compute_ru

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===== Using GPU with python  =====

Python offers some modules to communicate with a GPU\\
Both Tensorflow and Torch have been used on this cluster succesfully.\\
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An example setup using Python venv, run these commands :\\
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''python3 -m venv myTorch''\\
''source myTorch/bin/activate''\\
''pip install torch''\\
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The new Python virtual environment has been created and activated, **torch** package installed\\
And the correct **CUDA library** is also installed\\
Please run a simple test with python code to make sure you can communicate with the GPU\\
One example command to test torch\\
''if torch.cuda.is_available() print("GPU")''
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But you must run this code test with **sbatch**, send your job to the cluster\\
Create a python file dummy.py with the test-GPU command\\
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Update the sbatch script file, mine is called **oneJob.sh**\\
Remove lines below last #SBatch\\
Add 2 lines\\
''source myTorch/bin/activate''\\
''python3 dummy.py''\\
[SAVE]\\
Run the command ''sbatch oneJob.sh''\\
The response should be **Submitted batch job XXX**\\
Then check your log file (default name is myJob.log) for the result\\
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Installing Tensorflow is same process, we recommend using a different virtual environment if you want to try both of them\\
Make sure to instruct pip in this way, to get all necessary packages ''pip install "tensorflow[and-cuda]"''\\
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== Those version have also been used before and known to work ==
^Module^Version^Python version^
|Tensorflow |2.17.0 |3.11.10 |
|Torch |2.0.1 |3.9.7 |
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