From c5a3440912ef2af381009ef9ddcbf907ad6ced89 Mon Sep 17 00:00:00 2001
From: Elias Werner <eliwerner3@googlemail.com>
Date: Fri, 27 Aug 2021 12:58:45 +0200
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-# Keras
-
-This is an introduction on how to run a
-Keras machine learning application on the new machine learning partition
-of Taurus.
-
-Keras is a high-level neural network API,
-written in Python and capable of running on top of
-[TensorFlow](https://github.com/tensorflow/tensorflow).
-In this page, [Keras](https://www.tensorflow.org/guide/keras) will be
-considered as a TensorFlow's high-level API for building and training
-deep learning models. Keras includes support for TensorFlow-specific
-functionality, such as [eager execution](https://www.tensorflow.org/guide/keras#eager_execution)
-, [tf.data](https://www.tensorflow.org/api_docs/python/tf/data) pipelines
-and [estimators](https://www.tensorflow.org/guide/estimator).
-
-On the machine learning nodes (machine learning partition), you can use
-the tools from [IBM Power AI](./power_ai.md). PowerAI is an enterprise
-software distribution that combines popular open-source deep learning
-frameworks, efficient AI development tools (Tensorflow, Caffe, etc).
-
-In machine learning partition (modenv/ml) Keras is available as part of
-the Tensorflow library at Taurus and also as a separate module named
-"Keras". For using Keras in machine learning partition you have two
-options:
-
-- use Keras as part of the TensorFlow module;
-- use Keras separately and use Tensorflow as an interface between
- Keras and GPUs.
-
-**Prerequisites**: To work with Keras you, first of all, need
-[access](../access/ssh_login.md) for the Taurus system, loaded
-Tensorflow module on ml partition, activated Python virtual environment.
-Basic knowledge about Python, SLURM system also required.
-
-**Aim** of this page is to introduce users on how to start working with
-Keras and TensorFlow on the [HPC-DA](../jobs_and_resources/hpcda.md)
-system - part of the TU Dresden HPC system.
-
-There are three main options on how to work with Keras and Tensorflow on
-the HPC-DA: 1. Modules; 2. JupyterNotebook; 3. Containers. One of the
-main ways is using the **TODO LINK MISSING** (Modules
-system)(RuntimeEnvironment#Module_Environments) and Python virtual
-environment. Please see the
-[Python page](./python.md) for the HPC-DA
-system.
-
-The information about the Jupyter notebook and the **JupyterHub** could
-be found [here](../access/jupyterhub.md). The use of
-Containers is described [here](tensorflow_container_on_hpcda.md).
-
-Keras contains numerous implementations of commonly used neural-network
-building blocks such as layers,
-[objectives](https://en.wikipedia.org/wiki/Objective_function),
-[activation functions](https://en.wikipedia.org/wiki/Activation_function)
-[optimizers](https://en.wikipedia.org/wiki/Mathematical_optimization),
-and a host of tools
-to make working with image and text data easier. Keras, for example, has
-a library for preprocessing the image data.
-
-The core data structure of Keras is a
-**model**, a way to organize layers. The Keras functional API is the way
-to go for defining as simple (sequential) as complex models, such as
-multi-output models, directed acyclic graphs, or models with shared
-layers.
-
-## Getting started with Keras
-
-This example shows how to install and start working with TensorFlow and
-Keras (using the module system). To get started, import [tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras)
-as part of your TensorFlow program setup.
-tf.keras is TensorFlow's implementation of the [Keras API
-specification](https://keras.io/). This is a modified example that we
-used for the [Tensorflow page](./tensorflow.md).
-
-```bash
-srun -p ml --gres=gpu:1 -n 1 --pty --mem-per-cpu=8000 bash
-
-module load modenv/ml #example output: The following have been reloaded with a version change: 1) modenv/scs5 => modenv/ml
-
-mkdir python-virtual-environments
-cd python-virtual-environments
-module load TensorFlow #example output: Module TensorFlow/1.10.0-PythonAnaconda-3.6 and 1 dependency loaded.
-which python
-python3 -m venv --system-site-packages env #create virtual environment "env" which inheriting with global site packages
-source env/bin/activate #example output: (env) bash-4.2$
-module load TensorFlow
-python
-import tensorflow as tf
-from tensorflow.keras import layers
-
-print(tf.VERSION) #example output: 1.10.0
-print(tf.keras.__version__) #example output: 2.1.6-tf
-```
-
-As was said the core data structure of Keras is a **model**, a way to
-organize layers. In Keras, you assemble *layers* to build *models*. A
-model is (usually) a graph of layers. For our example we use the most
-common type of model is a stack of layers. The below [example](https://www.tensorflow.org/guide/keras#model_subclassing)
-of using the advanced model with model
-subclassing and custom layers illustrate using TF-Keras API.
-
-```python
-import tensorflow as tf
-from tensorflow.keras import layers
-import numpy as np
-
-# Numpy arrays to train and evaluate a model
-data = np.random.random((50000, 32))
-labels = np.random.random((50000, 10))
-
-# Create a custom layer by subclassing
-class MyLayer(layers.Layer):
-
- def __init__(self, output_dim, **kwargs):
- self.output_dim = output_dim
- super(MyLayer, self).__init__(**kwargs)
-
-# Create the weights of the layer
- def build(self, input_shape):
- shape = tf.TensorShape((input_shape[1], self.output_dim))
-# Create a trainable weight variable for this layer
- self.kernel = self.add_weight(name='kernel',
- shape=shape,
- initializer='uniform',
- trainable=True)
- super(MyLayer, self).build(input_shape)
-# Define the forward pass
- def call(self, inputs):
- return tf.matmul(inputs, self.kernel)
-
-# Specify how to compute the output shape of the layer given the input shape.
- def compute_output_shape(self, input_shape):
- shape = tf.TensorShape(input_shape).as_list()
- shape[-1] = self.output_dim
- return tf.TensorShape(shape)
-
-# Serializing the layer
- def get_config(self):
- base_config = super(MyLayer, self).get_config()
- base_config['output_dim'] = self.output_dim
- return base_config
-
- @classmethod
- def from_config(cls, config):
- return cls(**config)
-# Create a model using your custom layer
-model = tf.keras.Sequential([
- MyLayer(10),
- layers.Activation('softmax')])
-
-# The compile step specifies the training configuration
-model.compile(optimizer=tf.compat.v1.train.RMSPropOptimizer(0.001),
- loss='categorical_crossentropy',
- metrics=['accuracy'])
-
-# Trains for 10 epochs(steps).
-model.fit(data, labels, batch_size=32, epochs=10)
-```
-
-## Running the sbatch script on ML modules (modenv/ml)
-
-Generally, for machine learning purposes ml partition is used but for
-some special issues, SCS5 partition can be useful. The following sbatch
-script will automatically execute the above Python script on ml
-partition. If you have a question about the sbatch script see the
-article about [SLURM](./../jobs_and_resources/binding_and_distribution_of_tasks.md).
-Keep in mind that you need to put the executable file (Keras_example) with
-python code to the same folder as bash script or specify the path.
-
-```bash
-#!/bin/bash
-#SBATCH --mem=4GB # specify the needed memory
-#SBATCH -p ml # specify ml partition
-#SBATCH --gres=gpu:1 # use 1 GPU per node (i.e. use one GPU per task)
-#SBATCH --nodes=1 # request 1 node
-#SBATCH --time=00:05:00 # runs for 5 minutes
-#SBATCH -c 16 # how many cores per task allocated
-#SBATCH -o HLR_Keras_example.out # save output message under HLR_${SLURMJOBID}.out
-#SBATCH -e HLR_Keras_example.err # save error messages under HLR_${SLURMJOBID}.err
-
-module load modenv/ml
-module load TensorFlow
-
-python Keras_example.py
-
-## when finished writing, submit with: sbatch <script_name>
-```
-
-Output results and errors file you can see in the same folder in the
-corresponding files after the end of the job. Part of the example
-output:
-
-```
-......
-Epoch 9/10
-50000/50000 [==============================] - 2s 37us/sample - loss: 11.5159 - acc: 0.1000
-Epoch 10/10
-50000/50000 [==============================] - 2s 37us/sample - loss: 11.5159 - acc: 0.1020
-```
-
-## Tensorflow 2
-
-[TensorFlow 2.0](https://blog.tensorflow.org/2019/09/tensorflow-20-is-now-available.html)
-is a significant milestone for the
-TensorFlow and the community. There are multiple important changes for
-users.
-
-Tere are a number of TensorFlow 2 modules for both ml and scs5
-partitions in Taurus (2.0 (anaconda), 2.0 (python), 2.1 (python))
-(11.04.20). Please check **TODO MISSING DOC**(the software modules list)(./SoftwareModulesList.md
-for the information about available
-modules.
-
-<span style="color:red">**NOTE**</span>: Tensorflow 2 of the
-current version is loading by default as a Tensorflow module.
-
-TensorFlow 2.0 includes many API changes, such as reordering arguments,
-renaming symbols, and changing default values for parameters. Thus in
-some cases, it makes code written for the TensorFlow 1 not compatible
-with TensorFlow 2. However, If you are using the high-level APIs
-**(tf.keras)** there may be little or no action you need to take to make
-your code fully TensorFlow 2.0 [compatible](https://www.tensorflow.org/guide/migrate).
-It is still possible to run 1.X code,
-unmodified ([except for contrib](https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md)
-), in TensorFlow 2.0:
-
-```python
-import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior() #instead of "import tensorflow as tf"
-```
-
-To make the transition to TF 2.0 as seamless as possible, the TensorFlow
-team has created the [tf_upgrade_v2](https://www.tensorflow.org/guide/upgrade)
-utility to help transition legacy code to the new API.
-
-## F.A.Q:
--
GitLab