TensorFlow
Deep learning framework for Python.
News
5.10.2022 Due to Puhti's update to Red Hat Enterprise Linux 8 (RHEL8), the number of fully supported TensorFlow versions has been reduced. Previously deprecated conda-based versions have been removed. Please contact our servicedesk if you really need access to older versions.
5.5.2022 Due to Mahti's update to Red Hat Enterprise Linux 8 (RHEL8), the number of fully supported TensorFlow versions has been reduced. Please contact our servicedesk if you really need access to other versions.
4.2.2022 All old TensorFlow versions which were based on direct Conda installations have been deprecated, and we encourage users to move to newer versions. Read more on our separate Conda deprecation page.
Available
Currently supported TensorFlow versions:
| Version | Module | Puhti | Mahti | LUMI | Notes |
|---|---|---|---|---|---|
| 2.15.0 | tensorflow/2.15 |
X | X | - | default version |
| 2.14.0 | tensorflow/2.14 |
X | X | - | |
| 2.13.0 | tensorflow/2.13 |
X | X | - | |
| 2.12.0 | tensorflow/2.12 |
X | X | X* | |
| 2.11.0 | tensorflow/2.11 |
X | X | X* | |
| 2.10.0 | tensorflow/2.10 |
X | X | X* | |
| 2.9.0 | tensorflow/2.9 |
X | X | X* | |
| 2.8.0 | tensorflow/2.8 |
X | X | X* | |
| 2.7.0 | tensorflow/2.7 |
(x) | (x) | - | |
| 2.6.0 | tensorflow/2.6 |
(x) | (x) | - | |
| 2.5.0 | tensorflow/2.5 |
(x) | (x) | - | |
| 2.4.1 | tensorflow/2.4 |
(x) | (x) | - | |
| 2.4.0 | tensorflow/2.4-sng |
(x) | - | - | |
| 2.3.0 | tensorflow/2.3 |
(x) | - | - | |
| 2.2.0 | tensorflow/2.2 |
(x) | - | - | |
| 1.15.5 | tensorflow/1.15 |
(x) | - | - |
Includes TensorFlow and Keras with GPU support via CUDA/ROCm.
Versions marked with "(x)" are based on old Red Hat Enterprise Linux 7
(RHEL7) images, and are no longer fully supported. In particular MPI
and Horovod are not expected to work anymore with these modules. If
you still wish to access these versions, you need to enable old RHEL7
modules by module use /appl/soft/ai/rhel7/modulefiles/.
Versions in LUMI, marked as "X*" are still experimental with limited support. They are still subject to change at any time without notice, and for example multi-node jobs are know not to work properly yet.
If you find that some package is missing, you can often install it
yourself using pip install. It is recommended to use Python virtual
environments. See our Python documentation for more information on
how to install packages
yourself.
If you think that some important package should be included in the
module provided by CSC, please contact our
servicedesk.
All modules are based on containers using Apptainer (previously known
as Singularity). Wrapper scripts have been provided so that common
commands such as python, python3, pip and pip3 should work as
normal. For other commands, you need to prefix them with
apptainer_wrapper exec, for example apptainer_wrapper exec
huggingface-cli. For more information, see CSC's general
instructions on how to run Apptainer
containers.
Some modules support Horovod, which is our recommended framework for multi-node jobs, i.e., jobs needing more than 4 GPUs. Horovod can also be used with single-node jobs for 2-4 GPUs. For more information, read the Multi-GPU section in our machine learning guide.
License
TensorFlow is licensed under Apache License 2.0.
Usage
To use the default version of TensorFlow on Puhti or Mahti, initialize it with:
module load tensorflow
To access TensorFlow on LUMI:
module use /appl/local/csc/modulefiles/
module load tensorflow
Note that LUMI versions are still considered experimental with limited support. They are still subject to change at any time without notice, and for example multi-node jobs are know not to work properly yet.
If you wish to have a specific version (see above for available versions), use:
module load tensorflow/2.12
Please note that the module already includes CUDA and cuDNN libraries, so there is no need to load cuda and cudnn modules separately!
This command will also show all available versions:
module avail tensorflow
To check the exact packages and versions included in the loaded module you can run:
list-packages
Warning
Note that login nodes are not intended for heavy computing, please use slurm batch jobs instead. See our instructions on how to use the batch job system.
Example batch script
Example batch script for reserving one GPU and 1/4 of the available CPU cores in a single node:
#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpu
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --mem=80G
#SBATCH --time=1:00:00
#SBATCH --gres=gpu:v100:1
module load tensorflow/2.14
srun python3 myprog.py <options>
#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpusmall
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --time=1:00:00
#SBATCH --gres=gpu:a100:1
module load tensorflow/2.14
srun python3 myprog.py <options>
#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small-g
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=7
#SBATCH --gpus-per-node=1
#SBATCH --mem=60G
#SBATCH --time=1:00:00
module use /appl/local/csc/modulefiles/
module load tensorflow/2.12
srun python3 myprog.py <options>
Please read the section on Efficient GPU utilization in our Machine learning guide to learn how to use the GPU efficiently.
Big datasets, multi-GPU and multi-node jobs
If you are working with big datasets, or datasets that contain a lot of files, please read the data section of our Machine learning guide. In particular, please do not read a huge number of files from the shared file system, use fast local disk or package your data into larger files instead!
For multi-GPU and multi-node please read the Multi-GPU and multi-node section of our Machine learning guide