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--- technical:recipes:keras-in-virtualenv [2020-02-04 12:25] – [Create Keras Virtualenv] frey
+++ technical:recipes:keras-in-virtualenv [2021-08-06 13:00] (current) – frey
@@ Line 30: / Line 30: @@
 [(my_workgroup:user)@login01 ~]$ vpkg_require intel-python/2019u5:python3
 Adding package `intel-python/2019u5:python3` to your environment
-(root) [(it_nss:frey)@login01 ~]$
+(root) [(my_workgroup:user)@login01 ~]$
 </code>
@@ Line 38: / Line 38: @@
 <code bash>
-(root) [(it_nss:frey)@login01 ~]$ conda create --prefix=${WORKDIR}/sw/keras/20200204-sklearn keras numpy pip
+(root) [(my_workgroup:user)@login01 ~]$ conda create --prefix=${WORKDIR}/sw/keras/20200204-sklearn keras numpy pip
 </code>
@@ Line 50: / Line 50: @@
 <code bash>
-(root) [(it_nss:frey)@login01 ~]$ vpkg_rollback
+(root) [(my_workgroup:user)@login01 ~]$ vpkg_rollback
-[(it_nss:frey)@login01 ~]$
+[(my_workgroup:user)@login01 ~]$
 </code>
@@ Line 63: / Line 63: @@
     prefix: /work/my_workgroup/sw/keras
     description: KERAS Python environments
+    flags:
+        - no-standard-paths
     actions:
         - action: source
           script:
-              sh: anaconda-activate.sh
+              sh: intel-python.sh
           order: failure-first
           success: 0
@@ Line 82: / Line 84: @@
     prefix: /work/my_workgroup/sw/keras
     description: KERAS Python environments
+    flags:
+        - no-standard-paths
     actions:
         - action: source
           script:
-              sh: anaconda-activate.sh
+              sh: intel-python.sh
           order: failure-first
           success: 0
@@ Line 105: / Line 109: @@
 <note tip>On Caviness after a user has used the ''workgroup'' command, VALET searches for package definitions in ''${WORKDIR}/sw/valet'' by default.</note>
+===== Install SKLearn =====
+The SKLearn package is not present in the conda online repositories, but it can be installed using ''pip.''  First, activate the new Keras virtualenv:
+<code bash>
+[(my_workgroup:user)@login01 ~]$ vpkg_require keras/20200204:sklearn
+Adding dependency `intel-python/2019u5:python3` to your environment
+Adding package `keras/20200204:sklearn` to your environment
+(/work/my_workgroup/sw/keras/20200204-sklearn) [(my_workgroup:user)@login01 keras]$ which pip
+/work/my_workgroup/sw/keras/20200204-sklearn/bin/pip
+</code>
+A prefix has reappeared on the prompt — the path at which the new Keras virtualenv was created — and the ''pip'' command refers to an executable within that directory tree.  The virtualenv is ready to have SKLearn installed.
+<code bash>
+(/work/my_workgroup/sw/keras/20200204-sklearn) [(my_workgroup:user)@login01 ~]$ pip install sklearn
+Collecting sklearn
+  Downloading ..
+    :
+Successfully built sklearn
+Installing collected packages: joblib, scikit-learn, sklearn
+Successfully installed joblib-0.14.1 scikit-learn-0.22.1 sklearn-0.0
+(/work/my_workgroup/sw/keras/20200204-sklearn) [(my_workgroup:user)@login01 ~]$ python3 -c "import sklearn;print(sklearn.__version__)"
+.22.1
+</code>
+The Keras environment with SKLearn is now ready for use.
+===== Job Scripts =====
+Any job scripts you submit that want to run scripts using this virtualenv should include something like the following toward its end:
+<code>
+#
+# Setup Keras virtualenv:
+#
+vpkg_require keras/20200204:sklearn
+#
+# Run a Python script in that virtualenv:
+#
+python3 my_keras_work.py
+rc=$?
+#
+# Do cleanup work, etc....
+#
+#
+# Exit with whatever exit code our Python script handed back:
+#
+exit $rc
+</code>