From 88272887d2e3beee590b9510f1b78199a56736c2 Mon Sep 17 00:00:00 2001 From: Zongheng Yang Date: Sat, 24 Jul 2021 00:05:08 +0000 Subject: [PATCH 01/20] Minor change --- models/official/resnet/resnet_main.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index fe8f97c5..c3e47aed 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -19,6 +19,7 @@ from __future__ import print_function import os +import pprint import time from absl import app @@ -686,6 +687,7 @@ def main(unused_argv): params.validate() params.lock() + tf.logging.info('Params:\n%s', pprint.pformat(params.as_dict())) tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver( FLAGS.tpu if (FLAGS.tpu or params.use_tpu) else '', @@ -839,6 +841,10 @@ def main(unused_argv): max_steps=params.train_steps, hooks=hooks) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', + params.train_steps, elapsed_time) + else: assert FLAGS.mode == 'train_and_eval' while current_step < params.train_steps: From 6544625e6d13bbc4f9e589d84bc34ddcede7dd34 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 27 Jul 2021 18:00:23 +0000 Subject: [PATCH 02/20] Multi-GPU Training --- .../official/resnet/configs/resnet_config.py | 4 +- models/official/resnet/resnet_main.py | 15 +- models/official/resnet/run.log | 1 + models/run.log | 161104 +++++++++++++++ 4 files changed, 161114 insertions(+), 10 deletions(-) create mode 100644 models/official/resnet/run.log create mode 100644 models/run.log diff --git a/models/official/resnet/configs/resnet_config.py b/models/official/resnet/configs/resnet_config.py index 64e892b6..117b6604 100644 --- a/models/official/resnet/configs/resnet_config.py +++ b/models/official/resnet/configs/resnet_config.py @@ -24,11 +24,11 @@ 'train_steps': 112590, 'base_learning_rate': 0.1, 'iterations_per_loop': 1251, - 'use_tpu': True, + 'use_tpu': False, 'num_cores': 8, 'enable_lars': False, 'transpose_input': True, - 'precision': 'bfloat16', + 'precision': 'float32', 'num_label_classes': 1000, 'use_cache': True, 'use_async_checkpointing': False, diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index c3e47aed..00de664b 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -455,10 +455,10 @@ def restore_scaffold(): export_outputs={ 'classify': tf.estimator.export.PredictOutput(predictions)}, scaffold_fn=scaffold_fn) - + #print(params) # If necessary, in the model_fn, use params['batch_size'] instead the batch # size flags (--train_batch_size or --eval_batch_size). - batch_size = params['batch_size'] # pylint: disable=unused-variable + #batch_size = params['batch_size'] # pylint: disable=unused-variable # Calculate loss, which includes softmax cross entropy and L2 regularization. one_hot_labels = tf.one_hot(labels, params['num_label_classes']) @@ -689,20 +689,19 @@ def main(unused_argv): params.lock() tf.logging.info('Params:\n%s', pprint.pformat(params.as_dict())) - tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver( - FLAGS.tpu if (FLAGS.tpu or params.use_tpu) else '', - zone=FLAGS.tpu_zone, - project=FLAGS.gcp_project) - + tpu_cluster_resolver = None + devices = tf.config.list_physical_devices('GPU') if params.use_async_checkpointing: save_checkpoints_steps = None else: save_checkpoints_steps = max(5000, params.iterations_per_loop) config = tf.estimator.tpu.RunConfig( - cluster=tpu_cluster_resolver, + cluster=None, #tpu_cluster_resolver, model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=FLAGS.log_step_count_steps, + train_distribute=tf.distribute.MirroredStrategy(), + eval_distribute=tf.distribute.MirroredStrategy(), session_config=tf.ConfigProto( graph_options=tf.GraphOptions( rewrite_options=rewriter_config_pb2.RewriterConfig( diff --git a/models/official/resnet/run.log b/models/official/resnet/run.log new file mode 100644 index 00000000..809a2de8 --- /dev/null +++ b/models/official/resnet/run.log @@ -0,0 +1 @@ +python3: can't open file 'official/resnet/resnet_main.py': [Errno 2] No such file or directory diff --git a/models/run.log b/models/run.log new file mode 100644 index 00000000..f4e9b317 --- /dev/null +++ b/models/run.log @@ -0,0 +1,161104 @@ +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. +Instructions for updating: +non-resource variables are not supported in the long term +W0727 11:12:34.394004 140180445390656 flags_to_params.py:44] Flag tpu is None. +INFO:tensorflow:Params: +{'augment_name': None, + 'base_learning_rate': 0.1, + 'bn_momentum': 0.9, + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_last', + 'drop_connect_rate': None, + 'dropblock_groups': '', + 'dropblock_keep_prob': None, + 'dropblock_size': 7, + 'dropout_rate': None, + 'enable_lars': False, + 'eval_batch_size': 1024, + 'image_size': 224, + 'iterations_per_loop': 1251, + 'label_smoothing': 0.0, + 'model_dir': '/home/ubuntu/model', + 'momentum': 0.9, + 'moving_average_decay': 0, + 'norm_act_layer': 'bn_relu', + 'num_cores': 8, + 'num_eval_images': 50000, + 'num_label_classes': 1000, + 'num_parallel_calls': 8, + 'num_train_images': 1281167, + 'poly_rate': 0.0, + 'pre_activation': False, + 'precision': 'float32', + 'randaug_magnitude': None, + 'randaug_num_layers': None, + 'replace_stem_max_pool': False, + 'resnet_depth': 50, + 'resnetd_shortcut': False, + 'se_ratio': None, + 'skip_host_call': False, + 'train_batch_size': 128, + 'train_steps': 112590, + 'transpose_input': True, + 'use_async_checkpointing': False, + 'use_cache': True, + 'use_resnetd_stem': False, + 'use_tpu': False, + 'weight_decay': 0.0001} +I0727 11:12:34.394836 140180445390656 resnet_main.py:690] Params: +{'augment_name': None, + 'base_learning_rate': 0.1, + 'bn_momentum': 0.9, + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_last', + 'drop_connect_rate': None, + 'dropblock_groups': '', + 'dropblock_keep_prob': None, + 'dropblock_size': 7, + 'dropout_rate': None, + 'enable_lars': False, + 'eval_batch_size': 1024, + 'image_size': 224, + 'iterations_per_loop': 1251, + 'label_smoothing': 0.0, + 'model_dir': '/home/ubuntu/model', + 'momentum': 0.9, + 'moving_average_decay': 0, + 'norm_act_layer': 'bn_relu', + 'num_cores': 8, + 'num_eval_images': 50000, + 'num_label_classes': 1000, + 'num_parallel_calls': 8, + 'num_train_images': 1281167, + 'poly_rate': 0.0, + 'pre_activation': False, + 'precision': 'float32', + 'randaug_magnitude': None, + 'randaug_num_layers': None, + 'replace_stem_max_pool': False, + 'resnet_depth': 50, + 'resnetd_shortcut': False, + 'se_ratio': None, + 'skip_host_call': False, + 'train_batch_size': 128, + 'train_steps': 112590, + 'transpose_input': True, + 'use_async_checkpointing': False, + 'use_cache': True, + 'use_resnetd_stem': False, + 'use_tpu': False, + 'weight_decay': 0.0001} +WARNING:tensorflow:From official/resnet/resnet_main.py:698: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. + +W0727 11:12:35.106326 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:698: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. + +INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +I0727 11:12:41.998410 140180445390656 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +I0727 11:12:42.075105 140180445390656 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +WARNING:tensorflow:From official/resnet/resnet_main.py:709: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. + +W0727 11:12:42.075695 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:709: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. + +WARNING:tensorflow:From official/resnet/resnet_main.py:712: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. + +W0727 11:12:42.075878 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:712: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. + +INFO:tensorflow:Initializing RunConfig with distribution strategies. +I0727 11:12:42.076311 140180445390656 run_config.py:584] Initializing RunConfig with distribution strategies. +INFO:tensorflow:Not using Distribute Coordinator. +I0727 11:12:42.076480 140180445390656 estimator_training.py:167] Not using Distribute Coordinator. +WARNING:tensorflow:From official/resnet/resnet_main.py:715: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. + +W0727 11:12:42.076694 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:715: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. + +INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { + rewrite_options { + disable_meta_optimizer: true + } +} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +I0727 11:12:42.077999 140180445390656 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { + rewrite_options { + disable_meta_optimizer: true + } +} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +INFO:tensorflow:_TPUContext: eval_on_tpu True +I0727 11:12:42.078345 140180445390656 tpu_context.py:271] _TPUContext: eval_on_tpu True +WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. +W0727 11:12:42.078552 140180445390656 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. +INFO:tensorflow:Precision: float32 +I0727 11:12:42.078651 140180445390656 resnet_main.py:728] Precision: float32 +INFO:tensorflow:Using dataset: /home/ubuntu/data/ +I0727 11:12:42.078749 140180445390656 resnet_main.py:752] Using dataset: /home/ubuntu/data/ +INFO:tensorflow:Training for 112590 steps (11.25 epochs in total). Current step 0. +I0727 11:12:42.079294 140180445390656 resnet_main.py:814] Training for 112590 steps (11.25 epochs in total). Current step 0. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. +W0727 11:12:42.097877 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. +W0727 11:12:42.163720 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. +W0727 11:12:42.166103 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +Instructions for updating: +`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. +W0727 11:12:42.765567 140180445390656 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +Instructions for updating: +`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. +INFO:tensorflow:Calling model_fn. +I0727 11:12:43.647521 140158129137408 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:12:46.066568 140158129137408 api.py:479] Running train on CPU/GPU +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. + +W0727 11:12:46.982038 140158129137408 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. + +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. + warnings.warn('`tf.layers.conv2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. + warnings.warn('`layer.apply` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). + '`tf.layers.batch_normalization` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. + warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. + warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. + warnings.warn('`tf.layers.dense` is deprecated and ' +INFO:tensorflow:Calling model_fn. +I0727 11:13:00.984426 140158120744704 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:00.986140 140158120744704 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:03.850445 140158112352000 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:03.852002 140158112352000 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:07.033149 140158103959296 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:07.034896 140158103959296 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:10.330595 140158095566592 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:10.332391 140158095566592 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:14.047538 140158087173888 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:14.049701 140158087173888 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:18.110300 140157533550336 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:18.112006 140157533550336 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0727 11:13:22.676995 140157525157632 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0727 11:13:22.678644 140157525157632 api.py:479] Running train on CPU/GPU +INFO:tensorflow:batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 +I0727 11:13:27.478203 140180445390656 cross_device_ops.py:847] batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.537037 140158129137408 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.570353 140158120744704 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.602451 140158112352000 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.634366 140158103959296 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.667575 140158095566592 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.701061 140158087173888 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.733644 140157533550336 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0727 11:13:57.765744 140157525157632 api.py:479] Done calling model_fn. +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:13:57.767505 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Create CheckpointSaverHook. +I0727 11:14:00.855510 140180445390656 basic_session_run_hooks.py:546] Create CheckpointSaverHook. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +W0727 11:14:26.454797 140180445390656 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.500183 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.506530 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.519586 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.525996 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.538837 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.545017 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.558121 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.564764 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0727 11:14:30.577740 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Graph was finalized. +I0727 11:14:36.764134 140180445390656 monitored_session.py:246] Graph was finalized. +INFO:tensorflow:Running local_init_op. +I0727 11:15:13.579779 140180445390656 session_manager.py:505] Running local_init_op. +INFO:tensorflow:Done running local_init_op. +I0727 11:15:18.693089 140180445390656 session_manager.py:508] Done running local_init_op. +INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... +I0727 11:17:45.677532 140180445390656 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 0... +INFO:tensorflow:Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +I0727 11:17:46.694157 140180445390656 basic_session_run_hooks.py:618] Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... +I0727 11:18:08.125375 140180445390656 basic_session_run_hooks.py:626] Calling checkpoint listeners after saving checkpoint 0... +INFO:tensorflow:global_step/sec: 0.0114676 +I0727 11:21:30.464733 140180445390656 tpu_estimator.py:2391] global_step/sec: 0.0114676 +INFO:tensorflow:examples/sec: 1.46785 +I0727 11:21:30.466175 140180445390656 tpu_estimator.py:2392] examples/sec: 1.46785 +INFO:tensorflow:global_step/sec: 0.870898 +I0727 11:21:31.612610 140180445390656 tpu_estimator.py:2391] global_step/sec: 0.870898 +INFO:tensorflow:examples/sec: 111.475 +I0727 11:21:31.612941 140180445390656 tpu_estimator.py:2392] 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+I0727 15:29:38.959500 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.73493 +INFO:tensorflow:examples/sec: 222.072 +I0727 15:29:38.959813 140180445390656 tpu_estimator.py:2392] examples/sec: 222.072 +INFO:tensorflow:global_step/sec: 1.70676 +I0727 15:29:39.545411 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.70676 +INFO:tensorflow:examples/sec: 218.466 +I0727 15:29:39.545729 140180445390656 tpu_estimator.py:2392] examples/sec: 218.466 +INFO:tensorflow:global_step/sec: 1.67061 +I0727 15:29:40.143989 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.67061 +INFO:tensorflow:examples/sec: 213.838 +I0727 15:29:40.144303 140180445390656 tpu_estimator.py:2392] examples/sec: 213.838 +INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 25000... +I0727 15:29:40.733674 140180445390656 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 25000... +INFO:tensorflow:Saving checkpoints for 25000 into /home/ubuntu/model/model.ckpt. +I0727 15:29:40.733892 140180445390656 basic_session_run_hooks.py:618] Saving checkpoints for 25000 into /home/ubuntu/model/model.ckpt. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/saver.py:970: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. +Instructions for updating: +Use standard file APIs to delete files with this prefix. +W0727 15:29:40.987785 140180445390656 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/saver.py:970: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. +Instructions for updating: +Use standard file APIs to delete files with this prefix. +INFO:tensorflow:Calling checkpoint listeners 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218.361 +INFO:tensorflow:global_step/sec: 1.69347 +I0727 18:00:14.934791 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.69347 +INFO:tensorflow:examples/sec: 216.764 +I0727 18:00:14.935105 140180445390656 tpu_estimator.py:2392] examples/sec: 216.764 +INFO:tensorflow:global_step/sec: 1.76444 +I0727 18:00:15.501590 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.76444 +INFO:tensorflow:examples/sec: 225.848 +I0727 18:00:15.501971 140180445390656 tpu_estimator.py:2392] examples/sec: 225.848 From 41a7b12bd4e02bdf42ece31c42cd8333384b27b5 Mon Sep 17 00:00:00 2001 From: Michael Luo Date: Tue, 27 Jul 2021 11:20:10 -0700 Subject: [PATCH 03/20] Delete run.log --- models/official/resnet/run.log | 1 - 1 file changed, 1 deletion(-) delete mode 100644 models/official/resnet/run.log diff --git a/models/official/resnet/run.log b/models/official/resnet/run.log deleted file mode 100644 index 809a2de8..00000000 --- a/models/official/resnet/run.log +++ /dev/null @@ -1 +0,0 @@ -python3: can't open file 'official/resnet/resnet_main.py': [Errno 2] No such file or directory From 2e08e0e27200354171efdb3860417a4b0f861bee Mon Sep 17 00:00:00 2001 From: Michael Luo Date: Tue, 27 Jul 2021 11:20:29 -0700 Subject: [PATCH 04/20] Delete run.log --- models/run.log | 161104 ---------------------------------------------- 1 file changed, 161104 deletions(-) delete mode 100644 models/run.log diff --git a/models/run.log b/models/run.log deleted file mode 100644 index f4e9b317..00000000 --- a/models/run.log +++ /dev/null @@ -1,161104 +0,0 @@ -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. -Instructions for updating: -non-resource variables are not supported in the long term -W0727 11:12:34.394004 140180445390656 flags_to_params.py:44] Flag tpu is None. -INFO:tensorflow:Params: -{'augment_name': None, - 'base_learning_rate': 0.1, - 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', - 'data_format': 'channels_last', - 'drop_connect_rate': None, - 'dropblock_groups': '', - 'dropblock_keep_prob': None, - 'dropblock_size': 7, - 'dropout_rate': None, - 'enable_lars': False, - 'eval_batch_size': 1024, - 'image_size': 224, - 'iterations_per_loop': 1251, - 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', - 'momentum': 0.9, - 'moving_average_decay': 0, - 'norm_act_layer': 'bn_relu', - 'num_cores': 8, - 'num_eval_images': 50000, - 'num_label_classes': 1000, - 'num_parallel_calls': 8, - 'num_train_images': 1281167, - 'poly_rate': 0.0, - 'pre_activation': False, - 'precision': 'float32', - 'randaug_magnitude': None, - 'randaug_num_layers': None, - 'replace_stem_max_pool': False, - 'resnet_depth': 50, - 'resnetd_shortcut': False, - 'se_ratio': None, - 'skip_host_call': False, - 'train_batch_size': 128, - 'train_steps': 112590, - 'transpose_input': True, - 'use_async_checkpointing': False, - 'use_cache': True, - 'use_resnetd_stem': False, - 'use_tpu': False, - 'weight_decay': 0.0001} -I0727 11:12:34.394836 140180445390656 resnet_main.py:690] Params: -{'augment_name': None, - 'base_learning_rate': 0.1, - 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', - 'data_format': 'channels_last', - 'drop_connect_rate': None, - 'dropblock_groups': '', - 'dropblock_keep_prob': None, - 'dropblock_size': 7, - 'dropout_rate': None, - 'enable_lars': False, - 'eval_batch_size': 1024, - 'image_size': 224, - 'iterations_per_loop': 1251, - 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', - 'momentum': 0.9, - 'moving_average_decay': 0, - 'norm_act_layer': 'bn_relu', - 'num_cores': 8, - 'num_eval_images': 50000, - 'num_label_classes': 1000, - 'num_parallel_calls': 8, - 'num_train_images': 1281167, - 'poly_rate': 0.0, - 'pre_activation': False, - 'precision': 'float32', - 'randaug_magnitude': None, - 'randaug_num_layers': None, - 'replace_stem_max_pool': False, - 'resnet_depth': 50, - 'resnetd_shortcut': False, - 'se_ratio': None, - 'skip_host_call': False, - 'train_batch_size': 128, - 'train_steps': 112590, - 'transpose_input': True, - 'use_async_checkpointing': False, - 'use_cache': True, - 'use_resnetd_stem': False, - 'use_tpu': False, - 'weight_decay': 0.0001} -WARNING:tensorflow:From official/resnet/resnet_main.py:698: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. - -W0727 11:12:35.106326 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:698: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. - -INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -I0727 11:12:41.998410 140180445390656 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -I0727 11:12:42.075105 140180445390656 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -WARNING:tensorflow:From official/resnet/resnet_main.py:709: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. - -W0727 11:12:42.075695 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:709: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. - -WARNING:tensorflow:From official/resnet/resnet_main.py:712: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. - -W0727 11:12:42.075878 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:712: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. - -INFO:tensorflow:Initializing RunConfig with distribution strategies. -I0727 11:12:42.076311 140180445390656 run_config.py:584] Initializing RunConfig with distribution strategies. -INFO:tensorflow:Not using Distribute Coordinator. -I0727 11:12:42.076480 140180445390656 estimator_training.py:167] Not using Distribute Coordinator. -WARNING:tensorflow:From official/resnet/resnet_main.py:715: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. - -W0727 11:12:42.076694 140180445390656 module_wrapper.py:138] From official/resnet/resnet_main.py:715: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. - -INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { - rewrite_options { - disable_meta_optimizer: true - } -} -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -I0727 11:12:42.077999 140180445390656 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { - rewrite_options { - disable_meta_optimizer: true - } -} -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -INFO:tensorflow:_TPUContext: eval_on_tpu True -I0727 11:12:42.078345 140180445390656 tpu_context.py:271] _TPUContext: eval_on_tpu True -WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. -W0727 11:12:42.078552 140180445390656 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. -INFO:tensorflow:Precision: float32 -I0727 11:12:42.078651 140180445390656 resnet_main.py:728] Precision: float32 -INFO:tensorflow:Using dataset: /home/ubuntu/data/ -I0727 11:12:42.078749 140180445390656 resnet_main.py:752] Using dataset: /home/ubuntu/data/ -INFO:tensorflow:Training for 112590 steps (11.25 epochs in total). Current step 0. -I0727 11:12:42.079294 140180445390656 resnet_main.py:814] Training for 112590 steps (11.25 epochs in total). Current step 0. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -W0727 11:12:42.097877 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. -W0727 11:12:42.163720 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -W0727 11:12:42.166103 140180445390656 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. -Instructions for updating: -`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -W0727 11:12:42.765567 140180445390656 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. -Instructions for updating: -`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -INFO:tensorflow:Calling model_fn. -I0727 11:12:43.647521 140158129137408 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:12:46.066568 140158129137408 api.py:479] Running train on CPU/GPU -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. - -W0727 11:12:46.982038 140158129137408 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. - -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. - warnings.warn('`tf.layers.conv2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. - warnings.warn('`layer.apply` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). - '`tf.layers.batch_normalization` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. - warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. - warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. - warnings.warn('`tf.layers.dense` is deprecated and ' -INFO:tensorflow:Calling model_fn. -I0727 11:13:00.984426 140158120744704 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:00.986140 140158120744704 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:03.850445 140158112352000 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:03.852002 140158112352000 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:07.033149 140158103959296 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:07.034896 140158103959296 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:10.330595 140158095566592 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:10.332391 140158095566592 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:14.047538 140158087173888 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:14.049701 140158087173888 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:18.110300 140157533550336 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:18.112006 140157533550336 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0727 11:13:22.676995 140157525157632 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0727 11:13:22.678644 140157525157632 api.py:479] Running train on CPU/GPU -INFO:tensorflow:batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 -I0727 11:13:27.478203 140180445390656 cross_device_ops.py:847] batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.537037 140158129137408 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.570353 140158120744704 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.602451 140158112352000 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.634366 140158103959296 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.667575 140158095566592 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.701061 140158087173888 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.733644 140157533550336 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0727 11:13:57.765744 140157525157632 api.py:479] Done calling model_fn. -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:13:57.767505 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Create CheckpointSaverHook. -I0727 11:14:00.855510 140180445390656 basic_session_run_hooks.py:546] Create CheckpointSaverHook. -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -W0727 11:14:26.454797 140180445390656 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.500183 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.506530 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.519586 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.525996 140180445390656 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then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.564764 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0727 11:14:30.577740 140180445390656 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Graph was finalized. -I0727 11:14:36.764134 140180445390656 monitored_session.py:246] Graph was finalized. -INFO:tensorflow:Running local_init_op. -I0727 11:15:13.579779 140180445390656 session_manager.py:505] Running local_init_op. -INFO:tensorflow:Done running local_init_op. -I0727 11:15:18.693089 140180445390656 session_manager.py:508] Done running local_init_op. -INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... -I0727 11:17:45.677532 140180445390656 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218.361 -INFO:tensorflow:global_step/sec: 1.69347 -I0727 18:00:14.934791 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.69347 -INFO:tensorflow:examples/sec: 216.764 -I0727 18:00:14.935105 140180445390656 tpu_estimator.py:2392] examples/sec: 216.764 -INFO:tensorflow:global_step/sec: 1.76444 -I0727 18:00:15.501590 140180445390656 tpu_estimator.py:2391] global_step/sec: 1.76444 -INFO:tensorflow:examples/sec: 225.848 -I0727 18:00:15.501971 140180445390656 tpu_estimator.py:2392] examples/sec: 225.848 From 9e40451a38ae1bd2499a903ce2c5b4b7c5a61a22 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 3 Aug 2021 09:55:58 +0000 Subject: [PATCH 05/20] okie --- models/official/resnet/resnet_main.py | 34 ++++++++++++++++++++++++--- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 00de664b..c59b3020 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -229,6 +229,14 @@ 'export_moving_average', False, 'Whether to export model using moving average variables.') +flags.DEFINE_bool( + 'amp', False, + 'Whether to use automated mixed precision.') + +flags.DEFINE_bool( + 'xla', False, + 'Whether to use accelerated linear algebra.') + # The input tensor is in the range of [0, 255], we need to scale them to the # range of [0, 1] @@ -507,6 +515,8 @@ def restore_scaffold(): # user, this should look like regular synchronous training. optimizer = tf.tpu.CrossShardOptimizer(optimizer) + if params['amp']: + optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) @@ -685,6 +695,22 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) + # Enable AMP + amp = params.amp + xla = params.xla + # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow + # Step 2: add --amp flag + import os + print(amp, xla) + if amp: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" + os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = "1" + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1" + if xla: + # For XLA + # Ru this TF_XLA_FLAGS=--tf_xla_auto_jit=2 python official/resnet/resnet_main.py --use_tpu=False --mode=train --data_dir=/home/ubuntu/data/tf_records --model_dir=/home/ubuntu/model --train_batch_size=100 --train_steps=112590 --iterations_per_loop=1251 2>&1 | tee run.log + os.environ['TF_XLA_FLAGS'] = 'TF_XLA_FLAGS=--tf_xla_auto_jit=2'#'--tf_xla_enable_xla_devices' + params.validate() params.lock() tf.logging.info('Params:\n%s', pprint.pformat(params.as_dict())) @@ -695,6 +721,10 @@ def main(unused_argv): save_checkpoints_steps = None else: save_checkpoints_steps = max(5000, params.iterations_per_loop) + + # Enable XLA + session_config = tf.GraphOptions(rewrite_options=rewriter_config_pb2.RewriterConfig(disable_meta_optimizer=True)) + config = tf.estimator.tpu.RunConfig( cluster=None, #tpu_cluster_resolver, model_dir=FLAGS.model_dir, @@ -703,9 +733,7 @@ def main(unused_argv): train_distribute=tf.distribute.MirroredStrategy(), eval_distribute=tf.distribute.MirroredStrategy(), session_config=tf.ConfigProto( - graph_options=tf.GraphOptions( - rewrite_options=rewriter_config_pb2.RewriterConfig( - disable_meta_optimizer=True))), + graph_options=session_config), tpu_config=tf.estimator.tpu.TPUConfig( iterations_per_loop=params.iterations_per_loop, num_shards=params.num_cores, From 3017e065b9fce49c6af4c8d80465bca8f364ff1e Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 3 Aug 2021 09:59:53 +0000 Subject: [PATCH 06/20] ok --- models/official/resnet/resnet_main.py | 4 +- models/run.log | 266 ++++++++++++++++++++++++++ 2 files changed, 268 insertions(+), 2 deletions(-) create mode 100644 models/run.log diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index c59b3020..a982a980 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -730,8 +730,8 @@ def main(unused_argv): model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=FLAGS.log_step_count_steps, - train_distribute=tf.distribute.MirroredStrategy(), - eval_distribute=tf.distribute.MirroredStrategy(), + train_distribute=tf.distribute.MultiWorkerMirroredStrategy(), + eval_distribute=tf.distribute.MultiWorkerMirroredStrategy(), session_config=tf.ConfigProto( graph_options=session_config), tpu_config=tf.estimator.tpu.TPUConfig( diff --git a/models/run.log b/models/run.log new file mode 100644 index 00000000..ed9e6bc4 --- /dev/null +++ b/models/run.log @@ -0,0 +1,266 @@ +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. +Instructions for updating: +non-resource variables are not supported in the long term +W0803 09:39:46.753082 139809987172160 flags_to_params.py:44] Flag tpu is None. +INFO:tensorflow:Params: +{'augment_name': None, + 'base_learning_rate': 0.1, + 'bn_momentum': 0.9, + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_last', + 'drop_connect_rate': None, + 'dropblock_groups': '', + 'dropblock_keep_prob': None, + 'dropblock_size': 7, + 'dropout_rate': None, + 'enable_lars': False, + 'eval_batch_size': 1024, + 'image_size': 224, + 'iterations_per_loop': 1251, + 'label_smoothing': 0.0, + 'model_dir': '/home/ubuntu/model', + 'momentum': 0.9, + 'moving_average_decay': 0, + 'norm_act_layer': 'bn_relu', + 'num_cores': 8, + 'num_eval_images': 50000, + 'num_label_classes': 1000, + 'num_parallel_calls': 8, + 'num_train_images': 1281167, + 'poly_rate': 0.0, + 'pre_activation': False, + 'precision': 'float32', + 'randaug_magnitude': None, + 'randaug_num_layers': None, + 'replace_stem_max_pool': False, + 'resnet_depth': 50, + 'resnetd_shortcut': False, + 'se_ratio': None, + 'skip_host_call': False, + 'train_batch_size': 128, + 'train_steps': 112590, + 'transpose_input': True, + 'use_async_checkpointing': False, + 'use_cache': True, + 'use_resnetd_stem': False, + 'use_tpu': False, + 'weight_decay': 0.0001} +I0803 09:39:46.753675 139809987172160 resnet_main.py:699] Params: +{'augment_name': None, + 'base_learning_rate': 0.1, + 'bn_momentum': 0.9, + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_last', + 'drop_connect_rate': None, + 'dropblock_groups': '', + 'dropblock_keep_prob': None, + 'dropblock_size': 7, + 'dropout_rate': None, + 'enable_lars': False, + 'eval_batch_size': 1024, + 'image_size': 224, + 'iterations_per_loop': 1251, + 'label_smoothing': 0.0, + 'model_dir': '/home/ubuntu/model', + 'momentum': 0.9, + 'moving_average_decay': 0, + 'norm_act_layer': 'bn_relu', + 'num_cores': 8, + 'num_eval_images': 50000, + 'num_label_classes': 1000, + 'num_parallel_calls': 8, + 'num_train_images': 1281167, + 'poly_rate': 0.0, + 'pre_activation': False, + 'precision': 'float32', + 'randaug_magnitude': None, + 'randaug_num_layers': None, + 'replace_stem_max_pool': False, + 'resnet_depth': 50, + 'resnetd_shortcut': False, + 'se_ratio': None, + 'skip_host_call': False, + 'train_batch_size': 128, + 'train_steps': 112590, + 'transpose_input': True, + 'use_async_checkpointing': False, + 'use_cache': True, + 'use_resnetd_stem': False, + 'use_tpu': False, + 'weight_decay': 0.0001} +WARNING:tensorflow:From official/resnet/resnet_main.py:711: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. + +W0803 09:39:47.604891 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:711: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. + +INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +I0803 09:39:52.503415 139809987172160 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +I0803 09:39:52.578093 139809987172160 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') +WARNING:tensorflow:From official/resnet/resnet_main.py:720: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. + +W0803 09:39:52.578595 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:720: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. + +WARNING:tensorflow:From official/resnet/resnet_main.py:723: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. + +W0803 09:39:52.578706 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:723: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. + +INFO:tensorflow:Initializing RunConfig with distribution strategies. +I0803 09:39:52.579045 139809987172160 run_config.py:584] Initializing RunConfig with distribution strategies. +INFO:tensorflow:Not using Distribute Coordinator. +I0803 09:39:52.579163 139809987172160 estimator_training.py:167] Not using Distribute Coordinator. +WARNING:tensorflow:From official/resnet/resnet_main.py:726: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. + +W0803 09:39:52.579271 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:726: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. + +INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { + rewrite_options { + disable_meta_optimizer: true + } +} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +I0803 09:39:52.580219 139809987172160 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { + rewrite_options { + disable_meta_optimizer: true + } +} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +INFO:tensorflow:_TPUContext: eval_on_tpu True +I0803 09:39:52.580475 139809987172160 tpu_context.py:271] _TPUContext: eval_on_tpu True +WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. +W0803 09:39:52.580622 139809987172160 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. +INFO:tensorflow:Precision: float32 +I0803 09:39:52.580673 139809987172160 resnet_main.py:739] Precision: float32 +INFO:tensorflow:Using dataset: /home/ubuntu/data/ +I0803 09:39:52.580726 139809987172160 resnet_main.py:763] Using dataset: /home/ubuntu/data/ +INFO:tensorflow:Training for 112590 steps (11.25 epochs in total). Current step 0. +I0803 09:39:52.581054 139809987172160 resnet_main.py:825] Training for 112590 steps (11.25 epochs in total). Current step 0. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. +W0803 09:39:52.593989 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. +W0803 09:39:52.640557 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. +WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. +W0803 09:39:52.642029 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +Instructions for updating: +Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +Instructions for updating: +`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. +W0803 09:39:53.051217 139809987172160 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +Instructions for updating: +`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. +INFO:tensorflow:Calling model_fn. +I0803 09:39:53.607462 139781631624960 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:39:55.182162 139781631624960 api.py:479] Running train on CPU/GPU +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. + +W0803 09:39:55.797909 139781631624960 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. + +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. + warnings.warn('`tf.layers.conv2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. + warnings.warn('`layer.apply` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). + '`tf.layers.batch_normalization` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. + warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. + warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. + warnings.warn('`tf.layers.dense` is deprecated and ' +INFO:tensorflow:Calling model_fn. +I0803 09:40:04.542257 139781623232256 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:04.543516 139781623232256 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:06.340046 139781614839552 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:06.341055 139781614839552 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:08.405181 139781606446848 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:08.406299 139781606446848 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:10.533782 139780314625792 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:10.534827 139780314625792 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:12.904958 139780306233088 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:12.906047 139780306233088 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:15.546494 139780297840384 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:15.547550 139780297840384 api.py:479] Running train on CPU/GPU +INFO:tensorflow:Calling model_fn. +I0803 09:40:18.545397 139780289447680 api.py:479] Calling model_fn. +INFO:tensorflow:Running train on CPU/GPU +I0803 09:40:18.546474 139780289447680 api.py:479] Running train on CPU/GPU +INFO:tensorflow:batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 +I0803 09:40:21.705314 139809987172160 cross_device_ops.py:847] batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.880970 139781631624960 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.903018 139781623232256 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.924298 139781614839552 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.945406 139781606446848 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.967176 139780314625792 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:41.989030 139780306233088 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:42.010580 139780297840384 api.py:479] Done calling model_fn. +INFO:tensorflow:Done calling model_fn. +I0803 09:40:42.031991 139780289447680 api.py:479] Done calling model_fn. +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:40:42.033955 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Create CheckpointSaverHook. +I0803 09:40:43.885584 139809987172160 basic_session_run_hooks.py:546] Create CheckpointSaverHook. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +W0803 09:41:01.146029 139809987172160 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.536312 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.540358 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.548108 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.551898 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.559479 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.563181 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.570720 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.574940 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0803 09:41:03.582626 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Graph was finalized. +I0803 09:41:07.170394 139809987172160 monitored_session.py:246] Graph was finalized. +INFO:tensorflow:Running local_init_op. +I0803 09:41:36.430108 139809987172160 session_manager.py:505] Running local_init_op. +INFO:tensorflow:Done running local_init_op. +I0803 09:41:40.273209 139809987172160 session_manager.py:508] Done running local_init_op. +INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... +I0803 09:43:18.839676 139809987172160 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 0... +INFO:tensorflow:Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +I0803 09:43:19.693045 139809987172160 basic_session_run_hooks.py:618] Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... +I0803 09:43:34.784770 139809987172160 basic_session_run_hooks.py:626] Calling checkpoint listeners after saving checkpoint 0... From 74aadabfdac337c0e0c1e4a4aeaa757f16099776 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 3 Aug 2021 10:07:26 +0000 Subject: [PATCH 07/20] ok --- models/official/resnet/resnet_main.py | 10 +- models/run.log | 182 +++++++------------------- 2 files changed, 49 insertions(+), 143 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index a982a980..b337b613 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -515,7 +515,7 @@ def restore_scaffold(): # user, this should look like regular synchronous training. optimizer = tf.tpu.CrossShardOptimizer(optimizer) - if params['amp']: + if True: optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. @@ -696,8 +696,8 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) # Enable AMP - amp = params.amp - xla = params.xla + amp = True + xla = False # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow # Step 2: add --amp flag import os @@ -730,8 +730,8 @@ def main(unused_argv): model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=FLAGS.log_step_count_steps, - train_distribute=tf.distribute.MultiWorkerMirroredStrategy(), - eval_distribute=tf.distribute.MultiWorkerMirroredStrategy(), + train_distribute=tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy(), + eval_distribute=tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy(), session_config=tf.ConfigProto( graph_options=session_config), tpu_config=tf.estimator.tpu.TPUConfig( diff --git a/models/run.log b/models/run.log index ed9e6bc4..744d507e 100644 --- a/models/run.log +++ b/models/run.log @@ -1,12 +1,12 @@ WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term -W0803 09:39:46.753082 139809987172160 flags_to_params.py:44] Flag tpu is None. +W0803 10:06:59.058344 140214897387328 flags_to_params.py:44] Flag tpu is None. INFO:tensorflow:Params: {'augment_name': None, 'base_learning_rate': 0.1, 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', + 'data_dir': '/data/tfrecords', 'data_format': 'channels_last', 'drop_connect_rate': None, 'dropblock_groups': '', @@ -18,7 +18,7 @@ INFO:tensorflow:Params: 'image_size': 224, 'iterations_per_loop': 1251, 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', + 'model_dir': './model', 'momentum': 0.9, 'moving_average_decay': 0, 'norm_act_layer': 'bn_relu', @@ -38,18 +38,18 @@ INFO:tensorflow:Params: 'se_ratio': None, 'skip_host_call': False, 'train_batch_size': 128, - 'train_steps': 112590, + 'train_steps': 4096, 'transpose_input': True, 'use_async_checkpointing': False, 'use_cache': True, 'use_resnetd_stem': False, 'use_tpu': False, 'weight_decay': 0.0001} -I0803 09:39:46.753675 139809987172160 resnet_main.py:699] Params: +I0803 10:06:59.059037 140214897387328 resnet_main.py:716] Params: {'augment_name': None, 'base_learning_rate': 0.1, 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', + 'data_dir': '/data/tfrecords', 'data_format': 'channels_last', 'drop_connect_rate': None, 'dropblock_groups': '', @@ -61,7 +61,7 @@ I0803 09:39:46.753675 139809987172160 resnet_main.py:699] Params: 'image_size': 224, 'iterations_per_loop': 1251, 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', + 'model_dir': './model', 'momentum': 0.9, 'moving_average_decay': 0, 'norm_act_layer': 'bn_relu', @@ -81,186 +81,92 @@ I0803 09:39:46.753675 139809987172160 resnet_main.py:699] Params: 'se_ratio': None, 'skip_host_call': False, 'train_batch_size': 128, - 'train_steps': 112590, + 'train_steps': 4096, 'transpose_input': True, 'use_async_checkpointing': False, 'use_cache': True, 'use_resnetd_stem': False, 'use_tpu': False, 'weight_decay': 0.0001} -WARNING:tensorflow:From official/resnet/resnet_main.py:711: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. +WARNING:tensorflow:From official/resnet/resnet_main.py:728: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. -W0803 09:39:47.604891 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:711: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. +W0803 10:06:59.918581 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:728: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. -INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -I0803 09:39:52.503415 139809987172160 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -I0803 09:39:52.578093 139809987172160 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0', '/replica:0/task:0/device:GPU:1', '/replica:0/task:0/device:GPU:2', '/replica:0/task:0/device:GPU:3', '/replica:0/task:0/device:GPU:4', '/replica:0/task:0/device:GPU:5', '/replica:0/task:0/device:GPU:6', '/replica:0/task:0/device:GPU:7') -WARNING:tensorflow:From official/resnet/resnet_main.py:720: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. +INFO:tensorflow:Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') +I0803 10:07:04.749025 140214897387328 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') +INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO +I0803 10:07:04.749967 140214897387328 collective_all_reduce_strategy.py:404] Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO +INFO:tensorflow:Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') +I0803 10:07:04.751258 140214897387328 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') +INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO +I0803 10:07:04.751380 140214897387328 collective_all_reduce_strategy.py:404] Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO +WARNING:tensorflow:From official/resnet/resnet_main.py:737: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. -W0803 09:39:52.578595 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:720: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. +W0803 10:07:04.751748 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:737: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. -WARNING:tensorflow:From official/resnet/resnet_main.py:723: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. +WARNING:tensorflow:From official/resnet/resnet_main.py:740: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. -W0803 09:39:52.578706 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:723: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. +W0803 10:07:04.751852 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:740: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. INFO:tensorflow:Initializing RunConfig with distribution strategies. -I0803 09:39:52.579045 139809987172160 run_config.py:584] Initializing RunConfig with distribution strategies. +I0803 10:07:04.752192 140214897387328 run_config.py:584] Initializing RunConfig with distribution strategies. INFO:tensorflow:Not using Distribute Coordinator. -I0803 09:39:52.579163 139809987172160 estimator_training.py:167] Not using Distribute Coordinator. -WARNING:tensorflow:From official/resnet/resnet_main.py:726: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. +I0803 10:07:04.752317 140214897387328 estimator_training.py:167] Not using Distribute Coordinator. +WARNING:tensorflow:From official/resnet/resnet_main.py:743: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. -W0803 09:39:52.579271 139809987172160 module_wrapper.py:138] From official/resnet/resnet_main.py:726: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. +W0803 10:07:04.752419 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:743: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. -INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { +INFO:tensorflow:Using config: {'_model_dir': './model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { rewrite_options { disable_meta_optimizer: true } } -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -I0803 09:39:52.580219 139809987172160 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +I0803 10:07:04.755181 140214897387328 estimator.py:192] Using config: {'_model_dir': './model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { rewrite_options { disable_meta_optimizer: true } } -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} INFO:tensorflow:_TPUContext: eval_on_tpu True -I0803 09:39:52.580475 139809987172160 tpu_context.py:271] _TPUContext: eval_on_tpu True +I0803 10:07:04.755454 140214897387328 tpu_context.py:271] _TPUContext: eval_on_tpu True WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. -W0803 09:39:52.580622 139809987172160 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. +W0803 10:07:04.755591 140214897387328 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. INFO:tensorflow:Precision: float32 -I0803 09:39:52.580673 139809987172160 resnet_main.py:739] Precision: float32 -INFO:tensorflow:Using dataset: /home/ubuntu/data/ -I0803 09:39:52.580726 139809987172160 resnet_main.py:763] Using dataset: /home/ubuntu/data/ -INFO:tensorflow:Training for 112590 steps (11.25 epochs in total). Current step 0. -I0803 09:39:52.581054 139809987172160 resnet_main.py:825] Training for 112590 steps (11.25 epochs in total). Current step 0. +I0803 10:07:04.755653 140214897387328 resnet_main.py:756] Precision: float32 +INFO:tensorflow:Using dataset: /data/tfrecords +I0803 10:07:04.755709 140214897387328 resnet_main.py:780] Using dataset: /data/tfrecords +INFO:tensorflow:Training for 4096 steps (0.41 epochs in total). Current step 0. +I0803 10:07:04.756135 140214897387328 resnet_main.py:842] Training for 4096 steps (0.41 epochs in total). Current step 0. WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -W0803 09:39:52.593989 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +W0803 10:07:04.772450 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. -W0803 09:39:52.640557 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. +W0803 10:07:04.818538 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -W0803 09:39:52.642029 139809987172160 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +W0803 10:07:04.820030 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -W0803 09:39:53.051217 139809987172160 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +W0803 10:07:05.227103 140214897387328 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. INFO:tensorflow:Calling model_fn. -I0803 09:39:53.607462 139781631624960 api.py:479] Calling model_fn. +I0803 10:07:05.780834 140181474629376 api.py:479] Calling model_fn. INFO:tensorflow:Running train on CPU/GPU -I0803 09:39:55.182162 139781631624960 api.py:479] Running train on CPU/GPU +I0803 10:07:07.341922 140181474629376 api.py:479] Running train on CPU/GPU WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. -W0803 09:39:55.797909 139781631624960 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. +W0803 10:07:07.944726 140181474629376 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. - warnings.warn('`tf.layers.conv2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. - warnings.warn('`layer.apply` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). - '`tf.layers.batch_normalization` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. - warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. - warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. - warnings.warn('`tf.layers.dense` is deprecated and ' -INFO:tensorflow:Calling model_fn. -I0803 09:40:04.542257 139781623232256 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:04.543516 139781623232256 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:06.340046 139781614839552 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:06.341055 139781614839552 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:08.405181 139781606446848 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:08.406299 139781606446848 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:10.533782 139780314625792 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:10.534827 139780314625792 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:12.904958 139780306233088 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:12.906047 139780306233088 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:15.546494 139780297840384 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:15.547550 139780297840384 api.py:479] Running train on CPU/GPU -INFO:tensorflow:Calling model_fn. -I0803 09:40:18.545397 139780289447680 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0803 09:40:18.546474 139780289447680 api.py:479] Running train on CPU/GPU -INFO:tensorflow:batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 -I0803 09:40:21.705314 139809987172160 cross_device_ops.py:847] batch_all_reduce: 161 all-reduces with algorithm = nccl, num_packs = 1 -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.880970 139781631624960 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.903018 139781623232256 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.924298 139781614839552 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.945406 139781606446848 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.967176 139780314625792 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:41.989030 139780306233088 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:42.010580 139780297840384 api.py:479] Done calling model_fn. -INFO:tensorflow:Done calling model_fn. -I0803 09:40:42.031991 139780289447680 api.py:479] Done calling model_fn. -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:40:42.033955 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Create CheckpointSaverHook. -I0803 09:40:43.885584 139809987172160 basic_session_run_hooks.py:546] Create CheckpointSaverHook. -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -W0803 09:41:01.146029 139809987172160 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.536312 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.540358 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.548108 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.551898 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.559479 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.563181 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.570720 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.574940 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0803 09:41:03.582626 139809987172160 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Graph was finalized. -I0803 09:41:07.170394 139809987172160 monitored_session.py:246] Graph was finalized. -INFO:tensorflow:Running local_init_op. -I0803 09:41:36.430108 139809987172160 session_manager.py:505] Running local_init_op. -INFO:tensorflow:Done running local_init_op. -I0803 09:41:40.273209 139809987172160 session_manager.py:508] Done running local_init_op. -INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... -I0803 09:43:18.839676 139809987172160 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 0... -INFO:tensorflow:Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. -I0803 09:43:19.693045 139809987172160 basic_session_run_hooks.py:618] Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. -INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... -I0803 09:43:34.784770 139809987172160 basic_session_run_hooks.py:626] Calling checkpoint listeners after saving checkpoint 0... From 863f5c6374bd750826e665e537cef90583a6f214 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 6 Aug 2021 21:02:06 +0000 Subject: [PATCH 08/20] temp --- models/official/resnet/resnet_main.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index b337b613..c1cb0625 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -724,14 +724,15 @@ def main(unused_argv): # Enable XLA session_config = tf.GraphOptions(rewrite_options=rewriter_config_pb2.RewriterConfig(disable_meta_optimizer=True)) + strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.NcclAllReduce()) config = tf.estimator.tpu.RunConfig( cluster=None, #tpu_cluster_resolver, model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=FLAGS.log_step_count_steps, - train_distribute=tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy(), - eval_distribute=tf.compat.v1.distribute.experimental.MultiWorkerMirroredStrategy(), + train_distribute=strategy, + eval_distribute=strategy, session_config=tf.ConfigProto( graph_options=session_config), tpu_config=tf.estimator.tpu.TPUConfig( From 349f196e6f4ce87c8f0c2c300aa7778b6c930e0a Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 6 Aug 2021 21:14:26 +0000 Subject: [PATCH 09/20] lmao --- models/official/resnet/configs/resnet_config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/official/resnet/configs/resnet_config.py b/models/official/resnet/configs/resnet_config.py index 117b6604..693449d6 100644 --- a/models/official/resnet/configs/resnet_config.py +++ b/models/official/resnet/configs/resnet_config.py @@ -27,7 +27,7 @@ 'use_tpu': False, 'num_cores': 8, 'enable_lars': False, - 'transpose_input': True, + 'transpose_input': False, 'precision': 'float32', 'num_label_classes': 1000, 'use_cache': True, From 9f7d4c1a2d3e45e1f6f3faa56ff07dc16a035c7d Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 6 Aug 2021 21:27:49 +0000 Subject: [PATCH 10/20] ok --- models/official/resnet/configs/resnet_config.py | 2 +- models/official/resnet/resnet_main.py | 8 ++++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/models/official/resnet/configs/resnet_config.py b/models/official/resnet/configs/resnet_config.py index 693449d6..83fb1d80 100644 --- a/models/official/resnet/configs/resnet_config.py +++ b/models/official/resnet/configs/resnet_config.py @@ -44,7 +44,7 @@ 'dropblock_size': 7, 'pre_activation': False, 'norm_act_layer': 'bn_relu', - 'data_format': 'channels_last', + 'data_format': 'channels_first', 'augment_name': None, 'randaug_magnitude': None, 'randaug_num_layers': None, diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index c1cb0625..4d45c604 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -377,8 +377,12 @@ def resnet_model_fn(features, labels, mode, params): features = tf.transpose(features, [3, 0, 1, 2]) # HWCN to NHWC # Normalize the image to zero mean and unit variance. - features -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=features.dtype) - features /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=features.dtype) + if params['data_format'] == 'channels_first': + features -= tf.constant(MEAN_RGB, shape=[3, 1, 1], dtype=features.dtype) + features /= tf.constant(STDDEV_RGB, shape=[3, 1, 1], dtype=features.dtype) + else: + features -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=features.dtype) + features /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=features.dtype) # DropBlock keep_prob for the 4 block groups of ResNet architecture. # None means applying no DropBlock at the corresponding block group. From 50c0fc6181949bd283caacdb6c555b50e60dc437 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 6 Aug 2021 21:53:08 +0000 Subject: [PATCH 11/20] Turn off amp --- models/official/resnet/resnet_main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 4d45c604..3bff5035 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -519,7 +519,7 @@ def restore_scaffold(): # user, this should look like regular synchronous training. optimizer = tf.tpu.CrossShardOptimizer(optimizer) - if True: + if False: optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. @@ -700,7 +700,7 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) # Enable AMP - amp = True + amp = False xla = False # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow # Step 2: add --amp flag From c875534155f2fb43d89b2e0285c8c99e75e0bd22 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 6 Aug 2021 22:14:33 +0000 Subject: [PATCH 12/20] enable amp --- models/official/resnet/resnet_main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 3bff5035..4d45c604 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -519,7 +519,7 @@ def restore_scaffold(): # user, this should look like regular synchronous training. optimizer = tf.tpu.CrossShardOptimizer(optimizer) - if False: + if True: optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. @@ -700,7 +700,7 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) # Enable AMP - amp = False + amp = True xla = False # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow # Step 2: add --amp flag From 8a68cc5a3755060c1ec785e4c1c7f27552906292 Mon Sep 17 00:00:00 2001 From: Zongheng Yang Date: Mon, 9 Aug 2021 23:55:24 +0000 Subject: [PATCH 13/20] Make AMP work. --- models/official/resnet/resnet_main.py | 88 +++++++++++++++++++++------ 1 file changed, 71 insertions(+), 17 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 4d45c604..e1ee5bbc 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -12,7 +12,49 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Train a ResNet-50 model on ImageNet on TPU.""" +"""Train a ResNet-50 model on ImageNet on TPU. + +# Train on GPUs. +python resnet_main.py --use_tpu=False --mode=train \ + --model_dir=/home/ubuntu/resnet-model \ + --data_dir=gs://cloud-tpu-test-datasets/fake_imagenet \ + --train_batch_size=256 --train_steps=112590 --iterations_per_loop=1251 2>&1 | tee run.log + +V100 16GB + + Problem: bs=256 has some improvement over bs=128, but only ~1.+x + +float32, BS=128: 365 imgs/sec +AMP float16, BS=128: 730 imgs/sec +AMP float16, BS=256: 750 imgs/sec *** Why? +TODO: enable XLA + + amp noxla + - bs=256 OOMs + - bs=128 - 360 imgs/sec + + disable_meta_optimizer=False + - bs=256 - 750 imgs/sec + - bs=128 - 730 imgs/sec + + noamp noxla + - bs=256 OOMs + - bs=128 - 365 imgs/sec + + disable_meta_optimizer=False + - bs=256 OOMs + - bs=128 - 370 imgs/sec + + ------- + + amp xla + - bs=256 OOMs + - bs=128 - 360 imgs/sec + + disable_meta_optimizer=False + + (jit={1,2,fusible}) no longer printing thput, altho GPU has util. +""" from __future__ import absolute_import from __future__ import division @@ -467,10 +509,9 @@ def restore_scaffold(): export_outputs={ 'classify': tf.estimator.export.PredictOutput(predictions)}, scaffold_fn=scaffold_fn) - #print(params) # If necessary, in the model_fn, use params['batch_size'] instead the batch # size flags (--train_batch_size or --eval_batch_size). - #batch_size = params['batch_size'] # pylint: disable=unused-variable + batch_size = params['batch_size'] # pylint: disable=unused-variable # Calculate loss, which includes softmax cross entropy and L2 regularization. one_hot_labels = tf.one_hot(labels, params['num_label_classes']) @@ -519,7 +560,7 @@ def restore_scaffold(): # user, this should look like regular synchronous training. optimizer = tf.tpu.CrossShardOptimizer(optimizer) - if True: + if FLAGS.amp: optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. @@ -700,20 +741,29 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) # Enable AMP - amp = True - xla = False + amp = FLAGS.amp + xla = FLAGS.xla # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow # Step 2: add --amp flag - import os - print(amp, xla) if amp: - os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" - os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = "1" - os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING"] = "1" + pass + # These two lines have no effect. + # os.environ['TF_CPP_VMODULE'] = 'auto_mixed_precision=2' + # os.environ['TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_LOG_PATH'] = '/home/ubuntu/amp-log' + + # These three lines are redundant. + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE'] = '1' + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1' + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING'] = '1' if xla: - # For XLA - # Ru this TF_XLA_FLAGS=--tf_xla_auto_jit=2 python official/resnet/resnet_main.py --use_tpu=False --mode=train --data_dir=/home/ubuntu/data/tf_records --model_dir=/home/ubuntu/model --train_batch_size=100 --train_steps=112590 --iterations_per_loop=1251 2>&1 | tee run.log - os.environ['TF_XLA_FLAGS'] = 'TF_XLA_FLAGS=--tf_xla_auto_jit=2'#'--tf_xla_enable_xla_devices' + # https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/compiler/jit/flags.cc#L78-L87 + # 1: on for things very likely to be improved + # 2: on for everything + # fusible: only for Tensorflow operations that XLA knows how to fuse + # + # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=1' + os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2' + # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' params.validate() params.lock() @@ -726,9 +776,13 @@ def main(unused_argv): else: save_checkpoints_steps = max(5000, params.iterations_per_loop) - # Enable XLA - session_config = tf.GraphOptions(rewrite_options=rewriter_config_pb2.RewriterConfig(disable_meta_optimizer=True)) - strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.NcclAllReduce()) + session_config = tf.GraphOptions( + rewrite_options=rewriter_config_pb2.RewriterConfig( + # disable_meta_optimizer=True, # NOTE: this line would keep AMP disabled. + auto_mixed_precision=1 if FLAGS.amp else 2, + )) + strategy = tf.distribute.MirroredStrategy( + cross_device_ops=tf.distribute.NcclAllReduce()) config = tf.estimator.tpu.RunConfig( cluster=None, #tpu_cluster_resolver, From 09199b8d8a8434ee57361d62e3c735af8d6aaed0 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 10 Aug 2021 11:02:58 +0000 Subject: [PATCH 14/20] Added static loss + Nvidia config --- .../official/resnet/configs/resnet_config.py | 2 +- models/official/resnet/resnet_main.py | 22 +- models/run.log | 1684 ++++++++++++++++- 3 files changed, 1643 insertions(+), 65 deletions(-) diff --git a/models/official/resnet/configs/resnet_config.py b/models/official/resnet/configs/resnet_config.py index 83fb1d80..440f9c64 100644 --- a/models/official/resnet/configs/resnet_config.py +++ b/models/official/resnet/configs/resnet_config.py @@ -30,7 +30,7 @@ 'transpose_input': False, 'precision': 'float32', 'num_label_classes': 1000, - 'use_cache': True, + 'use_cache': False, 'use_async_checkpointing': False, 'image_size': 224, 'momentum': 0.9, diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index e1ee5bbc..7bd9df81 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -279,6 +279,8 @@ 'xla', False, 'Whether to use accelerated linear algebra.') +flags.DEFINE_integer('loss_scale', -1, 'Loss Scale for AMP.') + # The input tensor is in the range of [0, 255], we need to scale them to the # range of [0, 1] @@ -561,7 +563,7 @@ def restore_scaffold(): optimizer = tf.tpu.CrossShardOptimizer(optimizer) if FLAGS.amp: - optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer) + optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer, loss_scale='dynamic' if FLAGS.loss_scale==-1 else FLAGS.loss_scale) # Batch normalization requires UPDATE_OPS to be added as a dependency to # the train operation. update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) @@ -740,12 +742,24 @@ def main(unused_argv): params = flags_to_params.override_params_from_input_flags(params, FLAGS) + # From Nvidia Repo, explained here: https://github.com/NVIDIA/DeepLearningExamples/issues/57 + os.environ['CUDA_CACHE_DISABLE'] = '0' + os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private' + os.environ['TF_GPU_THREAD_COUNT'] = '2' + os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1' + os.environ['TF_ADJUST_HUE_FUSED'] = '1' + os.environ['TF_ADJUST_SATURATION_FUSED'] = '1' + os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1' + os.environ['TF_SYNC_ON_FINISH'] = '0' + os.environ['TF_AUTOTUNE_THRESHOLD'] = '2' + os.environ['TF_DISABLE_NVTX_RANGES'] = '1' # Enable AMP amp = FLAGS.amp xla = FLAGS.xla # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow # Step 2: add --amp flag if amp: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" pass # These two lines have no effect. # os.environ['TF_CPP_VMODULE'] = 'auto_mixed_precision=2' @@ -762,8 +776,10 @@ def main(unused_argv): # fusible: only for Tensorflow operations that XLA knows how to fuse # # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=1' - os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2' - # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' + # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2' + # Best Performing XLA Option + os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' + os.environ["TF_XLA_FLAGS"] = (os.environ.get("TF_XLA_FLAGS", "") + " --tf_xla_enable_lazy_compilation=false") params.validate() params.lock() diff --git a/models/run.log b/models/run.log index 744d507e..32a04aa1 100644 --- a/models/run.log +++ b/models/run.log @@ -1,13 +1,15 @@ WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term -W0803 10:06:59.058344 140214897387328 flags_to_params.py:44] Flag tpu is None. -INFO:tensorflow:Params: +W0810 11:00:07.484305 139645157615424 flags_to_params.py:44] Flag tpu is None. +> /home/ubuntu/tpu/models/official/resnet/resnet_main.py(781)main() +-> os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' +(Pdb) INFO:tensorflow:Params: {'augment_name': None, 'base_learning_rate': 0.1, 'bn_momentum': 0.9, - 'data_dir': '/data/tfrecords', - 'data_format': 'channels_last', + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_first', 'drop_connect_rate': None, 'dropblock_groups': '', 'dropblock_keep_prob': None, @@ -18,7 +20,7 @@ INFO:tensorflow:Params: 'image_size': 224, 'iterations_per_loop': 1251, 'label_smoothing': 0.0, - 'model_dir': './model', + 'model_dir': '/home/ubuntu/model', 'momentum': 0.9, 'moving_average_decay': 0, 'norm_act_layer': 'bn_relu', @@ -37,20 +39,20 @@ INFO:tensorflow:Params: 'resnetd_shortcut': False, 'se_ratio': None, 'skip_host_call': False, - 'train_batch_size': 128, - 'train_steps': 4096, - 'transpose_input': True, + 'train_batch_size': 256, + 'train_steps': 112590, + 'transpose_input': False, 'use_async_checkpointing': False, - 'use_cache': True, + 'use_cache': False, 'use_resnetd_stem': False, 'use_tpu': False, 'weight_decay': 0.0001} -I0803 10:06:59.059037 140214897387328 resnet_main.py:716] Params: +I0810 11:00:10.608073 139645157615424 resnet_main.py:781] Params: {'augment_name': None, 'base_learning_rate': 0.1, 'bn_momentum': 0.9, - 'data_dir': '/data/tfrecords', - 'data_format': 'channels_last', + 'data_dir': '/home/ubuntu/data/', + 'data_format': 'channels_first', 'drop_connect_rate': None, 'dropblock_groups': '', 'dropblock_keep_prob': None, @@ -61,7 +63,7 @@ I0803 10:06:59.059037 140214897387328 resnet_main.py:716] Params: 'image_size': 224, 'iterations_per_loop': 1251, 'label_smoothing': 0.0, - 'model_dir': './model', + 'model_dir': '/home/ubuntu/model', 'momentum': 0.9, 'moving_average_decay': 0, 'norm_act_layer': 'bn_relu', @@ -80,93 +82,1653 @@ I0803 10:06:59.059037 140214897387328 resnet_main.py:716] Params: 'resnetd_shortcut': False, 'se_ratio': None, 'skip_host_call': False, - 'train_batch_size': 128, - 'train_steps': 4096, - 'transpose_input': True, + 'train_batch_size': 256, + 'train_steps': 112590, + 'transpose_input': False, 'use_async_checkpointing': False, - 'use_cache': True, + 'use_cache': False, 'use_resnetd_stem': False, 'use_tpu': False, 'weight_decay': 0.0001} -WARNING:tensorflow:From official/resnet/resnet_main.py:728: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. +INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0',) +I0810 11:00:11.449082 139645157615424 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0',) +WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. -W0803 10:06:59.918581 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:728: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. +W0810 11:00:11.490467 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. -INFO:tensorflow:Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') -I0803 10:07:04.749025 140214897387328 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') -INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO -I0803 10:07:04.749967 140214897387328 collective_all_reduce_strategy.py:404] Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO -INFO:tensorflow:Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') -I0803 10:07:04.751258 140214897387328 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7') -INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO -I0803 10:07:04.751380 140214897387328 collective_all_reduce_strategy.py:404] Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0', '/device:GPU:1', '/device:GPU:2', '/device:GPU:3', '/device:GPU:4', '/device:GPU:5', '/device:GPU:6', '/device:GPU:7'), communication = CommunicationImplementation.AUTO -WARNING:tensorflow:From official/resnet/resnet_main.py:737: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. +WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. -W0803 10:07:04.751748 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:737: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. +W0810 11:00:11.490733 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. -WARNING:tensorflow:From official/resnet/resnet_main.py:740: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. +WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. -W0803 10:07:04.751852 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:740: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. +W0810 11:00:11.490892 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. INFO:tensorflow:Initializing RunConfig with distribution strategies. -I0803 10:07:04.752192 140214897387328 run_config.py:584] Initializing RunConfig with distribution strategies. +I0810 11:00:11.491120 139645157615424 run_config.py:584] Initializing RunConfig with distribution strategies. INFO:tensorflow:Not using Distribute Coordinator. -I0803 10:07:04.752317 140214897387328 estimator_training.py:167] Not using Distribute Coordinator. -WARNING:tensorflow:From official/resnet/resnet_main.py:743: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. +I0810 11:00:11.491270 139645157615424 estimator_training.py:167] Not using Distribute Coordinator. +WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. -W0803 10:07:04.752419 140214897387328 module_wrapper.py:138] From official/resnet/resnet_main.py:743: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. +W0810 11:00:11.491413 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. -INFO:tensorflow:Using config: {'_model_dir': './model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { +INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { rewrite_options { - disable_meta_optimizer: true + auto_mixed_precision: ON } } -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -I0803 10:07:04.755181 140214897387328 estimator.py:192] Using config: {'_model_dir': './model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +I0810 11:00:11.492113 139645157615424 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { rewrite_options { - disable_meta_optimizer: true + auto_mixed_precision: ON } } -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} +, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} INFO:tensorflow:_TPUContext: eval_on_tpu True -I0803 10:07:04.755454 140214897387328 tpu_context.py:271] _TPUContext: eval_on_tpu True +I0810 11:00:11.492438 139645157615424 tpu_context.py:271] _TPUContext: eval_on_tpu True WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. -W0803 10:07:04.755591 140214897387328 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. +W0810 11:00:11.492663 139645157615424 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. INFO:tensorflow:Precision: float32 -I0803 10:07:04.755653 140214897387328 resnet_main.py:756] Precision: float32 -INFO:tensorflow:Using dataset: /data/tfrecords -I0803 10:07:04.755709 140214897387328 resnet_main.py:780] Using dataset: /data/tfrecords -INFO:tensorflow:Training for 4096 steps (0.41 epochs in total). Current step 0. -I0803 10:07:04.756135 140214897387328 resnet_main.py:842] Training for 4096 steps (0.41 epochs in total). Current step 0. +I0810 11:00:11.492757 139645157615424 resnet_main.py:781] Precision: float32 +INFO:tensorflow:Using dataset: /home/ubuntu/data/ +I0810 11:00:11.492849 139645157615424 resnet_main.py:781] Using dataset: /home/ubuntu/data/ +INFO:tensorflow:Training for 112590 steps (22.50 epochs in total). Current step 0. +I0810 11:00:11.493233 139645157615424 resnet_main.py:781] Training for 112590 steps (22.50 epochs in total). Current step 0. WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -W0803 10:07:04.772450 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. +W0810 11:00:11.508014 139645157615424 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. -W0803 10:07:04.818538 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:360: shuffle_and_repeat (from tensorflow.python.data.experimental.ops.shuffle_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by `tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take care of using the fused implementation. WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -W0803 10:07:04.820030 140214897387328 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. +W0810 11:00:11.566509 139645157615424 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -W0803 10:07:05.227103 140214897387328 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. +W0810 11:00:12.158080 139645157615424 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: `seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. INFO:tensorflow:Calling model_fn. -I0803 10:07:05.780834 140181474629376 api.py:479] Calling model_fn. +I0810 11:00:12.779572 139639704770304 api.py:479] Calling model_fn. INFO:tensorflow:Running train on CPU/GPU -I0803 10:07:07.341922 140181474629376 api.py:479] Running train on CPU/GPU -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. +I0810 11:00:15.165064 139639704770304 api.py:479] Running train on CPU/GPU +WARNING:tensorflow:AutoGraph could not transform and will run it as-is. +Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. +Cause: could not get source code +To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert +W0810 11:00:15.863449 139639704770304 ag_logging.py:146] AutoGraph could not transform and will run it as-is. +Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. +Cause: could not get source code +To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. + warnings.warn('`tf.layers.conv2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. + warnings.warn('`layer.apply` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). + '`tf.layers.batch_normalization` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. + warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. + warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' +/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. + warnings.warn('`tf.layers.dense` is deprecated and ' +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/experimental/loss_scale.py:470: FixedLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version. +Instructions for updating: +Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of FixedLossScale +W0810 11:00:18.342297 139639704770304 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/experimental/loss_scale.py:470: FixedLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version. +Instructions for updating: +Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of FixedLossScale +WARNING:tensorflow:From official/resnet/resnet_main.py:673: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. -W0803 10:07:07.944726 140181474629376 module_wrapper.py:138] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/autograph/converters/directives.py:130: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. +W0810 11:00:20.390120 139639704770304 module_wrapper.py:138] From official/resnet/resnet_main.py:673: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. +INFO:tensorflow:Done calling model_fn. +I0810 11:00:21.082230 139639704770304 api.py:479] Done calling model_fn. +INFO:tensorflow:Create CheckpointSaverHook. +I0810 11:00:21.893251 139645157615424 basic_session_run_hooks.py:546] Create CheckpointSaverHook. +WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +W0810 11:00:22.565945 139645157615424 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. +Instructions for updating: +Use the iterator's `initializer` property instead. +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.262583 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.265484 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.273587 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.276298 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.284469 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.287177 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.295237 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.297972 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.306059 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +I0810 11:00:23.308802 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). +INFO:tensorflow:Graph was finalized. +I0810 11:00:24.604404 139645157615424 monitored_session.py:246] Graph was finalized. +INFO:tensorflow:Running local_init_op. +I0810 11:00:29.342514 139645157615424 session_manager.py:505] Running local_init_op. +INFO:tensorflow:Done running local_init_op. +I0810 11:00:29.460495 139645157615424 session_manager.py:508] Done running local_init_op. +INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... +I0810 11:00:35.547602 139645157615424 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 0... +INFO:tensorflow:Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +I0810 11:00:35.554989 139645157615424 basic_session_run_hooks.py:618] Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. +INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... +I0810 11:00:36.940120 139645157615424 basic_session_run_hooks.py:626] Calling checkpoint listeners after saving checkpoint 0... +INFO:tensorflow:global_step/sec: 0.0674846 +I0810 11:01:29.060101 139645157615424 tpu_estimator.py:2391] 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11:02:44.802129 139645157615424 tpu_estimator.py:2392] examples/sec: 1237.22 +INFO:tensorflow:global_step/sec: 4.86949 +I0810 11:02:45.007092 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.86949 +INFO:tensorflow:examples/sec: 1246.59 +I0810 11:02:45.007491 139645157615424 tpu_estimator.py:2392] examples/sec: 1246.59 +INFO:tensorflow:global_step/sec: 4.92383 +I0810 11:02:45.210191 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.92383 +INFO:tensorflow:examples/sec: 1260.5 +I0810 11:02:45.210618 139645157615424 tpu_estimator.py:2392] examples/sec: 1260.5 +INFO:tensorflow:global_step/sec: 4.92238 +I0810 11:02:45.413343 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.92238 +INFO:tensorflow:examples/sec: 1260.13 +I0810 11:02:45.413766 139645157615424 tpu_estimator.py:2392] examples/sec: 1260.13 +INFO:tensorflow:global_step/sec: 4.89193 +I0810 11:02:45.617760 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.89193 +INFO:tensorflow:examples/sec: 1252.33 +I0810 11:02:45.618169 139645157615424 tpu_estimator.py:2392] examples/sec: 1252.33 +INFO:tensorflow:global_step/sec: 4.75845 +I0810 11:02:45.827893 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.75845 +INFO:tensorflow:examples/sec: 1218.16 +I0810 11:02:45.828269 139645157615424 tpu_estimator.py:2392] examples/sec: 1218.16 From 222cc86b58effc1d073308f313125a65710b39f5 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 10 Aug 2021 21:45:25 +0000 Subject: [PATCH 15/20] ok --- models/official/resnet/resnet_main.py | 13 +- models/run.log | 1734 ------------------------- 2 files changed, 4 insertions(+), 1743 deletions(-) delete mode 100644 models/run.log diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 7bd9df81..bae2f03a 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -513,7 +513,7 @@ def restore_scaffold(): scaffold_fn=scaffold_fn) # If necessary, in the model_fn, use params['batch_size'] instead the batch # size flags (--train_batch_size or --eval_batch_size). - batch_size = params['batch_size'] # pylint: disable=unused-variable + # batch_size = params['batch_size'] # pylint: disable=unused-variable # Calculate loss, which includes softmax cross entropy and L2 regularization. one_hot_labels = tf.one_hot(labels, params['num_label_classes']) @@ -797,8 +797,8 @@ def main(unused_argv): # disable_meta_optimizer=True, # NOTE: this line would keep AMP disabled. auto_mixed_precision=1 if FLAGS.amp else 2, )) - strategy = tf.distribute.MirroredStrategy( - cross_device_ops=tf.distribute.NcclAllReduce()) + # strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.NcclAllReduce()) + strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(communication=tf.distribute.experimental.CollectiveCommunication.NCCL) config = tf.estimator.tpu.RunConfig( cluster=None, #tpu_cluster_resolver, @@ -808,12 +808,7 @@ def main(unused_argv): train_distribute=strategy, eval_distribute=strategy, session_config=tf.ConfigProto( - graph_options=session_config), - tpu_config=tf.estimator.tpu.TPUConfig( - iterations_per_loop=params.iterations_per_loop, - num_shards=params.num_cores, - per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig - .PER_HOST_V2)) # pylint: disable=line-too-long + graph_options=session_config),) # pylint: disable=line-too-long resnet_classifier = tf.estimator.tpu.TPUEstimator( use_tpu=params.use_tpu, diff --git a/models/run.log b/models/run.log deleted file mode 100644 index 32a04aa1..00000000 --- a/models/run.log +++ /dev/null @@ -1,1734 +0,0 @@ -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. -Instructions for updating: -non-resource variables are not supported in the long term -W0810 11:00:07.484305 139645157615424 flags_to_params.py:44] Flag tpu is None. -> /home/ubuntu/tpu/models/official/resnet/resnet_main.py(781)main() --> os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' -(Pdb) INFO:tensorflow:Params: -{'augment_name': None, - 'base_learning_rate': 0.1, - 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', - 'data_format': 'channels_first', - 'drop_connect_rate': None, - 'dropblock_groups': '', - 'dropblock_keep_prob': None, - 'dropblock_size': 7, - 'dropout_rate': None, - 'enable_lars': False, - 'eval_batch_size': 1024, - 'image_size': 224, - 'iterations_per_loop': 1251, - 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', - 'momentum': 0.9, - 'moving_average_decay': 0, - 'norm_act_layer': 'bn_relu', - 'num_cores': 8, - 'num_eval_images': 50000, - 'num_label_classes': 1000, - 'num_parallel_calls': 8, - 'num_train_images': 1281167, - 'poly_rate': 0.0, - 'pre_activation': False, - 'precision': 'float32', - 'randaug_magnitude': None, - 'randaug_num_layers': None, - 'replace_stem_max_pool': False, - 'resnet_depth': 50, - 'resnetd_shortcut': False, - 'se_ratio': None, - 'skip_host_call': False, - 'train_batch_size': 256, - 'train_steps': 112590, - 'transpose_input': False, - 'use_async_checkpointing': False, - 'use_cache': False, - 'use_resnetd_stem': False, - 'use_tpu': False, - 'weight_decay': 0.0001} -I0810 11:00:10.608073 139645157615424 resnet_main.py:781] Params: -{'augment_name': None, - 'base_learning_rate': 0.1, - 'bn_momentum': 0.9, - 'data_dir': '/home/ubuntu/data/', - 'data_format': 'channels_first', - 'drop_connect_rate': None, - 'dropblock_groups': '', - 'dropblock_keep_prob': None, - 'dropblock_size': 7, - 'dropout_rate': None, - 'enable_lars': False, - 'eval_batch_size': 1024, - 'image_size': 224, - 'iterations_per_loop': 1251, - 'label_smoothing': 0.0, - 'model_dir': '/home/ubuntu/model', - 'momentum': 0.9, - 'moving_average_decay': 0, - 'norm_act_layer': 'bn_relu', - 'num_cores': 8, - 'num_eval_images': 50000, - 'num_label_classes': 1000, - 'num_parallel_calls': 8, - 'num_train_images': 1281167, - 'poly_rate': 0.0, - 'pre_activation': False, - 'precision': 'float32', - 'randaug_magnitude': None, - 'randaug_num_layers': None, - 'replace_stem_max_pool': False, - 'resnet_depth': 50, - 'resnetd_shortcut': False, - 'se_ratio': None, - 'skip_host_call': False, - 'train_batch_size': 256, - 'train_steps': 112590, - 'transpose_input': False, - 'use_async_checkpointing': False, - 'use_cache': False, - 'use_resnetd_stem': False, - 'use_tpu': False, - 'weight_decay': 0.0001} -INFO:tensorflow:Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0',) -I0810 11:00:11.449082 139645157615424 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/replica:0/task:0/device:GPU:0',) -WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. - -W0810 11:00:11.490467 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.RunConfig is deprecated. Please use tf.compat.v1.estimator.tpu.RunConfig instead. - -WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. - -W0810 11:00:11.490733 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead. - -WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. - -W0810 11:00:11.490892 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.InputPipelineConfig is deprecated. Please use tf.compat.v1.estimator.tpu.InputPipelineConfig instead. - -INFO:tensorflow:Initializing RunConfig with distribution strategies. -I0810 11:00:11.491120 139645157615424 run_config.py:584] Initializing RunConfig with distribution strategies. -INFO:tensorflow:Not using Distribute Coordinator. -I0810 11:00:11.491270 139645157615424 estimator_training.py:167] Not using Distribute Coordinator. -WARNING:tensorflow:From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. - -W0810 11:00:11.491413 139645157615424 module_wrapper.py:138] From official/resnet/resnet_main.py:781: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead. - -INFO:tensorflow:Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { - rewrite_options { - auto_mixed_precision: ON - } -} -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -I0810 11:00:11.492113 139645157615424 estimator.py:192] Using config: {'_model_dir': '/home/ubuntu/model', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 5000, '_save_checkpoints_secs': None, '_session_config': graph_options { - rewrite_options { - auto_mixed_precision: ON - } -} -, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None, '_tpu_config': TPUConfig(iterations_per_loop=1251, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': None} -INFO:tensorflow:_TPUContext: eval_on_tpu True -I0810 11:00:11.492438 139645157615424 tpu_context.py:271] _TPUContext: eval_on_tpu True -WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False. -W0810 11:00:11.492663 139645157615424 tpu_context.py:273] eval_on_tpu ignored because use_tpu is False. -INFO:tensorflow:Precision: float32 -I0810 11:00:11.492757 139645157615424 resnet_main.py:781] Precision: float32 -INFO:tensorflow:Using dataset: /home/ubuntu/data/ -I0810 11:00:11.492849 139645157615424 resnet_main.py:781] Using dataset: /home/ubuntu/data/ -INFO:tensorflow:Training for 112590 steps (22.50 epochs in total). Current step 0. -I0810 11:00:11.493233 139645157615424 resnet_main.py:781] Training for 112590 steps (22.50 epochs in total). Current step 0. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -W0810 11:00:11.508014 139645157615424 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:356: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`. -WARNING:tensorflow:From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -W0810 11:00:11.566509 139645157615424 deprecation.py:339] From /home/ubuntu/tpu/models/official/resnet/imagenet_input.py:206: map_and_batch (from tensorflow.python.data.experimental.ops.batching) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Dataset.map(map_func, num_parallel_calls)` followed by `tf.data.Dataset.batch(batch_size, drop_remainder)`. Static tf.data optimizations will take care of using the fused implementation. -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. -Instructions for updating: -`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -W0810 11:00:12.158080 139645157615424 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. -Instructions for updating: -`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead. -INFO:tensorflow:Calling model_fn. -I0810 11:00:12.779572 139639704770304 api.py:479] Calling model_fn. -INFO:tensorflow:Running train on CPU/GPU -I0810 11:00:15.165064 139639704770304 api.py:479] Running train on CPU/GPU -WARNING:tensorflow:AutoGraph could not transform and will run it as-is. -Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. -Cause: could not get source code -To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert -W0810 11:00:15.863449 139639704770304 ag_logging.py:146] AutoGraph could not transform and will run it as-is. -Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. -Cause: could not get source code -To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/convolutional.py:414: UserWarning: `tf.layers.conv2d` is deprecated and will be removed in a future version. Please Use `tf.keras.layers.Conv2D` instead. - warnings.warn('`tf.layers.conv2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1719: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead. - warnings.warn('`layer.apply` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/normalization.py:308: UserWarning: `tf.layers.batch_normalization` is deprecated and will be removed in a future version. Please use `tf.keras.layers.BatchNormalization` instead. In particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not be used (consult the `tf.keras.layers.BatchNormalization` documentation). - '`tf.layers.batch_normalization` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:310: UserWarning: `tf.layers.max_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.MaxPooling2D` instead. - warnings.warn('`tf.layers.max_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/pooling.py:236: UserWarning: `tf.layers.average_pooling2d` is deprecated and will be removed in a future version. Please use `tf.keras.layers.AveragePooling2D` instead. - warnings.warn('`tf.layers.average_pooling2d` is deprecated and ' -/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/keras/legacy_tf_layers/core.py:171: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. - warnings.warn('`tf.layers.dense` is deprecated and ' -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/experimental/loss_scale.py:470: FixedLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version. -Instructions for updating: -Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of FixedLossScale -W0810 11:00:18.342297 139639704770304 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/gpu_cuda11.0/lib/python3.7/site-packages/tensorflow/python/training/experimental/loss_scale.py:470: FixedLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version. -Instructions for updating: -Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of FixedLossScale -WARNING:tensorflow:From official/resnet/resnet_main.py:673: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. - -W0810 11:00:20.390120 139639704770304 module_wrapper.py:138] From official/resnet/resnet_main.py:673: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead. - -INFO:tensorflow:Done calling model_fn. -I0810 11:00:21.082230 139639704770304 api.py:479] Done calling model_fn. -INFO:tensorflow:Create CheckpointSaverHook. -I0810 11:00:21.893251 139645157615424 basic_session_run_hooks.py:546] Create CheckpointSaverHook. -WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -W0810 11:00:22.565945 139645157615424 deprecation.py:339] From /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:96: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. -Instructions for updating: -Use the iterator's `initializer` property instead. -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.262583 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.265484 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.273587 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.276298 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.284469 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.287177 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.295237 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.297972 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.306059 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -I0810 11:00:23.308802 139645157615424 cross_device_ops.py:565] Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). -INFO:tensorflow:Graph was finalized. -I0810 11:00:24.604404 139645157615424 monitored_session.py:246] Graph was finalized. -INFO:tensorflow:Running local_init_op. -I0810 11:00:29.342514 139645157615424 session_manager.py:505] Running local_init_op. -INFO:tensorflow:Done running local_init_op. -I0810 11:00:29.460495 139645157615424 session_manager.py:508] Done running local_init_op. -INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... -I0810 11:00:35.547602 139645157615424 basic_session_run_hooks.py:614] Calling checkpoint listeners before saving checkpoint 0... -INFO:tensorflow:Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. -I0810 11:00:35.554989 139645157615424 basic_session_run_hooks.py:618] Saving checkpoints for 0 into /home/ubuntu/model/model.ckpt. -INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... -I0810 11:00:36.940120 139645157615424 basic_session_run_hooks.py:626] Calling checkpoint listeners after saving checkpoint 0... -INFO:tensorflow:global_step/sec: 0.0674846 -I0810 11:01:29.060101 139645157615424 tpu_estimator.py:2391] 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-I0810 11:02:45.618169 139645157615424 tpu_estimator.py:2392] examples/sec: 1252.33 -INFO:tensorflow:global_step/sec: 4.75845 -I0810 11:02:45.827893 139645157615424 tpu_estimator.py:2391] global_step/sec: 4.75845 -INFO:tensorflow:examples/sec: 1218.16 -I0810 11:02:45.828269 139645157615424 tpu_estimator.py:2392] examples/sec: 1218.16 From fb36b0e9669f6413a51b347b14b6feccb5eab521 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Tue, 7 Sep 2021 20:55:33 +0000 Subject: [PATCH 16/20] GPU Inference --- models/official/resnet/resnet_main.py | 28 ++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index bae2f03a..7619e6ec 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -63,7 +63,7 @@ import os import pprint import time - +import sys from absl import app from absl import flags from absl import logging @@ -647,6 +647,7 @@ def metric_fn(labels, logits): Returns: A dict of the metrics to return from evaluation. """ + #logits = tf.Print(logits, [tf.shape(logits)]) predictions = tf.argmax(logits, axis=1) top_1_accuracy = tf.metrics.accuracy(labels, predictions) in_top_5 = tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32) @@ -669,7 +670,6 @@ def eval_scaffold(): saver = tf.train.Saver(restore_vars_dict) return tf.train.Scaffold(saver=saver) scaffold_fn = eval_scaffold - return tf.estimator.tpu.TPUEstimatorSpec( mode=mode, loss=loss, @@ -808,7 +808,8 @@ def main(unused_argv): train_distribute=strategy, eval_distribute=strategy, session_config=tf.ConfigProto( - graph_options=session_config),) # pylint: disable=line-too-long + graph_options=session_config, + allow_soft_placement=True),) # pylint: disable=line-too-long resnet_classifier = tf.estimator.tpu.TPUEstimator( use_tpu=params.use_tpu, @@ -866,9 +867,11 @@ def main(unused_argv): steps_per_epoch = params.num_train_images // params.train_batch_size eval_steps = params.num_eval_images // params.eval_batch_size + + if FLAGS.mode == 'infer': + inf_steps = 10000 if FLAGS.mode == 'eval': - # Run evaluation when there's a new checkpoint for ckpt in tf.train.checkpoints_iterator( FLAGS.model_dir, timeout=FLAGS.eval_timeout): @@ -889,7 +892,6 @@ def main(unused_argv): tf.logging.info( 'Evaluation finished after training step %d', current_step) break - except tf.errors.NotFoundError: # Since the coordinator is on a different job than the TPU worker, # sometimes the TPU worker does not finish initializing until long after @@ -897,8 +899,20 @@ def main(unused_argv): # file could have been deleted already. tf.logging.info( 'Checkpoint %s no longer exists, skipping checkpoint', ckpt) - - else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval' + elif FLAGS.mode == 'infer': + # Run evaluation when there's a new checkpoint + for ckpt in tf.train.checkpoints_iterator( + FLAGS.model_dir, timeout=FLAGS.eval_timeout): + tf.logging.info('Model Serving (over Imagenet Training).') + start_timestamp = time.time() # This time will include compilation time + inf_results = resnet_classifier.evaluate( + input_fn=imagenet_train.input_fn, + steps=inf_steps, + checkpoint_path=ckpt) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info('Inference results: %s. Elapsed seconds: %d', + inf_results, elapsed_time) + else: try: current_step = tf.train.load_variable(FLAGS.model_dir, tf.GraphKeys.GLOBAL_STEP) From 19e64965ebd196af284bdca449e9acc27491a978 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Thu, 7 Oct 2021 22:45:20 +0000 Subject: [PATCH 17/20] Added requirements --- models/official/resnet/requirements.txt | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 models/official/resnet/requirements.txt diff --git a/models/official/resnet/requirements.txt b/models/official/resnet/requirements.txt new file mode 100644 index 00000000..c4d62149 --- /dev/null +++ b/models/official/resnet/requirements.txt @@ -0,0 +1,2 @@ +tensorflow==2.4.0 +pyyaml From adf2a26f29bab6fca0cf495129604814f3d8fc42 Mon Sep 17 00:00:00 2001 From: Michael Luo Date: Thu, 7 Oct 2021 17:32:15 -0700 Subject: [PATCH 18/20] Small c hange --- models/official/resnet/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/official/resnet/requirements.txt b/models/official/resnet/requirements.txt index c4d62149..675ad86d 100644 --- a/models/official/resnet/requirements.txt +++ b/models/official/resnet/requirements.txt @@ -1,2 +1,2 @@ -tensorflow==2.4.0 +tensorflow==2.5.0 pyyaml From 9459fee8893366848d0544c97f45313ce16f3a00 Mon Sep 17 00:00:00 2001 From: Ubuntu Date: Fri, 8 Oct 2021 02:44:34 +0000 Subject: [PATCH 19/20] temp --- models/official/resnet/resnet_main.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 7619e6ec..8f7592f5 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -801,7 +801,7 @@ def main(unused_argv): strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(communication=tf.distribute.experimental.CollectiveCommunication.NCCL) config = tf.estimator.tpu.RunConfig( - cluster=None, #tpu_cluster_resolver, + #cluster=None, #tpu_cluster_resolver, model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, log_step_count_steps=FLAGS.log_step_count_steps, @@ -947,10 +947,8 @@ def main(unused_argv): save_steps=FLAGS.profile_every_n_steps, output_dir=FLAGS.model_dir, tpu=FLAGS.tpu) ) - resnet_classifier.train( - input_fn=imagenet_train.input_fn, - max_steps=params.train_steps, - hooks=hooks) + tf.estimator.train_and_evaluate(resnet_classifier, train_spec=tf.estimator.TrainSpec(input_fn=imagenet_train.input_fn, max_steps=params.train_steps, hooks=hooks), eval_spec=tf.estimator.EvalSpec(input_fn=imagenet_eval.input_fn, steps=None, start_delay_secs=1e9)) + #resnet_classifier.train(input_fn=imagenet_train.input_fn,max_steps=params.train_steps,hooks=hooks) elapsed_time = int(time.time() - start_timestamp) tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', @@ -963,8 +961,9 @@ def main(unused_argv): # At the end of training, a checkpoint will be written to --model_dir. next_checkpoint = min(current_step + FLAGS.steps_per_eval, params.train_steps) - resnet_classifier.train( - input_fn=imagenet_train.input_fn, max_steps=next_checkpoint) + tf.estimator.train_and_evaluate(resnet_classifier, train_spec=tf.estimator.TrainSpec(input_fn=imagenet_train.input_fn, max_steps=next_checkpoint), eval_spec=tf.estimator.EvalSpec()) + #resnet_classifier.train( + #input_fn=imagenet_train.input_fn, max_steps=next_checkpoint) current_step = next_checkpoint tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', From d01593ddff4a569c5cc986ef8e9c581ff25d782b Mon Sep 17 00:00:00 2001 From: Michael Luo Date: Sun, 11 Sep 2022 18:36:28 -0700 Subject: [PATCH 20/20] Add Resnet inference --- models/official/resnet/resnet_main.py | 1448 +++++++++++++------------ 1 file changed, 769 insertions(+), 679 deletions(-) diff --git a/models/official/resnet/resnet_main.py b/models/official/resnet/resnet_main.py index 8f7592f5..726d13cb 100644 --- a/models/official/resnet/resnet_main.py +++ b/models/official/resnet/resnet_main.py @@ -91,7 +91,8 @@ FAKE_DATA_DIR = 'gs://cloud-tpu-test-datasets/fake_imagenet' flags.DEFINE_integer( - 'resnet_depth', default=None, + 'resnet_depth', + default=None, help=('Depth of ResNet model to use. Must be one of {18, 34, 50, 101, 152,' ' 200}. ResNet-18 and 34 use the pre-activation residual blocks' ' without bottleneck layers. The other models use pre-activation' @@ -99,64 +100,75 @@ ' more memory and may require reducing --train_batch_size to prevent' ' running out of memory.')) -flags.DEFINE_integer( - 'num_train_images', default=None, help='Size of training data set.') +flags.DEFINE_integer('num_train_images', + default=None, + help='Size of training data set.') -flags.DEFINE_integer( - 'num_eval_images', default=None, help='Size of evaluation data set.') +flags.DEFINE_integer('num_eval_images', + default=None, + help='Size of evaluation data set.') -flags.DEFINE_integer( - 'num_label_classes', default=None, help='Number of classes, at least 2') +flags.DEFINE_integer('num_label_classes', + default=None, + help='Number of classes, at least 2') flags.DEFINE_string( - 'data_format', default=None, + 'data_format', + default=None, help=('A flag to override the data format used in the model. The value' ' is either channels_first or channels_last. To run the network on' ' CPU or TPU, channels_last should be used. For GPU, channels_first' ' will improve performance.')) -flags.DEFINE_bool( - 'transpose_input', default=None, - help='Use TPU double transpose optimization') +flags.DEFINE_bool('transpose_input', + default=None, + help='Use TPU double transpose optimization') -flags.DEFINE_bool( - 'use_cache', default=None, help=('Enable cache for training input.')) +flags.DEFINE_bool('use_cache', + default=None, + help=('Enable cache for training input.')) flags.DEFINE_integer('image_size', None, 'The input image size.') flags.DEFINE_string( - 'dropblock_groups', None, - help=('A string containing comma separated integers indicating ResNet ' - 'block groups to apply DropBlock. `3,4` means to apply DropBlock to ' - 'block groups 3 and 4. Use an empty string to not apply DropBlock to ' - 'any block group.')) + 'dropblock_groups', + None, + help=( + 'A string containing comma separated integers indicating ResNet ' + 'block groups to apply DropBlock. `3,4` means to apply DropBlock to ' + 'block groups 3 and 4. Use an empty string to not apply DropBlock to ' + 'any block group.')) flags.DEFINE_float( - 'dropblock_keep_prob', default=None, + 'dropblock_keep_prob', + default=None, help=('keep_prob parameter of DropBlock. Will not be used if ' 'dropblock_groups is empty.')) flags.DEFINE_integer( - 'dropblock_size', default=None, + 'dropblock_size', + default=None, help=('size parameter of DropBlock. Will not be used if dropblock_groups ' 'is empty.')) -flags.DEFINE_boolean( - 'pre_activation', default=None, - help=('Whether to use pre-activation ResNet (ResNet-v2)')) +flags.DEFINE_boolean('pre_activation', + default=None, + help=('Whether to use pre-activation ResNet (ResNet-v2)')) -flags.DEFINE_string( - 'norm_act_layer', default=None, - help='One of {"bn_relu", "evonorm_b0", "evonorm_s0"}.') +flags.DEFINE_string('norm_act_layer', + default=None, + help='One of {"bn_relu", "evonorm_b0", "evonorm_s0"}.') flags.DEFINE_integer( - 'profile_every_n_steps', default=0, + 'profile_every_n_steps', + default=0, help=('Number of steps between collecting profiles if larger than 0')) -flags.DEFINE_string( - 'mode', default='train_and_eval', - help='One of {"train_and_eval", "train", "eval"}.') +flags.DEFINE_string('mode', + default='train_and_eval', + help='One of {"train_and_eval", "train", "eval"}.') flags.DEFINE_integer( - 'steps_per_eval', default=1251, + 'steps_per_eval', + default=1251, help=('Controls how often evaluation is performed. Since evaluation is' ' fairly expensive, it is advised to evaluate as infrequently as' ' possible (i.e. up to --train_steps, which evaluates the model only' @@ -168,36 +180,32 @@ help='Maximum seconds between checkpoints before evaluation terminates.') flags.DEFINE_integer( - 'num_parallel_calls', default=None, + 'num_parallel_calls', + default=None, help=('Number of parallel threads in CPU for the input pipeline.' ' Recommended value is the number of cores per CPU host.')) flags.DEFINE_integer( - 'num_cores', default=None, + 'num_cores', + default=None, help=('Number of TPU cores in total. For a single TPU device, this is 8' ' because each TPU has 4 chips each with 2 cores.')) flags.DEFINE_string( 'bigtable_project', None, 'The Cloud Bigtable project. If None, --gcp_project will be used.') -flags.DEFINE_string( - 'bigtable_instance', None, - 'The Cloud Bigtable instance to load data from.') -flags.DEFINE_string( - 'bigtable_table', 'imagenet', - 'The Cloud Bigtable table to load data from.') -flags.DEFINE_string( - 'bigtable_train_prefix', 'train_', - 'The prefix identifying training rows.') -flags.DEFINE_string( - 'bigtable_eval_prefix', 'validation_', - 'The prefix identifying evaluation rows.') -flags.DEFINE_string( - 'bigtable_column_family', 'tfexample', - 'The column family storing TFExamples.') -flags.DEFINE_string( - 'bigtable_column_qualifier', 'example', - 'The column name storing TFExamples.') +flags.DEFINE_string('bigtable_instance', None, + 'The Cloud Bigtable instance to load data from.') +flags.DEFINE_string('bigtable_table', 'imagenet', + 'The Cloud Bigtable table to load data from.') +flags.DEFINE_string('bigtable_train_prefix', 'train_', + 'The prefix identifying training rows.') +flags.DEFINE_string('bigtable_eval_prefix', 'validation_', + 'The prefix identifying evaluation rows.') +flags.DEFINE_string('bigtable_column_family', 'tfexample', + 'The column family storing TFExamples.') +flags.DEFINE_string('bigtable_column_qualifier', 'example', + 'The column name storing TFExamples.') flags.DEFINE_string( 'export_dir', @@ -205,57 +213,63 @@ help=('The directory where the exported SavedModel will be stored.')) flags.DEFINE_bool( - 'export_to_tpu', default=False, + 'export_to_tpu', + default=False, help=('Whether to export additional metagraph with "serve, tpu" tags' ' in addition to "serve" only metagraph.')) -flags.DEFINE_float( - 'base_learning_rate', default=None, - help=('Base learning rate when train batch size is 256.')) +flags.DEFINE_float('base_learning_rate', + default=None, + help=('Base learning rate when train batch size is 256.')) -flags.DEFINE_float( - 'momentum', default=None, - help=('Momentum parameter used in the MomentumOptimizer.')) +flags.DEFINE_float('momentum', + default=None, + help=('Momentum parameter used in the MomentumOptimizer.')) -flags.DEFINE_float( - 'weight_decay', default=None, - help=('Weight decay coefficiant for l2 regularization.')) +flags.DEFINE_float('weight_decay', + default=None, + help=('Weight decay coefficiant for l2 regularization.')) flags.DEFINE_float( - 'label_smoothing', default=None, + 'label_smoothing', + default=None, help=('Label smoothing parameter used in the softmax_cross_entropy')) flags.DEFINE_bool('enable_lars', default=None, help=('Enable LARS optimizer for large batch training.')) -flags.DEFINE_float('poly_rate', default=None, +flags.DEFINE_float('poly_rate', + default=None, help=('Set LARS/Poly learning rate.')) -flags.DEFINE_bool( - 'use_async_checkpointing', default=None, help=('Enable async checkpoint')) +flags.DEFINE_bool('use_async_checkpointing', + default=None, + help=('Enable async checkpoint')) -flags.DEFINE_integer('log_step_count_steps', 64, 'The number of steps at ' - 'which the global step information is logged.') +flags.DEFINE_integer( + 'log_step_count_steps', 64, 'The number of steps at ' + 'which the global step information is logged.') flags.DEFINE_string( - 'augment_name', default=None, + 'augment_name', + default=None, help='`string` that is the name of the augmentation method' - 'to apply to the image. `autoaugment` if AutoAugment is to be used or' - '`randaugment` if RandAugment is to be used. If the value is `None` no' - 'augmentation method will be applied applied. See autoaugment.py for ' - 'more details.') - + 'to apply to the image. `autoaugment` if AutoAugment is to be used or' + '`randaugment` if RandAugment is to be used. If the value is `None` no' + 'augmentation method will be applied applied. See autoaugment.py for ' + 'more details.') flags.DEFINE_integer( - 'randaug_num_layers', default=None, + 'randaug_num_layers', + default=None, help='If RandAug is used, what should the number of layers be.' - 'See autoaugment.py for detailed description.') + 'See autoaugment.py for detailed description.') -flags.DEFINE_integer( - 'randaug_magnitude', default=None, - help='If RandAug is used, what should the magnitude be. ' - 'See autoaugment.py for detailed description.') +flags.DEFINE_integer('randaug_magnitude', + default=None, + help='If RandAug is used, what should the magnitude be. ' + 'See autoaugment.py for detailed description.') # Inference configuration. flags.DEFINE_bool( @@ -267,20 +281,23 @@ flags.DEFINE_multi_integer( 'inference_batch_sizes', [8], 'Known inference batch sizes used to warm up for each core.') -flags.DEFINE_bool( - 'export_moving_average', False, - 'Whether to export model using moving average variables.') +flags.DEFINE_bool('export_moving_average', False, + 'Whether to export model using moving average variables.') -flags.DEFINE_bool( - 'amp', False, - 'Whether to use automated mixed precision.') +flags.DEFINE_bool('amp', False, 'Whether to use automated mixed precision.') -flags.DEFINE_bool( - 'xla', False, - 'Whether to use accelerated linear algebra.') +flags.DEFINE_bool('xla', False, 'Whether to use accelerated linear algebra.') flags.DEFINE_integer('loss_scale', -1, 'Loss Scale for AMP.') +flags.DEFINE_integer( + 'infer_steps', + default=10000, + help=('Number of batches to plug through the network for inference')) + +flags.DEFINE_integer('infer_batch_size', + default=32, + help=('Batch size for infeernce')) # The input tensor is in the range of [0, 255], we need to scale them to the # range of [0, 1] @@ -289,19 +306,19 @@ def get_lr_schedule(train_steps, num_train_images, train_batch_size): - """learning rate schedule.""" - steps_per_epoch = np.floor(num_train_images / train_batch_size) - train_epochs = train_steps / steps_per_epoch - return [ # (multiplier, epoch to start) tuples - (1.0, np.floor(5 / 90 * train_epochs)), - (0.1, np.floor(30 / 90 * train_epochs)), - (0.01, np.floor(60 / 90 * train_epochs)), - (0.001, np.floor(80 / 90 * train_epochs)) - ] + """learning rate schedule.""" + steps_per_epoch = np.floor(num_train_images / train_batch_size) + train_epochs = train_steps / steps_per_epoch + return [ # (multiplier, epoch to start) tuples + (1.0, np.floor(5 / 90 * train_epochs)), + (0.1, np.floor(30 / 90 * train_epochs)), + (0.01, np.floor(60 / 90 * train_epochs)), + (0.001, np.floor(80 / 90 * train_epochs)) + ] def learning_rate_schedule(params, current_epoch): - """Handles linear scaling rule, gradual warmup, and LR decay. + """Handles linear scaling rule, gradual warmup, and LR decay. The learning rate starts at 0, then it increases linearly per step. After 5 epochs we reach the base learning rate (scaled to account @@ -317,34 +334,33 @@ def learning_rate_schedule(params, current_epoch): Returns: A scaled `Tensor` for current learning rate. """ - scaled_lr = params['base_learning_rate'] * ( - params['train_batch_size'] / 256.0) + scaled_lr = params['base_learning_rate'] * (params['train_batch_size'] / + 256.0) - lr_schedule = get_lr_schedule( - train_steps=params['train_steps'], - num_train_images=params['num_train_images'], - train_batch_size=params['train_batch_size']) - decay_rate = (scaled_lr * lr_schedule[0][0] * - current_epoch / lr_schedule[0][1]) - for mult, start_epoch in lr_schedule: - decay_rate = tf.where(current_epoch < start_epoch, - decay_rate, scaled_lr * mult) - return decay_rate + lr_schedule = get_lr_schedule(train_steps=params['train_steps'], + num_train_images=params['num_train_images'], + train_batch_size=params['train_batch_size']) + decay_rate = (scaled_lr * lr_schedule[0][0] * current_epoch / + lr_schedule[0][1]) + for mult, start_epoch in lr_schedule: + decay_rate = tf.where(current_epoch < start_epoch, decay_rate, + scaled_lr * mult) + return decay_rate def get_ema_vars(): - """Get all exponential moving average (ema) variables.""" - ema_vars = tf.trainable_variables() + tf.get_collection('moving_vars') - for v in tf.global_variables(): - # We maintain mva for batch norm moving mean and variance as well. - if 'moving_mean' in v.name or 'moving_variance' in v.name: - ema_vars.append(v) - return list(set(ema_vars)) + """Get all exponential moving average (ema) variables.""" + ema_vars = tf.trainable_variables() + tf.get_collection('moving_vars') + for v in tf.global_variables(): + # We maintain mva for batch norm moving mean and variance as well. + if 'moving_mean' in v.name or 'moving_variance' in v.name: + ema_vars.append(v) + return list(set(ema_vars)) def get_pretrained_variables_to_restore(checkpoint_path, load_moving_average=False): - """Gets veriables_to_restore mapping from pretrained checkpoint. + """Gets veriables_to_restore mapping from pretrained checkpoint. Args: checkpoint_path: String. Path of checkpoint. @@ -354,41 +370,42 @@ def get_pretrained_variables_to_restore(checkpoint_path, Returns: Mapping of variables to restore. """ - checkpoint_reader = tf.train.load_checkpoint(checkpoint_path) - variable_shape_map = checkpoint_reader.get_variable_to_shape_map() - - variables_to_restore = {} - ema_vars = get_ema_vars() - for v in tf.global_variables(): - # Skip variables if they are in excluded scopes. - is_excluded = False - for scope in ['global_step', 'ExponentialMovingAverage']: - if scope in v.op.name: - is_excluded = True - break - if is_excluded: - tf.logging.info('Exclude [%s] from loading from checkpoint.', v.op.name) - continue - variable_name_ckpt = v.op.name - if load_moving_average and v in ema_vars: - # To load moving average variables into non-moving version for - # fine-tuning, maps variables here manually. - variable_name_ckpt = v.op.name + '/ExponentialMovingAverage' - - if variable_name_ckpt not in variable_shape_map: - tf.logging.info( - 'Skip init [%s] from [%s] as it is not in the checkpoint', - v.op.name, variable_name_ckpt) - continue - - variables_to_restore[variable_name_ckpt] = v - tf.logging.info('Init variable [%s] from [%s] in ckpt', v.op.name, - variable_name_ckpt) - return variables_to_restore + checkpoint_reader = tf.train.load_checkpoint(checkpoint_path) + variable_shape_map = checkpoint_reader.get_variable_to_shape_map() + + variables_to_restore = {} + ema_vars = get_ema_vars() + for v in tf.global_variables(): + # Skip variables if they are in excluded scopes. + is_excluded = False + for scope in ['global_step', 'ExponentialMovingAverage']: + if scope in v.op.name: + is_excluded = True + break + if is_excluded: + tf.logging.info('Exclude [%s] from loading from checkpoint.', + v.op.name) + continue + variable_name_ckpt = v.op.name + if load_moving_average and v in ema_vars: + # To load moving average variables into non-moving version for + # fine-tuning, maps variables here manually. + variable_name_ckpt = v.op.name + '/ExponentialMovingAverage' + + if variable_name_ckpt not in variable_shape_map: + tf.logging.info( + 'Skip init [%s] from [%s] as it is not in the checkpoint', + v.op.name, variable_name_ckpt) + continue + + variables_to_restore[variable_name_ckpt] = v + tf.logging.info('Init variable [%s] from [%s] in ckpt', v.op.name, + variable_name_ckpt) + return variables_to_restore def resnet_model_fn(features, labels, mode, params): - """The model_fn for ResNet to be used with TPUEstimator. + """The model_fn for ResNet to be used with TPUEstimator. Args: features: `Tensor` of batched images. If transpose_input is enabled, it @@ -402,181 +419,195 @@ def resnet_model_fn(features, labels, mode, params): Returns: A `TPUEstimatorSpec` for the model """ - is_training = (mode == tf.estimator.ModeKeys.TRAIN) - - if isinstance(features, dict): - features = features['feature'] - - # In most cases, the default data format NCHW instead of NHWC should be - # used for a significant performance boost on GPU/TPU. NHWC should be used - # only if the network needs to be run on CPU since the pooling operations - # are only supported on NHWC. - if params['data_format'] == 'channels_first': - assert not params['transpose_input'] # channels_first only for GPU - features = tf.transpose(features, [0, 3, 1, 2]) - - if params['transpose_input'] and mode != tf.estimator.ModeKeys.PREDICT: - image_size = tf.sqrt(tf.shape(features)[0] / (3 * tf.shape(labels)[0])) - features = tf.reshape(features, [image_size, image_size, 3, -1]) - features = tf.transpose(features, [3, 0, 1, 2]) # HWCN to NHWC - - # Normalize the image to zero mean and unit variance. - if params['data_format'] == 'channels_first': - features -= tf.constant(MEAN_RGB, shape=[3, 1, 1], dtype=features.dtype) - features /= tf.constant(STDDEV_RGB, shape=[3, 1, 1], dtype=features.dtype) - else: - features -= tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=features.dtype) - features /= tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=features.dtype) - - # DropBlock keep_prob for the 4 block groups of ResNet architecture. - # None means applying no DropBlock at the corresponding block group. - dropblock_keep_probs = [None] * 4 - if params['dropblock_groups']: - # Scheduled keep_prob for DropBlock. - train_steps = tf.cast(params['train_steps'], tf.float32) - current_step = tf.cast(tf.train.get_global_step(), tf.float32) - current_ratio = current_step / train_steps - dropblock_keep_prob = (1 - current_ratio * ( - 1 - params['dropblock_keep_prob'])) - - # Computes DropBlock keep_prob for different block groups of ResNet. - dropblock_groups = [int(x) for x in params['dropblock_groups'].split(',')] - for block_group in dropblock_groups: - if block_group < 1 or block_group > 4: + is_training = (mode == tf.estimator.ModeKeys.TRAIN) + + if isinstance(features, dict): + features = features['feature'] + + # In most cases, the default data format NCHW instead of NHWC should be + # used for a significant performance boost on GPU/TPU. NHWC should be used + # only if the network needs to be run on CPU since the pooling operations + # are only supported on NHWC. + if params['data_format'] == 'channels_first': + assert not params['transpose_input'] # channels_first only for GPU + features = tf.transpose(features, [0, 3, 1, 2]) + + if params['transpose_input'] and mode != tf.estimator.ModeKeys.PREDICT: + image_size = tf.sqrt(tf.shape(features)[0] / (3 * tf.shape(labels)[0])) + features = tf.reshape(features, [image_size, image_size, 3, -1]) + features = tf.transpose(features, [3, 0, 1, 2]) # HWCN to NHWC + + # Normalize the image to zero mean and unit variance. + if params['data_format'] == 'channels_first': + features -= tf.constant(MEAN_RGB, + shape=[3, 1, 1], + dtype=features.dtype) + features /= tf.constant(STDDEV_RGB, + shape=[3, 1, 1], + dtype=features.dtype) + else: + features -= tf.constant(MEAN_RGB, + shape=[1, 1, 3], + dtype=features.dtype) + features /= tf.constant(STDDEV_RGB, + shape=[1, 1, 3], + dtype=features.dtype) + + # DropBlock keep_prob for the 4 block groups of ResNet architecture. + # None means applying no DropBlock at the corresponding block group. + dropblock_keep_probs = [None] * 4 + if params['dropblock_groups']: + # Scheduled keep_prob for DropBlock. + train_steps = tf.cast(params['train_steps'], tf.float32) + current_step = tf.cast(tf.train.get_global_step(), tf.float32) + current_ratio = current_step / train_steps + dropblock_keep_prob = (1 - current_ratio * + (1 - params['dropblock_keep_prob'])) + + # Computes DropBlock keep_prob for different block groups of ResNet. + dropblock_groups = [ + int(x) for x in params['dropblock_groups'].split(',') + ] + for block_group in dropblock_groups: + if block_group < 1 or block_group > 4: + raise ValueError( + 'dropblock_groups should be a comma separated list of integers ' + 'between 1 and 4 (dropblcok_groups: {}).'.format( + params['dropblock_groups'])) + dropblock_keep_probs[block_group - 1] = 1 - ( + (1 - dropblock_keep_prob) / 4.0**(4 - block_group)) + + has_moving_average_decay = (params['moving_average_decay'] > 0) + if has_moving_average_decay and params['bn_momentum'] > 0: raise ValueError( - 'dropblock_groups should be a comma separated list of integers ' - 'between 1 and 4 (dropblcok_groups: {}).' - .format(params['dropblock_groups'])) - dropblock_keep_probs[block_group - 1] = 1 - ( - (1 - dropblock_keep_prob) / 4.0**(4 - block_group)) - - has_moving_average_decay = (params['moving_average_decay'] > 0) - if has_moving_average_decay and params['bn_momentum'] > 0: - raise ValueError( - 'Should not use exponential moving average and batch norm momentum') - - # This nested function allows us to avoid duplicating the logic which - # builds the network, for different values of --precision. - def build_network(): - network = resnet_model.resnet( - resnet_depth=params['resnet_depth'], - num_classes=params['num_label_classes'], - dropblock_size=params['dropblock_size'], - dropblock_keep_probs=dropblock_keep_probs, - pre_activation=params['pre_activation'], - norm_act_layer=params['norm_act_layer'], - data_format=params['data_format'], - se_ratio=params['se_ratio'], - drop_connect_rate=params['drop_connect_rate'], - use_resnetd_stem=params['use_resnetd_stem'], - resnetd_shortcut=params['resnetd_shortcut'], - replace_stem_max_pool=params['replace_stem_max_pool'], - dropout_rate=params['dropout_rate'], - bn_momentum=params['bn_momentum']) - return network( - inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN)) - - if params['precision'] == 'bfloat16': - with tf.tpu.bfloat16_scope(): - logits = build_network() - logits = tf.cast(logits, tf.float32) - elif params['precision'] == 'float32': - logits = build_network() - - if mode == tf.estimator.ModeKeys.PREDICT: - scaffold_fn = None - if FLAGS.export_moving_average: - # If the model is trained with moving average decay, to match evaluation - # metrics, we need to export the model using moving average variables. - restore_checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir) - variables_to_restore = get_pretrained_variables_to_restore( - restore_checkpoint, load_moving_average=True) - tf.logging.info('Restoring from the latest checkpoint: %s', - restore_checkpoint) - tf.logging.info(str(variables_to_restore)) - - def restore_scaffold(): - saver = tf.train.Saver(variables_to_restore) - return tf.train.Scaffold(saver=saver) - - scaffold_fn = restore_scaffold - - predictions = { - 'classes': tf.argmax(logits, axis=1), - 'probabilities': tf.nn.softmax(logits, name='softmax_tensor') - } - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=predictions, - export_outputs={ - 'classify': tf.estimator.export.PredictOutput(predictions)}, - scaffold_fn=scaffold_fn) - # If necessary, in the model_fn, use params['batch_size'] instead the batch - # size flags (--train_batch_size or --eval_batch_size). - # batch_size = params['batch_size'] # pylint: disable=unused-variable - - # Calculate loss, which includes softmax cross entropy and L2 regularization. - one_hot_labels = tf.one_hot(labels, params['num_label_classes']) - cross_entropy = tf.losses.softmax_cross_entropy( - logits=logits, - onehot_labels=one_hot_labels, - label_smoothing=params['label_smoothing']) - - # Add weight decay to the loss for non-batch-normalization variables. - if params['enable_lars']: - loss = cross_entropy - else: - loss = cross_entropy + params['weight_decay'] * tf.add_n([ - tf.nn.l2_loss(v) - for v in tf.trainable_variables() - if 'batch_normalization' not in v.name and 'evonorm' not in v.name - ]) - - global_step = tf.train.get_global_step() - if has_moving_average_decay: - ema = tf.train.ExponentialMovingAverage( - decay=params['moving_average_decay'], num_updates=global_step) - ema_vars = get_ema_vars() - - host_call = None - if mode == tf.estimator.ModeKeys.TRAIN: - # Compute the current epoch and associated learning rate from global_step. - global_step = tf.train.get_global_step() - steps_per_epoch = params['num_train_images'] / params['train_batch_size'] - current_epoch = (tf.cast(global_step, tf.float32) / - steps_per_epoch) - # LARS is a large batch optimizer. LARS enables higher accuracy at batch 16K - # and larger batch sizes. + 'Should not use exponential moving average and batch norm momentum' + ) + + # This nested function allows us to avoid duplicating the logic which + # builds the network, for different values of --precision. + def build_network(): + network = resnet_model.resnet( + resnet_depth=params['resnet_depth'], + num_classes=params['num_label_classes'], + dropblock_size=params['dropblock_size'], + dropblock_keep_probs=dropblock_keep_probs, + pre_activation=params['pre_activation'], + norm_act_layer=params['norm_act_layer'], + data_format=params['data_format'], + se_ratio=params['se_ratio'], + drop_connect_rate=params['drop_connect_rate'], + use_resnetd_stem=params['use_resnetd_stem'], + resnetd_shortcut=params['resnetd_shortcut'], + replace_stem_max_pool=params['replace_stem_max_pool'], + dropout_rate=params['dropout_rate'], + bn_momentum=params['bn_momentum']) + return network(inputs=features, + is_training=(mode == tf.estimator.ModeKeys.TRAIN)) + + if params['precision'] == 'bfloat16': + with tf.tpu.bfloat16_scope(): + logits = build_network() + logits = tf.cast(logits, tf.float32) + elif params['precision'] == 'float32': + logits = build_network() + + if mode == tf.estimator.ModeKeys.PREDICT: + scaffold_fn = None + if FLAGS.export_moving_average: + # If the model is trained with moving average decay, to match evaluation + # metrics, we need to export the model using moving average variables. + restore_checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir) + variables_to_restore = get_pretrained_variables_to_restore( + restore_checkpoint, load_moving_average=True) + tf.logging.info('Restoring from the latest checkpoint: %s', + restore_checkpoint) + tf.logging.info(str(variables_to_restore)) + + def restore_scaffold(): + saver = tf.train.Saver(variables_to_restore) + return tf.train.Scaffold(saver=saver) + + scaffold_fn = restore_scaffold + + predictions = { + 'classes': tf.argmax(logits, axis=1), + 'probabilities': tf.nn.softmax(logits, name='softmax_tensor') + } + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + 'classify': tf.estimator.export.PredictOutput(predictions) + }, + scaffold=scaffold_fn) + # If necessary, in the model_fn, use params['batch_size'] instead the batch + # size flags (--train_batch_size or --eval_batch_size). + # batch_size = params['batch_size'] # pylint: disable=unused-variable + + # Calculate loss, which includes softmax cross entropy and L2 regularization. + one_hot_labels = tf.one_hot(labels, params['num_label_classes']) + cross_entropy = tf.losses.softmax_cross_entropy( + logits=logits, + onehot_labels=one_hot_labels, + label_smoothing=params['label_smoothing']) + + # Add weight decay to the loss for non-batch-normalization variables. if params['enable_lars']: - learning_rate = 0.0 - optimizer = lars_util.init_lars_optimizer(current_epoch, params) + loss = cross_entropy else: - learning_rate = learning_rate_schedule(params, current_epoch) - optimizer = tf.train.MomentumOptimizer( - learning_rate=learning_rate, - momentum=params['momentum'], - use_nesterov=True) - if params['use_tpu']: - # When using TPU, wrap the optimizer with CrossShardOptimizer which - # handles synchronization details between different TPU cores. To the - # user, this should look like regular synchronous training. - optimizer = tf.tpu.CrossShardOptimizer(optimizer) - - if FLAGS.amp: - optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer, loss_scale='dynamic' if FLAGS.loss_scale==-1 else FLAGS.loss_scale) - # Batch normalization requires UPDATE_OPS to be added as a dependency to - # the train operation. - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step) + loss = cross_entropy + params['weight_decay'] * tf.add_n([ + tf.nn.l2_loss(v) for v in tf.trainable_variables() + if 'batch_normalization' not in v.name and 'evonorm' not in v.name + ]) + global_step = tf.train.get_global_step() if has_moving_average_decay: - with tf.control_dependencies([train_op]): - train_op = ema.apply(ema_vars) - - if not params['skip_host_call']: - def host_call_fn(gs, loss, lr, ce): - """Training host call. Creates scalar summaries for training metrics. + ema = tf.train.ExponentialMovingAverage( + decay=params['moving_average_decay'], num_updates=global_step) + ema_vars = get_ema_vars() + + host_call = None + if mode == tf.estimator.ModeKeys.TRAIN: + # Compute the current epoch and associated learning rate from global_step. + global_step = tf.train.get_global_step() + steps_per_epoch = params['num_train_images'] / params[ + 'train_batch_size'] + current_epoch = (tf.cast(global_step, tf.float32) / steps_per_epoch) + # LARS is a large batch optimizer. LARS enables higher accuracy at batch 16K + # and larger batch sizes. + if params['enable_lars']: + learning_rate = 0.0 + optimizer = lars_util.init_lars_optimizer(current_epoch, params) + else: + learning_rate = learning_rate_schedule(params, current_epoch) + optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, + momentum=params['momentum'], + use_nesterov=True) + if params['use_tpu']: + # When using TPU, wrap the optimizer with CrossShardOptimizer which + # handles synchronization details between different TPU cores. To the + # user, this should look like regular synchronous training. + optimizer = tf.tpu.CrossShardOptimizer(optimizer) + + if FLAGS.amp: + optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite( + optimizer, + loss_scale='dynamic' + if FLAGS.loss_scale == -1 else FLAGS.loss_scale) + # Batch normalization requires UPDATE_OPS to be added as a dependency to + # the train operation. + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies(update_ops): + train_op = optimizer.minimize(loss, global_step) + + if has_moving_average_decay: + with tf.control_dependencies([train_op]): + train_op = ema.apply(ema_vars) + + if not params['skip_host_call']: + + def host_call_fn(gs, loss, lr, ce): + """Training host call. Creates scalar summaries for training metrics. This function is executed on the CPU and should not directly reference any Tensors in the rest of the `model_fn`. To pass Tensors from the @@ -596,40 +627,41 @@ def host_call_fn(gs, loss, lr, ce): Returns: List of summary ops to run on the CPU host. """ - gs = gs[0] - # Host call fns are executed params['iterations_per_loop'] times after - # one TPU loop is finished, setting max_queue value to the same as - # number of iterations will make the summary writer only flush the data - # to storage once per loop. - with tf2.summary.create_file_writer( - FLAGS.model_dir, - max_queue=params['iterations_per_loop']).as_default(): - with tf2.summary.record_if(True): - tf2.summary.scalar('loss', loss[0], step=gs) - tf2.summary.scalar('learning_rate', lr[0], step=gs) - tf2.summary.scalar('current_epoch', ce[0], step=gs) - - return tf.summary.all_v2_summary_ops() - - # To log the loss, current learning rate, and epoch for Tensorboard, the - # summary op needs to be run on the host CPU via host_call. host_call - # expects [batch_size, ...] Tensors, thus reshape to introduce a batch - # dimension. These Tensors are implicitly concatenated to - # [params['batch_size']]. - gs_t = tf.reshape(global_step, [1]) - loss_t = tf.reshape(loss, [1]) - lr_t = tf.reshape(learning_rate, [1]) - ce_t = tf.reshape(current_epoch, [1]) - - host_call = (host_call_fn, [gs_t, loss_t, lr_t, ce_t]) - - else: - train_op = None - - eval_metrics = None - if mode == tf.estimator.ModeKeys.EVAL: - def metric_fn(labels, logits): - """Evaluation metric function. Evaluates accuracy. + gs = gs[0] + # Host call fns are executed params['iterations_per_loop'] times after + # one TPU loop is finished, setting max_queue value to the same as + # number of iterations will make the summary writer only flush the data + # to storage once per loop. + with tf2.summary.create_file_writer( + FLAGS.model_dir, + max_queue=params['iterations_per_loop']).as_default(): + with tf2.summary.record_if(True): + tf2.summary.scalar('loss', loss[0], step=gs) + tf2.summary.scalar('learning_rate', lr[0], step=gs) + tf2.summary.scalar('current_epoch', ce[0], step=gs) + + return tf.summary.all_v2_summary_ops() + + # To log the loss, current learning rate, and epoch for Tensorboard, the + # summary op needs to be run on the host CPU via host_call. host_call + # expects [batch_size, ...] Tensors, thus reshape to introduce a batch + # dimension. These Tensors are implicitly concatenated to + # [params['batch_size']]. + gs_t = tf.reshape(global_step, [1]) + loss_t = tf.reshape(loss, [1]) + lr_t = tf.reshape(learning_rate, [1]) + ce_t = tf.reshape(current_epoch, [1]) + + host_call = (host_call_fn, [gs_t, loss_t, lr_t, ce_t]) + + else: + train_op = None + + eval_metrics = None + if mode == tf.estimator.ModeKeys.EVAL: + + def metric_fn(labels, logits): + """Evaluation metric function. Evaluates accuracy. This function is executed on the CPU and should not directly reference any Tensors in the rest of the `model_fn`. To pass Tensors from the model @@ -647,40 +679,43 @@ def metric_fn(labels, logits): Returns: A dict of the metrics to return from evaluation. """ - #logits = tf.Print(logits, [tf.shape(logits)]) - predictions = tf.argmax(logits, axis=1) - top_1_accuracy = tf.metrics.accuracy(labels, predictions) - in_top_5 = tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32) - top_5_accuracy = tf.metrics.mean(in_top_5) - - return { - 'top_1_accuracy': top_1_accuracy, - 'top_5_accuracy': top_5_accuracy, - } - - eval_metrics = (metric_fn, [labels, logits]) - - # Prepares scaffold_fn if needed. - scaffold_fn = None - restore_vars_dict = None - if not is_training and has_moving_average_decay: - # Load moving average variables for eval. - restore_vars_dict = ema.variables_to_restore(ema_vars) - def eval_scaffold(): - saver = tf.train.Saver(restore_vars_dict) - return tf.train.Scaffold(saver=saver) - scaffold_fn = eval_scaffold - return tf.estimator.tpu.TPUEstimatorSpec( - mode=mode, - loss=loss, - train_op=train_op, - host_call=host_call, - eval_metrics=eval_metrics, - scaffold_fn=scaffold_fn) + #logits = tf.Print(logits, [tf.shape(logits)]) + predictions = tf.argmax(logits, axis=1) + top_1_accuracy = tf.metrics.accuracy(labels, predictions) + in_top_5 = tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32) + top_5_accuracy = tf.metrics.mean(in_top_5) + + return { + 'top_1_accuracy': top_1_accuracy, + 'top_5_accuracy': top_5_accuracy, + } + + eval_metrics = (metric_fn, [labels, logits]) + + # Prepares scaffold_fn if needed. + scaffold_fn = None + restore_vars_dict = None + if not is_training and has_moving_average_decay: + # Load moving average variables for eval. + restore_vars_dict = ema.variables_to_restore(ema_vars) + + def eval_scaffold(): + saver = tf.train.Saver(restore_vars_dict) + return tf.train.Scaffold(saver=saver) + + scaffold_fn = eval_scaffold + return tf.estimator.tpu.TPUEstimatorSpec( + mode=mode, + loss=loss, + train_op=train_op, + host_call=host_call, + eval_metrics=eval_metrics, + scaffold_fn=scaffold_fn, + ) def _verify_non_empty_string(value, field_name): - """Ensures that a given proposed field value is a non-empty string. + """Ensures that a given proposed field value is a non-empty string. Args: value: proposed value for the field. @@ -692,315 +727,370 @@ def _verify_non_empty_string(value, field_name): Raises: ValueError: the value is not a string, or is a blank string. """ - if not isinstance(value, str): - raise ValueError( - 'Bigtable parameter "%s" must be a string.' % field_name) - if not value: - raise ValueError( - 'Bigtable parameter "%s" must be non-empty.' % field_name) - return value + if not isinstance(value, str): + raise ValueError('Bigtable parameter "%s" must be a string.' % + field_name) + if not value: + raise ValueError('Bigtable parameter "%s" must be non-empty.' % + field_name) + return value def _select_tables_from_flags(): - """Construct training and evaluation Bigtable selections from flags. + """Construct training and evaluation Bigtable selections from flags. Returns: [training_selection, evaluation_selection] """ - project = _verify_non_empty_string( - FLAGS.bigtable_project or FLAGS.gcp_project, - 'project') - instance = _verify_non_empty_string(FLAGS.bigtable_instance, 'instance') - table = _verify_non_empty_string(FLAGS.bigtable_table, 'table') - train_prefix = _verify_non_empty_string(FLAGS.bigtable_train_prefix, - 'train_prefix') - eval_prefix = _verify_non_empty_string(FLAGS.bigtable_eval_prefix, - 'eval_prefix') - column_family = _verify_non_empty_string(FLAGS.bigtable_column_family, - 'column_family') - column_qualifier = _verify_non_empty_string(FLAGS.bigtable_column_qualifier, - 'column_qualifier') - return [ # pylint: disable=g-complex-comprehension - imagenet_input.BigtableSelection( - project=project, - instance=instance, - table=table, - prefix=p, - column_family=column_family, - column_qualifier=column_qualifier) - for p in (train_prefix, eval_prefix) - ] + project = _verify_non_empty_string( + FLAGS.bigtable_project or FLAGS.gcp_project, 'project') + instance = _verify_non_empty_string(FLAGS.bigtable_instance, 'instance') + table = _verify_non_empty_string(FLAGS.bigtable_table, 'table') + train_prefix = _verify_non_empty_string(FLAGS.bigtable_train_prefix, + 'train_prefix') + eval_prefix = _verify_non_empty_string(FLAGS.bigtable_eval_prefix, + 'eval_prefix') + column_family = _verify_non_empty_string(FLAGS.bigtable_column_family, + 'column_family') + column_qualifier = _verify_non_empty_string( + FLAGS.bigtable_column_qualifier, 'column_qualifier') + return [ # pylint: disable=g-complex-comprehension + imagenet_input.BigtableSelection(project=project, + instance=instance, + table=table, + prefix=p, + column_family=column_family, + column_qualifier=column_qualifier) + for p in (train_prefix, eval_prefix) + ] def main(unused_argv): - params = params_dict.ParamsDict( - resnet_config.RESNET_CFG, resnet_config.RESNET_RESTRICTIONS) - params = params_dict.override_params_dict( - params, FLAGS.config_file, is_strict=True) - params = params_dict.override_params_dict( - params, FLAGS.params_override, is_strict=True) - - params = flags_to_params.override_params_from_input_flags(params, FLAGS) - - # From Nvidia Repo, explained here: https://github.com/NVIDIA/DeepLearningExamples/issues/57 - os.environ['CUDA_CACHE_DISABLE'] = '0' - os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private' - os.environ['TF_GPU_THREAD_COUNT'] = '2' - os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1' - os.environ['TF_ADJUST_HUE_FUSED'] = '1' - os.environ['TF_ADJUST_SATURATION_FUSED'] = '1' - os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1' - os.environ['TF_SYNC_ON_FINISH'] = '0' - os.environ['TF_AUTOTUNE_THRESHOLD'] = '2' - os.environ['TF_DISABLE_NVTX_RANGES'] = '1' - # Enable AMP - amp = FLAGS.amp - xla = FLAGS.xla - # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow - # Step 2: add --amp flag - if amp: - os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" - pass - # These two lines have no effect. - # os.environ['TF_CPP_VMODULE'] = 'auto_mixed_precision=2' - # os.environ['TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_LOG_PATH'] = '/home/ubuntu/amp-log' - - # These three lines are redundant. - # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE'] = '1' - # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1' - # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING'] = '1' - if xla: - # https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/compiler/jit/flags.cc#L78-L87 - # 1: on for things very likely to be improved - # 2: on for everything - # fusible: only for Tensorflow operations that XLA knows how to fuse - # - # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=1' - # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2' - # Best Performing XLA Option - os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' - os.environ["TF_XLA_FLAGS"] = (os.environ.get("TF_XLA_FLAGS", "") + " --tf_xla_enable_lazy_compilation=false") - - params.validate() - params.lock() - tf.logging.info('Params:\n%s', pprint.pformat(params.as_dict())) - - tpu_cluster_resolver = None - devices = tf.config.list_physical_devices('GPU') - if params.use_async_checkpointing: - save_checkpoints_steps = None - else: - save_checkpoints_steps = max(5000, params.iterations_per_loop) - - session_config = tf.GraphOptions( - rewrite_options=rewriter_config_pb2.RewriterConfig( - # disable_meta_optimizer=True, # NOTE: this line would keep AMP disabled. - auto_mixed_precision=1 if FLAGS.amp else 2, - )) - # strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.NcclAllReduce()) - strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(communication=tf.distribute.experimental.CollectiveCommunication.NCCL) - - config = tf.estimator.tpu.RunConfig( - #cluster=None, #tpu_cluster_resolver, - model_dir=FLAGS.model_dir, - save_checkpoints_steps=save_checkpoints_steps, - log_step_count_steps=FLAGS.log_step_count_steps, - train_distribute=strategy, - eval_distribute=strategy, - session_config=tf.ConfigProto( - graph_options=session_config, - allow_soft_placement=True),) # pylint: disable=line-too-long - - resnet_classifier = tf.estimator.tpu.TPUEstimator( - use_tpu=params.use_tpu, - model_fn=resnet_model_fn, - config=config, - params=params.as_dict(), - train_batch_size=params.train_batch_size, - eval_batch_size=params.eval_batch_size, - export_to_tpu=FLAGS.export_to_tpu) - - assert (params.precision == 'bfloat16' or - params.precision == 'float32'), ( - 'Invalid value for precision parameter; ' - 'must be bfloat16 or float32.') - tf.logging.info('Precision: %s', params.precision) - use_bfloat16 = params.precision == 'bfloat16' - - # Input pipelines are slightly different (with regards to shuffling and - # preprocessing) between training and evaluation. - if FLAGS.bigtable_instance: - tf.logging.info('Using Bigtable dataset, table %s', FLAGS.bigtable_table) - select_train, select_eval = _select_tables_from_flags() - imagenet_train, imagenet_eval = [ - imagenet_input.ImageNetBigtableInput( # pylint: disable=g-complex-comprehension - is_training=is_training, - use_bfloat16=use_bfloat16, - transpose_input=params.transpose_input, - selection=selection, - augment_name=FLAGS.augment_name, - randaug_num_layers=FLAGS.randaug_num_layers, - randaug_magnitude=FLAGS.randaug_magnitude) - for (is_training, selection) in [(True, - select_train), (False, select_eval)] - ] - else: - if FLAGS.data_dir == FAKE_DATA_DIR: - tf.logging.info('Using fake dataset.') + params = params_dict.ParamsDict(resnet_config.RESNET_CFG, + resnet_config.RESNET_RESTRICTIONS) + params = params_dict.override_params_dict(params, + FLAGS.config_file, + is_strict=True) + params = params_dict.override_params_dict(params, + FLAGS.params_override, + is_strict=True) + + params = flags_to_params.override_params_from_input_flags(params, FLAGS) + + # From Nvidia Repo, explained here: https://github.com/NVIDIA/DeepLearningExamples/issues/57 + os.environ['CUDA_CACHE_DISABLE'] = '0' + os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private' + os.environ['TF_GPU_THREAD_COUNT'] = '2' + os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1' + os.environ['TF_ADJUST_HUE_FUSED'] = '1' + os.environ['TF_ADJUST_SATURATION_FUSED'] = '1' + os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1' + os.environ['TF_SYNC_ON_FINISH'] = '0' + os.environ['TF_AUTOTUNE_THRESHOLD'] = '2' + os.environ['TF_DISABLE_NVTX_RANGES'] = '1' + # Enable AMP + amp = FLAGS.amp + xla = FLAGS.xla + # Step 1: Set up Docker https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow + # Step 2: add --amp flag + if amp: + os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" + pass + # These two lines have no effect. + # os.environ['TF_CPP_VMODULE'] = 'auto_mixed_precision=2' + # os.environ['TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_LOG_PATH'] = '/home/ubuntu/amp-log' + + # These three lines are redundant. + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE'] = '1' + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1' + # os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING'] = '1' + if xla: + # https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/compiler/jit/flags.cc#L78-L87 + # 1: on for things very likely to be improved + # 2: on for everything + # fusible: only for Tensorflow operations that XLA knows how to fuse + # + # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=1' + # os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2' + # Best Performing XLA Option + os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=fusible' + os.environ["TF_XLA_FLAGS"] = ( + os.environ.get("TF_XLA_FLAGS", "") + + " --tf_xla_enable_lazy_compilation=false") + + params.validate() + params.lock() + tf.logging.info('Params:\n%s', pprint.pformat(params.as_dict())) + + tpu_cluster_resolver = None + devices = tf.config.list_physical_devices('GPU') + if params.use_async_checkpointing: + save_checkpoints_steps = None else: - tf.logging.info('Using dataset: %s', FLAGS.data_dir) - imagenet_train, imagenet_eval = [ - imagenet_input.ImageNetInput( # pylint: disable=g-complex-comprehension - is_training=is_training, - data_dir=FLAGS.data_dir, - transpose_input=params.transpose_input, - cache=params.use_cache and is_training, - image_size=params.image_size, - num_parallel_calls=params.num_parallel_calls, - include_background_label=(params.num_label_classes == 1001), - use_bfloat16=use_bfloat16, - augment_name=FLAGS.augment_name, - randaug_num_layers=FLAGS.randaug_num_layers, - randaug_magnitude=FLAGS.randaug_magnitude) - for is_training in [True, False] - ] + save_checkpoints_steps = max(5000, params.iterations_per_loop) + + session_config = tf.GraphOptions( + rewrite_options=rewriter_config_pb2.RewriterConfig( + # disable_meta_optimizer=True, # NOTE: this line would keep AMP disabled. + auto_mixed_precision=1 if FLAGS.amp else 2, )) + # strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.NcclAllReduce()) + strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy( + communication=tf.distribute.experimental.CollectiveCommunication.NCCL) + + config = tf.estimator.tpu.RunConfig( + #cluster=None, #tpu_cluster_resolver, + model_dir=FLAGS.model_dir, + save_checkpoints_steps=save_checkpoints_steps, + log_step_count_steps=FLAGS.log_step_count_steps, + train_distribute=strategy, + eval_distribute=strategy, + session_config=tf.ConfigProto( + graph_options=session_config, + allow_soft_placement=True), ) # pylint: disable=line-too-long + + resnet_classifier = tf.estimator.tpu.TPUEstimator( + use_tpu=params.use_tpu, + model_fn=resnet_model_fn, + config=config, + params=params.as_dict(), + train_batch_size=params.train_batch_size, + eval_batch_size=params.eval_batch_size, + predict_batch_size=FLAGS.infer_batch_size, + export_to_tpu=FLAGS.export_to_tpu) + + assert (params.precision == 'bfloat16' or params.precision + == 'float32'), ('Invalid value for precision parameter; ' + 'must be bfloat16 or float32.') + tf.logging.info('Precision: %s', params.precision) + use_bfloat16 = params.precision == 'bfloat16' + + # Input pipelines are slightly different (with regards to shuffling and + # preprocessing) between training and evaluation. + if FLAGS.bigtable_instance: + tf.logging.info('Using Bigtable dataset, table %s', + FLAGS.bigtable_table) + select_train, select_eval = _select_tables_from_flags() + imagenet_train, imagenet_eval = [ + imagenet_input.ImageNetBigtableInput( # pylint: disable=g-complex-comprehension + is_training=is_training, + use_bfloat16=use_bfloat16, + transpose_input=params.transpose_input, + selection=selection, + augment_name=FLAGS.augment_name, + randaug_num_layers=FLAGS.randaug_num_layers, + randaug_magnitude=FLAGS.randaug_magnitude) + for (is_training, + selection) in [(True, select_train), (False, select_eval)] + ] + else: + if FLAGS.data_dir == FAKE_DATA_DIR: + tf.logging.info('Using fake dataset.') + else: + tf.logging.info('Using dataset: %s', FLAGS.data_dir) + imagenet_train, imagenet_eval = [ + imagenet_input.ImageNetInput( # pylint: disable=g-complex-comprehension + is_training=is_training, + data_dir=FLAGS.data_dir, + transpose_input=params.transpose_input, + cache=params.use_cache and is_training, + image_size=params.image_size, + num_parallel_calls=params.num_parallel_calls, + include_background_label=(params.num_label_classes == 1001), + use_bfloat16=use_bfloat16, + augment_name=FLAGS.augment_name, + randaug_num_layers=FLAGS.randaug_num_layers, + randaug_magnitude=FLAGS.randaug_magnitude) + for is_training in [True, False] + ] - steps_per_epoch = params.num_train_images // params.train_batch_size - eval_steps = params.num_eval_images // params.eval_batch_size - - if FLAGS.mode == 'infer': - inf_steps = 10000 - - if FLAGS.mode == 'eval': - # Run evaluation when there's a new checkpoint - for ckpt in tf.train.checkpoints_iterator( - FLAGS.model_dir, timeout=FLAGS.eval_timeout): - tf.logging.info('Starting to evaluate.') - try: - start_timestamp = time.time() # This time will include compilation time - eval_results = resnet_classifier.evaluate( - input_fn=imagenet_eval.input_fn, - steps=eval_steps, - checkpoint_path=ckpt) - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info('Eval results: %s. Elapsed seconds: %d', - eval_results, elapsed_time) - - # Terminate eval job when final checkpoint is reached - current_step = int(os.path.basename(ckpt).split('-')[1]) - if current_step >= params.train_steps: - tf.logging.info( - 'Evaluation finished after training step %d', current_step) - break - except tf.errors.NotFoundError: - # Since the coordinator is on a different job than the TPU worker, - # sometimes the TPU worker does not finish initializing until long after - # the CPU job tells it to start evaluating. In this case, the checkpoint - # file could have been deleted already. - tf.logging.info( - 'Checkpoint %s no longer exists, skipping checkpoint', ckpt) - elif FLAGS.mode == 'infer': - # Run evaluation when there's a new checkpoint - for ckpt in tf.train.checkpoints_iterator( - FLAGS.model_dir, timeout=FLAGS.eval_timeout): - tf.logging.info('Model Serving (over Imagenet Training).') - start_timestamp = time.time() # This time will include compilation time - inf_results = resnet_classifier.evaluate( - input_fn=imagenet_train.input_fn, - steps=inf_steps, - checkpoint_path=ckpt) - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info('Inference results: %s. Elapsed seconds: %d', - inf_results, elapsed_time) - else: - try: - current_step = tf.train.load_variable(FLAGS.model_dir, - tf.GraphKeys.GLOBAL_STEP) - except (TypeError, ValueError, tf.errors.NotFoundError): - current_step = 0 steps_per_epoch = params.num_train_images // params.train_batch_size - tf.logging.info('Training for %d steps (%.2f epochs in total). Current' - ' step %d.', - params.train_steps, - params.train_steps / steps_per_epoch, - current_step) - - start_timestamp = time.time() # This time will include compilation time - - if FLAGS.mode == 'train': - hooks = [] - if params.use_async_checkpointing: + eval_steps = params.num_eval_images // params.eval_batch_size + + if FLAGS.mode == 'eval': + # Run evaluation when there's a new checkpoint + for ckpt in tf.train.checkpoints_iterator(FLAGS.model_dir, + timeout=FLAGS.eval_timeout): + tf.logging.info('Starting to evaluate.') + try: + start_timestamp = time.time( + ) # This time will include compilation time + eval_results = resnet_classifier.evaluate( + input_fn=imagenet_eval.input_fn, + steps=eval_steps, + checkpoint_path=ckpt) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info('Eval results: %s. Elapsed seconds: %d', + eval_results, elapsed_time) + + # Terminate eval job when final checkpoint is reached + current_step = int(os.path.basename(ckpt).split('-')[1]) + if current_step >= params.train_steps: + tf.logging.info( + 'Evaluation finished after training step %d', + current_step) + break + except tf.errors.NotFoundError: + # Since the coordinator is on a different job than the TPU worker, + # sometimes the TPU worker does not finish initializing until long after + # the CPU job tells it to start evaluating. In this case, the checkpoint + # file could have been deleted already. + tf.logging.info( + 'Checkpoint %s no longer exists, skipping checkpoint', + ckpt) + elif FLAGS.mode == 'infer': + for ckpt in tf.train.checkpoints_iterator(FLAGS.model_dir, + timeout=FLAGS.eval_timeout): + ckpt_path = ckpt + break + total_steps = FLAGS.infer_steps + warmup_inf_steps = 50 + counter = 0 + inf_times = [] + while counter < total_steps + warmup_inf_steps: + for batch in resnet_classifier.predict( + input_fn=imagenet_train.input_fn, + checkpoint_path=ckpt_path, + yield_single_examples=False): + start_time = time.time() + if counter > warmup_inf_steps: + inf_times.append(start_time - end_time) + counter += 1 + end_time = time.time() + if counter % 1000 == 0: + print('Evaluation Iter ' + str(counter)) + if counter >= total_steps + warmup_inf_steps: + break + import numpy as np + inf_times = np.array(inf_times) + print('Throughput: ' + str(FLAGS.infer_batch_size * FLAGS.infer_steps / + np.sum(inf_times)) + 'samples/sec') + print('Mean Latency: ' + str(1000.0 * np.mean(inf_times)) + ' ms') + print('P90 Latency: ' + str(1000.0 * np.percentile(inf_times, 90)) + + ' ms') + print('P95 Latency: ' + str(1000.0 * np.percentile(inf_times, 95)) + + ' ms') + print('P99 Latency: ' + str(1000.0 * np.percentile(inf_times, 99)) + + ' ms') + # # Run evaluation when there's a new checkpoint + # for ckpt in tf.train.checkpoints_iterator( + # FLAGS.model_dir, timeout=FLAGS.eval_timeout): + # tf.logging.info('Model Serving (over Imagenet Training).') + # start_timestamp = time.time() # This time will include compilation time + # inf_results = resnet_classifier.evaluate( + # input_fn=imagenet_train.input_fn, + # steps=inf_steps, + # checkpoint_path=ckpt) + # elapsed_time = int(time.time() - start_timestamp) + # tf.logging.info('Inference results: %s. Elapsed seconds: %d', + # inf_results, elapsed_time) + else: try: - from tensorflow.contrib.tpu.python.tpu import async_checkpoint # pylint: disable=g-import-not-at-top - except ImportError as e: - logging.exception( - 'Async checkpointing is not supported in TensorFlow 2.x') - raise e - - hooks.append( - async_checkpoint.AsyncCheckpointSaverHook( - checkpoint_dir=FLAGS.model_dir, - save_steps=max(5000, params.iterations_per_loop))) - if FLAGS.profile_every_n_steps > 0: - hooks.append( - tpu_profiler_hook.TPUProfilerHook( - save_steps=FLAGS.profile_every_n_steps, - output_dir=FLAGS.model_dir, tpu=FLAGS.tpu) + current_step = tf.train.load_variable(FLAGS.model_dir, + tf.GraphKeys.GLOBAL_STEP) + except (TypeError, ValueError, tf.errors.NotFoundError): + current_step = 0 + steps_per_epoch = params.num_train_images // params.train_batch_size + tf.logging.info( + 'Training for %d steps (%.2f epochs in total). Current' + ' step %d.', params.train_steps, + params.train_steps / steps_per_epoch, current_step) + + start_timestamp = time.time( + ) # This time will include compilation time + + if FLAGS.mode == 'train': + hooks = [] + if params.use_async_checkpointing: + try: + from tensorflow.contrib.tpu.python.tpu import async_checkpoint # pylint: disable=g-import-not-at-top + except ImportError as e: + logging.exception( + 'Async checkpointing is not supported in TensorFlow 2.x' + ) + raise e + + hooks.append( + async_checkpoint.AsyncCheckpointSaverHook( + checkpoint_dir=FLAGS.model_dir, + save_steps=max(5000, params.iterations_per_loop))) + if FLAGS.profile_every_n_steps > 0: + hooks.append( + tpu_profiler_hook.TPUProfilerHook( + save_steps=FLAGS.profile_every_n_steps, + output_dir=FLAGS.model_dir, + tpu=FLAGS.tpu)) + tf.estimator.train_and_evaluate( + resnet_classifier, + train_spec=tf.estimator.TrainSpec( + input_fn=imagenet_train.input_fn, + max_steps=params.train_steps, + hooks=hooks), + eval_spec=tf.estimator.EvalSpec( + input_fn=imagenet_eval.input_fn, + steps=None, + start_delay_secs=1e9)) + #resnet_classifier.train(input_fn=imagenet_train.input_fn,max_steps=params.train_steps,hooks=hooks) + + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info( + 'Finished training up to step %d. Elapsed seconds %d.', + params.train_steps, elapsed_time) + + else: + assert FLAGS.mode == 'train_and_eval' + while current_step < params.train_steps: + # Train for up to steps_per_eval number of steps. + # At the end of training, a checkpoint will be written to --model_dir. + next_checkpoint = min(current_step + FLAGS.steps_per_eval, + params.train_steps) + tf.estimator.train_and_evaluate( + resnet_classifier, + train_spec=tf.estimator.TrainSpec( + input_fn=imagenet_train.input_fn, + max_steps=next_checkpoint), + eval_spec=tf.estimator.EvalSpec()) + #resnet_classifier.train( + #input_fn=imagenet_train.input_fn, max_steps=next_checkpoint) + current_step = next_checkpoint + + tf.logging.info( + 'Finished training up to step %d. Elapsed seconds %d.', + next_checkpoint, int(time.time() - start_timestamp)) + + # Evaluate the model on the most recent model in --model_dir. + # Since evaluation happens in batches of --eval_batch_size, some images + # may be excluded modulo the batch size. As long as the batch size is + # consistent, the evaluated images are also consistent. + tf.logging.info('Starting to evaluate.') + eval_results = resnet_classifier.evaluate( + input_fn=imagenet_eval.input_fn, + steps=params.num_eval_images // params.eval_batch_size) + tf.logging.info('Eval results at step %d: %s', next_checkpoint, + eval_results) + + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info( + 'Finished training up to step %d. Elapsed seconds %d.', + params.train_steps, elapsed_time) + + if FLAGS.export_dir is not None: + # The guide to serve a exported TensorFlow model is at: + # https://www.tensorflow.org/serving/serving_basic + tf.logging.info('Starting to export model.') + export_path = resnet_classifier.export_saved_model( + export_dir_base=FLAGS.export_dir, + serving_input_receiver_fn=imagenet_input.image_serving_input_fn ) - tf.estimator.train_and_evaluate(resnet_classifier, train_spec=tf.estimator.TrainSpec(input_fn=imagenet_train.input_fn, max_steps=params.train_steps, hooks=hooks), eval_spec=tf.estimator.EvalSpec(input_fn=imagenet_eval.input_fn, steps=None, start_delay_secs=1e9)) - #resnet_classifier.train(input_fn=imagenet_train.input_fn,max_steps=params.train_steps,hooks=hooks) - - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', - params.train_steps, elapsed_time) - - else: - assert FLAGS.mode == 'train_and_eval' - while current_step < params.train_steps: - # Train for up to steps_per_eval number of steps. - # At the end of training, a checkpoint will be written to --model_dir. - next_checkpoint = min(current_step + FLAGS.steps_per_eval, - params.train_steps) - tf.estimator.train_and_evaluate(resnet_classifier, train_spec=tf.estimator.TrainSpec(input_fn=imagenet_train.input_fn, max_steps=next_checkpoint), eval_spec=tf.estimator.EvalSpec()) - #resnet_classifier.train( - #input_fn=imagenet_train.input_fn, max_steps=next_checkpoint) - current_step = next_checkpoint - - tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', - next_checkpoint, int(time.time() - start_timestamp)) - - # Evaluate the model on the most recent model in --model_dir. - # Since evaluation happens in batches of --eval_batch_size, some images - # may be excluded modulo the batch size. As long as the batch size is - # consistent, the evaluated images are also consistent. - tf.logging.info('Starting to evaluate.') - eval_results = resnet_classifier.evaluate( - input_fn=imagenet_eval.input_fn, - steps=params.num_eval_images // params.eval_batch_size) - tf.logging.info('Eval results at step %d: %s', - next_checkpoint, eval_results) - - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info('Finished training up to step %d. Elapsed seconds %d.', - params.train_steps, elapsed_time) - - if FLAGS.export_dir is not None: - # The guide to serve a exported TensorFlow model is at: - # https://www.tensorflow.org/serving/serving_basic - tf.logging.info('Starting to export model.') - export_path = resnet_classifier.export_saved_model( - export_dir_base=FLAGS.export_dir, - serving_input_receiver_fn=imagenet_input.image_serving_input_fn) - if FLAGS.add_warmup_requests: - inference_warmup.write_warmup_requests( - export_path, - FLAGS.model_name, - params.image_size, - batch_sizes=FLAGS.inference_batch_sizes, - image_format='JPEG') + if FLAGS.add_warmup_requests: + inference_warmup.write_warmup_requests( + export_path, + FLAGS.model_name, + params.image_size, + batch_sizes=FLAGS.inference_batch_sizes, + image_format='JPEG') if __name__ == '__main__': - tf.logging.set_verbosity(tf.logging.INFO) - tf.disable_v2_behavior() - app.run(main) + tf.logging.set_verbosity(tf.logging.INFO) + tf.disable_v2_behavior() + app.run(main)