covalent-ecs-plugin 0.32.0

Description:

covalentecsplugin 0.32.0

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",
ecs_cluster_name="covalent-fargate-cluster",
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


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 Apache License 2.0. See the LICENSE file or contact the support team for more details.