Difference between revisions of "ACLAB"
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= General Information = | = General Information = | ||
− | :* ACLAB is a private virtual cluster in Red Cloud with restricted access to the following groups: ktb1_0005 | + | :* ACLAB is a private virtual cluster in Red Cloud with restricted access to the following groups: ktb1_0005. Please see [[Virtual_Cluster_in_Red_Cloud | Virtual Cluster in Red Cloud page]] for more usage information. |
:** PIs are Adam Anderson and Eve DeRosa. | :** PIs are Adam Anderson and Eve DeRosa. | ||
:* Head node: '''aclab.cac.cornell.edu''' ([[#How To Login|access via ssh]]) | :* Head node: '''aclab.cac.cornell.edu''' ([[#How To Login|access via ssh]]) |
Revision as of 09:58, 6 May 2021
General Information
- ACLAB is a private virtual cluster in Red Cloud with restricted access to the following groups: ktb1_0005. Please see Virtual Cluster in Red Cloud page for more usage information.
- PIs are Adam Anderson and Eve DeRosa.
- Head node: aclab.cac.cornell.edu (access via ssh)
- OpenHPC deployment running Centos 8
- Cluster scheduler: slurm
- compute nodes - on demand via slurm
- data on the ACLAB cluster storage is NOT backed up
- Please send any questions and report problems to: cac-help@cornell.edu
- ACLAB is a private virtual cluster in Red Cloud with restricted access to the following groups: ktb1_0005. Please see Virtual Cluster in Red Cloud page for more usage information.
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/1.73.0 netcdf/4.7.3 py3-scipy/1.5.1 fftw/3.3.8 opencoarrays/2.8.0 scalapack/2.1.0 hypre/2.18.1 petsc/3.13.1 slepc/3.13.2 mfem/4.1 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 mpich/3.3.2-ofi openmpi4/4.0.4 (L) hdf5/1.10.6 mvapich2/2.3.2 py3-numpy/1.19.0 metis/5.1.0 openblas/0.3.7 superlu/5.2.1 -------------------------- /opt/ohpc/pub/modulefiles --------------------------- autotools (L) libfabric/1.10.1 (L) pmix/3.1.4 cmake/3.16.2 ohpc (L) prun/2.0 (L) gnu9/9.3.0 (L) os ucx/1.8.0 (L) Where: 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
pip
first:
pip install --upgrade pip
- Install the module:
pip install <module name>
- List installed python modules in the environment:
pip list modules
- Examples: Install
tensorflow
andkeras
like 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