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TensorFlow

User documentation

BETA VERSION, problems may occur and may not be solved quickly, and the documentation needs further development.

The TensorFlow container is developed by AMD specifically for LUMI and contains the necessary parts to run TensorFlow on LUMI, including the plugin needed for RCCL when doing distributed AI, and a suitable version of ROCm for the version of TensorFlow. Horovod is also provided, with support for Cray MPI.

The EasyBuild installation with the EasyConfigs mentioned below will do three things:

  1. It will copy the container to your own file space. We realise containers can be big, but it ensures that you have complete control over when a container is removed again.

    We will remove a container from the system when it is not sufficiently functional anymore, but the container may still work for you. E.g., after an upgrade of the network drivers on LUMI, the RCCL plugin for the LUMI Slingshot interconnect may be broken, but if you run on only one node TensorFlow may still work for you.

    If you prefer to use the centrally provided container, you can remove your copy after loading of the module with rm $SIF followed by reloading the module. This is however at your own risk.

  2. It will create a module file. When loading the module, a number of environment variables will be set to help you use the module and to make it easy to swap the module with a different version in your job scripts.

    • SIF and SIFTENSORFLOW both contain the name and full path of the singularity container file.

    • SINGULARITY_BINDPATH will mount all necessary directories from the system, including everything that is needed to access the project, scratch and flash file systems.

    • RUNSCRIPTS and RUNSCRIPTSTENSORFLOW contain the full path of the directory containing some sample run script(s) that can be used to run software in the container, or as inspiration for your own variants.

  3. It creates currently 1 script in the $RUNSCRIPTS directory:

    • conda-python-simple: This initialises Python in the container and then calls Python with the arguments of conda-python-simple. It can be used, e.g., to run commands through Python that utilise a single task but all GPUs.

The container uses a miniconda environment in which Python and its packages are installed. That environment needs to be activated in the container when running, which can be done with the command that is available in the container as the environment variable WITH_CONDA (which for this container is source /opt/miniconda3/bin/activate tensorflow).

The container (when used with SINGULARITY_BINDPATH of the module) also provides the wrapper script /runscripts/conda-python-simple to start the Python command from the conda environment in the container. That script is also available outside the container for inspection after loading the module as $RUNSCRIPTS/conda-python-simple and you can use that script as a source of inspiration to develop a script that more directly executes your commands or does additional initialisations.

Example (in an interactive session):

salloc -N1 -pstandard-g -t 30:00
module load LUMI TensorFlow/2.11.1-rocm-5.5.1-python-3.10-horovod-0.28.1-singularity-20231110
srun -N1 -n1 --gpus 8 singularity exec $SIF /runscripts/python-conda-simple \
    -c 'import tensorflow'
(and the warning shown about being built with the oneAPI Deep Neural Network Library is just a warning, as AVX2 and FMA are indeed the instructions that should be used on the LUMI CPUs).

After loading the module, the docker definition file used when building the container is available in the $EBROOTTENSORFLOW/share/docker-defs subdirectory. As it requires some licensed components from LUMI and some other files that are not included, it currently cannot be used to reconstruct the container and extend its definition.

Installation

To install the container with EasyBuild, follow the instructions in the EasyBuild section of the LUMI documentation, section "Software", and use the dummy partition container, e.g.:

module load LUMI partition/container EasyBuild-user
eb TensorFlow-2.11.1-rocm-5.5.1-python-3.10-horovod-0.28.1-singularity-20231110.eb

To use the container after installation, the EasyBuild-user module is not needed nor is the container partition. The module will be available in all versions of the LUMI stack and in the CrayEnv stack (provided the environment variable EBU_USER_PREFIX points to the right location).

Singularity containers with modules for binding and extras

Install with the EasyBuild-user module in partition/container: 

module load LUMI partition/container EasyBuild-user
eb <easyconfig>
The module will be available in all versions of the LUMI stack and in the CrayEnv stack.

To access module help after installation use module spider TensorFlow/<version>.

EasyConfig: