🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across most categories, with no network calls or credential risks identified. The minor concerns about shell commands and metadata do not strongly suggest malicious activity.

Low network and credential risks
Normal use of shell commands for Git interaction
Unclear maintainer details

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Shell commands are used to interact with Git, likely for version control purposes, indicating normal development activities.
Obfuscation: The observed pattern is likely obfuscation to avoid code analysis, but does not indicate malicious intent.
Credentials: No patterns indicative of credential harvesting were found.
Metadata: The maintainer has a new or inactive account and lacks detailed author information, which may indicate potential unreliability.

📦 Package Quality Overall: Low (4.6/10)

○ 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://aistairc.github.io/aiaccel/
Detailed PyPI description (1740 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

92 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

4 unique contributor(s) across 100 commits in aistairc/aiaccel
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

) self.model.eval() def __call__(self, wav: torch.Tensor) -> torc

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

repository path result = subprocess.run(["git", "rev-parse", "--show-toplevel"], cwd=module_path, ca
check git_ignore result = subprocess.run(["git", "check-ignore", module_path], cwd=repository_path, c
# get commit id result = subprocess.run(["git", "rev-parse", "HEAD"], cwd=repository_path, capture_o
check git status result = subprocess.run(["git", "status", "-s"], cwd=repository_path, capture_output
f.write(job_script) subprocess.run(f"bash {job_filename}", shell=True, check=True) if __name_
.unlink(missing_ok=True) subprocess.run(f"{qsub} {qsub_args} {job_filename}", shell=True, check=True

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: aist.go.jp>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aistairc/aiaccel appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aiaccel

Create a mini-application that accelerates machine learning research using the 'aiaccel' package. Your task is to develop a utility that streamlines the process of running multiple machine learning experiments on different datasets. This tool should allow researchers to define their models, datasets, and hyperparameters once, then execute these experiments across various configurations in parallel. Here’s how your application should work:

1. **Setup**: Begin by installing the 'aiaccel' package if it isn't already installed. Use pip or conda to manage dependencies.
2. **Configuration File Creation**: Develop a feature where users can input experiment details through a configuration file. This file should include sections for specifying datasets, machine learning models, and hyperparameter ranges.
3. **Experiment Execution**: Utilize 'aiaccel' to run these experiments in parallel. Ensure the application supports both local execution and remote execution on a cluster, depending on user preference.
4. **Result Aggregation**: After all experiments complete, aggregate results from each run into a single report. This report should summarize performance metrics like accuracy, precision, recall, etc., for each model-dataset combination.
5. **Visualization**: Integrate basic visualization tools within the application to graphically represent the aggregated results, helping researchers quickly identify trends and optimal configurations.
6. **User Interface**: While command-line interaction is acceptable, consider developing a simple GUI for easier interaction, especially for non-technical users.
7. **Documentation & Examples**: Provide comprehensive documentation and example configuration files to help new users get started easily.

The 'aiaccel' package will primarily be used for managing job submissions, tracking progress, and handling the distributed execution of experiments. Explore its documentation to understand how to leverage its capabilities effectively.