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TensorFlow

TensorFlow is an open source library for machine learning.

Installation

An more in-depth tutorial on installing and using TensorFlow on Apocrita is also available on our blog.

Installing with pip

Package naming and installing specific versions

For releases 1.15 and older, the CPU and GPU pip packages are separate. If using one of these older releases, we recommend installing the GPU package because TensorFlow programs typically run much faster on a GPU, compared to CPU. Researchers need to request permission to be added to the list of GPU node users.

To select a specific version, use the pip standard method, noting that other versions may have been built with different CUDA libraries. To install version 1.15, run pip install tensorflow-gpu==1.15. Removing the version number installs the latest release version.

The TensorFlow package may be installed using pip in a virtualenv, which uses packages from the Python Package Index.

Loading a CUDNN module will also load the corresponding CUDA module as a prerequisite. These libraries are required to be loaded to utilise GPU acceleration within TensorFlow. Make sure to check for any errors in the job output, as an incorrect CUDA or CUDNN module version will usually result in the GPU not being used.

Loading a TensorRT module will also load the corresponding CUDNN module (and therefore CUDA) as a prerequisite.

Initial setup:

module load python
virtualenv tfenv
source tfenv/bin/activate
pip install tensorflow

If you have any other additional python package dependencies, these should be installed into your virtualenv with additional pip install commands, or in bulk, using a requirements file

Subsequent activation as part of a GPU job:

module load python
module load cudnn/8.1.1-cuda11.2
source tfenv/bin/activate

Installing with conda

If you prefer to use conda environments, the approach is slightly different as conda supports a variety of CUDA versions and will install requirements as conda packages within your virtual environment. Note that while the pip packages are officially supported by TensorFlow, the conda packages are built and supported by Anaconda.

Conda package availability and disk space

Conda tends to pull in a lot of packages, consuming more space than pip virtualenvs. Additionally, pip tends to have a wider range of third-party packages than conda.

Initial setup:

module load anaconda3
conda create -n tensorgpu
conda activate tensorgpu
conda install tensorflow-gpu

Subsequent activation as part of a GPU job:

module load anaconda3
conda activate tensorgpu

Using containers

If you have certain requirements that are not satisfiable by pip or conda (e.g. extra operating system packages not available on Apocrita), then it may be possible to solve this with a Singularity container. For most requirements, the pip method is recommended, since it is easier to maintain and add packages to a user-controlled virtualenv.

A list of existing TensorFlow containers can be found in the /data/containers/tensorflow directory on Apocrita, which can be customised to add the required packages.

Example jobs

Checking that the GPU is being used correctly

Running ssh <nodename> nvidia-smi will query the GPU status on a node. You can find out the node your job is using with the qstat command.

In all examples below, the file tf_test.py contains the following Python code:

import tensorflow as tf
print(tf.config.experimental.list_physical_devices(device_type="GPU"))

Simple GPU job using virtualenv

This assumes an existing virtualenv named tfenv created as shown above.

#!/bin/bash
#$ -cwd
#$ -j y
#$ -pe smp 8
#$ -l h_rt=240:0:0
#$ -l gpu=1

module load python
module load cudnn/8.1.1-cuda11.2
source tfenv/bin/activate
python tf_test.py

Simple GPU job using conda

This assumes an existing conda env named tensorgpu created as shown above.

#!/bin/bash
#$ -cwd
#$ -j y
#$ -pe smp 8
#$ -l h_rt=240:0:0
#$ -l gpu=1

module load anaconda3
conda activate tensorgpu
python tf_test.py

CPU-only example using virtualenv

This assumes an existing virtualenv named tfenv created as shown above.

#!/bin/bash
#$ -cwd
#$ -pe smp 1
#$ -l h_rt=1:0:0
#$ -l h_vmem=1G

module load python
source tfenv/bin/activate
python -c 'import tensorflow as tf; print(tf.__version__)'

Submit the script to the job scheduler and the TensorFlow version number will be recorded in the job output file.

Simple GPU job using a container

#!/bin/bash
#$ -cwd
#$ -j y
#$ -pe smp 8
#$ -l h_rt=240:0:0
#$ -l gpu=1

singularity exec --nv \
/data/containers/tensorflow/tensorflow-1.8-python3-ubuntu-16.04.img \
python -c 'import tensorflow as tf; print(tf.__version__)'

Singularity GPU support

The --nv flag is required for GPU support and passes through the appropriate GPU drivers and libraries from the host to the container.

GPU machine learning example

This example demonstrates some real-life code which uses 1 GPU on a node. The source can be found in the references section below.

#!/bin/bash
#$ -cwd
#$ -j y
#$ -pe smp 8
#$ -l h_rt=240:0:0
#$ -l gpu=1

module load python
module load cudnn/8.1.1-cuda11.2
source tfenv/bin/activate
python mnist_classify.py

References