Difference between revisions of "WALLE2 Cluster"
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= Scheduler/Queues = | = Scheduler/Queues = | ||
| − | Slurm | + | There are two Slurm partitions: normal and gpu. If no partition is specified in the <code>sbatch</code> or <code>srun</code> command, the <code>normal</code> partition is the default. |
| − | To request access to GPUs | + | There are two types of GPUs, V100 (quantity 2) and T4 (quantity 2). To request access to GPUs, you must specify BOTH the gpu partition (-p gpu) AND gpu type (--gres=gpu:<gpu>:<number of gpus>). |
| − | + | Examples for submitting a job to the <code>gpu</code> partition. | |
| − | + | 1. To request 1 NVidia V100 GPU (node g0001 has 2 V100s): | |
| − | # | + | #<code>srun -p gpu --gres=gpu:v100:1 -pty bash</code> |
| − | # | + | 2. To request 1 T4 GPU (g0002 has 2 T4s): |
| + | #<code>srun -p gpu --gres=gpu:t4:1 -pty bash</code> | ||
See the [[ Slurm#Requesting_GPUs | Requesting GPUs ]] section for information on how to request GPUs on compute nodes for your jobs. | See the [[ Slurm#Requesting_GPUs | Requesting GPUs ]] section for information on how to request GPUs on compute nodes for your jobs. | ||
Revision as of 12:48, 1 March 2022
General Information
- Walle2 is a private cluster. Access is restricted to the ek436_0001 group.
- Head node: walle2.cac.cornell.edu (access via ssh)
- OpenHPC deployment running Rocky Linux 8
- Cluster scheduler: slurm (See CAC slurm documentation for more info)
- compute nodes:
- 5 non-GPU nodes (c0001-c0005): 40 cores/80 threads, 768 GB of RAM
- 2 GPU nodes (g0001: 2 x NVidia V100, g0002: 2 x NVidia T4): 24 cores/48 threads, 384 GB of RAM
- Data on the walle cluster storage are NOT backed up.
- Please send any questions and report problems to: cac-help@cornell.edu
Scheduler/Queues
There are two Slurm partitions: normal and gpu. If no partition is specified in the sbatch or srun command, the normal partition is the default.
There are two types of GPUs, V100 (quantity 2) and T4 (quantity 2). To request access to GPUs, you must specify BOTH the gpu partition (-p gpu) AND gpu type (--gres=gpu:<gpu>:<number of gpus>).
Examples for submitting a job to the gpu partition.
1. To request 1 NVidia V100 GPU (node g0001 has 2 V100s):
srun -p gpu --gres=gpu:v100:1 -pty bash
2. To request 1 T4 GPU (g0002 has 2 T4s):
srun -p gpu --gres=gpu:t4:1 -pty bash
See the Requesting GPUs section for information on how to request GPUs on compute nodes for your jobs.
| Slurm Partition | Description | Time Limit |
|---|---|---|
| normal* | Non GPU nodes | None |
| gpu | Nvidia V100 or T4 GPU nodes | None |
Software
Work with Environment Modules
Set up the working environment for each package using the module command. The module command will activate dependent modules if there are any.
To show currently loaded modules: (These modules are loaded by default system configurations)
-bash-4.2$ module list Currently Loaded Modules: 1) autotools 3) gnu9/9.3.0 5) libfabric/1.10.1 7) ohpc 2) prun/2.0 4) ucx/1.8.0 6) openmpi4/4.0.4
To show all available modules:
-bash-4.2$ module avail ------------------- /opt/ohpc/pub/moduledeps/gnu9-openmpi4 -------------------- adios/1.13.1 netcdf-fortran/4.5.2 py3-mpi4py/3.0.3 boost/1.75.0 netcdf/4.7.3 py3-scipy/1.5.1 fftw/3.3.8 (L) opencoarrays/2.9.2 scalapack/2.1.0 hypre/2.18.1 petsc/3.14.4 slepc/3.14.2 jdftx/1.6.0 (L) petsc/3.15.0 (D) slepc/3.15.0 (D) mfem/4.2 phdf5/1.10.6 superlu_dist/6.1.1 mumps/5.2.1 pnetcdf/1.12.1 trilinos/13.0.0 netcdf-cxx/4.3.1 ptscotch/6.0.6 ------------------------ /opt/ohpc/pub/moduledeps/gnu9 ------------------------ gsl/2.6 (L) mpich/3.3.2-ofi py3-numpy/1.19.0 hdf5/1.10.6 (L) mvapich2/2.3.4 superlu/5.2.1 impi/2021.2.0 openblas/0.3.7 (L) metis/5.1.0 openmpi4/4.0.5 (L) -------------------------- /opt/ohpc/pub/modulefiles -------------------------- autotools (L) julia/1.6.1 os cmake/3.19.4 libfabric/1.11.2 (L) prun/2.1 (L) cuda/11.3 mkl/2021.2.0.610 (L) py3-libs gnu9/9.3.0 (L) octave/6.3.0 (L) ucx/1.9.0 (L) intel/2021.2.0.610 ohpc (L) Where: D: Default Module L: Module is loaded Use "module spider" to find all possible modules and extensions. Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys".
To load a module and verify:
-bash-4.2$ module load cmake -bash-4.2$ module list Currently Loaded Modules: 1) autotools 3) gnu9/9.3.0 5) libfabric/1.10.1 7) ohpc 2) prun/2.0 4) ucx/1.8.0 6) openmpi4/4.0.4 8) cmake/3.16.2
Manage Modules in Your Python Virtual Environment
python 3.6.8 is installed. Users can manage their own python environment (including installing needed modules) using virtual environments. Please see the documentation on virtual environments on python.org for details.
Create Virtual Environment
You can create as many virtual environments, each in their own directory, as needed.
python3 -m venv <your virtual environment directory>
Activate Virtual Environment
You need to activate a virtual environment before using it:
source <your virtual environment directory>/bin/activate
Install Python Modules Using pip
After activating your virtual environment, you can now install python modules for the activated environment:
- It's always a good idea to update
pipfirst:
pip install --upgrade pip
- Install the module:
pip install <module name>
- List installed python modules in the environment:
pip list modules
- Examples: Install
tensorflowandkeraslike this:
-bash-4.2$ python3 -m venv tensorflow
-bash-4.2$ source tensorflow/bin/activate
(tensorflow) -bash-4.2$ pip install --upgrade pip
Collecting pip
Using cached https://files.pythonhosted.org/packages/30/db/9e38760b32e3e7f40cce46dd5fb107b8c73840df38f0046d8e6514e675a1/pip-19.2.3-py2.py3-none-any.whl
Installing collected packages: pip
Found existing installation: pip 18.1
Uninstalling pip-18.1:
Successfully uninstalled pip-18.1
Successfully installed pip-19.2.3
(tensorflow) -bash-4.2$ pip install tensorflow keras
Collecting tensorflow
Using cached https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
:
:
:
Successfully installed absl-py-0.8.0 astor-0.8.0 gast-0.2.2 google-pasta-0.1.7 grpcio-1.23.0 h5py-2.9.0 keras-2.2.5 keras-applications-1.0.8 keras-preprocessing-1.1.0 markdown-3.1.1 numpy-1.17.1 protobuf-3.9.1 pyyaml-5.1.2 scipy-1.3.1 six-1.12.0 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 termcolor-1.1.0 werkzeug-0.15.5 wheel-0.33.6 wrapt-1.11.2
(tensorflow) -bash-4.2$ pip list modules
Package Version
-------------------- -------
absl-py 0.8.0
astor 0.8.0
gast 0.2.2
google-pasta 0.1.7
grpcio 1.23.0
h5py 2.9.0
Keras 2.2.5
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.0
Markdown 3.1.1
numpy 1.17.1
pip 19.2.3
protobuf 3.9.1
PyYAML 5.1.2
scipy 1.3.1
setuptools 40.6.2
six 1.12.0
tensorboard 1.14.0
tensorflow 1.14.0
tensorflow-estimator 1.14.0
termcolor 1.1.0
Werkzeug 0.15.5
wheel 0.33.6
wrapt 1.11.2
Software List
Software Path Notes *GNU Compilers 9.4.0 /opt/ohpc/pub/compiler/gcc/9.4.0 module load gnu9/9.4.0 Intel Compilers (2021 Update 2) /opt/intel/oneapi/compiler/2021.2.0 module load intel/2021.2.0.610 MKL 2021.2.0.610 (2021 Update 2) /opt/intel/oneapi/mkl/2021.2.0 module load mkl/2021.2.0.610 *openmpi 4.0.5 - /opt/ohpc/pub/mpi/openmpi4-gnu9
- /opt/ohpc/pub/mpi/openmpi4-intel
module load openmpi4 Intel MPI 2021.2.0 /opt/intel/oneapi/mpi/2021.2.0 module load impi/2021.2.0 Julia 1.6.1 /opt/ohpc/pub/compiler/julia/1.6.1 module load julia/1.6.1 JDFTx 1.6.0 /opt/ohpc/pub/apps/jdftx/1.6.0 module load jdftx/1.6.0 CUDA 11.5 /usr/local/cuda-11.5 module load cuda/11.5 petsc / petsc4py 3.15.0 - /opt/ohpc/pub/libs/gnu9/openmpi4/petsc/3.15.0
- /opt/ohpc/pub/libs/intel/impi/petsc/3.15.0
module load petsc/3.15.0 slepc / slepc4py 3.15.0 - /opt/ohpc/pub/libs/gnu9/openmpi4/slepc/3.15.0
- /opt/ohpc/pub/libs/intel/impi/slepc/3.15.0
module load slepc/3.15.0 Python3 Modules (pytorch with CUDA support, numpy, scipy, matplotlib, pandas tensorflow, keras, sklearn, umap)
/opt/ohpc/pub/utils/py3-libs/ module load py3-libs