aws-parallelcluster

v3.15.1 safe
4.0
Medium Risk

AWS ParallelCluster is an AWS supported Open Source cluster management tool to deploy and manage HPC clusters in the AWS cloud.

🤖 AI Analysis

Final verdict: SAFE

The package is generally safe with low risks in network and shell execution areas. The obfuscation and credential handling raise minor concerns, but these do not strongly indicate malicious activity.

  • moderate credential risk due to handling AWS credentials via environment variables
  • some level of obfuscation noted, but incomplete and likely benign
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages that do not require internet access for their primary functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands which aligns with typical package behavior.
  • Obfuscation: The detected pattern appears to be partial and incomplete, likely part of a larger codebase that handles encoded data, possibly for configuration or metadata.
  • Credentials: The code accesses AWS credentials through environment variables which is a standard practice but without proper context, there's a concern it might not handle them securely, posing a potential risk.
  • Metadata: The author has only one package, which might indicate a new or less active account but does not necessarily suggest malicious intent.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.aws.amazon.com/parallelcluster/
  • Brief PyPI description (786 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 34 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in aws/aws-parallelcluster
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • oded", False): body = base64.b64decode(body) if isinstance(body, str): body = body.enco
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 5 credential access pattern(s)

  • ck_id, region=os.environ.get("AWS_DEFAULT_REGION"), version=get_installed_vers
  • ck.id, region=os.environ.get("AWS_DEFAULT_REGION"), version=cluster.stack.vers
  • updated_time), region=os.environ.get("AWS_DEFAULT_REGION"), cluster_status=cluster_status,
  • ck.id, region=os.environ.get("AWS_DEFAULT_REGION"), version=stack.version,
  • be set") region_backup = os.environ.get("AWS_DEFAULT_REGION") LOGGER.info("Setting AWS Region to %s",
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aws/aws-parallelcluster appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aws-parallelcluster 
Your task is to create a small but powerful utility that leverages the AWS ParallelCluster Python package to streamline the deployment and management of High Performance Computing (HPC) clusters on AWS. This utility will serve as a user-friendly interface for users who want to quickly set up, manage, and tear down HPC clusters without needing deep knowledge of AWS infrastructure services.

### Project Overview:
1. **Project Name:** HPC Cluster Manager
2. **Goal:** Develop a command-line tool that simplifies the process of deploying, managing, and decommissioning HPC clusters using AWS ParallelCluster.
3. **Target Audience:** Researchers, data scientists, and engineers who require scalable computing resources for heavy computational tasks.

### Core Features:
- **Cluster Deployment:** Users should be able to specify cluster configuration parameters such as instance type, number of nodes, and storage options through a simple CLI.
- **Cluster Management:** The utility should provide commands to start, stop, and restart clusters.
- **Resource Monitoring:** Integrate monitoring capabilities to display real-time resource usage metrics like CPU and memory utilization.
- **Cost Estimation:** Offer a feature to estimate costs based on the selected cluster configuration and runtime.
- **Documentation:** Provide clear documentation on how to use the tool, including setup instructions and examples.

### Utilization of AWS ParallelCluster:
- Use the AWS ParallelCluster SDK to interact with AWS services and automate the creation and management of HPC clusters.
- Leverage AWS ParallelCluster's ability to define custom cluster configurations through YAML files, allowing for flexible and repeatable deployments.
- Implement error handling and logging mechanisms to ensure robust operation of the utility.

### Development Steps:
1. **Setup Environment:** Install necessary Python packages, including `aws-parallelcluster`, and configure AWS credentials.
2. **Design CLI Interface:** Create a basic CLI structure using Python libraries like Click or Argparse.
3. **Implement Deployment Logic:** Write functions to handle cluster deployment using AWS ParallelCluster API calls.
4. **Add Management Commands:** Extend functionality to include commands for starting, stopping, and restarting clusters.
5. **Integrate Monitoring:** Fetch and display real-time metrics from AWS CloudWatch or similar services.
6. **Cost Estimation Module:** Estimate costs based on AWS pricing models and provided cluster configurations.
7. **Testing and Validation:** Thoroughly test the utility with various cluster configurations to ensure reliability.
8. **Documentation:** Prepare comprehensive documentation detailing installation, usage, and troubleshooting tips.

### Expected Outcome:
By the end of this project, you will have developed a robust, user-friendly utility that significantly reduces the complexity of managing HPC clusters on AWS, making it accessible to a broader audience.

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