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Commit c812945a authored by Christoph Lehmann's avatar Christoph Lehmann
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software/machine_learning.md: some polishing

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5 merge requests!333Draft: update NGC containers,!322Merge preview into main,!319Merge preview into main,!279Draft: Machine Learning restructuring,!258Data Analytics restructuring
# Machine Learning # Machine Learning
This is an introduction of how to run machine learning applications on ZIH systems. This is an introduction of how to run machine learning applications on ZIH systems.
For machine learning purposes, we recommend to use the **Alpha** and/or **ML** partitions. For machine learning purposes, we recommend to use the [Alpha](#alpha-partition) and/or [ML](#ml-partition) partitions.
## ML partition ## ML Partition
The compute nodes of the ML partition are built on the base of [Power9](https://www.ibm.com/it-infrastructure/power/power9) The compute nodes of the ML partition are built on the base of [Power9](https://www.ibm.com/it-infrastructure/power/power9)
architecture from IBM. The system was created for AI challenges, analytics and working with architecture from IBM. The system was created for AI challenges, analytics and working with
...@@ -79,7 +79,7 @@ After accessing JupyterHub, you can start a new session and configure it. For ma ...@@ -79,7 +79,7 @@ After accessing JupyterHub, you can start a new session and configure it. For ma
purposes, select either **Alpha** or **ML** partition and the resources, your application requires. purposes, select either **Alpha** or **ML** partition and the resources, your application requires.
In your session you can use [Python](../data_analytics_with_python/#jupyter-notebooks), [R](../data_analytics_with_r/#r-in-jupyterhub) In your session you can use [Python](../data_analytics_with_python/#jupyter-notebooks), [R](../data_analytics_with_r/#r-in-jupyterhub)
or [R studio](data_analytics_with_rstudio) for your machine learning and data science topics. or [R studio](../data_analytics_with_rstudio/) for your machine learning and data science topics.
## Machine Learning with Containers ## Machine Learning with Containers
...@@ -112,7 +112,7 @@ marie@ml$ singularity run --nv my-ML-container.sif #run my-ML-container.sif c ...@@ -112,7 +112,7 @@ marie@ml$ singularity run --nv my-ML-container.sif #run my-ML-container.sif c
The following NVIDIA libraries are available on all nodes: The following NVIDIA libraries are available on all nodes:
| | | | Name | Path |
|-------|-----------------------------------------| |-------|-----------------------------------------|
| NCCL | `/usr/local/cuda/targets/ppc64le-linux` | | NCCL | `/usr/local/cuda/targets/ppc64le-linux` |
| cuDNN | `/usr/local/cuda/targets/ppc64le-linux` | | cuDNN | `/usr/local/cuda/targets/ppc64le-linux` |
...@@ -129,7 +129,7 @@ marie@compute$ export NCCL_MIN_NRINGS=4 ...@@ -129,7 +129,7 @@ marie@compute$ export NCCL_MIN_NRINGS=4
The following HPC related software is installed on all nodes: The following HPC related software is installed on all nodes:
| | | | Name | Path |
|------------------|--------------------------| |------------------|--------------------------|
| IBM Spectrum MPI | `/opt/ibm/spectrum_mpi/` | | IBM Spectrum MPI | `/opt/ibm/spectrum_mpi/` |
| PGI compiler | `/opt/pgi/` | | PGI compiler | `/opt/pgi/` |
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