Metadata-Version: 2.1
Name: covalent-ecs-plugin
Version: 0.21.0rc0
Summary: Covalent ECS Plugin
Home-page: https://github.com/AgnostiqHQ/covalent-ecs-plugin
Author: Agnostiq
Author-email: support@agnostiq.ai
Maintainer: Agnostiq
License: GNU Affero GPL v3.0
Download-URL: https://github.com/AgnostiqHQ/covalent-ecs-plugin/archive/v0.21.0.tar.gz
Description: &nbsp;
        
        <div align="center">
        
        <img src="https://raw.githubusercontent.com/AgnostiqHQ/covalent-ecs-plugin/main/assets/aws_ecs_readme_banner.jpg" width=150%>
        
        [![covalent](https://img.shields.io/badge/covalent-0.177.0-purple)](https://github.com/AgnostiqHQ/covalent)
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        </div>
        
        ## 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)](https://docs.aws.amazon.com/ecs/index.html) where the tasks are run using Fargate.
        
        ## 1. Installation
        
        To use this plugin with Covalent, install it using `pip`:
        
        ```sh
        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](https://covalent.readthedocs.io/en/latest/concepts/concepts.html#depspip) which will be required to execute the electrons.
        
        ```python
        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_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.
        
        ```sh
        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](#-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](https://covalent.readthedocs.io/en/latest/how_to/config/customization.html) under the section `[executors.ecs]`
        
        For more information about all of the possible configuration values, visit our [read the docs (RTD) guide](https://covalent.readthedocs.io/en/latest/api/executors/awsecs.html)
        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](https://aws.amazon.com/s3/), [ECR](https://aws.amazon.com/ecr/), and [ECS](https://aws.amazon.com/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](https://github.com/AgnostiqHQ/covalent) and the official [documentation](https://covalent.readthedocs.io/en/latest/).
        
        ## Release Notes
        
        Release notes are available in the [Changelog](https://github.com/AgnostiqHQ/covalent-ecs-executor/blob/main/CHANGELOG.md).
        
        ## 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](https://github.com/AgnostiqHQ/covalent-ecs-executor/blob/main/LICENSE) file or contact the [support team](mailto:support@agnostiq.ai) for more details.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: Plugins
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator
Classifier: Topic :: Software Development
Classifier: Topic :: System :: Distributed Computing
Description-Content-Type: text/markdown
