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README.md

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+---
+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](https://meta-pytorch.org/OpenEnv/) environment for the **Meta PyTorch Hackathon** where an AI agent investigates and diagnoses broken PyTorch training jobs.
+
+## Quick Start
+
+```python
+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, scheduler mismatch |
+| `hard` | ⭐⭐⭐ | Silent failure — memory leak, AMP overflow, red herrings |
+
+## 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.
+
+## 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