covalent-awsbatch-plugin 0.42.0

Covalent AWS Batch Plugin

 

Covalent AWS Batch Plugin

Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware.

This executor plugin interfaces Covalent with AWS Batch which allows tasks in a covalent workflow to be executed as AWS batch jobs.

1. Installation

To use this plugin with Covalent, simply install it using pip:

pip install covalent-awsbatch-plugin

2. Usage Example

This is an example of how a workflow can be adapted to utilize the AWS Batch Executor. Here we train a simple Support Vector Machine (SVM) model and use an existing AWS Batch Compute environment to run the train_svm electron as a batch job. We also note we require DepsPip to install the dependencies when creating the batch job.

from numpy.random import permutation
from sklearn import svm, datasets
import covalent as ct

deps_pip = ct.DepsPip(
	packages=["numpy==1.23.2", "scikit-learn==1.1.2"]
)

executor = ct.executor.AWSBatchExecutor(
    s3_bucket_name = "covalent-batch-qa-job-resources",
    batch_queue = "covalent-batch-qa-queue",
    batch_execution_role_name = "ecsTaskExecutionRole",
    batch_job_role_name = "covalent-batch-qa-job-role",
    batch_job_log_group_name = "covalent-batch-qa-log-group",
    vcpu = 2, # Number of vCPUs to allocate
    memory = 3.75, # Memory in GB to allocate
    time_limit = 300, # Time limit of job in seconds
)

# Use executor plugin to train our SVM model.
@ct.electron(
    executor=executor,
    deps_pip=deps_pip
)
def train_svm(data, C, gamma):
    X, y = data
    clf = svm.SVC(C=C, gamma=gamma)
    clf.fit(X[90:], y[90:])
    return clf

@ct.electron
def load_data():
    iris = datasets.load_iris()
    perm = permutation(iris.target.size)
    iris.data = iris.data[perm]
    iris.target = iris.target[perm]
    return iris.data, iris.target

@ct.electron
def score_svm(data, clf):
    X_test, y_test = data
    return clf.score(
    	X_test[:90],
	 	y_test[:90]
    )

@ct.lattice
def run_experiment(C=1.0, gamma=0.7):
    data = load_data()
    clf = train_svm(
    	data=data,
    	C=C,
    	gamma=gamma
    )
    score = score_svm(
    	data=data,
	 	clf=clf
    )
    return score

# Dispatch the workflow
dispatch_id = ct.dispatch(run_experiment)(
	C=1.0,
	gamma=0.7
)

# Wait for our result and get result value
result = ct.get_result(dispatch_id=dispatch_id, wait=True).result

print(result)

During the execution of the workflow one can navigate to the UI to see the status of the workflow, once completed however the above script should also output a value with the score of our model.

0.9777777777777777

3. Configuration

There are many configuration options that can be passed in to the class ct.executor.AWSBatchExecutor or by modifying the covalent config file under the section [executors.awsbatch]

For more information about all of the possible configuration values visit our read the docs (RTD) guide for this plugin.

4. Required AWS Resources

In order to run your workflows with covalent there are a few notable AWS resources that need to be provisioned first.

For more information regarding which cloud resources need to be provisioned visit our read the docs (RTD) guide for this plugin.

The required AWS resources include a Batch Job Definition, Batch Job Role, Batch Queue, Batch Compute Environment, Log Group, Subnet, VPC, and an S3 Bucket.

Getting Started with Covalent

For more information on how to get started with Covalent, check out the project homepage and the official documentation.

Release Notes

Release notes for this plugin are available in the Changelog.

Citation

Please 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.5903364

License

Covalent is licensed under the Apache License 2.0. See the LICENSE file or contact the support team for more details.