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How-To: Setup TensorFlow

TensorFlow is a package for deep learning with optional support for GPUs. You can find the original TensorFlow installation instructions here.

This article describes how to set up TensorFlow with GPU support using Conda. This how-to assumes that you have just connected to a GPU node via srun --mem=10g --partition=gpu --gres=gpu:tesla:1 --pty bash -i. Note that you will need to allocate "enough" memory, otherwise your python session will be Killed because of too little memory. You should read the How-To: Connect to GPU Nodes tutorial on an explanation of how to do this and to learn how to register for GPU usage.

This tutorial assumes, that conda has been set up as described in [Software Management]((../../best-practice/software-installation-with-conda.md).

Create conda environment

We recommend that you install mamba first with conda install -y mamba and use this C++ reimplementation of the conda command as follows.

$ conda create -y -n python-tf tensorflow-gpu
$ conda activate python-tf

Let us verify that we have Python and TensorFlow installed. You might get different versions you could pin the version on installing with `conda create -y -n python-tf python==3.9.10 tensorflow-gpu==2.6.2

$ python --version
Python 3.9.10
$ python -c 'import tensorflow; print(tensorflow.__version__)'
2.6.2

We thus end up with an installation of Python 3.9.10 with tensorflow 2.6.2.

Run TensorFlow Example

Let us now see whether TensorFlow has recognized our GPU correctly.

$ python
>>> import tensorflow as tf
>>> print("TensorFlow version:", tf.__version__)
TensorFlow version: 2.6.2
>>> print(tf.config.list_physical_devices())
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Yay, we can proceed to run the Quickstart Tutorial.

>>> mnist = tf.keras.datasets.mnist
>>> (x_train, y_train), (x_test, y_test) = mnist.load_data()
>>> x_train, x_test = x_train / 255.0, x_test / 255.0
>>> model = tf.keras.models.Sequential([
...   tf.keras.layers.Flatten(input_shape=(28, 28)),
...   tf.keras.layers.Dense(128, activation='relu'),
...   tf.keras.layers.Dropout(0.2),
...   tf.keras.layers.Dense(10)
... ])
>>> predictions = model(x_train[:1]).numpy()
>>> predictions
array([[-0.50569224,  0.26386747,  0.43226188,  0.61226094,  0.09630793,
         0.34400576,  0.9819117 , -0.3693726 ,  0.5221357 ,  0.3323232 ]],
      dtype=float32)
>>> tf.nn.softmax(predictions).numpy()
array([[0.04234391, 0.09141268, 0.10817807, 0.12951255, 0.07731011,
        0.09903987, 0.18743432, 0.04852816, 0.11835073, 0.09788957]],
      dtype=float32)
>>> loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
>>> loss_fn(y_train[:1], predictions).numpy()
2.3122327
>>> model.compile(optimizer='adam',
...               loss=loss_fn,
...               metrics=['accuracy'])
>>> model.fit(x_train, y_train, epochs=5)
2022-03-09 17:53:47.237997: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.2918 - accuracy: 0.9151
Epoch 2/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.1444 - accuracy: 0.9561
Epoch 3/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.1082 - accuracy: 0.9674
Epoch 4/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0898 - accuracy: 0.9720
Epoch 5/5
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0773 - accuracy: 0.9756
<keras.callbacks.History object at 0x154e81360190>
>>> model.evaluate(x_test,  y_test, verbose=2)
313/313 - 0s - loss: 0.0713 - accuracy: 0.9785
[0.0713074803352356, 0.9785000085830688]
>>> probability_model = tf.keras.Sequential([
...   model,
...   tf.keras.layers.Softmax()
... ])
>>> probability_model(x_test[:5])
<tf.Tensor: shape=(5, 10), dtype=float32, numpy=
array([[1.2339272e-06, 6.5599060e-10, 1.0560590e-06, 5.9356184e-06,
        5.3691075e-12, 1.4447859e-07, 5.4218874e-13, 9.9996936e-01,
        1.0347234e-07, 2.2147648e-05],
       [2.9887938e-06, 6.8461006e-05, 9.9991941e-01, 7.2003731e-06,
        2.9751782e-13, 8.2818183e-08, 1.4307782e-06, 2.3203837e-13,
        4.7433215e-07, 2.9504194e-14],
       [1.8058477e-06, 9.9928612e-01, 7.8716243e-05, 3.9140195e-06,
        3.0842333e-05, 9.4537208e-06, 2.2774333e-05, 4.5549971e-04,
        1.1015874e-04, 6.9138093e-07],
       [9.9978787e-01, 3.0206781e-08, 2.8528208e-05, 8.5581682e-08,
        1.3851340e-07, 2.3634559e-06, 1.8480707e-05, 1.0153375e-04,
        1.1583331e-07, 6.0887167e-05],
       [6.4914235e-07, 2.5808356e-08, 1.8225538e-06, 2.3215563e-09,
        9.9588013e-01, 4.6049720e-08, 3.8903639e-07, 2.9772724e-05,
        4.3141077e-07, 4.0867776e-03]], dtype=float32)>
>>> exit()

Writing TensorFlow Slurm Jobs

Writing Slurm jobs using TensorFlow is as easy as creating the following scripts.

tf_script.py

#/usr/bin/env python

import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print(tf.config.list_physical_devices())

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

predictions = model(x_train[:1]).numpy()
print(predictions)

print(tf.nn.softmax(predictions).numpy())

# ... and so on ;-)

tf_job.sh

#!/usr/bin/bash

#SBATCH --job-name=tf-job
#SBATCH --mem=10g
#SBATCH --partition=gpu
#SBATCH --gres=gpu:tesla:1

source $HOME/work/miniconda3/bin/activate
conda activate python-tf

python tf_script.py &>tf-out.txt

And then calling

$ sbatch tf_job.sh

You can find the reuslts in tf-out.txt after completion.

$ cat tf-out.txt 
2022-03-09 18:05:54.628846: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-09 18:05:56.999848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 30988 MB memory:  -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:18:00.0, compute capability: 7.0
TensorFlow version: 2.6.2
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
[[-0.07757086  0.04676083  0.9420195  -0.59902835 -0.26286742 -0.392514
   0.3231195  -0.17169198  0.3480805   0.37013203]]
[[0.07963609 0.09017922 0.22075593 0.04727634 0.06616627 0.05812084
  0.11888511 0.07248258 0.12188996 0.12460768]]

Last update: November 25, 2022