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Release v0.11.0

PaddlePaddle Fluid

  • Release 0.11.0 includes a new feature PaddlePaddle Fluid. Fluid is designed to allow users to program like PyTorch and TensorFlow Eager Execution. In these systems, there is no longer the concept model and applications do not include a symbolic description of a graph of operators nor a sequence of layers. Instead, applications look exactly like a usual program that describes a process of training or inference. The difference between Fluid and PyTorch or Eager Execution is that Fluid doesn't rely on Python's control-flow, if-then-else nor for. Instead, Fluid provides its C++ implementations and their Python binding using the with statement. For an example

    https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44

  • In 0.11.0, we provides a C++ class Executor to run a Fluid program. Executor works like an interpreter. In future version, we will improve Executor into a debugger like GDB, and we might provide some compilers, which, for example, takes an application like the above one, and outputs an equivalent C++ source program, which can be compiled using nvcc to generate binaries that use CUDA, or using icc to generate binaries that make full use of Intel CPUs.

New Features

Improvements

  • Build and install using a single whl package.
  • Custom evaluating in V2 API.
  • Change PADDLE_ONLY_CPU to PADDLE_WITH_GPU, since we will support many kinds of devices.
  • Remove buggy BarrierStat.
  • Clean and remove unused functions in paddle::Parameter.
  • Remove ProtoDataProvider.
  • Huber loss supports both regression and classification.
  • Add the stride parameter for sequence pooling layers.
  • Enable v2 API use cudnn batch normalization automatically.
  • The BN layer's parameter can be shared by a fixed the parameter name.
  • Support variable-dimension input feature for 2D convolution operation.
  • Refine cmake about CUDA to automatically detect GPU architecture.
  • Improved website navigation.

Bug Fixes

  • Fix bug in ROI pooling. cc9a761
  • Fix AUC is zero when label is dense vector. #5274
  • Fix bug in WarpCTC layer.

Release v0.10.0

We are glad to release version 0.10.0. In this version, we are happy to release the new Python API.

  • Our old Python API is kind of out of date. It's hard to learn and hard to use. To write a PaddlePaddle program using the old API, we'd have to write at least two Python files: one data provider and another one that defines the network topology. Users start a PaddlePaddle job by running the paddle_trainer C++ program, which calls Python interpreter to run the network topology configuration script and then start the training loop, which iteratively calls the data provider function to load minibatches. This prevents us from writing a Python program in a modern way, e.g., in the Jupyter Notebook.

  • The new API, which we often refer to as the v2 API, allows us to write much shorter Python programs to define the network and the data in a single .py file. Also, this program can run in Jupyter Notebook, since the entry point is in Python program and PaddlePaddle runs as a shared library loaded and invoked by this Python program.

Basing on the new API, we delivered an online interative book, Deep Learning 101 and its Chinese version.

We also worked on updating our online documentation to describe the new API. But this is an ongoing work. We will release more documentation improvements in the next version.

We also worked on bring the new API to distributed model training (via MPI and Kubernetes). This work is ongoing. We will release more about it in the next version.

New Features

  • We release new Python API.
  • Deep Learning 101 book in English and Chinese.
  • Support rectangle input for CNN.
  • Support stride pooling for seqlastin and seqfirstin.
  • Expose seq_concat_layer/seq_reshape_layer in trainer_config_helpers.
  • Add dataset package: CIFAR, MNIST, IMDB, WMT14, CONLL05, movielens, imikolov.
  • Add Priorbox layer for Single Shot Multibox Detection.
  • Add smooth L1 cost.
  • Add data reader creator and data reader decorator for v2 API.
  • Add the CPU implementation of cmrnorm projection.

Improvements

  • Support Python virtualenv for paddle_trainer.
  • Add pre-commit hooks, used for automatically format our code.
  • Upgrade protobuf to version 3.x.
  • Add an option to check data type in Python data provider.
  • Speedup the backward of average layer on GPU.
  • Documentation refinement.
  • Check dead links in documents using Travis-CI.
  • Add a example for explaining sparse_vector.
  • Add ReLU in layer_math.py
  • Simplify data processing flow for Quick Start.
  • Support CUDNN Deconv.
  • Add data feeder in v2 API.
  • Support predicting the samples from sys.stdin for sentiment demo.
  • Provide multi-proccess interface for image preprocessing.
  • Add benchmark document for v1 API.
  • Add ReLU in layer_math.py.
  • Add packages for automatically downloading public datasets.
  • Rename Argument::sumCost to Argument::sum since class Argument is nothing with cost.
  • Expose Argument::sum to Python
  • Add a new TensorExpression implementation for matrix-related expression evaluations.
  • Add lazy assignment for optimizing the calculation of a batch of multiple expressions.
  • Add abstract calss Function and its implementation:
    • PadFunc and PadGradFunc.
    • ContextProjectionForwardFunc and ContextProjectionBackwardFunc.
    • CosSimBackward and CosSimBackwardFunc.
    • CrossMapNormalFunc and CrossMapNormalGradFunc.
    • MulFunc.
  • Add class AutoCompare and FunctionCompare, which make it easier to write unit tests for comparing gpu and cpu version of a function.
  • Generate libpaddle_test_main.a and remove the main function inside the test file.
  • Support dense numpy vector in PyDataProvider2.
  • Clean code base, remove some copy-n-pasted code snippets:
    • Extract RowBuffer class for SparseRowMatrix.
    • Clean the interface of GradientMachine.
    • Use override keyword in layer.
    • Simplify Evaluator::create, use ClassRegister to create Evaluators.
  • Check MD5 checksum when downloading demo's dataset.
  • Add paddle::Error which intentially replace LOG(FATAL) in Paddle.

Bug Fixes

  • Check layer input types for recurrent_group.
  • Don't run clang-format with .cu source files.
  • Fix bugs with LogActivation.
  • Fix the bug that runs test_layerHelpers multiple times.
  • Fix the bug that the seq2seq demo exceeds protobuf message size limit.
  • Fix the bug in dataprovider converter in GPU mode.
  • Fix a bug in GatedRecurrentLayer.
  • Fix bug for BatchNorm when testing more than one models.
  • Fix broken unit test of paramRelu.
  • Fix some compile-time warnings about CpuSparseMatrix.
  • Fix MultiGradientMachine error when trainer_count > batch_size.
  • Fix bugs that prevents from asynchronous data loading in PyDataProvider2.

Release v0.9.0

New Features:

  • New Layers
    • bilinear interpolation layer.
    • spatial pyramid-pool layer.
    • de-convolution layer.
    • maxout layer.
  • Support rectangle padding, stride, window and input for Pooling Operation.
  • Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON.
  • Expose cost_weight/nce_layer in trainer_config_helpers
  • Add FAQ, concepts, h-rnn docs.
  • Add Bidi-LSTM and DB-LSTM to quick start demo @alvations
  • Add usage track scripts.

Improvements

  • Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed.
  • Add code coverage tools.
  • Refine convolution layer to speedup and reduce GPU memory.
  • Speed up PyDataProvider2
  • Add ubuntu deb package build scripts.
  • Make Paddle use git-flow branching model.
  • PServer support no parameter blocks.

Bug Fixes

  • add zlib link to py_paddle
  • add input sparse data check for sparse layer at runtime
  • Bug fix for sparse matrix multiplication
  • Fix floating-point overflow problem of tanh
  • Fix some nvcc compile options
  • Fix a bug in yield dictionary in DataProvider
  • Fix SRL hang when exit.

Release v0.8.0beta.1

New features:

  • Mac OSX is supported by source code. #138

    • Both GPU and CPU versions of PaddlePaddle are supported.

  • Support CUDA 8.0

  • Enhance PyDataProvider2

    • Add dictionary yield format. PyDataProvider2 can yield a dictionary with key is data_layer's name, value is features.
    • Add min_pool_size to control memory pool in provider.

  • Add deb install package & docker image for no_avx machines.

    • Especially for cloud computing and virtual machines

  • Automatically disable avx instructions in cmake when machine's CPU don't support avx instructions.

  • Add Parallel NN api in trainer_config_helpers.

  • Add travis ci for Github

Bug fixes:

  • Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers
  • Check if PaddlePaddle is installed when unittest.
  • Fix bugs in GTX series GPU
  • Fix bug in MultinomialSampler

Also more documentation was written since last release.

Release v0.8.0beta.0

PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version.