aws-parallelcluster
AWS ParallelCluster is an AWS supported Open Source cluster management tool to deploy and manage HPC clusters in the AWS cloud.
Decision gist · record as of 2026-08-14
Yes—if you need to deploy HPC clusters on AWS. The package is production-stable, actively maintained, has no known vulnerabilities, and carries a permissive license. The 29 runtime dependencies are substantial but reflect the complexity of orchestrating AWS infrastructure; they are not a red flag. Install it if cluster management is your goal; skip it if you only need to run individual EC2 instances or submit jobs to an existing cluster.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- AWS credentials must be configured (via environment variables, ~/.aws/config, or IAM role); an AWS account with appropriate permissions is required.
- Low install friction with a pure-Python wheel.
- Active maintenance with recent commits and 887 repository stars.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—suitable for production deployments without licensing concerns.
last release 2026-05-28 (78 days) · last repo commit 2026-08-14 · 887 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 93,430 downloads/mo, #13,386 on PyPI
Alternatives
Verify before relying
pip install aws-parallelcluster
from aws_parallelcluster import api
# Create or update a cluster via the API
api.create_cluster(cluster_name='my-hpc', cluster_config={...})- Whether the 29 runtime dependencies (including AWS CDK modules and Flask) are all required for basic cluster deployment or only for advanced features.
- Whether AWS credentials and account setup are prerequisites beyond the Python environment.
What it is and what it does
AWS ParallelCluster is an open-source cluster management tool built on the CfnCluster project that automates the deployment of HPC environments on AWS. It handles provisioning of compute resources, shared filesystems, and batch schedulers (such as AWS Batch and Slurm) through a single configuration interface. The tool is designed to support both rapid proof-of-concept deployments and production-scale HPC workloads.
The package depends on AWS CDK modules for infrastructure-as-code provisioning, boto3 for AWS API interaction, and Flask/Connexion for its control plane API. It uses Jinja2 for templating, marshmallow for configuration validation, and tabulate for CLI output. This dependency footprint reflects its role as a full-stack cluster orchestration platform rather than a lightweight utility.
Use it for
- Deploy a Slurm-based HPC cluster on AWS for scientific computing workloads without manual infrastructure setup.
- Build a genomics portal or similar domain-specific workflow platform on top of managed HPC infrastructure.
- Rapidly prototype HPC cluster configurations and scale them to production with minimal operational overhead.
- Manage multi-node batch processing jobs using AWS Batch as the scheduler backend.
- Automate shared filesystem provisioning (EFS, FSx) alongside compute resources for data-intensive applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes—if you need to deploy HPC clusters on AWS.
The package is production-stable, actively maintained, has no known vulnerabilities, and carries a permissive license. The 29 runtime dependencies are substantial but reflect the complexity of orchestrating AWS infrastructure; they are not a red flag. Install it if cluster management is your goal; skip it if you only need to run individual EC2 instances or submit jobs to an existing cluster.
Install
aws-parallelcluster on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with recent commits and 887 repository stars. Supports current Python versions (3.9–3.12) and marked as production-stable.
AWS credentials must be configured (via environment variables, ~/.aws/config, or IAM role); an AWS account with appropriate permissions is required.
License in practice
Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—suitable for production deployments without licensing concerns.
Quickstart
pip install aws-parallelcluster
from aws_parallelcluster import api
# Create or update a cluster via the API
api.create_cluster(cluster_name='my-hpc', cluster_config={...})Verify before relying
- Whether the 29 runtime dependencies (including AWS CDK modules and Flask) are all required for basic cluster deployment or only for advanced features.
- Whether AWS credentials and account setup are prerequisites beyond the Python environment.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 29 packagessetuptoolsboto3tabulatePyYAMLjinja2marshmallowpackagingaws-cdk.coreaws-cdk.aws-batchaws_cdk.aws-cloudwatchaws-cdk.aws-codebuildaws-cdk.aws-dynamodbaws-cdk.aws-ec2aws-cdk.aws-efsaws-cdk.aws-eventsaws-cdk.aws-fsxaws-cdk.aws-imagebuilderaws-cdk.aws-iamaws_cdk.aws-lambdaaws-cdk.aws-logsaws-cdk.aws-route53aws-cdk.aws-ssmaws-cdk.aws-sqsaws-cdk.aws-cloudformationconnexionjmespathjsiiwerkzeugflask |
| Maintenance | Actively maintained 78 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 93,430 / month, #13,386 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: aws_parallelcluster-3.15.1-py3-none-any.whl
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See also clusterscope · ClusterShell · dask-jobqueue · ipyparallel · anyscale · slurm-usage · lithops · submitit · distributed · hydra-submitit-launcher