Package: covalent-gcpbatch-plugin
Covalent Google Cloud Platform (GCP) Batch PluginCovalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. This executor plugin interfaces Covalent withGCP Batch.1. InstallationTo use this plugin with Covalent, install it usingpip:pipinstallcovalent-gcpbatch-plugin2. Usage ExampleThis is an example of how a workflow can be constructed to use the GCP Batch executor. In the example, we train a Support Vector Machine (SVM) and use an instance of the executor to execute thetrain_svmelectron. Note that we also requireDepsPipwhich will be required to execute the electrons.fromnumpy.randomimportpermutationfromsklearnimportsvm,datasetsimportcovalentasctdeps_pip=ct.DepsPip(packages=["numpy==1.22.4","scikit-learn==1.1.2"])executor=ct.executor.GCPBatchExecutor(project_id='covalent_gcp_batch',region='us-east1',bucket_name='covalent-storage-bucket',container_image_uri='us-east1-docker.pkg.dev/covalent_gcp_batch_/covalent/covalent-gcpbatch-executor',service_account_email='covalentsaaccount@covalenttesting.iam.gserviceaccount.com',vcpus=2,# Number of vCPUs to allocatememory=512,# Memory in MB to allocatetime_limit=300,# Time limit of job in secondspoll_freq=3,# Number of seconds to pause before polling for the job's status)# Use executor plugin to train our SVM model@ct.electron(executor=executor,deps_pip=deps_pip)deftrain_svm(data,C,gamma):X,y=dataclf=svm.SVC(C=C,gamma=gamma)clf.fit(X[90:],y[90:])returnclf@ct.electrondefload_data():iris=datasets.load_iris()perm=permutation(iris.target.size)iris.data=iris.data[perm]iris.target=iris.target[perm]returniris.data,iris.target@ct.electrondefscore_svm(data,clf):X_test,y_test=datareturnclf.score(X_test[:90],y_test[:90])@ct.latticedefrun_experiment(C=1.0,gamma=0.7):data=load_data()clf=train_svm(data=data,C=C,gamma=gamma)returnscore_svm(data=data,clf=clf)# Dispatch the workflow.dispatch_id=ct.dispatch(run_experiment)(C=1.0,gamma=0.7)# Wait for our result and get result valueresult=ct.get_result(dispatch_id,wait=True).resultprint(result)During the execution of the workflow, one can navigate to the UI to see the status of the workflow. Once completed, the above script should also output a value with the score of our model.0.8666666666666667In order for the above workflow to run successfully, one has to provision the required cloud resources as mentioned in the sectionRequired GCP Batch Resources.3. ConfigurationThere are many configuration options that can be passed in to the classct.executor.GCPBatchExecutoror by modifying thecovalent config fileunder the section[executors.gcpbatch].For more information about all of the possible configuration values visit ourread the docs (RTD) guidefor this plugin.4. Required GCP ResourcesIn order to run your workflows with covalent there are a few notable GCP resources that need to be provisioned first. The required resources are Google storage bucket, docker artifact registry and service account.For more information regarding which cloud resources need to be provisioned visit ourread the docs (RTD) guidefor this plugin.Getting Started with CovalentFor more information on how to get started with Covalent, check out the projecthomepageand the officialdocumentation.Release NotesRelease notes are available in theChangelog.CitationPlease use the following citation in any publications:W. J. Cunningham, S. K. Radha, F. Hasan, J. Kanem, S. W. Neagle, and S. Sanand.Covalent.Zenodo, 2022.https://doi.org/10.5281/zenodo.5903364LicenseCovalent is licensed under the Apache License 2.0. See theLICENSEfile or contact thesupport teamfor more details.