This page documents the creation of a Python virtual environment (virtualenv) containing the TensorFlow software for machine learning on the Caviness HPC system1). It assumes that the user is adding the software to the workgroup storage.
Prepare to add software in the standard sub-directories of the workgroup storage:
[user@login01 ~]$ workgroup -g my_workgroup [(my_workgroup:user)@login01 ~]$ mkdir --mode=2775 --parent ${WORKDIR}/sw/tensorflow [(my_workgroup:user)@login01 ~]$ mkdir --mode=2775 --parent ${WORKDIR}/sw/valet
These commands create any missing directories. All directories created will have group-write and -inherit permissions.
The Intel Python distribution will form the basis for the Keras virtualenv, so add it to the environment:
[(my_workgroup:user)@login01 ~]$ vpkg_require intel-python/2020u2:python3 Adding package `intel-python/2020u2:python3` to your environment (base) [(my_workgroup:user)@login01 ~]$
Notice the prompt changed: the text (base) now prefixes it, indicating the directory that contains the active Python virtualenv.
The conda search tensorflow command can be used to locate the specific version you wish to install. Two examples are shown: TensorFlow release at least 2.0 with GPU support; and an Intel-optimized version of TensorFlow 2.3.
(base) [frey@login00 ~]$ conda search 'tensorflow>=2.0=gpu*' Loading channels: done # Name Version Build Channel tensorflow 2.0.0 gpu_py27hb041a2f_0 pkgs/main tensorflow 2.0.0 gpu_py36h6b29c10_0 pkgs/main tensorflow 2.0.0 gpu_py37h768510d_0 pkgs/main tensorflow 2.1.0 gpu_py27h9cdf9a9_0 pkgs/main tensorflow 2.1.0 gpu_py36h2e5cdaa_0 pkgs/main tensorflow 2.1.0 gpu_py37h7a4bb67_0 pkgs/main tensorflow 2.2.0 gpu_py36hf933387_0 pkgs/main tensorflow 2.2.0 gpu_py37h1a511ff_0 pkgs/main tensorflow 2.2.0 gpu_py38hb782248_0 pkgs/main (base) [frey@login00 ~]$ conda search 'tensorflow[version=2.3,channel=intel]' Loading channels: done # Name Version Build Channel tensorflow 2.3.0 py36_0 intel tensorflow 2.3.0 py37_0 intel tensorflow 2.3.0 py38_0 intel
All versions of the TensorFlow virtualenv will be stored in the common base directory, $WORKDIR/sw/tensorflow; each virtualenv must have a unique name that will become the VALET version of TensorFlow. In this tutorial, the latest version of TensorFlow (with GPU support) is version 2.2.0, but the newest non-GPU version available with Python 3.8 is 2.3.0. An appropriate version for the former would be 2.2.0:gpu and the latter 2.3.0:intel,python3.8. Those versions can be translated to VALET-friendly directory names:
[(my_workgroup:user)@login01 ~]$ vpkg_id2path --version-id=2.2.0:gpu 2.2.0-gpu [(my_workgroup:user)@login01 ~]$ mkdir --mode=3750 ${WORKDIR}/sw/tensorflow/2.2.0-gpu [(my_workgroup:user)@login01 ~]$ vpkg_id2path --version-id=2.3.0:intel,python3.8 2.3.0-intel-python3.8 [(my_workgroup:user)@login01 ~]$ mkdir --mode=3750 ${WORKDIR}/sw/tensorflow/2.3.0-intel-python3.8
The virtualenvs are created using the --prefix option to specify the directories created above:
(base) [(my_workgroup:user)@login01 ~]$ conda create --prefix=${WORKDIR}/sw/tensorflow/2.2.0-gpu 'tensorflow[version=2.2.0,build=gpu_py38hb782248_0]' WARNING: A directory already exists at the target location '/work/it_nss/sw/tensorflow/2.2.0-gpu' but it is not a conda environment. Continue creating environment (y/[n])? y : Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate /work/it_nss/sw/tensorflow/2.2.0-gpu # # To deactivate an active environment, use # # $ conda deactivate
We're not going to activate that virtualenv – we will install the other one next:
(base) [(it_nss:frey)@login00 ~]$ conda create --prefix=${WORKDIR}/sw/tensorflow/2.3.0-intel-python3.8 'tensorflow[version=2.3.0,build=py38_0,channel=intel]' WARNING: A directory already exists at the target location '/work/it_nss/sw/tensorflow/2.3.0-intel-python3.8' but it is not a conda environment. Continue creating environment (y/[n])? y : Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate /work/it_nss/sw/tensorflow/2.3.0-intel-python3.8 # # To deactivate an active environment, use # # $ conda deactivate
Ignore that conda activate command as well. Rollback the intel-python environment changes before proceeding:
(base) [(my_workgroup:user)@login01 ~]$ vpkg_rollback [(my_workgroup:user)@login01 ~]$
Notice the (base) has disappeared from the prompt, indicating that the baseline virtualenv has been deactivated.
Assuming the workgroup does not already have a TensorFlow VALET package definition, the following text:
tensorflow:
prefix: /work/my_workgroup/sw/tensorflow
description: TensorFlow Python environments
flags:
- no-standard-paths
actions:
- action: source
script:
sh: anaconda-activate.sh
order: failure-first
success: 0
versions:
"2.2.0:gpu":
description: 2.2.0 with GPU support
dependencies:
- intel-python/2020u2:python3
"2.3.0:intel,python3.8":
description: 2.3.0 with Python 3.8, Intel optimizations
dependencies:
- intel-python/2020u2:python3
would be added to ${WORKDIR}/sw/valet/tensorflow.vpkg_yaml. If that file already exists, add your new version at the same level as others:
tensorflow:
prefix: /work/my_workgroup/sw/tensorflow
description: TensorFlow Python environments
flags:
- no-standard-paths
actions:
- action: source
script:
sh: anaconda-activate.sh
order: failure-first
success: 0
versions:
"2.2.0:gpu":
description: 2.2.0 with GPU support
dependencies:
- intel-python/2020u2:python3
"2.3.0:intel,python3.8":
description: 2.3.0 with Python 3.8, Intel optimizations
dependencies:
- intel-python/2020u2:python3
"1.8.0":
description: 1.8.0 from pkgs/main
dependencies:
- intel-python/2018u3:python3
prefix: /work/my_workgroup/sw/tensorflow for your workgroup (e.g. If my workgroup is it_nss, then use I would use prefix: /work/it_nss/sw/tensorflow).
workgroup command, VALET searches for package definitions in ${WORKDIR}/sw/valet by default. VALET also searches a ~/.valet directory (in your home directory) if it exists, so that's the best location for personal package definitions – for software you've installed in your home directory, for example.
With a properly-constructed package definition file, you can now check for your versions of TensorFlow:
[(it_nss:frey)@login00 ~]$ vpkg_versions tensorflow Available versions in package (* = default version): [/work/my_workgroup/sw/valet/tensorflow.vpkg_yaml] tensorflow TensorFlow Python environments * 2.2.0:gpu 2.2.0 with GPU support 2.3.0:intel,python3.8 2.3.0 with Python 3.8, Intel optimizations :
Any job scripts you submit that want to run scripts using this virtualenv should include something like the following toward its end:
# # Setup TensorFlow virtualenv: # vpkg_require tensorflow/2.3.0:intel,python3.8 # # Run a Python script in that virtualenv: # python3 my_tf_work.py rc=$? # # Do cleanup work, etc.... # # # Exit with whatever exit code our Python script handed back: # exit $rc