Tensorflow

Tensorflow is a Python deep learning package from Google. The easiest way to use install it is to build a Python virtualenv with it in it.

First load GPU modules to allow access to accelerated GPGPU training.

module add cuda/9.0.176 cudnn/v7.0.5

Next you will want to create a virtualenv and source into it.

$ python3 -m venv env
$ source env/bin/activate
(env) $

The next step is to ensure you have a recent copy of pip in your virtualenv.

(env) $ pip install --upgrade pip
Collecting pip
  Downloading https://files.pythonhosted.org/packages/a4/6d/6463d49a933f547439d6b5b98b46af8742cc03ae83543e4d7688c2420f8b/pip-21.3.1-py3-none-any.whl (1.7MB)
    100% |████████████████████████████████| 1.7MB 1.2MB/s
Installing collected packages: pip
  Found existing installation: pip 9.0.3
    Uninstalling pip-9.0.3:
      Successfully uninstalled pip-9.0.3
Successfully installed pip-21.3.1

Then you can now install the Tensorflow wheel through pip.

(env) $ pip install --upgrade tensorflow-gpu
Collecting tensorflow-gpu
  Downloading tensorflow_gpu-1.1.0-cp27-cp27mu-manylinux1_x86_64.whl (84.1MB)
    100% |████████████████████████████████| 84.1MB 15kB/s
Collecting wheel (from tensorflow-gpu)
  Downloading wheel-0.29.0-py2.py3-none-any.whl (66kB)
    100% |████████████████████████████████| 71kB 2.0MB/s
...

Finally start up a python shell (or install ipython through pip) and import Tensorflow.

(env)[derek@ramawks76:/scratch0/derek ] $ python
Python 2.7.5 (default, Aug  2 2016, 04:20:16)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.1.0'

You can then try a more rigourous test by running the following example.

import tensorflow as tf
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(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

To use this install after you close the shell you did this install in, you will need to both add the correct Cuda/cuDNN modules and activate the virtualenv by the source command. This includes any time you are submitting to Slurm or other resource managers.