Summary day 4

Keypoints

  • Parallel

    • You deploy cores and nodes via SLURM, either in interactive mode or batch

    • In Python, threads, distributed and MPI parallelization and DASK can be used.

  • GPUs

    • You deploy GPU nodes via SLURM, either in interactive mode or batch

    • In Python the numba package is handy

  • Machine Learning and Deep Learning

    • General overview of ML/DL with Python.

    • General overview of installed ML/DL tools at HPCs.

    • The loading of PyTorch, TensorFlow, Scikit-learn are slightly different at the clusters

      • UPPMAX: All tools are available from the module python_ML_packages/3.11.8

      • HPC2N:

        • For TensorFlow ml GCC/12.3.0  OpenMPI/4.1.5 TensorFlow/2.15.1-CUDA-12.1.1 scikit-learn/1.4.2 Tkinter/3.11.3 matplotlib/3.7.2

        • For the Pytorch: ml GCC/12.3.0  OpenMPI/4.1.5 PyTorch/2.1.2-CUDA-12.1.1 scikit-learn/1.4.2 Tkinter/3.11.3 matplotlib/3.7.2

      • LUNARC:

        • For TensorFlow module load GCC/11.3.0 Python/3.10.4 SciPy-bundle/2022.05 TensorFlow/2.11.0-CUDA-11.7.0 scikit-learn/1.1.2

        • For Pytorch module load GCC/11.3.0 Python/3.10.4 SciPy-bundle/2022.05 PyTorch/1.12.1-CUDA-11.7.0 scikit-learn/1.1.2

      • NSC: For Tetralith, use virtual environment. Pytorch and TensorFlow might coming soon to the cluster!

      • PDC: For both TensorFlow and Pytorch : module load PDC singularity/4.1.1-cpeGNU-23.12

  • Dimensionality reduction techniques: PCA, t-SNE, UMAP.