covalent-ecs-plugin 0.17.0rc0

Covalent ECS Plugin

 

Covalent ECS Plugin

Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. This executor plugin interfaces Covalent with AWS Elastic Container Service (ECS) where the tasks are run using Fargate.

1. Installation

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

pip install covalent-ecs-plugin

2. Usage Example

This is an example of how a workflow can be constructed to use the AWS ECS executor. In the example, we train a Support Vector Machine (SVM) and use an instance of the executor to execute the train_svm electron. Note that we also require DepsPip which will be required to execute the electrons.

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

deps_pip = ct.DepsPip(
    packages=["numpy==1.22.4", "scikit-learn==1.1.2"]
)

executor = ct.executor.ECSExecutor(
    s3_bucket_name="covalent-fargate-task-resources",
    ecr_repo_name="covalent-fargate-task-images",
    ecs_cluster_name="covalent-fargate-cluster",
    ecs_task_family_name="covalent-fargate-tasks",
    ecs_task_execution_role_name="ecsTaskExecutionRole",
    ecs_task_role_name="CovalentFargateTaskRole",
    ecs_task_subnet_id="subnet-871545e1",
    ecs_task_security_group_id="sg-0043541a",
    ecs_task_log_group_name="covalent-fargate-task-logs",
    vcpu=1,
    memory=2,
    poll_freq=10,
)


# 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, 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, the above script should also output a value with the score of our model.

0.8666666666666667

In order for the above workflow to run successfully, one has to provision the required cloud resources as mentioned in the section Required AWS Resources.

3. Configuration

There are many configuration options that can be passed into the ct.executor.ECSExecutor class or by modifying the covalent config file under the section [executors.ecs]

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 for workflows to leverage this executor, users must ensure that all the necessary IAM permissions are properly setup and configured. This executor uses the S3, ECR, and ECS services to execute an electron, thus the required IAM roles and policies must be configured correctly. Precisely, the following resources are needed for the executor to run any dispatched electrons properly.

Resource	Config Name	Description
IAM Role	ecs_task_execution_role_name	The IAM role used by the ECS agent
IAM Role	ecs_task_role_name	The IAM role used by the container during runtime
S3 Bucket	s3_bucket_name	The name of the S3 bucket where objects are stored
ECR repository	ecr_repo_name	The name of the ECR repository where task images are stored
ECS Cluster	ecs_cluster_name	The name of the ECS cluster on which your tasks are executed
ECS Task Family	ecs_task_family_name	The name of the task family that specifies container information for a user, project, or experiment
VPC Subnet	ecs_task_subnet_id	The ID of the subnet where instances are created
Security group	ecs_task_security_group_id	The ID of the security group for task instances
Cloudwatch log group	ecs_task_log_group_name	The name of the CloudWatch log group where container logs are stored
CPU	vCPU	The number of vCPUs available to a task
Memory	memory	The memory (in GB) available to a task

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 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 GNU Affero GPL 3.0 License. Covalent may be distributed under other licenses upon request. See the LICENSE file or contact the support team for more details.