Tensorflow is a commonly used Python package for deep learning.

Basic usage

First, check the tutorials up to and including GPU computing.

With tensorflow, you have to decide at install time if you want a version that runs on CPUs or GPUs. This means that we can’t install it for everyone and expect it to work everywhere - you have to load something different if you want it to run on login node/regular nodes (probably for testing) or GPU nodes. You probably want to use GPUs.

The basic way to use is via the Python in the anaconda3 module (or anaconda2) - but these modules have the GPU version installed, so you can’t run or test on the login node.

If you module spider anaconda3 (or 2), you can see several versions ending in -cpu or -gpu. These have respectively the CPU and GPU versions of tensorflow installed.

If you use GPUs, you need --constraint='kepler|pascal|volta' in order to select a GPU new enough to run tensorflow. (Note that as we get never cards, this will need further updating).

Simple Tensorflow/Keras model

Let’s run the MNIST example from Tensorflow’s tutorials:

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)

The full code for the example is in tensorflow_mnist.py. One can run this example with srun:

wget https://raw.githubusercontent.com/AaltoScienceIT/scicomp-docs/master/triton/examples/tensorflow/tensorflow_mnist.py
module load anaconda3/latest
srun -t 00:15:00 --gres=gpu:1 python tensorflow_mnist.py

or with sbatch by submitting tensorflow_mnist.sh:

#SBATCH --gres=gpu:1
#SBATCH --time=00:15:00

module load anaconda3/latest

python tensorflow_mnist.py

Do note that by default Keras downloads datasets to $HOME/.keras/datasets.