README.md

metadata

title: PyTorch Debug Env
emoji: 🔥
colorFrom: yellow
colorTo: red
sdk: docker
app_port: 7860
short_description: RL environment for diagnosing broken PyTorch training jobs
tags:
  - openenv
  - pytorch
  - reinforcement-learning
  - debugging
  - ml-training
  - agent
pinned: true

PyTorch Debug Env 🔥

A complete OpenEnv environment for the Meta PyTorch Hackathon where an AI agent investigates and diagnoses broken PyTorch training jobs.

Quick Start

from openenv import AutoEnv, AutoAction

env = AutoEnv.from_env("ArchCoder/pytorch-debug-env")
Action = AutoAction.from_env("ArchCoder/pytorch-debug-env")

with env.sync() as client:
    result = client.reset(task_id="easy")
    action = Action(
        current_hypothesis={
            "bug_type": "missing_zero_grad",
            "affected_file": "train.py",
            "confidence": 0.7
        },
        commit_diagnosis=False
    )
    step_result = client.step(action)

API Endpoints

Endpoint	Method	Description
/	GET	Environment info
/health	GET	Health check
/reset?task_id=easy	POST	Start new episode
/step	POST	Submit hypothesis + action
/state	GET	Current episode state

Tasks

Task	Difficulty	Description
easy	⭐	Single-file bug — missing zero_grad, wrong loss
medium	⭐⭐	Multi-file root cause — data leakage, learning-rate misconfig
hard	⭐⭐⭐	Silent failure — memory leak, AMP overflow, red herrings

Each difficulty draws from multiple bug templates, so repeated runs do not recycle the same exact failure.

Reward Structure

• Hypothesis delta (60%) — reward for improving your bug hypothesis each step
• Investigation (20%) — reward for inspecting the right files
• Final diagnosis (20%) — accuracy of committed diagnosis vs ground truth

Scores range from 0.0 to 1.0. Partial credit for correct bug category on hard tasks.

Investigation Actions

• reveal_file: reveal a file from the synthetic repo
• extend_loss_curve: reveal more loss-curve points
• extend_gpu_profile: reveal more GPU profile points
• reveal_log_chunk: append additional training log lines
• run_diagnostic: expose a diagnostic summary report

Reproducibility

Use SEED to make scenario selection and artifacts deterministic across runs:

set SEED=42
python inference.py

Baseline Scores

Run inference.py with a fixed SEED to record your baseline scores. The script prints per-task [END] lines with the final rewards.

Example template (fill after running):

Model	Seed	Easy	Medium	Hard
gpt-3.5-turbo	42	0.xx	0.xx	0.xx

Environment State

Each episode provides a synthetic PyTorch repo with:

• Source files (train.py, model/, data/, config/)
• Loss curves and GPU memory profiles
• Training logs with realistic noise and red herrings

The agent reveals files progressively across up to 5–6 steps, refining its hypothesis before committing a final diagnosis.

Author

Priyansh Saxena — IIIT Gwalior