Move interactive API examples to openfl-contrib

    As we deprecate the interactive API by a more generalized form with the workflow API, we
    decided to keep the examples in this repo.

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/README.md

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+# Federated FLAX CIFAR-10 CNN Tutorial
+
+### 1. About dataset
+
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
+
+Define the below param in envoy.yaml config to shard the dataset across participants/envoy.
+- rank_worldsize
+
+### 2. About model
+
+A simple multi-layer CNN is used with XLA compiled and Auto-grad based parameter updates.
+Definition provided in the notebook.
+
+### 3. Notebook Overview
+
+1. Class `CustomTrainState` - Subclasses `flax.training.TrainState`
+    - Variable `opt_vars` to keep track of generic optimizer variables.
+    - Method `update_state` to update the OpenFL `ModelInterface` registered state with the new_state returned within the `TaskInterface` registered training loop.
+
+2. Method `create_train_state`: Creates a new `TrainState` by encapsulating model layer definitions, random model parameters, and optax optimizer state.
+
+3. Method `apply_model` (`@jax.jit` decorated function): It takes a TrainState, images, and labels as parameters. It computes and returns the gradients, loss, and accuracy. These gradients are applied to a given state in the `update_model` method (`@jax.jit` decorated function) and a new TrainState instance is returned.
+
+### 4. How to run this tutorial (without TLS and locally as a simulation):
+
+0. Pre-requisites:
+
+    - Nvidia Driver >= 495.29.05
+    - CUDA >= 11.1.105
+    - cuDNN >= 8
+
+    Activate virtual environment (Python - 3.8.10) and install packages from requirements.txt
+
+    Set the variable `DEFAULT_DEVICE to 'CPU' or 'GPU'` in `start_envoy.sh` and notebook to enforce/control the execution platform.
+
+```sh
+cd Flax_CNN_CIFAR
+pip install -r requirements.txt
+```
+
+1. Run director:
+
+```sh
+cd director
+./start_director.sh
+```
+
+2. Run envoy:
+
+```sh
+cd envoy
+./start_envoy.sh "envoy_identifier" envoy_config.yaml
+```
+
+Optional: start second envoy:
+
+- Copy `envoy` folder to another place and follow the same process as above:
+
+```sh
+./start_envoy.sh "envoy_identifier_2" envoy_config_2.yaml
+```
+
+3. Run `FLAX_CIFAR10_CNN.ipynb` jupyter notebook:
+
+```sh
+cd workspace
+jupyter lab FLAX_CIFAR10_CNN.ipynb
+```
+
+4. Visualization:
+
+```
+tensorboard --logdir logs/
+```
+
+
+### 5. Known issues
+
+1. #### CUDA_ERROR_OUT_OF_MEMORY Exception - JAX XLA pre-allocates 90% of the GPU at start
+
+- set XLA_PYTHON_CLIENT_PREALLOCATE to start with a small memory footprint.
+```
+%env XLA_PYTHON_CLIENT_PREALLOCATE=false
+```
+OR
+
+- Below flag to restrict max GPU allocation to 50%
+```
+%env XLA_PYTHON_CLIENT_MEM_FRACTION=.5
+```
+
+
+2. #### Tensorflow pre-allocates 90% of the GPU (Potential OOM Errors).
+
+- set TF_FORCE_GPU_ALLOW_GROWTH to start with a small memory footprint.
+```
+%env TF_FORCE_GPU_ALLOW_GROWTH=true
+```
+
+3. #### DNN library Not found error
+
+- Make sure the jaxlib(cuda version), Nvidia Driver, CUDA and cuDNN versions are specific, relevant and compatible as per the documentation.
+- Reference:
+    -   CUDA and cuDNN Compatibility Matrix: https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html
+    -   Official JAX Compatible CUDA Releases: https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/director/director_config.yaml

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+settings:
+  listen_host: localhost
+  listen_port: 50055
+  sample_shape: ['32', '32', '3'] # [[shape], channel]
+  target_shape: ['1']
+  envoy_health_check_period: 5  # in seconds

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/director/start_director.sh

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+#!/bin/bash
+set -e
+
+fx director start --disable-tls -c director_config.yaml

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/envoy/cifar10_shard_descriptor.py

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+# Copyright (C) 2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""CIFAR10 Shard Descriptor (using `TFDS` API)"""
+import jax.numpy as jnp
+import logging
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from typing import List, Tuple
+from openfl.interface.interactive_api.shard_descriptor import ShardDescriptor
+
+logger = logging.getLogger(__name__)
+
+
+class CIFAR10ShardDescriptor(ShardDescriptor):
+    """
+    CIFAR10 Shard Descriptor
+
+    This example is based on `tfds` data loader.
+    Note that the ingestion of any model/task requires an iterable dataloader.
+    Hence, it is possible to utilize these pipelines without explicit need of a
+    new interface.
+    """
+
+    def __init__(
+            self,
+            rank_worldsize: str = '1, 1',
+            **kwargs
+    ) -> None:
+        """Download/Prepare CIFAR10 dataset"""
+        self.rank, self.worldsize = tuple(int(num) for num in rank_worldsize.split(','))
+
+        # Load dataset
+        train_ds, valid_ds = self._download_and_prepare_dataset(self.rank, self.worldsize)
+
+        # Set attributes
+        self._sample_shape = train_ds['image'].shape[1:]
+        self._target_shape = tf.expand_dims(train_ds['label'], -1).shape[1:]
+
+        self.splits = {
+            'train': train_ds,
+            'valid': valid_ds
+        }
+
+    def _download_and_prepare_dataset(self, rank: int, worldsize: int) -> Tuple[dict]:
+        """
+        Download, Cache CIFAR10 and prepare `tfds` builder.
+
+        Provide `rank` and `worldsize` to virtually split dataset across shards
+        uniquely for each client for simulation purposes.
+
+        Returns:
+        Tuple (train_dict, test_dict) of dictionary with JAX DeviceArray (image and label)
+        dict['image'] -> DeviceArray float32
+        dict['label'] -> DeviceArray int32
+        {'image' : DeviceArray(...), 'label' : DeviceArray(...)}
+
+        """
+
+        dataset_builder = tfds.builder('cifar10')
+        dataset_builder.download_and_prepare()
+
+        datasets = dataset_builder.as_dataset()
+
+        train_shard_size = int(len(datasets['train']) / worldsize)
+        test_shard_size = int(len(datasets['test']) / worldsize)
+
+        self.train_segment = f'train[{train_shard_size * (rank - 1)}:{train_shard_size * rank}]'
+        self.test_segment = f'test[{test_shard_size * (rank - 1)}:{test_shard_size * rank}]'
+        train_dataset = dataset_builder.as_dataset(split=self.train_segment, batch_size=-1)
+        test_dataset = dataset_builder.as_dataset(split=self.test_segment, batch_size=-1)
+        train_ds = tfds.as_numpy(train_dataset)
+        test_ds = tfds.as_numpy(test_dataset)
+
+        train_ds['image'] = jnp.float32(train_ds['image']) / 255.
+        test_ds['image'] = jnp.float32(test_ds['image']) / 255.
+        train_ds['label'] = jnp.int32(train_ds['label'])
+        test_ds['label'] = jnp.int32(test_ds['label'])
+
+        return train_ds, test_ds
+
+    def get_shard_dataset_types(self) -> List[str]:
+        """Get available split names"""
+        return list(self.splits)
+
+    def get_split(self, name: str) -> tf.data.Dataset:
+        """Return a shard dataset by type."""
+        if name not in self.splits:
+            raise Exception(f'Split name `{name}` not found.'
+                            f' Expected one of {list(self.splits.keys())}')
+        return self.splits[name]
+
+    @property
+    def sample_shape(self) -> List[str]:
+        """Return the sample shape info."""
+        return list(map(str, self._sample_shape))
+
+    @property
+    def target_shape(self) -> List[str]:
+        """Return the target shape info."""
+        return list(map(str, self._target_shape))
+
+    @property
+    def dataset_description(self) -> str:
+        """Return the dataset description."""
+        n_train = len(self.splits['train']['label'])
+        n_test = len(self.splits['valid']['label'])
+        return (f'CIFAR10 dataset, Shard Segments {self.train_segment}/{self.test_segment}, '
+                f'rank/world {self.rank}/{self.worldsize}.'
+                f'\n num_samples [Train/Valid]: [{n_train}/{n_test}]')

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/envoy/envoy_config_1.yaml

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+params:
+  cuda_devices: []
+
+optional_plugin_components: {}
+
+shard_descriptor:
+  template: cifar10_shard_descriptor.CIFAR10ShardDescriptor
+  params:
+    rank_worldsize: 1, 2

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/envoy/start_envoy.sh

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+#!/bin/bash
+set -e
+ENVOY_NAME=$1
+ENVOY_CONF=$2
+
+DEFAULT_DEVICE='CPU'
+
+if [[ $DEFAULT_DEVICE == 'CPU' ]]
+then
+    export JAX_PLATFORMS="cpu" # Force XLA to use CPU
+    export CUDA_VISIBLE_DEVICES='-1' # Force TF to use CPU
+else
+    export XLA_PYTHON_CLIENT_PREALLOCATE=false
+    export TF_FORCE_GPU_ALLOW_GROWTH=true
+fi
+
+fx envoy start -n "$ENVOY_NAME" --disable-tls --envoy-config-path "$ENVOY_CONF" -dh localhost -dp 50055

openfl_contrib_tutorials/interactive_api/Flax_CNN_CIFAR/requirements.txt

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+--find-links https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+jax
+jaxlib
+tensorflow==2.13
+tensorflow-datasets==4.6.0