azure-jobs

v0.3.5 safe
4.0
Medium Risk

A fast CLI for submitting and managing Azure ML jobs via pure REST APIs

🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legitimate use with a moderate level of risk due to its execution of shell commands and network interactions. However, there are no clear indicators of malicious intent.

  • Shell command execution
  • Low maintainer activity
Per-check LLM notes
  • Network: Network calls with retry logic and status checks are common in legitimate packages but should be scrutinized to ensure they align with expected functionality.
  • Shell: Execution of shell commands can be legitimate for certain operations but increases risk due to potential misuse for executing arbitrary code.
  • Obfuscation: The use of base64 decoding and HMAC is likely for legitimate cryptographic purposes rather than obfuscation.
  • Credentials: No clear patterns of credential harvesting are present in the provided snippet.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate a lower effort or abandoned project.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6039 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 196 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • on 429/5xx.""" session = requests.Session() retry = Retry( total=_RETRY_TOTAL, bac
  • ): resp = requests.get(urls[path], timeout=60) resp.raise_for_s
  • ): resp = requests.get(url, timeout=60) resp.raise_for_status()
  • as fh: resp = requests.put( url, data=fh,
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • hmac.new( base64.b64decode(creds.shared_key), string_to_sign.encode
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • }")) try: proc = subprocess.Popen( cmd, stdin=subprocess.PIPE,
  • text]) try: out = subprocess.run( cmd, capture_output=True, text=True, timeout=C.
  • try: result = subprocess.run( cmd, capture_output=True,
  • ause). """ tar_proc = subprocess.Popen( [ "tar", "cvf",
  • () try: result = subprocess.run( [ "kubectl", "e
  • , *ctx_args] result = subprocess.run( apply_cmd, capture_output=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: whu.edu.cn>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with azure-jobs 
Develop a command-line tool named 'AzureJobManager' using Python and the 'azure-jobs' package. This tool will enable users to manage their Azure Machine Learning jobs more efficiently through a user-friendly interface. The primary goal of this project is to streamline the process of submitting, monitoring, and managing jobs in Azure ML, thereby reducing the complexity associated with direct API interactions.

### Key Features:
1. **Job Submission**: Allow users to submit new jobs to Azure ML. Users should be able to specify job details such as experiment name, compute target, script path, and any necessary parameters.
2. **Job Monitoring**: Provide real-time monitoring capabilities where users can check the status of their jobs (e.g., queued, running, succeeded, failed).
3. **Job Management**: Enable functionalities to stop, resume, or delete jobs. Additionally, implement features to download logs and outputs from completed jobs.
4. **Experiment Tracking**: Track multiple experiments within the same workspace. Users should be able to view a summary of all jobs across different experiments.
5. **User Interface**: Design a clean and intuitive CLI interface that guides users through each operation step-by-step.
6. **Configuration Management**: Support configuration files for storing user credentials, workspace details, and default settings to minimize repetitive input.
7. **Error Handling**: Implement robust error handling to gracefully manage common issues such as invalid inputs, connection errors, and API failures.

### Utilizing 'azure-jobs':
- **Integration**: Integrate the 'azure-jobs' package to handle all interactions with Azure ML services via REST APIs. Leverage its capabilities to simplify the submission and management processes.
- **API Usage**: Use the 'azure-jobs' API methods for submitting jobs, retrieving job statuses, and managing job lifecycle operations. Ensure to document each API call and its corresponding functionality within your codebase.
- **Customization**: Customize the 'azure-jobs' package usage to fit the specific needs of 'AzureJobManager'. For instance, develop helper functions to format and validate job specifications before submission.
- **Documentation**: Provide comprehensive documentation on how to use 'AzureJobManager', including setup instructions, configuration examples, and sample workflows for common tasks.

### Expected Outcome:
By the end of this project, you should have a fully functional CLI tool capable of managing Azure ML jobs efficiently. The tool should significantly reduce the time and effort required to work with Azure ML jobs, making it a valuable addition to any data scientist's toolkit.

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