Metadata-Version: 2.4
Name: ml-intern-lab
Version: 0.1.0
Summary: Tiny reproducible ML experiment runner.
Author-email: Mukunda Katta <mukunda.vjcs6@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://gitlab.com/mukunda.vjcs6-group/ml-intern-lab
Project-URL: Repository, https://gitlab.com/mukunda.vjcs6-group/ml-intern-lab
Project-URL: Issues, https://gitlab.com/mukunda.vjcs6-group/ml-intern-lab/-/issues
Keywords: machine-learning,ai-agent,experiments,mlops
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# ml-intern-lab

An ML engineer agent sandbox for reading papers, running experiments, and shipping model reports.

This repo is inspired by the late-April 2026 trend around `huggingface/ml-intern` and agentic ML engineering workflows.

## Goal

Create a small lab where an agent can turn an ML idea into a tracked experiment: paper notes, dataset assumptions, training command, metrics, and a final report.

## First Workflow

```text
paper or idea -> experiment plan -> run baseline -> collect metrics -> write report
```

## Features To Build

- Paper summary template.
- Experiment plan schema.
- Dataset card generator.
- Baseline training runner.
- Report writer with metrics and next-step recommendations.

## Repository Shape

```text
experiments/
  0001-baseline/
templates/
  paper-note.md
  experiment-plan.json
  model-report.md
src/
  runner/
  reports/
```

## Milestone 1

- Done: add experiment templates.
- Done: implement a tiny local majority-class baseline runner with no external dependencies.
- Done: generate `metrics.json` and `model-report.md`.
- Done: add one sample experiment using a toy dataset.

## Run It

```bash
python -m ml_intern_lab.cli experiments/0001-baseline/experiment-plan.json
```

## Test It

```bash
PYTHONPATH=src python -m pytest
```

## Publish

This package is ready for GitLab Package Registry and PyPI releases. See [RELEASE.md](RELEASE.md).

## Trend Notes

- ML agents are moving from chat to execution.
- Reproducibility is the selling point: every report should link to config, data assumptions, and metrics.
- Start with classical ML baselines before adding GPUs or deep learning complexity.
