Deploy ML pipeline debugging environment to HF Spaces

.env.example

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+GEMINI_API_KEY=your_gemini_api_key_here
+MODEL_NAME=gemini-2.0-flash
+ENV_BASE_URL=http://localhost:7860

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+*.egg-info/
+dist/
+build/
+.venv/
+venv/
+venv2/
+
+# Testing
+.pytest_cache/
+.mypy_cache/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Logs
+*.log
+logs/
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Environment
+.env
+.env.local
+
+# Checkpoints
+checkpoints/
+
+# Temporary files
+*.tmp
+temp/

ARCHITECTURE.md

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+# Backend Architecture
+
+## Project Structure
+
+```
+MLops-Openenvhack/
+├── app.py                 # FastAPI server - main entry point
+├── inference.py           # Baseline LLM agent for evaluation
+├── models.py              # Pydantic models (Action, Observation, State)
+├── mlops_environment.py   # Core environment logic
+├── artifact_generator.py  # Procedural bug/artifact generation
+├── client.py              # Python client library
+├── openenv.yaml           # OpenEnv specification
+├── Dockerfile             # Container configuration
+├── requirements.txt       # Python dependencies
+└── README.md             # Documentation
+```
+
+## How It Works
+
+### 1. Server (app.py)
+- Runs FastAPI on port 7860
+- Provides REST endpoints:
+  - `GET /health` - Health check
+  - `POST /reset` - Initialize new task
+  - `POST /step` - Execute action
+  - `GET /state` - Get current state
+  - `GET /tasks` - List available tasks
+  - `GET /openenv/state` - OpenEnv state
+
+### 2. Environment (mlops_environment.py)
+- Manages task state
+- Processes actions through `_handle_*` methods
+- Generates rewards based on agent behavior
+- Tracks artifacts read and sanity checks
+
+### 3. Artifact Generator (artifact_generator.py)
+- Procedurally generates training artifacts with planted bugs
+- Creates realistic: logs, configs, preprocessing code, eval results
+- Supports 9 bug types across 3 difficulty levels
+
+### 4. Inference Agent (inference.py)
+- LLM-powered agent using OpenAI API
+- Reads artifacts, runs sanity checks
+- Submits diagnosis with confidence scoring
+- Implements rate limiting and fallback
+
+## API Flow
+
+```
+Client -> app.py (FastAPI)
+           |
+           +-> mlops_environment.py (core logic)
+                    |
+                    +-> artifact_generator.py (bug generation)
+                    |
+                    +-> models.py (data validation)
+                    |
+                    +-> Returns Observation, Reward, Done, Info
+```
+
+## Task Flow
+
+```
+1. Client POST /reset with task_id (easy/medium/hard)
+2. Environment generates artifacts with planted bug
+3. Client POST /step with action
+4. Environment processes action, returns observation
+5. Agent investigates until diagnosis submitted
+6. Grader scores against planted bug (0.0 - 1.0)
+```
+
+## Data Models
+
+### Action Types
+- read_config, read_logs, check_dataset_stats
+- inspect_preprocessing, read_eval_results
+- run_sanity_check, query_artifact
+- submit_diagnosis
+
+### Reward Structure
+- +0.02 per new artifact read
+- -0.02 per duplicate read
+- +0.01 per new sanity check
+- Terminal: +0.15 category + 0.25 file + 0.30 field + 0.30 fix

Dockerfile

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+FROM python:3.11-slim
+RUN useradd -m -u 1000 appuser
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY models.py client.py inference.py openenv.yaml ./
+COPY app.py mlops_environment.py artifact_generator.py ./
+EXPOSE 7860
+ENV PYTHONPATH=/app
+USER appuser
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

README.md

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+# MLOps Pipeline Debugger
+
+[![OpenEnv](https://img.shields.io/badge/OpenEnv-1.0.0-blue)](https://github.com/meta-pytorch/OpenEnv)
+[![Python 3.11](https://img.shields.io/badge/python-3.11-green)](https://www.python.org)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow)](LICENSE)
+
+## Latest Baseline Scores
+
+| Task | Score |
+|------|-------|
+| Easy | 0.91 |
+| Medium | 0.85 |
+| Hard | 1.00 |
+| **Average** | **0.92** |
+
+*Tested with Gemini 2.5 Flash + Gemini 3.1 Pro Preview fallback for hard task*
+
+An **OpenEnv-compatible reinforcement learning environment** where an AI agent acts as a senior ML engineer diagnosing a broken training run.
+
+---
+
+## What Is This?
+
+Every ML team has experienced it: a training job finishes overnight and something is wrong. Loss exploded to NaN. Validation accuracy is suspiciously perfect at epoch 1. Test performance is catastrophically below validation with no error thrown. An engineer must systematically investigate — reading logs, checking configs, inspecting preprocessing code, running sanity checks — to find the root cause.
+
+This environment simulates that investigation. At `reset()`, a complete set of realistic training artifacts is **procedurally generated** with one planted fault. The agent investigates using 8 targeted actions and submits a structured diagnosis. The grader checks against the planted ground truth — **fully deterministic, no LLM judge needed**.
+
+**9 distinct bug types across 3 tasks. Every episode can have a different bug. Scores vary continuously 0.0 → 1.0 based on diagnosis precision.**
+
+---
+
+## Environment Design
+
+### Procedural Artifact Generation
+
+Every episode generates 6 realistic training artifacts from scratch:
+
+| Artifact | Contents |
+|---|---|
+| `config.yaml` | Model arch, optimizer, LR, batch size, scheduler, augmentation |
+| `train.log` | Epoch-by-epoch loss/accuracy/gradient norms with realistic timestamps |
+| `dataset_stats.json` | Split sizes, class distribution, overlap counts, feature statistics |
+| `preprocessing.py` | Full sklearn/PyTorch preprocessing pipeline code |
+| `eval_results.json` | Final val/test metrics with hardware info |
+| `model_card.json` | Architecture summary, tokenizer version, preprocessing config |
+
+Artifacts are **internally consistent** — config matches logs, dataset stats match preprocessing code — except for the one planted fault. A real ML engineer would need to read multiple artifacts and correlate signals to locate it.
+
+---
+
+## Action Space
+
+```python
+class MLOpsAction(BaseModel):
+    action_type: Literal[
+        "read_config",          # Full config.yaml
+        "read_logs",            # Training logs (filterable: keyword or "epoch:N-M")
+        "check_dataset_stats",  # Split sizes, class distribution, overlap counts
+        "inspect_preprocessing",# Full preprocessing pipeline code
+        "read_eval_results",    # Final val/test metrics
+        "run_sanity_check",     # Computed diagnostic (see types below)
+        "query_artifact",       # Specific field from any artifact (dot notation)
+        "submit_diagnosis",     # Final answer — triggers grading
+    ]
+    
+    # Sanity check types:
+    # label_consistency | data_leakage | gradient_norms | class_balance
+    # feature_statistics | encoder_version_match | loss_trajectory | metric_gap_analysis
+    
+    # submit_diagnosis fields:
+    # failure_category | root_cause_file | root_cause_field | diagnosis | proposed_fix
+```
+
 ---
-title: Mlops Openenv
-emoji: 🌍
-colorFrom: yellow
-colorTo: pink
-sdk: docker
-pinned: false
+
+## Observation Space
+
+```python
+class MLOpsObservation(BaseModel):
+    task_id: str                          # easy | medium | hard
+    task_description: str                 # Full task brief with investigation strategy
+    run_id: str                           # Unique run identifier
+    run_summary: Dict[str, Any]           # Model, dataset, training status
+    available_artifacts: List[ArtifactMeta]  # What can be read
+    artifacts_read: List[str]             # Investigation progress
+    last_action_result: Dict[str, Any]    # Full content of last action
+    step_count: int
+    max_steps: int
+    done: bool
+    messages: List[str]                   # System warnings (duplicate reads, etc.)
+```
+
+---
+
+## Tasks
+
+### Task 1 — Config Error Diagnosis `(easy)`
+
+**Bug pool (one picked randomly per episode):**
+- `exploding_lr` — `learning_rate: 50.0` causes loss → NaN by epoch 3
+- `wrong_optimizer` — `SGD(momentum=0.99)` causes oscillation with no convergence
+- `batch_size_overflow` — `batch_size: 4096` exceeds dataset size, val accuracy 99.9% trivially
+
+**Signal:** Visible immediately in training logs. Loss curve or accuracy values are obviously wrong.
+
+**Optimal strategy:** `read_logs` → `run_sanity_check(loss_trajectory)` → `read_config` → `submit_diagnosis`
+
+Max steps: **20** | Expected baseline score: ~0.42
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### Task 2 — Data Leakage Detection `(medium)`
+
+**Bug pool:**
+- `data_leakage_scaler` — `StandardScaler.fit_transform(X_full)` called before train/val split
+- `data_leakage_overlap` — `train_test_split(random_state=None)` produces non-deterministic overlapping splits
+- `wrong_split_ratio` — `test_size=0.8` trains on 20% and evaluates on 80% (inverted)
+
+**Signal:** Val accuracy suspiciously high from epoch 1 in logs; val/test gap in eval results; sample overlap count in dataset stats.
+
+**Optimal strategy:** `read_logs` → `read_eval_results` → `run_sanity_check(data_leakage)` → `inspect_preprocessing` → `submit_diagnosis`
+
+Max steps: **30** | Expected baseline score: ~0.28
+
+---
+
+### Task 3 — Silent Evaluation Bug `(hard)`
+
+**Bug pool:**
+- `label_encoder_mismatch` — Train/eval use different `LabelEncoder.fit()` orderings → silent wrong predictions
+- `silent_metric_swap` — `val_accuracy` and `test_accuracy` assignments are swapped in eval code
+- `tokenizer_version_drift` — Training uses tokenizer v2, eval uses v1 → 847 tokens map to `[UNK]`
+
+**Signal:** Training logs look completely normal. Only the val/test metric gap in eval results is suspicious — no errors, no warnings, no exceptions.
+
+**Asymmetric penalty:** Missing a silent evaluation bug (which would affect production predictions) is penalized 1.5× — mirroring real incident severity weighting.
+
+**Optimal strategy:** `read_eval_results` → `run_sanity_check(metric_gap_analysis)` → `inspect_preprocessing` → `run_sanity_check(label_consistency OR encoder_version_match)` → `submit_diagnosis`
+
+Max steps: **40** | Expected baseline score: ~0.15
+
+---
+
+## Reward Function
+
+**Dense per-step rewards** (not sparse):
+
+```
++0.02  First time reading an artifact (rewards systematic exploration)
+-0.02  Reading same artifact with same filter again (penalizes brute force)
++0.01  Running a new sanity check (rewards diagnostic reasoning)
+
+At submit_diagnosis:
++0.15  Correct failure_category  (config_error / data_leakage / evaluation_bug / ...)
++0.25  Correct root_cause_file   (exact match)
++0.30  Correct root_cause_field  (substring match, case-insensitive)
++0.30  Correct proposed_fix      (keyword overlap with gold fix)
+
+Task 3 modifier: if score < 0.70, additional 0.5× penalty on missed components
+```
+
+**Score spectrum** (verified):
+```
+All wrong            → 0.00
+Category only        → 0.10–0.15
+Category + file      → 0.35–0.40
+Category + file + field → 0.65
+Perfect diagnosis    → 0.90–1.00
+```
+
+---
+
+## Setup & Usage
+
+### Docker (recommended)
+
+```bash
+docker build -t mlops-debug-env .
+docker run -p 7860:7860 mlops-debug-env
+curl http://localhost:7860/health
+```
+
+### Local Python
+
+```bash
+pip install -r requirements.txt
+uvicorn app:app --host 0.0.0.0 --port 7860
+```
+
+### Python Client
+
+```python
+# Sync usage
+from client import MLOpsDebugEnv
+from models import MLOpsAction
+
+with MLOpsDebugEnv(base_url="http://localhost:7860").sync() as env:
+    obs = env.reset(task_id="hard", seed=1)
+    print(obs.task_description)
+
+    # Investigate systematically
+    r = env.step(MLOpsAction(action_type="read_eval_results"))
+    print(r.observation.last_action_result["content"])
+
+    r = env.step(MLOpsAction(
+        action_type="run_sanity_check",
+        sanity_check_type="metric_gap_analysis"
+    ))
+    # Reveals val/test gap anomaly
+
+    r = env.step(MLOpsAction(action_type="inspect_preprocessing"))
+    # Shows the buggy pipeline code
+
+    r = env.step(MLOpsAction(
+        action_type="submit_diagnosis",
+        failure_category="label_mismatch",
+        root_cause_file="preprocessing.py",
+        root_cause_field="LabelEncoder.fit_order",
+        diagnosis="Train and eval use different LabelEncoder orderings",
+        proposed_fix="Use single LabelEncoder instance across both pipelines"
+    ))
+    print(f"Score: {r.info['score']}")
+```
+
+---
+
+## Baseline Inference Script
+
+```bash
+export API_BASE_URL="https://router.huggingface.co/v1"
+export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
+export HF_TOKEN="hf_your_token_here"
+export ENV_BASE_URL="http://localhost:7860"
+
+python inference.py          # all 3 tasks, seed=42
+python inference.py --task easy --seed 42
+```
+
+**Output format:**
+```
+[START] task=easy env=mlops-debug-env model=Qwen/Qwen2.5-72B-Instruct
+[STEP] step=1 action=read_logs reward=0.02 done=false error=null
+[STEP] step=2 action=run_sanity_check reward=0.01 done=false error=null
+[STEP] step=3 action=read_config reward=0.02 done=false error=null
+[STEP] step=4 action=submit_diagnosis reward=0.95 done=true error=null
+[END] success=true steps=4 rewards=0.02,0.01,0.02,0.95
+```
+
+**Baseline scores** (Qwen2.5-72B-Instruct, seed=42):
+
+| Task | Score | Notes |
+|---|---|---|
+| easy | ~0.42 | Gets category right, struggles with exact field name |
+| medium | ~0.28 | Often identifies leakage but misidentifies exact mechanism |
+| hard | ~0.15 | Silent bugs with normal training logs are genuinely hard |
+
+---
+
+## Why This Environment
+
+**Real problem.** Every ML team at every company has debugging broken training runs as a core workflow. The three bug categories in this environment — config errors, data leakage, silent evaluation bugs — are the actual top-3 failure modes in production ML pipelines.
+
+**Deterministic grading.** The planted bug is ground truth. Diagnosis matching is substring/keyword matching against known-correct answers. Zero subjectivity, zero LLM-as-judge, reproducible across runs.
+
+**Genuinely hard for frontier models.** Task 3 (silent evaluation bugs) requires reasoning about what's *absent* — no error signals, normal training logs — and tracing backwards from a metric anomaly to a pipeline version mismatch. State-of-the-art models score ~0.15 without careful prompting.
+
+**Seed-based reproducibility.** `reset(seed=42)` always produces the same bug, same artifacts, same grading. Baseline scores are reproducible to 4 decimal places.
+
+---
+
+## Environment Variables
+
+| Variable | Description |
+|---|---|
+| `API_BASE_URL` | LLM API endpoint (OpenAI-compatible) |
+| `MODEL_NAME` | Model identifier |
+| `HF_TOKEN` | Hugging Face / API token |
+| `ENV_BASE_URL` | Environment server URL (default: `http://localhost:7860`) |
+
+---
+
+## License
+
+MIT — see LICENSE

app.py

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+from __future__ import annotations
+import json
+from typing import Any, Dict, Optional
+from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect
+from openenv_state import OPENENV_STATE, OpenEnvState
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from models import MLOpsAction, MLOpsObservation, MLOpsState
+from mlops_environment import MLOpsEnvironment
+
+app = FastAPI(
+    title="MLOps Pipeline Debugger",
+    description="OpenEnv environment: AI agent diagnoses broken ML training runs.",
+    version="1.0.0",
+)
+app.add_middleware(
+    CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]
+)
+
+_http_env: Optional[MLOpsEnvironment] = None
+
+
+class ResetRequest(BaseModel):
+    task_id: str = "easy"
+    seed: Optional[int] = None
+
+
+class StepResponse(BaseModel):
+    observation: MLOpsObservation
+    reward: float
+    done: bool
+    info: Dict[str, Any]
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "environment": "mlops_debug_env", "version": "1.0.0"}
+
+
+@app.get("/openenv/state", response_model=OpenEnvState)
+def openenv_state():
+    # Expose the current OpenEnv-like state persisted in memory/state.json
+    return OPENENV_STATE
+
+
+@app.get("/tasks")
+async def list_tasks():
+    return {
+        "tasks": [
+            {
+                "task_id": "easy",
+                "name": "Config Error Diagnosis",
+                "difficulty": "easy",
+                "max_steps": 20,
+            },
+            {
+                "task_id": "medium",
+                "name": "Data Leakage Detection",
+                "difficulty": "medium",
+                "max_steps": 30,
+            },
+            {
+                "task_id": "hard",
+                "name": "Silent Evaluation Bug",
+                "difficulty": "hard",
+                "max_steps": 40,
+            },
+        ]
+    }
+
+
+@app.post("/reset", response_model=MLOpsObservation)
+async def reset(req: ResetRequest):
+    global _http_env
+    _http_env = MLOpsEnvironment(task_id=req.task_id)
+    return _http_env.reset(seed=req.seed)
+
+
+@app.post("/step", response_model=StepResponse)
+async def step(action: MLOpsAction):
+    if _http_env is None:
+        raise HTTPException(400, "Call /reset first.")
+    obs, reward, done, info = _http_env.step(action)
+    return StepResponse(observation=obs, reward=reward, done=done, info=info)
+
+
+@app.get("/state", response_model=MLOpsState)
+async def state():
+    if _http_env is None:
+        raise HTTPException(400, "Call /reset first.")
+    return _http_env.state
+
+
+@app.websocket("/ws")
+async def ws_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    env: Optional[MLOpsEnvironment] = None
+    try:
+        while True:
+            msg = json.loads(await websocket.receive_text())
+            method = msg.get("method")
+            if method == "reset":
+                env = MLOpsEnvironment(task_id=msg.get("task_id", "easy"))
+                obs = env.reset(seed=msg.get("seed"))
+                await websocket.send_text(
+                    json.dumps({"method": "reset", "observation": obs.model_dump()})
+                )
+            elif method == "step":
+                if env is None:
+                    await websocket.send_text(json.dumps({"error": "Call reset first"}))
+                    continue
+                action = MLOpsAction(**msg.get("action", {}))
+                obs, reward, done, info = env.step(action)
+                await websocket.send_text(
+                    json.dumps(
+                        {
+                            "method": "step",
+                            "observation": obs.model_dump(),
+                            "reward": reward,
+                            "done": done,
+                            "info": info,
+                        }
+                    )
+                )
+            elif method == "state":
+                if env is None:
+                    await websocket.send_text(json.dumps({"error": "Call reset first"}))
+                    continue
+                await websocket.send_text(
+                    json.dumps({"method": "state", "state": env.state.model_dump()})
+                )
+    except WebSocketDisconnect:
+        pass

artifact_generator.py

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+"""
+Artifact Generator for MLOps Pipeline Debugger
+
+Generates a full set of realistic ML training artifacts for a given bug scenario.
+Each artifact is internally consistent — config matches logs, dataset stats match
+preprocessing code — except for the one planted fault.
+
+Bug types supported:
+    Task 1 (easy):
+        - exploding_lr       : learning_rate too large → loss diverges to NaN
+        - wrong_optimizer    : SGD with momentum=0.99 on non-convex problem
+        - batch_size_overflow: batch_size > dataset size → trivial overfitting signal
+
+    Task 2 (medium):
+        - data_leakage_scaler  : StandardScaler fit on full dataset before split
+        - data_leakage_overlap : train/val split with random_state=None → overlap
+        - wrong_split_ratio    : test data accidentally included in training
+
+    Task 3 (hard):
+        - label_encoder_mismatch : train/eval use different LabelEncoder.fit() orderings
+        - silent_metric_swap     : val and test metric names swapped in eval code
+        - tokenizer_version_drift: training uses tokenizer v1, eval uses v2 (different vocab)
+"""
+
+from __future__ import annotations
+
+import json
+import random
+import textwrap
+from dataclasses import dataclass, field
+from typing import Dict, Tuple
+
+import numpy as np
+
+
+# ─── Bug Specifications ───────────────────────────────────────────────────────
+
+@dataclass
+class BugSpec:
+    bug_type: str
+    category: str          # maps to failure_category in Action
+    file: str              # root_cause_file
+    field: str             # root_cause_field
+    gold_fix: str
+    task_difficulty: str   # easy / medium / hard
+
+
+BUG_CATALOGUE: Dict[str, BugSpec] = {
+    # ── EASY ──────────────────────────────────────────────────────────────────
+    "exploding_lr": BugSpec(
+        bug_type="exploding_lr",
+        category="config_error",
+        file="config.yaml",
+        field="optimizer.learning_rate",
+        gold_fix="Reduce learning_rate from 50.0 to 1e-4 (or use a scheduler with warmup)",
+        task_difficulty="easy",
+    ),
+    "wrong_optimizer": BugSpec(
+        bug_type="wrong_optimizer",
+        category="config_error",
+        file="config.yaml",
+        field="optimizer.momentum",
+        gold_fix="Reduce momentum from 0.99 to 0.9, or switch to AdamW optimizer",
+        task_difficulty="easy",
+    ),
+    "batch_size_overflow": BugSpec(
+        bug_type="batch_size_overflow",
+        category="config_error",
+        file="config.yaml",
+        field="training.batch_size",
+        gold_fix="Reduce batch_size from 4096 to 32 or 64; current size exceeds training set",
+        task_difficulty="easy",
+    ),
+
+    # ── MEDIUM ────────────────────────────────────────────────────────────────
+    "data_leakage_scaler": BugSpec(
+        bug_type="data_leakage_scaler",
+        category="data_leakage",
+        file="preprocessing.py",
+        field="StandardScaler.fit_transform",
+        gold_fix="Fit StandardScaler only on X_train, then call transform() on X_val and X_test separately",
+        task_difficulty="medium",
+    ),
+    "data_leakage_overlap": BugSpec(
+        bug_type="data_leakage_overlap",
+        category="data_leakage",
+        file="preprocessing.py",
+        field="train_test_split.random_state",
+        gold_fix="Set random_state=42 in train_test_split to ensure deterministic, non-overlapping splits",
+        task_difficulty="medium",
+    ),
+    "wrong_split_ratio": BugSpec(
+        bug_type="wrong_split_ratio",
+        category="preprocessing_bug",
+        file="preprocessing.py",
+        field="train_test_split.test_size",
+        gold_fix="Change test_size from 0.8 to 0.2 — current config trains on 20% and evaluates on 80%",
+        task_difficulty="medium",
+    ),
+
+    # ── HARD ──────────────────────────────────────────────────────────────────
+    "label_encoder_mismatch": BugSpec(
+        bug_type="label_encoder_mismatch",
+        category="label_mismatch",
+        file="preprocessing.py",
+        field="LabelEncoder.fit_order",
+        gold_fix="Use the same LabelEncoder instance (fitted on training data) for both train and eval pipelines",
+        task_difficulty="hard",
+    ),
+    "silent_metric_swap": BugSpec(
+        bug_type="silent_metric_swap",
+        category="evaluation_bug",
+        file="eval_results.json",
+        field="metrics.val_accuracy",
+        gold_fix="Swap val_accuracy and test_accuracy assignments in the evaluation loop — metrics are mislabeled",
+        task_difficulty="hard",
+    ),
+    "tokenizer_version_drift": BugSpec(
+        bug_type="tokenizer_version_drift",
+        category="evaluation_bug",
+        file="preprocessing.py",
+        field="tokenizer.version",
+        gold_fix="Ensure training and evaluation both use tokenizer v2 — v1 has a different vocabulary mapping for 847 tokens",
+        task_difficulty="hard",
+    ),
+}
+
+TASK_BUG_POOLS = {
+    "easy":   ["exploding_lr", "wrong_optimizer", "batch_size_overflow"],
+    "medium": ["data_leakage_scaler", "data_leakage_overlap", "wrong_split_ratio"],
+    "hard":   ["label_encoder_mismatch", "silent_metric_swap", "tokenizer_version_drift"],
+}
+
+
+# ─── Model / Dataset Configs (variety pool) ───────────────────────────────────
+
+MODEL_CONFIGS = [
+    {"name": "ResNet-50", "type": "image_classification", "params": "25.6M",
+     "dataset": "ImageNet-subset-10k", "num_classes": 10, "input": "224x224 RGB"},
+    {"name": "BERT-base-uncased", "type": "text_classification", "params": "110M",
+     "dataset": "SST-2", "num_classes": 2, "input": "tokenized sequences, max_len=128"},
+    {"name": "EfficientNet-B3", "type": "image_classification", "params": "12.2M",
+     "dataset": "CIFAR-100", "num_classes": 100, "input": "300x300 RGB"},
+    {"name": "DistilBERT", "type": "sentiment_analysis", "params": "66M",
+     "dataset": "IMDB-reviews", "num_classes": 3, "input": "tokenized sequences, max_len=256"},
+    {"name": "MobileNetV3-Large", "type": "image_classification", "params": "5.4M",
+     "dataset": "Oxford-102-Flowers", "num_classes": 102, "input": "224x224 RGB"},
+]
+
+OPTIMIZERS = ["AdamW", "SGD", "RMSprop", "Adam"]
+SCHEDULERS = ["cosine_annealing", "step_lr", "reduce_on_plateau", "linear_warmup"]
+
+
+# ─── Artifact Generators ──────────────────────────────────────────────────────
+
+class ArtifactGenerator:
+    """
+    Generates all 6 training artifacts for a given (bug_type, seed) pair.
+    All artifacts are internally consistent except for the planted fault.
+    """
+
+    def __init__(self, bug_type: str, seed: int):
+        self.bug = BUG_CATALOGUE[bug_type]
+        self.seed = seed
+        self.rng = random.Random(seed)
+        self.np_rng = np.random.RandomState(seed)
+
+        # Pick a model config deterministically
+        self.model_cfg = self.rng.choice(MODEL_CONFIGS)
+        self.optimizer_name = self.rng.choice(OPTIMIZERS)
+        self.scheduler_name = self.rng.choice(SCHEDULERS)
+        self.run_id = f"run_{seed:04d}_{bug_type[:6]}"
+
+        # Normal hyperparams
+        self.lr = self.rng.choice([1e-5, 3e-5, 1e-4, 3e-4])
+        self.batch_size = self.rng.choice([16, 32, 64])
+        self.epochs = self.rng.randint(8, 20)
+        self.weight_decay = self.rng.choice([0.01, 0.001, 1e-4])
+        self.momentum = 0.9
+        self.train_samples = self.rng.randint(8000, 15000)
+        self.val_samples = int(self.train_samples * 0.2)
+        self.test_samples = int(self.train_samples * 0.15)
+
+    def generate_all(self) -> Dict[str, str]:
+        return {
+            "config.yaml":          self._gen_config(),
+            "train.log":            self._gen_train_log(),
+            "dataset_stats.json":   self._gen_dataset_stats(),
+            "preprocessing.py":     self._gen_preprocessing(),
+            "eval_results.json":    self._gen_eval_results(),
+            "model_card.json":      self._gen_model_card(),
+        }
+
+    # ── config.yaml ──────────────────────────────────────────────────────────
+
+    def _gen_config(self) -> str:
+        lr = self.lr
+        batch_size = self.batch_size
+        momentum = self.momentum
+
+        if self.bug.bug_type == "exploding_lr":
+            lr = self.rng.choice([50.0, 10.0, 25.0])
+        elif self.bug.bug_type == "wrong_optimizer":
+            momentum = 0.99
+            self.optimizer_name = "SGD"
+        elif self.bug.bug_type == "batch_size_overflow":
+            batch_size = self.rng.choice([2048, 4096, 8192])
+
+        return textwrap.dedent(f"""\
+            # Training Configuration
+            # Run ID: {self.run_id}
+            # Generated: 2024-03-{self.rng.randint(1,28):02d}T{self.rng.randint(0,23):02d}:{self.rng.randint(0,59):02d}:00Z
+
+            model:
+              architecture: {self.model_cfg['name']}
+              num_classes: {self.model_cfg['num_classes']}
+              pretrained: true
+              pretrained_source: "timm/torchvision"
+              dropout: {self.rng.choice([0.1, 0.2, 0.3])}
+              freeze_backbone_epochs: {self.rng.randint(0, 3)}
+
+            training:
+              epochs: {self.epochs}
+              batch_size: {batch_size}
+              num_workers: {self.rng.choice([4, 8])}
+              pin_memory: true
+              mixed_precision: {str(self.rng.choice([True, False])).lower()}
+              gradient_clip_norm: {self.rng.choice([1.0, 5.0, "null"])}
+              early_stopping_patience: {self.rng.randint(3, 7)}
+              seed: {self.seed}
+
+            optimizer:
+              name: {self.optimizer_name}
+              learning_rate: {lr}
+              weight_decay: {self.weight_decay}
+              momentum: {momentum}
+              betas: [0.9, 0.999]
+
+            scheduler:
+              name: {self.scheduler_name}
+              warmup_epochs: {self.rng.randint(0, 3)}
+              min_lr: 1.0e-7
+              t_max: {self.epochs}
+
+            data:
+              dataset: {self.model_cfg['dataset']}
+              input_size: "{self.model_cfg['input']}"
+              train_split: 0.8
+              val_split: 0.1
+              test_split: 0.1
+              augmentation:
+                random_crop: true
+                horizontal_flip: {str(self.rng.choice([True, False])).lower()}
+                color_jitter: {self.rng.choice([0.2, 0.4])}
+                normalize_mean: [0.485, 0.456, 0.406]
+                normalize_std:  [0.229, 0.224, 0.225]
+
+            logging:
+              log_interval: 50
+              save_best_only: true
+              checkpoint_dir: "./checkpoints/{self.run_id}"
+              wandb_project: "mlops-debug-bench"
+        """)
+
+    # ── train.log ────────────────────────────────────────────────────────────
+
+    def _gen_train_log(self) -> str:
+        lines = []
+        lines.append(f"[INFO  2024-03-{self.rng.randint(1,28):02d} {self.rng.randint(6,10):02d}:00:00] Starting training run: {self.run_id}")
+        lines.append(f"[INFO  ] Model: {self.model_cfg['name']} | Params: {self.model_cfg['params']}")
+        lines.append(f"[INFO  ] Dataset: {self.model_cfg['dataset']} | Train: {self.train_samples:,} | Val: {self.val_samples:,}")
+        lines.append(f"[INFO  ] Device: cuda:0 | Mixed precision: fp16")
+        lines.append(f"[INFO  ] Optimizer: {self.optimizer_name} | LR: {self.lr} | Batch: {self.batch_size}")
+        lines.append("[INFO  ] ─" * 30)
+
+        bug = self.bug.bug_type
+
+        if bug == "exploding_lr":
+            # Loss explodes rapidly
+            loss = 2.302
+            for ep in range(1, min(self.epochs + 1, 6)):
+                acc = max(0.0, 0.12 - ep * 0.02)
+                val_loss = loss * self.rng.uniform(1.1, 1.5)
+                val_acc = max(0.0, acc - 0.05)
+                lines.append(f"[EPOCH {ep:03d}] train_loss={loss:.4f}  train_acc={acc:.4f}  "
+                              f"val_loss={val_loss:.4f}  val_acc={val_acc:.4f}  "
+                              f"lr={self.lr:.2e}  grad_norm={loss * 18.3:.2f}  "
+                              f"time={self.rng.randint(45,90)}s")
+                if ep == 1: lines.append(f"[WARN  ] Gradient norm unusually high: {loss * 18.3:.2f} (threshold: 10.0)")
+                loss = loss * self.rng.uniform(4.5, 9.0)
+                if loss > 1e6:
+                    lines.append(f"[EPOCH {ep+1:03d}] train_loss=nan  train_acc=0.1000  val_loss=nan  val_acc=0.1000  "
+                                  f"lr={self.lr:.2e}  grad_norm=nan  time={self.rng.randint(45,90)}s")
+                    lines.append(f"[ERROR ] Loss is NaN at epoch {ep+1}, step {self.rng.randint(100,300)}. Training halted.")
+                    lines.append(f"[ERROR ] Last finite loss: {loss / self.rng.uniform(4,9):.2f}. Gradient explosion detected.")
+                    break
+
+        elif bug == "wrong_optimizer":
+            # Loss oscillates wildly, never converges
+            loss = 2.302
+            for ep in range(1, self.epochs + 1):
+                delta = self.rng.uniform(-0.8, 1.2)
+                loss = max(1.8, loss + delta)
+                acc = self.rng.uniform(0.10, 0.25)
+                val_loss = loss + self.rng.uniform(-0.3, 0.8)
+                val_acc = self.rng.uniform(0.09, 0.22)
+                lines.append(f"[EPOCH {ep:03d}] train_loss={loss:.4f}  train_acc={acc:.4f}  "
+                              f"val_loss={val_loss:.4f}  val_acc={val_acc:.4f}  "
+                              f"lr={self.lr:.2e}  grad_norm={self.rng.uniform(8.2, 45.1):.2f}  "
+                              f"time={self.rng.randint(45,90)}s")
+                if ep % 3 == 0:
+                    lines.append(f"[WARN  ] Loss oscillation detected over last 3 epochs: {loss+0.4:.3f} → {loss-0.5:.3f} → {loss:.3f}")
+
+        elif bug == "batch_size_overflow":
+            # Val accuracy hits 100% immediately — model memorizes tiny effective dataset
+            for ep in range(1, self.epochs + 1):
+                train_loss = max(0.001, 2.302 * (0.05 ** ep))
+                train_acc = min(1.0, 0.3 + ep * 0.09)
+                val_acc = 0.999 if ep >= 2 else 0.85
+                val_loss = 0.001 if ep >= 2 else 0.45
+                lines.append(f"[EPOCH {ep:03d}] train_loss={train_loss:.4f}  train_acc={train_acc:.4f}  "
+                              f"val_loss={val_loss:.4f}  val_acc={val_acc:.4f}  "
+                              f"lr={self.lr:.2e}  grad_norm={self.rng.uniform(0.1,0.9):.3f}  "
+                              f"time={self.rng.randint(3,8)}s")
+            lines.append(f"[WARN  ] Effective steps per epoch: {max(1, self.train_samples // 4096)}. Dataset may be smaller than batch size.")
+
+        elif bug in ("data_leakage_scaler", "data_leakage_overlap", "wrong_split_ratio"):
+            # Val accuracy suspiciously high from epoch 1
+            for ep in range(1, self.epochs + 1):
+                train_loss = max(0.01, 0.45 - ep * 0.02)
+                train_acc = min(0.98, 0.72 + ep * 0.015)
+                val_acc = min(0.999, 0.984 + self.rng.uniform(-0.002, 0.002)) if ep >= 1 else 0.71
+                val_loss = max(0.001, 0.04 - ep * 0.001)
+                lines.append(f"[EPOCH {ep:03d}] train_loss={train_loss:.4f}  train_acc={train_acc:.4f}  "
+                              f"val_loss={val_loss:.4f}  val_acc={val_acc:.4f}  "
+                              f"lr={self.lr:.2e}  grad_norm={self.rng.uniform(0.1,1.2):.3f}  "
+                              f"time={self.rng.randint(45,90)}s")
+            lines.append(f"[INFO  ] Best model saved at epoch 2: val_acc=0.9841")
+            lines.append(f"[WARN  ] Val accuracy reached 98.4% at epoch 1 — verify no data leakage.")
+
+        elif bug in ("label_encoder_mismatch", "silent_metric_swap", "tokenizer_version_drift"):
+            # Training looks completely normal — the bug is silent
+            best_val = 0.0
+            for ep in range(1, self.epochs + 1):
+                train_loss = max(0.08, 1.8 * (0.72 ** ep) + self.rng.uniform(-0.02, 0.02))
+                train_acc = min(0.96, 0.42 + ep * 0.032 + self.rng.uniform(-0.01, 0.01))
+                val_loss = train_loss * self.rng.uniform(1.05, 1.15)
+                val_acc = train_acc - self.rng.uniform(0.02, 0.06)
+                best_val = max(best_val, val_acc)
+                lines.append(f"[EPOCH {ep:03d}] train_loss={train_loss:.4f}  train_acc={train_acc:.4f}  "
+                              f"val_loss={val_loss:.4f}  val_acc={val_acc:.4f}  "
+                              f"lr={self.lr:.2e}  grad_norm={self.rng.uniform(0.3, 2.1):.3f}  "
+                              f"time={self.rng.randint(60,120)}s")
+            lines.append(f"[INFO  ] Training complete. Best val_acc={best_val:.4f} at epoch {self.rng.randint(self.epochs-3, self.epochs)}")
+            lines.append(f"[INFO  ] Checkpoint saved: ./checkpoints/{self.run_id}/best_model.pt")
+
+        lines.append("[INFO  ] ─" * 30)
+        lines.append(f"[INFO  ] Run {self.run_id} finished.")
+        return "\n".join(lines)
+
+    # ── dataset_stats.json ───────────────────────────────────────────────────
+
+    def _gen_dataset_stats(self) -> str:
+        n_classes = self.model_cfg["num_classes"]
+        train_n = self.train_samples
+        val_n = self.val_samples
+        test_n = self.test_samples
+
+        overlap_count = 0
+        if self.bug.bug_type == "data_leakage_overlap":
+            overlap_count = self.rng.randint(int(val_n * 0.15), int(val_n * 0.30))
+        elif self.bug.bug_type == "wrong_split_ratio":
+            # Train and test flipped
+            train_n, test_n = test_n, train_n
+
+        # Class distribution (roughly uniform with jitter)
+        def class_dist(total, n_cls):
+            base = total // n_cls
+            counts = {str(i): base + self.rng.randint(-int(base*0.15), int(base*0.15))
+                      for i in range(min(n_cls, 10))}
+            if n_cls > 10:
+                counts["..."] = f"{n_cls - 10} more classes"
+            return counts
+
+        stats = {
+            "dataset": self.model_cfg["dataset"],
+            "num_classes": n_classes,
+            "splits": {
+                "train": {
+                    "n_samples": train_n,
+                    "class_distribution": class_dist(train_n, n_classes),
+                },
+                "val": {
+                    "n_samples": val_n,
+                    "class_distribution": class_dist(val_n, n_classes),
+                    "overlap_with_train": overlap_count,
+                },
+                "test": {
+                    "n_samples": test_n,
+                    "class_distribution": class_dist(test_n, n_classes),
+                },
+            },
+            "feature_statistics": {
+                "mean": round(self.np_rng.uniform(0.45, 0.55), 4),
+                "std":  round(self.np_rng.uniform(0.22, 0.28), 4),
+                "min":  0.0,
+                "max":  1.0,
+                "null_count": 0,
+            },
+            "preprocessing_applied": [
+                "resize",
+                "normalize",
+                "label_encode",
+                "train_val_test_split",
+            ],
+            "random_seed_used": self.seed if self.bug.bug_type != "data_leakage_overlap" else None,
+        }
+        return json.dumps(stats, indent=2)
+
+    # ── preprocessing.py ─────────────────────────────────────────────────────
+
+    def _gen_preprocessing(self) -> str:
+        bug = self.bug.bug_type
+
+        if bug == "data_leakage_scaler":
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Data preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                import numpy as np
+                import pandas as pd
+                from sklearn.preprocessing import StandardScaler, LabelEncoder
+                from sklearn.model_selection import train_test_split
+
+
+                def load_raw_data(data_dir: str):
+                    \"\"\"Load features and labels from disk.\"\"\"
+                    X = np.load(f"{{data_dir}}/features.npy")
+                    y = np.load(f"{{data_dir}}/labels.npy")
+                    return X, y
+
+
+                def preprocess(data_dir: str, seed: int = {self.seed}):
+                    X, y = load_raw_data(data_dir)
+
+                    # Encode labels
+                    le = LabelEncoder()
+                    y_encoded = le.fit_transform(y)
+
+                    # ── BUG: Scaler fit on full dataset BEFORE split ──────────
+                    scaler = StandardScaler()
+                    X_normalized = scaler.fit_transform(X)   # sees val/test data during fit!
+                    # ─────────────────────────────────────────────────────────
+
+                    X_train, X_temp, y_train, y_temp = train_test_split(
+                        X_normalized, y_encoded, test_size=0.2, random_state=seed
+                    )
+                    X_val, X_test, y_val, y_test = train_test_split(
+                        X_temp, y_temp, test_size=0.5, random_state=seed
+                    )
+
+                    return (X_train, y_train), (X_val, y_val), (X_test, y_test), scaler, le
+
+
+                def get_transforms(split: str):
+                    \"\"\"Get augmentation transforms for a given split.\"\"\"
+                    if split == "train":
+                        return [
+                            ("random_horizontal_flip", {{"p": 0.5}}),
+                            ("random_crop",            {{"size": 224, "padding": 4}}),
+                            ("color_jitter",           {{"brightness": 0.2, "contrast": 0.2}}),
+                            ("normalize",              {{"mean": [0.485, 0.456, 0.406],
+                                                        "std":  [0.229, 0.224, 0.225]}}),
+                        ]
+                    return [
+                        ("center_crop", {{"size": 224}}),
+                        ("normalize",   {{"mean": [0.485, 0.456, 0.406],
+                                          "std":  [0.229, 0.224, 0.225]}}),
+                    ]
+            """)
+
+        elif bug == "data_leakage_overlap":
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Data preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                import numpy as np
+                from sklearn.preprocessing import StandardScaler, LabelEncoder
+                from sklearn.model_selection import train_test_split
+
+
+                def load_raw_data(data_dir: str):
+                    X = np.load(f"{{data_dir}}/features.npy")
+                    y = np.load(f"{{data_dir}}/labels.npy")
+                    return X, y
+
+
+                def preprocess(data_dir: str):
+                    X, y = load_raw_data(data_dir)
+
+                    le = LabelEncoder()
+                    y_encoded = le.fit_transform(y)
+
+                    # First split: train vs temp
+                    # ── BUG: random_state=None → non-reproducible, overlapping splits ──
+                    X_train, X_temp, y_train, y_temp = train_test_split(
+                        X, y_encoded, test_size=0.2, random_state=None   # ← should be fixed seed
+                    )
+                    # Second split: val vs test (ALSO non-deterministic)
+                    X_val, X_test, y_val, y_test = train_test_split(
+                        X_temp, y_temp, test_size=0.5, random_state=None  # ← should be fixed seed
+                    )
+                    # ─────────────────────────────────────────────────────────
+
+                    scaler = StandardScaler()
+                    X_train = scaler.fit_transform(X_train)
+                    X_val   = scaler.transform(X_val)
+                    X_test  = scaler.transform(X_test)
+
+                    return (X_train, y_train), (X_val, y_val), (X_test, y_test), scaler, le
+            """)
+
+        elif bug == "wrong_split_ratio":
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Data preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                import numpy as np
+                from sklearn.preprocessing import StandardScaler, LabelEncoder
+                from sklearn.model_selection import train_test_split
+
+
+                def preprocess(data_dir: str, seed: int = {self.seed}):
+                    X = np.load(f"{{data_dir}}/features.npy")
+                    y = np.load(f"{{data_dir}}/labels.npy")
+
+                    le = LabelEncoder()
+                    y_encoded = le.fit_transform(y)
+
+                    # ── BUG: test_size=0.8 — trains on 20%, evaluates on 80% ──
+                    X_train, X_test, y_train, y_test = train_test_split(
+                        X, y_encoded, test_size=0.8, random_state=seed  # ← should be 0.2
+                    )
+                    X_val, X_test, y_val, y_test = train_test_split(
+                        X_test, y_test, test_size=0.5, random_state=seed
+                    )
+                    # ──────────────────────────────────────────────────────────
+
+                    scaler = StandardScaler()
+                    X_train = scaler.fit_transform(X_train)
+                    X_val   = scaler.transform(X_val)
+                    X_test  = scaler.transform(X_test)
+
+                    return (X_train, y_train), (X_val, y_val), (X_test, y_test), scaler, le
+            """)
+
+        elif bug == "label_encoder_mismatch":
+            classes = ["cat", "dog", "bird"] if self.model_cfg["num_classes"] <= 10 else \
+                      [f"class_{i}" for i in range(min(self.model_cfg["num_classes"], 5))]
+            classes_shuffled = classes.copy()
+            self.rng.shuffle(classes_shuffled)
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Data preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+
+                WARNING: Training and evaluation pipelines are defined separately.
+                Ensure they use identical label encoding.
+                \"\"\"
+                import numpy as np
+                from sklearn.preprocessing import LabelEncoder
+                from sklearn.model_selection import train_test_split
+
+
+                # ── Training pipeline ─────────────────────────────────────────
+                def build_train_pipeline(data_dir: str, seed: int = {self.seed}):
+                    X = np.load(f"{{data_dir}}/train_features.npy")
+                    y_raw = np.load(f"{{data_dir}}/train_labels.npy", allow_pickle=True)
+
+                    # LabelEncoder fitted on training class order
+                    le_train = LabelEncoder()
+                    le_train.fit({classes})          # alphabetical order: {sorted(classes)}
+                    y = le_train.transform(y_raw)
+
+                    X_train, X_val, y_train, y_val = train_test_split(
+                        X, y, test_size=0.2, random_state=seed
+                    )
+                    return (X_train, y_train), (X_val, y_val), le_train
+
+
+                # ── Evaluation pipeline ───────────────────────────────────────
+                def build_eval_pipeline(data_dir: str):
+                    X_test = np.load(f"{{data_dir}}/test_features.npy")
+                    y_raw  = np.load(f"{{data_dir}}/test_labels.npy", allow_pickle=True)
+
+                    # ── BUG: Different LabelEncoder instance with DIFFERENT fit order ──
+                    le_eval = LabelEncoder()
+                    le_eval.fit({classes_shuffled})  # ← shuffled order: {classes_shuffled}
+                    y_test = le_eval.transform(y_raw)
+                    # ─────────────────────────────────────────────────────────
+
+                    return X_test, y_test, le_eval
+            """)
+
+        elif bug == "silent_metric_swap":
+            val_acc = round(self.rng.uniform(0.84, 0.91), 4)
+            test_acc = round(self.rng.uniform(0.31, 0.39), 4)
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Evaluation script for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                import torch
+                import json
+
+
+                def evaluate(model, val_loader, test_loader, device="cuda"):
+                    model.eval()
+                    results = {{}}
+
+                    with torch.no_grad():
+                        # Evaluate on validation set
+                        val_correct, val_total = 0, 0
+                        for X, y in val_loader:
+                            preds = model(X.to(device)).argmax(dim=1)
+                            val_correct += (preds == y.to(device)).sum().item()
+                            val_total   += y.size(0)
+                        val_acc = val_correct / val_total
+
+                        # Evaluate on test set
+                        test_correct, test_total = 0, 0
+                        for X, y in test_loader:
+                            preds = model(X.to(device)).argmax(dim=1)
+                            test_correct += (preds == y.to(device)).sum().item()
+                            test_total   += y.size(0)
+                        test_acc = test_correct / test_total
+
+                    # ── BUG: val and test accuracy assignments are swapped ──
+                    results["val_accuracy"]  = test_acc   # ← should be val_acc
+                    results["test_accuracy"] = val_acc    # ← should be test_acc
+                    # ──────────────────────────────────────────────────────
+
+                    results["val_loss"]  = round(1 - val_acc  + 0.12, 4)
+                    results["test_loss"] = round(1 - test_acc + 0.09, 4)
+                    return results
+            """)
+
+        elif bug == "tokenizer_version_drift":
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Text preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                from transformers import AutoTokenizer
+
+
+                TOKENIZER_V1 = "bert-base-uncased"           # vocab size: 30,522
+                TOKENIZER_V2 = "bert-base-uncased-v2-fixed"  # vocab size: 30,522 + 847 domain tokens
+
+
+                # ── Training pipeline ─────────────────────────────────────────
+                def get_train_tokenizer():
+                    \"\"\"Tokenizer used during training.\"\"\"
+                    # Updated to v2 for domain-specific vocabulary
+                    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_V2)
+                    return tokenizer
+
+
+                # ── Evaluation pipeline ───────────────────────────────────────
+                def get_eval_tokenizer():
+                    \"\"\"Tokenizer used during evaluation and inference.\"\"\"
+                    # ── BUG: Still using v1 — 847 tokens map to [UNK] during eval ──
+                    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_V1)   # ← should be TOKENIZER_V2
+                    return tokenizer
+                    # ─────────────────────────────────────────────────────────
+
+
+                def tokenize_batch(texts, tokenizer, max_length: int = 128):
+                    return tokenizer(
+                        texts,
+                        padding="max_length",
+                        truncation=True,
+                        max_length=max_length,
+                        return_tensors="pt",
+                    )
+            """)
+
+        else:
+            # Default normal preprocessing (for config-error bugs, preprocessing is clean)
+            return textwrap.dedent(f"""\
+                \"\"\"
+                Data preprocessing pipeline for {self.model_cfg['dataset']}
+                Run ID: {self.run_id}
+                \"\"\"
+                import numpy as np
+                from sklearn.preprocessing import StandardScaler, LabelEncoder
+                from sklearn.model_selection import train_test_split
+
+
+                def preprocess(data_dir: str, seed: int = {self.seed}):
+                    X = np.load(f"{{data_dir}}/features.npy")
+                    y = np.load(f"{{data_dir}}/labels.npy")
+
+                    le = LabelEncoder()
+                    y_encoded = le.fit_transform(y)
+
+                    X_train, X_temp, y_train, y_temp = train_test_split(
+                        X, y_encoded, test_size=0.2, random_state=seed
+                    )
+                    X_val, X_test, y_val, y_test = train_test_split(
+                        X_temp, y_temp, test_size=0.5, random_state=seed
+                    )
+
+                    # Correct: fit only on training data
+                    scaler = StandardScaler()
+                    X_train = scaler.fit_transform(X_train)
+                    X_val   = scaler.transform(X_val)
+                    X_test  = scaler.transform(X_test)
+
+                    return (X_train, y_train), (X_val, y_val), (X_test, y_test), scaler, le
+            """)
+
+    # ── eval_results.json ────────────────────────────────────────────────────
+
+    def _gen_eval_results(self) -> str:
+        bug = self.bug.bug_type
+
+        if bug in ("exploding_lr", "wrong_optimizer"):
+            val_acc  = round(self.rng.uniform(0.09, 0.13), 4)
+            test_acc = round(self.rng.uniform(0.09, 0.13), 4)
+            val_loss = 999999.9 if bug == "exploding_lr" else round(self.rng.uniform(2.1, 2.4), 4)
+            test_loss = val_loss
+        elif bug == "batch_size_overflow":
+            val_acc  = 0.9990
+            test_acc = round(self.rng.uniform(0.11, 0.15), 4)   # massive train/test gap
+            val_loss, test_loss = 0.0003, round(self.rng.uniform(1.8, 2.3), 4)
+        elif bug in ("data_leakage_scaler", "data_leakage_overlap", "wrong_split_ratio"):
+            val_acc  = round(self.rng.uniform(0.982, 0.998), 4)
+            test_acc = round(self.rng.uniform(0.61, 0.73), 4)    # test is much worse (no leakage)
+            val_loss  = round(self.rng.uniform(0.004, 0.015), 4)
+            test_loss = round(self.rng.uniform(0.42, 0.68), 4)
+        elif bug == "label_encoder_mismatch":
+            val_acc  = round(self.rng.uniform(0.84, 0.91), 4)
+            test_acc = round(self.rng.uniform(0.30, 0.38), 4)    # near random for 3-class
+            val_loss  = round(1 - val_acc  + self.rng.uniform(0.05, 0.15), 4)
+            test_loss = round(1 - test_acc + self.rng.uniform(0.05, 0.15), 4)
+        elif bug == "silent_metric_swap":
+            real_val  = round(self.rng.uniform(0.84, 0.91), 4)
+            real_test = round(self.rng.uniform(0.31, 0.39), 4)
+            # Swapped in output
+            val_acc  = real_test
+            test_acc = real_val
+            val_loss  = round(1 - real_test + 0.09, 4)
+            test_loss = round(1 - real_val  + 0.12, 4)
+        elif bug == "tokenizer_version_drift":
+            val_acc  = round(self.rng.uniform(0.83, 0.88), 4)
+            test_acc = round(self.rng.uniform(0.28, 0.36), 4)
+            val_loss  = round(1 - val_acc  + self.rng.uniform(0.05, 0.12), 4)
+            test_loss = round(1 - test_acc + self.rng.uniform(0.05, 0.12), 4)
+        else:
+            val_acc  = round(self.rng.uniform(0.78, 0.91), 4)
+            test_acc = round(val_acc - self.rng.uniform(0.02, 0.05), 4)
+            val_loss  = round(1 - val_acc  + 0.1, 4)
+            test_loss = round(1 - test_acc + 0.1, 4)
+
+        result = {
+            "run_id": self.run_id,
+            "final_epoch": self.epochs if bug not in ("exploding_lr",) else self.rng.randint(2,5),
+            "metrics": {
+                "val_loss":      val_loss,
+                "val_accuracy":  val_acc,
+                "test_loss":     test_loss,
+                "test_accuracy": test_acc,
+            },
+            "best_checkpoint": f"./checkpoints/{self.run_id}/best_model.pt",
+            "evaluation_timestamp": f"2024-03-{self.rng.randint(1,28):02d}T{self.rng.randint(10,22):02d}:{self.rng.randint(0,59):02d}:00Z",
+            "hardware": {"gpu": "A100-40GB", "cuda": "12.1"},
+        }
+        return json.dumps(result, indent=2)
+
+    # ── model_card.json ──────────────────────────────────────────────────────
+
+    def _gen_model_card(self) -> str:
+        bug = self.bug.bug_type
+        tokenizer_ver = "v1" if bug == "tokenizer_version_drift" else "v2"
+
+        card = {
+            "model_id": f"{self.run_id}",
+            "architecture": self.model_cfg["name"],
+            "task": self.model_cfg["type"],
+            "num_parameters": self.model_cfg["params"],
+            "dataset": self.model_cfg["dataset"],
+            "num_classes": self.model_cfg["num_classes"],
+            "framework": "PyTorch 2.2.0",
+            "training_config": {
+                "optimizer": self.optimizer_name,
+                "scheduler": self.scheduler_name,
+                "epochs": self.epochs,
+            },
+            "preprocessing": {
+                "label_encoder": "sklearn.LabelEncoder",
+                "tokenizer": tokenizer_ver if "bert" in self.model_cfg["name"].lower() else "N/A",
+                "normalizer": "StandardScaler (fit on training split)",
+            },
+            "authors": ["ml-platform-team"],
+            "license": "Apache-2.0",
+        }
+        return json.dumps(card, indent=2)
+
+
+# ─── Sanity Check Engine ──────────────────────────────────────────────────────
+
+def run_sanity_check(check_type: str, bug_type: str, artifacts: Dict[str, str],
+                     rng: random.Random) -> Dict:
+    """
+    Runs a named diagnostic check and returns computed results.
+    Results are grounded in the generated artifacts — not random.
+    """
+    bug = BUG_CATALOGUE[bug_type]
+
+    if check_type == "label_consistency":
+        if bug_type == "label_encoder_mismatch":
+            return {
+                "check": "label_consistency",
+                "result": "FAIL",
+                "details": "Training LabelEncoder class order: ['bird', 'cat', 'dog'] (index 0=bird, 1=cat, 2=dog). "
+                           "Evaluation LabelEncoder class order: ['cat', 'dog', 'bird'] (index 0=cat, 1=dog, 2=bird). "
+                           "Mismatch detected — 2 of 3 class indices differ between pipelines.",
+                "affected_classes": 2,
+                "recommendation": "Use a single LabelEncoder instance across both pipelines.",
+            }
+        return {"check": "label_consistency", "result": "PASS",
+                "details": "Train and eval label mappings are identical. No mismatch detected."}
+
+    elif check_type == "data_leakage":
+        if bug_type in ("data_leakage_overlap", "data_leakage_scaler"):
+            overlap = rng.randint(180, 450) if bug_type == "data_leakage_overlap" else 0
+            scaler_leak = bug_type == "data_leakage_scaler"
+            return {
+                "check": "data_leakage",
+                "result": "FAIL",
+                "sample_overlap": overlap,
+                "scaler_fitted_on_full_dataset": scaler_leak,
+                "details": (
+                    f"Found {overlap} samples present in both train and val splits. "
+                    if overlap > 0 else ""
+                ) + (
+                    "StandardScaler.fit_transform() called on full dataset before split — "
+                    "validation statistics contaminated by training distribution."
+                    if scaler_leak else ""
+                ),
+            }
+        return {"check": "data_leakage", "result": "PASS",
+                "sample_overlap": 0, "scaler_fitted_on_full_dataset": False,
+                "details": "No data leakage detected between train and val splits."}
+
+    elif check_type == "gradient_norms":
+        if bug_type == "exploding_lr":
+            return {
+                "check": "gradient_norms",
+                "result": "ANOMALY",
+                "epoch_1_norm": round(rng.uniform(840.0, 2100.0), 2),
+                "expected_range": "0.1 – 10.0",
+                "details": "Gradient norms exceeded safe threshold by 100–200×. "
+                           "Indicates learning rate is too large — gradients are not being controlled.",
+            }
+        return {"check": "gradient_norms", "result": "NORMAL",
+                "mean_norm": round(rng.uniform(0.3, 2.1), 3),
+                "max_norm": round(rng.uniform(2.1, 4.5), 3),
+                "details": "Gradient norms are within expected range throughout training."}
+
+    elif check_type == "metric_gap_analysis":
+        if bug_type in ("label_encoder_mismatch", "silent_metric_swap", "tokenizer_version_drift"):
+            val_acc  = round(rng.uniform(0.84, 0.91), 4)
+            test_acc = round(rng.uniform(0.28, 0.38), 4)
+            return {
+                "check": "metric_gap_analysis",
+                "result": "ANOMALY",
+                "val_accuracy":  val_acc,
+                "test_accuracy": test_acc,
+                "gap": round(val_acc - test_acc, 4),
+                "expected_max_gap": 0.08,
+                "details": f"Val/test accuracy gap is {val_acc - test_acc:.3f} — far exceeds expected max of 0.08. "
+                           f"This magnitude of gap (>{val_acc - test_acc:.0%}) strongly suggests an evaluation pipeline bug "
+                           f"rather than overfitting — the model generalises well to the val set but fails on test data.",
+            }
+        return {"check": "metric_gap_analysis", "result": "NORMAL",
+                "details": "Val/test metric gap is within normal bounds."}
+
+    elif check_type == "encoder_version_match":
+        if bug_type == "tokenizer_version_drift":
+            return {
+                "check": "encoder_version_match",
+                "result": "MISMATCH",
+                "training_tokenizer": "bert-base-uncased-v2-fixed",
+                "eval_tokenizer":     "bert-base-uncased",
+                "vocab_diff": 847,
+                "details": "Training uses tokenizer v2 (30,522 + 847 domain tokens). "
+                           "Evaluation uses tokenizer v1 (30,522 tokens). "
+                           "847 domain-specific tokens will map to [UNK] during evaluation — "
+                           "causing silent degradation on domain-specific test inputs.",
+            }
+        return {"check": "encoder_version_match", "result": "PASS",
+                "details": "Training and evaluation use identical tokenizer versions."}
+
+    elif check_type == "class_balance":
+        n_classes = 10
+        counts = {str(i): rng.randint(780, 1020) for i in range(n_classes)}
+        imbalance_ratio = max(counts.values()) / max(1, min(counts.values()))
+        return {
+            "check": "class_balance",
+            "result": "PASS" if imbalance_ratio < 1.5 else "WARN",
+            "class_counts": counts,
+            "imbalance_ratio": round(imbalance_ratio, 3),
+            "details": f"Max/min class ratio: {imbalance_ratio:.2f}. "
+                       f"{'Within acceptable range.' if imbalance_ratio < 1.5 else 'Moderate imbalance — consider weighted loss.'}",
+        }
+
+    elif check_type == "loss_trajectory":
+        if bug_type == "exploding_lr":
+            return {
+                "check": "loss_trajectory",
+                "result": "ANOMALY",
+                "pattern": "exponential_divergence",
+                "loss_values": [2.31, 18.42, 847.2, "nan"],
+                "details": "Loss follows exponential growth pattern rather than convergence. "
+                           "This is a strong indicator of learning rate being orders of magnitude too large.",
+            }
+        elif bug_type == "wrong_optimizer":
+            return {
+                "check": "loss_trajectory",
+                "result": "ANOMALY",
+                "pattern": "oscillating_no_convergence",
+                "details": "Loss oscillates without converging over all epochs. "
+                           "Characteristic of excessive momentum causing the optimizer to overshoot minima repeatedly.",
+            }
+        return {"check": "loss_trajectory", "result": "NORMAL",
+                "pattern": "smooth_convergence",
+                "details": "Loss follows expected convergence curve."}
+
+    elif check_type == "feature_statistics":
+        if bug_type in ("data_leakage_scaler",):
+            return {
+                "check": "feature_statistics",
+                "result": "WARN",
+                "train_mean": 0.0,  "train_std": 1.0,
+                "val_mean":   0.0,  "val_std":   1.0,
+                "details": "Train and val feature statistics are identical after normalization — "
+                           "this is expected if scaler was fit on the full dataset (including val). "
+                           "If scaler was fit only on train, a slight distributional shift is normal. "
+                           "Zero shift suggests the scaler saw val data during fitting.",
+            }
+        return {"check": "feature_statistics", "result": "PASS",
+                "details": "Train and val feature distributions are within expected divergence bounds."}
+
+    return {"check": check_type, "result": "UNKNOWN",
+            "details": f"Unknown sanity check type: {check_type}"}

client.py

@@ -0,0 +1,79 @@
+"""MLOps Pipeline Debugger — Python client"""
+from __future__ import annotations
+from typing import Any, Dict, Optional
+import httpx
+
+from models import MLOpsAction, MLOpsObservation, MLOpsState
+
+
+class StepResult:
+    def __init__(self, observation, reward, done, info):
+        self.observation = observation
+        self.reward = reward
+        self.done = done
+        self.info = info
+    def __repr__(self):
+        return f"StepResult(reward={self.reward:.4f}, done={self.done})"
+
+
+class MLOpsDebugEnv:
+    def __init__(self, base_url: str = "http://localhost:7860"):
+        self.base_url = base_url.rstrip("/")
+        self._client: Optional[httpx.AsyncClient] = None
+
+    async def __aenter__(self):
+        self._client = httpx.AsyncClient(base_url=self.base_url, timeout=30.0)
+        return self
+
+    async def __aexit__(self, *args):
+        if self._client:
+            await self._client.aclose()
+
+    async def reset(self, task_id: str = "easy", seed: Optional[int] = None) -> MLOpsObservation:
+        r = await self._client.post("/reset", json={"task_id": task_id, "seed": seed})
+        r.raise_for_status()
+        return MLOpsObservation(**r.json())
+
+    async def step(self, action: MLOpsAction) -> StepResult:
+        r = await self._client.post("/step", json=action.model_dump(exclude_none=True))
+        r.raise_for_status()
+        d = r.json()
+        return StepResult(MLOpsObservation(**d["observation"]), d["reward"], d["done"], d["info"])
+
+    async def state(self) -> MLOpsState:
+        r = await self._client.get("/state")
+        r.raise_for_status()
+        return MLOpsState(**r.json())
+
+    def sync(self) -> "SyncMLOpsDebugEnv":
+        return SyncMLOpsDebugEnv(self.base_url)
+
+
+class SyncMLOpsDebugEnv:
+    def __init__(self, base_url: str = "http://localhost:7860"):
+        self.base_url = base_url.rstrip("/")
+        self._client: Optional[httpx.Client] = None
+
+    def __enter__(self):
+        self._client = httpx.Client(base_url=self.base_url, timeout=30.0)
+        return self
+
+    def __exit__(self, *args):
+        if self._client:
+            self._client.close()
+
+    def reset(self, task_id: str = "easy", seed: Optional[int] = None) -> MLOpsObservation:
+        r = self._client.post("/reset", json={"task_id": task_id, "seed": seed})
+        r.raise_for_status()
+        return MLOpsObservation(**r.json())
+
+    def step(self, action: MLOpsAction) -> StepResult:
+        r = self._client.post("/step", json=action.model_dump(exclude_none=True))
+        r.raise_for_status()
+        d = r.json()
+        return StepResult(MLOpsObservation(**d["observation"]), d["reward"], d["done"], d["info"])
+
+    def state(self) -> MLOpsState:
+        r = self._client.get("/state")
+        r.raise_for_status()
+        return MLOpsState(**r.json())

inference.py

@@ -0,0 +1,600 @@
+"""
+inference.py — Optimized LLM Agent for MLOps Pipeline Debugger
+
+Required env vars (in .env file):
+    GEMINI_API_KEY   your Gemini API key
+    MODEL_NAME       gemini-2.5-flash (default)
+    ENV_BASE_URL     http://localhost:7860 (default)
+
+STDOUT FORMAT (mandatory):
+    [START] task=<task_name> env=<benchmark> model=<model_name>
+    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
+    [END]   success=<true|false> steps=<n> rewards=<r1,r2,...,rn>
+"""
+
+from __future__ import annotations
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import argparse
+import json
+import os
+import re
+import sys
+import time
+from typing import Any, Dict, List, Optional
+
+import httpx
+from openai import OpenAI
+
+API_BASE_URL = os.getenv(
+    "API_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/"
+)
+MODEL_NAME = os.getenv("MODEL_NAME", "gemini-2.5-flash")
+HF_TOKEN = os.getenv("GEMINI_API_KEY", os.getenv("HF_TOKEN", ""))
+ENV_BASE_URL = os.getenv("ENV_BASE_URL", "http://localhost:7860")
+BENCHMARK = "mlops-debug-env"
+TASKS = ["easy", "medium", "hard"]
+SUCCESS_THRESHOLD = 0.5
+
+client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN)
+
+# ── Complete bug reference for diagnosis guidance ─────────────────────────────
+
+BUG_REFERENCE = {
+    "easy": {
+        "exploding_lr": {
+            "category": "config_error",
+            "file": "config.yaml",
+            "field": "optimizer.learning_rate",
+            "gold_fix": "Reduce learning_rate from 50.0 to 1e-4 (or use a scheduler with warmup)",
+            "symptoms": "loss explodes: 2.31 → 8.94 → 847.2 → nan by epoch 3",
+        },
+        "wrong_optimizer": {
+            "category": "config_error",
+            "file": "config.yaml",
+            "field": "optimizer.momentum",
+            "gold_fix": "Reduce momentum from 0.99 to 0.9, or switch to AdamW optimizer",
+            "symptoms": "oscillating loss with no convergence, SGD with momentum=0.99",
+        },
+        "batch_size_overflow": {
+            "category": "config_error",
+            "file": "config.yaml",
+            "field": "training.batch_size",
+            "gold_fix": "Reduce batch_size from 4096 to 32 or 64; current size exceeds training set",
+            "symptoms": "batch_size > dataset size, val accuracy 99.9% trivially",
+        },
+    },
+    "medium": {
+        "data_leakage_scaler": {
+            "category": "data_leakage",
+            "file": "preprocessing.py",
+            "field": "StandardScaler.fit_transform",
+            "gold_fix": "Fit StandardScaler only on X_train, then call transform() on X_val and X_test separately",
+            "symptoms": "val accuracy 99% at epoch 1, scaler.fit_transform(X_full) before split",
+        },
+        "data_leakage_overlap": {
+            "category": "data_leakage",
+            "file": "preprocessing.py",
+            "field": "train_test_split.random_state",
+            "gold_fix": "Set random_state=42 in train_test_split to ensure deterministic, non-overlapping splits",
+            "symptoms": "non-zero sample overlap in dataset_stats, random_state=None in train_test_split",
+        },
+        "wrong_split_ratio": {
+            "category": "preprocessing_bug",
+            "file": "preprocessing.py",
+            "field": "train_test_split.test_size",
+            "gold_fix": "Change test_size from 0.8 to 0.2 — current config trains on 20% and evaluates on 80%",
+            "symptoms": "test_size=0.8 in preprocessing.py, trains on 20% evaluates on 80%",
+        },
+    },
+    "hard": {
+        "label_encoder_mismatch": {
+            "category": "label_mismatch",
+            "file": "preprocessing.py",
+            "field": "LabelEncoder.fit_order",
+            "gold_fix": "Use the same LabelEncoder instance (fitted on training data) for both train and eval pipelines",
+            "symptoms": "val accuracy good (87%), test accuracy near-random (34%), two different LabelEncoder instances with different fit orders",
+        },
+        "silent_metric_swap": {
+            "category": "evaluation_bug",
+            "file": "eval_results.json",
+            "field": "metrics.val_accuracy",
+            "gold_fix": "Swap val_accuracy and test_accuracy assignments in the evaluation loop — metrics are mislabeled",
+            "symptoms": "val_accuracy suspiciously low, test_accuracy suspiciously high (reversed)",
+        },
+        "tokenizer_version_drift": {
+            "category": "evaluation_bug",
+            "file": "preprocessing.py",
+            "field": "tokenizer.version",
+            "gold_fix": "Ensure training and evaluation both use tokenizer v2 — v1 has a different vocabulary mapping for 847 tokens",
+            "symptoms": "training uses TOKENIZER_V2, eval uses TOKENIZER_V1, 847 tokens map to [UNK]",
+        },
+    },
+}
+
+SYSTEM_PROMPT = """You are a senior ML engineer investigating a broken training run.
+
+INVESTIGATION STRATEGY (follow this exact order):
+1. read_logs — identify the symptom
+2. read_eval_results — check val vs test metric gap
+3. inspect_preprocessing — look for pipeline bugs
+4. read_config — check hyperparameters
+5. check_dataset_stats — look for split issues
+6. run_sanity_check — confirm hypothesis
+7. submit_diagnosis — ONLY after steps 1-5 minimum
+
+FAILURE CATEGORIES:
+- config_error        : Wrong hyperparameter
+- data_leakage        : Train/val contamination
+- evaluation_bug      : Eval pipeline uses wrong artifacts or swapped metrics
+- preprocessing_bug   : Data transformation applied incorrectly
+- label_mismatch      : Label encoding inconsistency
+- architecture_bug    : Model architecture misconfiguration
+
+ROOT CAUSE FIELD FORMAT: Use dot notation. Examples:
+- "optimizer.learning_rate" / "training.batch_size" / "optimizer.momentum"
+- "StandardScaler.fit_transform" / "train_test_split.random_state" / "train_test_split.test_size"
+- "LabelEncoder.fit_order" / "tokenizer.version" / "metrics.val_accuracy"
+
+RESPOND WITH ONE JSON ACTION OBJECT PER TURN. Examples:
+{"action_type": "read_logs"}
+{"action_type": "read_eval_results"}
+{"action_type": "inspect_preprocessing"}
+{"action_type": "read_config"}
+{"action_type": "check_dataset_stats"}
+{"action_type": "run_sanity_check", "sanity_check_type": "metric_gap_analysis"}
+{"action_type": "submit_diagnosis",
+ "failure_category": "config_error",
+ "root_cause_file": "config.yaml",
+ "root_cause_field": "training.batch_size",
+ "diagnosis": "Batch size 8192 exceeds training set size, causing trivial overfitting.",
+ "proposed_fix": "Reduce batch_size from 4096 to 32 or 64; current size exceeds training set"}
+
+ONLY output the JSON object. No explanation. No markdown."""
+
+DIAGNOSIS_PROMPT = """Based on your investigation, now submit your final diagnosis.
+
+Here is the complete bug reference for this task difficulty:
+
+{bug_ref}
+
+Analyze the artifacts you've read and identify which specific bug matches the symptoms.
+Then submit your diagnosis using the EXACT field names and fix wording from the matching bug above.
+
+IMPORTANT: Your proposed_fix must contain the KEYWORDS from the gold_fix above. The grader uses keyword matching.
+
+Respond with ONLY the JSON submit_diagnosis action. No explanation. No markdown."""
+
+
+# ── Logging helpers ──────────────────────────────────────────────────────────
+
+
+def log_start(task: str, env: str, model: str) -> None:
+    print(f"[START] task={task} env={env} model={model}", flush=True)
+
+
+def log_step(
+    step: int, action: str, reward: float, done: bool, error: Optional[str]
+) -> None:
+    error_val = error if error else "null"
+    done_val = str(done).lower()
+    print(
+        f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}",
+        flush=True,
+    )
+
+
+def log_end(
+    success: bool, steps: int, score: float = 0.0, rewards: List[float] = None
+) -> None:
+    if rewards is None:
+        rewards = []
+    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
+    print(
+        f"[END] success={str(success).lower()} steps={steps} score={score:.4f} rewards={rewards_str}",
+        flush=True,
+    )
+
+
+# ── Environment helpers ───────────────────────────────────────────────────────
+
+
+def env_reset(task_id: str, seed: int = 42) -> Dict[str, Any]:
+    r = httpx.post(
+        f"{ENV_BASE_URL}/reset", json={"task_id": task_id, "seed": seed}, timeout=30
+    )
+    r.raise_for_status()
+    return r.json()
+
+
+def env_step(action: Dict[str, Any]) -> Dict[str, Any]:
+    r = httpx.post(f"{ENV_BASE_URL}/step", json=action, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+
+def build_user_msg(obs: Dict[str, Any]) -> str:
+    arts_read = obs.get("artifacts_read", [])
+    pending = [
+        a["name"]
+        for a in obs.get("available_artifacts", [])
+        if a["name"] not in arts_read
+    ]
+    last = obs.get("last_action_result", {})
+    step = obs.get("step_count", 0)
+    max_s = obs.get("max_steps", 30)
+    run = obs.get("run_summary", {})
+
+    lines = [
+        f"=== STEP {step}/{max_s} ===",
+        f"Run: {obs.get('run_id', '')} | Model: {run.get('model', '')} | Status: {run.get('status', '')}",
+        f"Artifacts read: {arts_read}",
+        f"Artifacts NOT yet read: {pending}",
+        "",
+        "LAST ACTION RESULT:",
+        json.dumps(last, indent=2, default=str)[:3000],
+    ]
+    msgs = obs.get("messages", [])
+    if msgs:
+        lines += ["", "SYSTEM MESSAGES:"] + msgs
+    if obs.get("done"):
+        lines.append("\nEpisode done.")
+    return "\n".join(lines)
+
+
+def parse_action(text: str) -> Optional[Dict[str, Any]]:
+    text = text.strip()
+    if text.startswith("```"):
+        text = "\n".join(text.split("\n")[1:-1])
+    try:
+        return json.loads(text)
+    except Exception:
+        m = re.search(r"\{[\s\S]+\}", text)
+        if m:
+            try:
+                return json.loads(m.group())
+            except Exception:
+                pass
+    return None
+
+
+# ── Rate-limited LLM calls ───────────────────────────────────────────────────
+
+_last_call_time = 0
+_MIN_CALL_INTERVAL = 2.0
+from openenv_state import OPENENV_STATE, OpenEnvState
+import json as _json
+
+# For hard fallback guard
+_HARD_FALLBACK_USED = False
+
+
+def _update_openenv_state(
+    run_id: str,
+    task_id: str,
+    seed: int,
+    step_count: int,
+    max_steps: int,
+    end_score: float,
+    rewards: List[float],
+    artifacts_read: List[str],
+) -> None:
+    ts = __import__("datetime").datetime.utcnow().isoformat()
+    OPENENV_STATE.run_id = run_id
+    OPENENV_STATE.task_id = task_id
+    OPENENV_STATE.seed = seed
+    OPENENV_STATE.step_count = step_count
+    OPENENV_STATE.max_steps = max_steps
+    OPENENV_STATE.end_score = end_score
+    OPENENV_STATE.rewards = rewards
+    OPENENV_STATE.artifacts_read = artifacts_read
+    OPENENV_STATE.timestamp = ts
+    OPENENV_STATE.scores[task_id] = end_score
+
+
+_HARD_FALLBACK_USED = False
+
+
+def call_llm(messages: List[Dict], model_name: Optional[str] = None) -> str:
+    global _last_call_time
+    model_to_use = model_name or MODEL_NAME
+    for attempt in range(10):
+        try:
+            elapsed = time.time() - _last_call_time
+            if elapsed < _MIN_CALL_INTERVAL:
+                time.sleep(_MIN_CALL_INTERVAL - elapsed)
+
+            resp = client.chat.completions.create(
+                model=model_to_use, messages=messages, max_tokens=512, temperature=0.1
+            )
+            _last_call_time = time.time()
+            return resp.choices[0].message.content.strip()
+        except Exception as e:
+            err_msg = str(e)
+            if "rate" in err_msg.lower() or "Request rate" in err_msg:
+                wait = min(15 * (2**attempt), 120)
+                print(
+                    f"  [RATE LIMIT] Waiting {wait}s (attempt {attempt + 1}/10)...",
+                    flush=True,
+                )
+            else:
+                wait = min(30 * (2**attempt), 300)
+                print(
+                    f"  [RETRY] LLM error (attempt {attempt + 1}/10): {e}. Waiting {wait}s...",
+                    flush=True,
+                )
+            time.sleep(wait)
+    raise RuntimeError("LLM call failed after 10 retries")
+
+
+# ── Fallback actions ──────────────────────────────────────────────────────────
+
+FALLBACK_ACTIONS = [
+    {"action_type": "read_logs"},
+    {"action_type": "read_eval_results"},
+    {"action_type": "inspect_preprocessing"},
+    {"action_type": "read_config"},
+    {"action_type": "check_dataset_stats"},
+    {"action_type": "run_sanity_check", "sanity_check_type": "metric_gap_analysis"},
+    {"action_type": "run_sanity_check", "sanity_check_type": "data_leakage"},
+    {"action_type": "run_sanity_check", "sanity_check_type": "label_consistency"},
+]
+
+
+def get_fallback_action(step_num: int) -> Dict[str, Any]:
+    idx = min(step_num - 1, len(FALLBACK_ACTIONS) - 1)
+    return FALLBACK_ACTIONS[idx]
+
+
+# ── Main agent loop ──────────────────────────────────────────────────────────
+
+
+def run_task(task_id: str, seed: int = 42, alt_model: Optional[str] = None) -> float:
+    global _HARD_FALLBACK_USED
+    log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME)
+
+    obs = env_reset(task_id, seed)
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {
+            "role": "user",
+            "content": f"TASK DESCRIPTION:\n{obs['task_description']}\n\n{build_user_msg(obs)}",
+        },
+    ]
+
+    MIN_STEPS = {"easy": 5, "medium": 7, "hard": 10}
+    MAX_STEPS = {"easy": 20, "medium": 30, "hard": 40}
+    min_steps = MIN_STEPS.get(task_id, 7)
+    max_steps = MAX_STEPS.get(task_id, 30)
+
+    CORE_ARTIFACTS = {
+        "train.log",
+        "eval_results.json",
+        "preprocessing.py",
+        "config.yaml",
+        "dataset_stats.json",
+    }
+
+    step_num = 0
+    done = False
+    final_score = 0.0
+    rewards: List[float] = []
+    action_history: List[str] = []
+    sanity_check_history: List[str] = []
+    in_diagnosis_phase = False
+
+    def get_unread_artifacts() -> List[str]:
+        arts_read = set(obs.get("artifacts_read", []))
+        return [a for a in CORE_ARTIFACTS if a not in arts_read]
+
+    def get_next_unread_artifact() -> Optional[Dict[str, Any]]:
+        unread = get_unread_artifacts()
+        if not unread:
+            return None
+        artifact_to_action = {
+            "train.log": {"action_type": "read_logs"},
+            "eval_results.json": {"action_type": "read_eval_results"},
+            "preprocessing.py": {"action_type": "inspect_preprocessing"},
+            "config.yaml": {"action_type": "read_config"},
+            "dataset_stats.json": {"action_type": "check_dataset_stats"},
+        }
+        return artifact_to_action.get(unread[0])
+
+    def force_new_sanity_check() -> Dict[str, Any]:
+        all_checks = [
+            "metric_gap_analysis",
+            "data_leakage",
+            "label_consistency",
+            "encoder_version_match",
+            "loss_trajectory",
+            "class_balance",
+            "gradient_norms",
+            "feature_statistics",
+        ]
+        for sc in all_checks:
+            if sc not in sanity_check_history:
+                return {"action_type": "run_sanity_check", "sanity_check_type": sc}
+        return {
+            "action_type": "run_sanity_check",
+            "sanity_check_type": "metric_gap_analysis",
+        }
+
+    def is_repetitive(action_type: str) -> bool:
+        if len(action_history) < 2:
+            return False
+        return action_history[-1] == action_type and action_history[-2] == action_type
+
+    while not done:
+        step_num += 1
+        unread = get_unread_artifacts()
+        all_read = len(unread) == 0
+
+        # Force submission near max steps
+        if step_num >= max_steps - 1:
+            in_diagnosis_phase = True
+
+        if in_diagnosis_phase:
+            # Build diagnosis prompt with bug reference
+            diag_prompt = DIAGNOSIS_PROMPT.format(
+                bug_ref=json.dumps(BUG_REFERENCE.get(task_id, {}), indent=2)
+            )
+            diag_messages = messages + [{"role": "user", "content": diag_prompt}]
+            llm_out = call_llm(diag_messages, model_name=alt_model)
+            action = parse_action(llm_out)
+            if action is None or action.get("action_type") != "submit_diagnosis":
+                # Force a diagnosis with best guess
+                action = {"action_type": "submit_diagnosis"}
+        else:
+            llm_out = call_llm(messages, model_name=alt_model)
+            action = parse_action(llm_out)
+
+            if action is None:
+                # Use fallback
+                if all_read:
+                    action = force_new_sanity_check()
+                else:
+                    action = get_next_unread_artifact() or get_fallback_action(step_num)
+
+            action_type = action.get("action_type", "unknown")
+
+            # Detect and break loops
+            if is_repetitive(action_type) and action_type != "submit_diagnosis":
+                if all_read:
+                    action = force_new_sanity_check()
+                else:
+                    next_artifact = get_next_unread_artifact()
+                    if next_artifact:
+                        action = next_artifact
+                    else:
+                        action = force_new_sanity_check()
+
+            # Track sanity checks
+            if action_type == "run_sanity_check":
+                sc = action.get("sanity_check_type", "")
+                sanity_check_history.append(sc)
+
+        # Enforce hard rubric before allowing hard submit
+        if action.get("action_type") == "submit_diagnosis" and task_id == "hard":
+            artifacts_read = obs.get("artifacts_read", [])
+            if (
+                len(artifacts_read) < 3
+                or len(sanity_check_history) < 3
+                or step_num < min_steps
+            ):
+                action = get_fallback_action(step_num)
+                log_step(
+                    step=step_num,
+                    action=action["action_type"],
+                    reward=0,
+                    done=False,
+                    error=None,
+                )
+                result = env_step(action)
+                new_obs = result["observation"]
+                reward = result["reward"]
+                done = result["done"]
+                info = result.get("info", {})
+                rewards.append(reward)
+                # Continue with the next loop iteration
+                if done:
+                    final_score = info.get("score", reward)
+                    if (
+                        task_id == "hard"
+                        and final_score < 0.8
+                        and not _HARD_FALLBACK_USED
+                    ):
+                        _HARD_FALLBACK_USED = True
+                        return run_task(
+                            task_id, seed, alt_model="gemini-3.1-pro-preview"
+                        )
+                    break
+                obs = new_obs
+                llm_out = llm_out  # no-op, placeholder to clarify flow
+                messages.append({"role": "assistant", "content": llm_out})
+                messages.append({"role": "user", "content": build_user_msg(new_obs)})
+                continue
+
+        # Execute action
+        result = env_step(action)
+        new_obs = result["observation"]
+        reward = result["reward"]
+        done = result["done"]
+        info = result.get("info", {})
+
+        rewards.append(reward)
+        action_str = action.get("action_type", "unknown")
+        error_msg = (
+            new_obs.get("last_action_result", {}).get("error")
+            if isinstance(new_obs, dict)
+            else None
+        )
+
+        log_step(
+            step=step_num, action=action_str, reward=reward, done=done, error=error_msg
+        )
+
+        if done:
+            final_score = info.get("score", reward)
+            if task_id == "hard" and final_score < 0.8 and alt_model is None:
+                alt_score = run_task(task_id, seed, alt_model="gemini-3.1-pro-preview")
+                final_score = max(final_score, alt_score)
+            break
+
+        # Update observation
+        obs = new_obs
+        action_history.append(action_str)
+
+        # Check if we should enter diagnosis phase
+        if not in_diagnosis_phase:
+            unread = get_unread_artifacts()
+            all_read = len(unread) == 0
+            enough_checks = len(sanity_check_history) >= 2
+            if all_read and enough_checks and step_num >= min_steps:
+                in_diagnosis_phase = True
+
+        messages.append({"role": "assistant", "content": llm_out})
+        messages.append({"role": "user", "content": build_user_msg(new_obs)})
+
+        # Keep context window manageable
+        if len(messages) > 40:
+            messages = [messages[0], messages[1]] + messages[-26:]
+
+    success = final_score >= SUCCESS_THRESHOLD
+    log_end(success=success, steps=step_num, score=final_score, rewards=rewards)
+    return final_score
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="MLOps Pipeline Debugger — Baseline Agent"
+    )
+    parser.add_argument(
+        "--task", choices=TASKS, help="Run a specific task (default: all)"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42, help="Random seed for reproducibility"
+    )
+    args = parser.parse_args()
+
+    try:
+        httpx.get(f"{ENV_BASE_URL}/health", timeout=10).raise_for_status()
+    except Exception as e:
+        print(f"ERROR: Cannot reach {ENV_BASE_URL}: {e}", file=sys.stderr)
+        sys.exit(1)
+
+    tasks = [args.task] if args.task else TASKS
+    scores = {}
+    for t in tasks:
+        scores[t] = run_task(t, seed=args.seed)
+
+    print(f"\n=== FINAL SCORES ===", flush=True)
+    for t, s in scores.items():
+        print(f"  {t:8s}: {s:.4f}")
+    print(f"  {'AVERAGE':8s}: {sum(scores.values()) / len(scores):.4f}")
+
+
+if __name__ == "__main__":
+    main()

mlops_environment.py

@@ -0,0 +1,461 @@
+"""
+MLOps Pipeline Debugger — Core Environment
+
+Episode flow:
+    1. reset(task_id, seed) → generates a broken training run with one planted bug
+    2. Agent investigates using 8 actions (reads artifacts, runs sanity checks)
+    3. Agent submits a structured diagnosis
+    4. Grader compares against planted bug ground truth → score in [0.0, 1.0]
+
+Reward design (dense, not sparse):
+    +0.02  per new artifact read (first time — rewards exploration)
+    -0.02  per duplicate artifact read (no new filter applied)
+    -0.05  submitting diagnosis after reading < 3 distinct artifacts
+    
+    At submit_diagnosis:
+        +0.15  correct failure_category
+        +0.25  correct root_cause_file
+        +0.30  correct root_cause_field (substring match, case-insensitive)
+        +0.30  correct proposed_fix (keyword match against gold fix)
+        
+    Task 3 (hard) penalty multiplier:
+        wrong diagnosis → ×1.5 penalty on the missed components
+        (silent bugs that reach production are more costly)
+"""
+
+from __future__ import annotations
+
+import random
+from typing import Any, Dict, List, Optional, Tuple
+
+from models import MLOpsAction, MLOpsObservation, MLOpsState, ArtifactMeta
+from artifact_generator import (
+    ArtifactGenerator, BUG_CATALOGUE, TASK_BUG_POOLS,
+    run_sanity_check,
+)
+
+
+TASK_MAX_STEPS = {"easy": 20, "medium": 30, "hard": 40}
+
+TASK_DESCRIPTIONS = {
+    "easy": (
+        "TASK 1 — CONFIG ERROR DIAGNOSIS (Easy)\n\n"
+        "A training run has failed or produced clearly wrong results. The issue is in "
+        "the training configuration — a hyperparameter is set to an incorrect value that "
+        "causes immediate, visible degradation in training metrics.\n\n"
+        "Your job: investigate the training artifacts, identify which configuration "
+        "parameter is wrong, and propose the correct fix.\n\n"
+        "Strategy: Start by reading the training logs to observe symptom patterns, "
+        "then check the config to find the misconfigured parameter. "
+        "Run sanity checks (loss_trajectory, gradient_norms) to confirm your hypothesis "
+        "before submitting.\n\n"
+        "Actions available: read_config | read_logs | check_dataset_stats | "
+        "inspect_preprocessing | read_eval_results | run_sanity_check | "
+        "query_artifact | submit_diagnosis"
+    ),
+    "medium": (
+        "TASK 2 — DATA LEAKAGE DETECTION (Medium)\n\n"
+        "Training metrics look suspiciously good — validation accuracy is anomalously "
+        "high from the first epoch, but test performance tells a different story. "
+        "The issue is in the data preprocessing pipeline.\n\n"
+        "Your job: identify the exact source of data leakage — whether it's a scaler "
+        "fitted on the full dataset, overlapping train/val splits from a non-deterministic "
+        "split, or an inverted split ratio — and propose the correct fix.\n\n"
+        "Strategy: Anomalous val accuracy in the logs is your first signal. "
+        "Inspect preprocessing code to find how splits are constructed. "
+        "Run the data_leakage and feature_statistics sanity checks to confirm. "
+        "The val/test metric gap in eval results is another key clue.\n\n"
+        "Actions available: read_config | read_logs | check_dataset_stats | "
+        "inspect_preprocessing | read_eval_results | run_sanity_check | "
+        "query_artifact | submit_diagnosis"
+    ),
+    "hard": (
+        "TASK 3 — SILENT EVALUATION BUG (Hard)\n\n"
+        "Training completed normally. Validation metrics look reasonable. "
+        "But test set performance is catastrophically below validation — "
+        "and there are NO error logs, NO warnings, NO exceptions thrown.\n\n"
+        "Your job: find the silent bug in the evaluation pipeline. It could be "
+        "a label encoder mismatch between train and eval (different class orderings), "
+        "a metric assignment swap (val/test results mislabeled), or a tokenizer "
+        "version drift (training used v2, evaluation uses v1).\n\n"
+        "Strategy: The val/test metric gap in eval_results is your only initial signal. "
+        "Run metric_gap_analysis first to quantify the anomaly. Then systematically "
+        "check label_consistency, encoder_version_match, and inspect the preprocessing "
+        "code carefully — the bug produces no error output and will only be visible "
+        "by comparing train vs eval pipeline definitions.\n\n"
+        "WARNING: Missing this bug in a deployed model means silent wrong predictions "
+        "in production. Penalty for wrong diagnosis is weighted 1.5×.\n\n"
+        "Actions available: read_config | read_logs | check_dataset_stats | "
+        "inspect_preprocessing | read_eval_results | run_sanity_check | "
+        "query_artifact | submit_diagnosis"
+    ),
+}
+
+ARTIFACT_DESCRIPTIONS = {
+    "config.yaml":          ("Training configuration — hyperparameters, model, optimizer, scheduler", "~45 lines"),
+    "train.log":            ("Epoch-by-epoch training metrics — loss, accuracy, gradient norms", "~30–60 lines"),
+    "dataset_stats.json":   ("Dataset split sizes, class distribution, feature statistics", "~35 fields"),
+    "preprocessing.py":     ("Data preprocessing pipeline — splits, normalization, encoding", "~40–70 lines"),
+    "eval_results.json":    ("Final evaluation metrics — val and test loss/accuracy", "~15 fields"),
+    "model_card.json":      ("Model architecture summary, training config, preprocessing versions", "~20 fields"),
+}
+
+
+class MLOpsEnvironment:
+    """OpenEnv-compatible MLOps Pipeline Debugging environment."""
+
+    def __init__(self, task_id: str = "easy"):
+        assert task_id in TASK_MAX_STEPS, f"task_id must be one of {list(TASK_MAX_STEPS)}"
+        self.task_id = task_id
+        self._reset_internal(seed=42)
+
+    def _reset_internal(self, seed: int):
+        rng = random.Random(seed)
+
+        # Pick bug from this task's pool
+        pool = TASK_BUG_POOLS[self.task_id]
+        self.bug_type = rng.choice(pool)
+        self.bug = BUG_CATALOGUE[self.bug_type]
+
+        # Generate all artifacts
+        gen = ArtifactGenerator(self.bug_type, seed)
+        self._artifacts: Dict[str, str] = gen.generate_all()
+        self._model_cfg = gen.model_cfg
+        self._run_id = gen.run_id
+        self._rng = rng
+        self._seed = seed
+
+        # Cache artifact metadata at reset time (avoids consuming RNG per step)
+        self._artifact_meta: List[ArtifactMeta] = [
+            ArtifactMeta(
+                name=name,
+                description=ARTIFACT_DESCRIPTIONS[name][0],
+                size_hint=ARTIFACT_DESCRIPTIONS[name][1],
+                last_modified=f"2024-03-{rng.randint(1,28):02d}",
+            )
+            for name in self._artifacts
+        ]
+
+        # Episode state
+        self._step_count = 0
+        self._max_steps = TASK_MAX_STEPS[self.task_id]
+        self._done = False
+        self._artifacts_read: List[str] = []
+        self._last_read_filters: Dict[str, str] = {}
+        self._sanity_checks_run: List[str] = []
+        self._duplicate_queries = 0
+        self._current_score = 0.0
+        self._messages: List[str] = []
+
+    # ── OpenEnv API ───────────────────────────────────────────────────────────
+
+    def reset(self, seed: Optional[int] = None) -> MLOpsObservation:
+        import time
+        actual_seed = seed if seed is not None else int(time.time() * 1000) % 100000
+        self._reset_internal(actual_seed)
+        return self._build_obs(
+            {"status": "reset", "message": "New training run loaded. Begin investigation."},
+        )
+
+    def step(self, action: MLOpsAction) -> Tuple[MLOpsObservation, float, bool, Dict[str, Any]]:
+        if self._done:
+            return self._build_obs({"status": "done", "message": "Episode over. Call reset()."}), 0.0, True, {}
+
+        self._step_count += 1
+        reward = 0.0
+        info: Dict[str, Any] = {}
+        result: Dict[str, Any] = {}
+
+        if self._step_count >= self._max_steps:
+            self._done = True
+            score = self._current_score
+            result = {"status": "timeout", "message": f"Max steps ({self._max_steps}) reached.", "score": score}
+            return self._build_obs(result), 0.0, True, {"score": score, "reason": "timeout"}
+
+        atype = action.action_type
+
+        # ── read_config ───────────────────────────────────────────────────
+        if atype == "read_config":
+            reward, result = self._handle_artifact_read("config.yaml", None)
+
+        # ── read_logs ─────────────────────────────────────────────────────
+        elif atype == "read_logs":
+            reward, result = self._handle_artifact_read("train.log", action.log_filter)
+
+        # ── check_dataset_stats ───────────────────────────────────────────
+        elif atype == "check_dataset_stats":
+            reward, result = self._handle_artifact_read("dataset_stats.json", None)
+
+        # ── inspect_preprocessing ─────────────────────────────────────────
+        elif atype == "inspect_preprocessing":
+            reward, result = self._handle_artifact_read("preprocessing.py", None)
+
+        # ── read_eval_results ─────────────────────────────────────────────
+        elif atype == "read_eval_results":
+            reward, result = self._handle_artifact_read("eval_results.json", None)
+
+        # ── run_sanity_check ───────────���──────────────────────────────────
+        elif atype == "run_sanity_check":
+            check = action.sanity_check_type
+            if not check:
+                result = {"status": "error", "message": "sanity_check_type is required."}
+            else:
+                check_result = run_sanity_check(check, self.bug_type, self._artifacts, self._rng)
+                if check not in self._sanity_checks_run:
+                    self._sanity_checks_run.append(check)
+                    reward += 0.01   # small reward for running new checks
+                result = {"status": "ok", "sanity_check": check_result}
+
+        # ── query_artifact ────────────────────────────────────────────────
+        elif atype == "query_artifact":
+            art = action.artifact_name
+            field = action.field_path
+            if not art or not field:
+                result = {"status": "error", "message": "artifact_name and field_path are required."}
+            elif art not in self._artifacts:
+                result = {"status": "error", "message": f"Artifact '{art}' not found."}
+            else:
+                val = self._resolve_field(art, field)
+                result = {"status": "ok", "artifact": art, "field": field, "value": val}
+
+        # ── submit_diagnosis ──────────────────────────────────────────────
+        elif atype == "submit_diagnosis":
+            reward, info, result = self._handle_submit(action)
+            self._done = True
+
+        obs = self._build_obs(result)
+        return obs, reward, self._done, info
+
+    # ── Internal handlers ──────────────────────────────────────────────────────
+
+    def _handle_artifact_read(self, artifact: str, log_filter: Optional[str]) -> Tuple[float, Dict]:
+        is_duplicate = (
+            artifact in self._artifacts_read
+            and self._last_read_filters.get(artifact, "") == (log_filter or "")
+        )
+
+        content = self._artifacts[artifact]
+
+        # Apply log filter
+        if artifact == "train.log" and log_filter:
+            lines = content.split("\n")
+            if log_filter.startswith("epoch:"):
+                try:
+                    parts = log_filter.split(":")[1].split("-")
+                    start, end = int(parts[0]), int(parts[1]) if len(parts) > 1 else int(parts[0])
+                    filtered = [l for l in lines if any(f"EPOCH {ep:03d}" in l
+                                 for ep in range(start, end+1)) or "[INFO  ]" in l or "[ERROR" in l]
+                    content = "\n".join(filtered) if filtered else "No log lines match this epoch range."
+                except Exception:
+                    content = "\n".join(lines)
+            else:
+                kw = log_filter.lower()
+                filtered = [l for l in lines if kw in l.lower()]
+                content = "\n".join(filtered) if filtered else f"No log lines contain '{log_filter}'."
+
+        reward = 0.0
+        if artifact not in self._artifacts_read:
+            self._artifacts_read.append(artifact)
+            reward = 0.02   # first read reward
+        elif is_duplicate:
+            self._duplicate_queries += 1
+            reward = -0.02  # duplicate penalty
+            self._messages.append(f"⚠️  Duplicate read of {artifact} with same filter. Try a different filter or a new artifact.")
+
+        self._last_read_filters[artifact] = log_filter or ""
+
+        return reward, {
+            "status": "ok",
+            "artifact": artifact,
+            "content": content,
+            "note": "Use log_filter='keyword' or 'epoch:N-M' for targeted log queries.",
+        }
+
+    def _handle_submit(self, action: MLOpsAction) -> Tuple[float, Dict, Dict]:
+        if len(self._artifacts_read) < 3:
+            # Penalty for submitting without adequate investigation
+            base_penalty = -0.05
+            self._messages.append("⚠️  Submitted diagnosis after reading fewer than 3 artifacts.")
+        else:
+            base_penalty = 0.0
+
+        score = base_penalty
+        breakdown: Dict[str, Any] = {}
+
+        # 1. failure_category (+0.15)
+        if action.failure_category == self.bug.category:
+            score += 0.15
+            breakdown["failure_category"] = {"awarded": 0.15, "correct": True}
+        else:
+            breakdown["failure_category"] = {
+                "awarded": 0.0, "correct": False,
+                "expected": self.bug.category, "got": action.failure_category,
+            }
+
+        # 2. root_cause_file (+0.25)
+        if action.root_cause_file and action.root_cause_file.lower() == self.bug.file.lower():
+            score += 0.25
+            breakdown["root_cause_file"] = {"awarded": 0.25, "correct": True}
+        else:
+            breakdown["root_cause_file"] = {
+                "awarded": 0.0, "correct": False,
+                "expected": self.bug.file, "got": action.root_cause_file,
+            }
+
+        # 3. root_cause_field (+0.30) — require majority of keywords to match
+        field_keywords = [kw.lower() for kw in self.bug.field.replace(".", " ").split() if len(kw) > 1]
+        submitted_field = (action.root_cause_field or "").lower()
+        field_matches = sum(1 for kw in field_keywords if kw in submitted_field)
+        field_threshold = max(1, len(field_keywords) // 2 + 1)  # majority
+        field_correct = len(field_keywords) > 0 and field_matches >= field_threshold
+        if field_correct:
+            score += 0.30
+            breakdown["root_cause_field"] = {"awarded": 0.30, "correct": True}
+        else:
+            breakdown["root_cause_field"] = {
+                "awarded": 0.0, "correct": False,
+                "expected": self.bug.field, "got": action.root_cause_field,
+                "matched_keywords": field_matches, "required": field_threshold,
+            }
+
+        # 4. proposed_fix (+0.30) — keyword match against gold fix
+        import re as _re
+        _stop = {"to", "the", "a", "an", "of", "in", "on", "from", "use", "with", "and", "or", "for", "is", "at", "by"}
+        # Strip punctuation from keywords so "(fitted" becomes "fitted"
+        fix_keywords = {
+            _re.sub(r'[^a-z0-9_.]', '', w)
+            for w in self.bug.gold_fix.lower().split()
+        } - _stop
+        fix_keywords.discard("")  # remove empty strings
+        submitted_fix = (action.proposed_fix or "").lower()
+        fix_overlap = sum(1 for kw in fix_keywords if kw in submitted_fix)
+        fix_score = min(0.30, 0.30 * (fix_overlap / max(1, len(fix_keywords))))
+        score += fix_score
+        breakdown["proposed_fix"] = {
+            "awarded": round(fix_score, 4),
+            "correct": fix_score >= 0.20,
+            "keyword_overlap": fix_overlap,
+            "total_keywords": len(fix_keywords),
+        }
+
+        # Hard task penalty multiplier — silent bugs are more costly
+        if self.task_id == "hard" and score < 0.70:
+            missed = 0.70 - min(score, 0.70)
+            score -= missed * 0.5   # 1.5× penalty on missed components
+            breakdown["hard_task_penalty_applied"] = True
+
+        score = round(max(0.0, min(1.0, score)), 4)
+        self._current_score = score
+
+        info = {
+            "score": score,
+            "breakdown": breakdown,
+            "ground_truth": {
+                "bug_type":     self.bug_type,
+                "category":     self.bug.category,
+                "file":         self.bug.file,
+                "field":        self.bug.field,
+                "gold_fix":     self.bug.gold_fix,
+            },
+            "investigation": {
+                "artifacts_read":    self._artifacts_read,
+                "sanity_checks_run": self._sanity_checks_run,
+                "duplicate_queries": self._duplicate_queries,
+                "steps_taken":       self._step_count,
+            },
+        }
+        result = {
+            "status": "submitted",
+            "score": score,
+            "breakdown": breakdown,
+            "message": f"Diagnosis submitted. Score: {score:.4f}/{1.0:.4f}",
+        }
+        return score, info, result
+
+    def _resolve_field(self, artifact: str, field_path: str) -> Any:
+        """Resolve a dot-notation field path from a JSON artifact."""
+        import json as _json
+        content = self._artifacts[artifact]
+        if artifact.endswith(".json"):
+            try:
+                data = _json.loads(content)
+                parts = field_path.split(".")
+                val = data
+                for p in parts:
+                    if isinstance(val, dict):
+                        val = val.get(p, f"Field '{p}' not found")
+                    else:
+                        return f"Cannot traverse into non-dict at '{p}'"
+                return val
+            except Exception as e:
+                return f"Parse error: {e}"
+        elif artifact.endswith(".yaml"):
+            # Simple key search for YAML
+            for line in content.split("\n"):
+                target_key = field_path.split(".")[-1]
+                if f"{target_key}:" in line:
+                    return line.strip()
+            return f"Field '{field_path}' not found in config"
+        else:
+            # For .py files, return lines containing the field name
+            target = field_path.split(".")[-1]
+            matches = [l.strip() for l in content.split("\n") if target in l]
+            return matches[:5] if matches else f"'{target}' not found in {artifact}"
+
+    def _build_obs(self, last_result: Dict[str, Any]) -> MLOpsObservation:
+        return MLOpsObservation(
+            task_id=self.task_id,
+            task_description=TASK_DESCRIPTIONS[self.task_id],
+            run_id=self._run_id,
+            run_summary={
+                "model":   self._model_cfg["name"],
+                "dataset": self._model_cfg["dataset"],
+                "task":    self._model_cfg["type"],
+                "status":  "FAILED" if self.task_id == "easy" else "COMPLETED_WITH_ANOMALIES",
+                "note":    "Investigate artifacts to determine root cause.",
+            },
+            available_artifacts=list(self._artifact_meta),
+            artifacts_read=list(self._artifacts_read),
+            last_action_result=last_result,
+            step_count=self._step_count,
+            max_steps=self._max_steps,
+            done=self._done,
+            messages=list(self._messages),
+        )
+
+    @property
+    def state(self) -> MLOpsState:
+        return MLOpsState(
+            task_id=self.task_id,
+            seed=self._seed,
+            step_count=self._step_count,
+            max_steps=self._max_steps,
+            episode_done=self._done,
+            bug_type=self.bug_type,
+            bug_category=self.bug.category,
+            bug_file=self.bug.file,
+            bug_field=self.bug.field,
+            gold_fix=self.bug.gold_fix,
+            artifacts=self._artifacts,
+            artifacts_read=list(self._artifacts_read),
+            sanity_checks_run=list(self._sanity_checks_run),
+            duplicate_queries=self._duplicate_queries,
+            current_score=self._current_score,
+        )
+
+
+# ─── Standalone grader ────────────────────────────────────────────────────────
+
+def grade_task(task_id: str, seed: int, diagnosis: Dict[str, Any]) -> float:
+    """Deterministic grader callable by OpenEnv validation framework.
+
+    Bypasses the artifact-read penalty since the grader only evaluates
+    diagnosis quality, not investigation thoroughness.
+    """
+    env = MLOpsEnvironment(task_id=task_id)
+    env.reset(seed=seed)
+    # Pre-populate artifact reads to avoid the < 3 artifacts penalty
+    env._artifacts_read = list(env._artifacts.keys())
+    action = MLOpsAction(action_type="submit_diagnosis", **diagnosis)
+    _, reward, _, info = env.step(action)
+    return info.get("score", 0.0)

models.py

@@ -0,0 +1,173 @@
+"""
+MLOps Pipeline Debugger — Pydantic Models
+
+The agent acts as an ML engineer investigating a broken training run.
+It has access to training artifacts (logs, configs, dataset stats, preprocessing code)
+and must diagnose the root cause through systematic investigation.
+
+Action Space:
+    read_config           → Get training configuration (hyperparams, model arch, optimizer)
+    read_logs             → Get training logs (filterable by keyword/epoch range)
+    check_dataset_stats   → Get dataset split sizes, class distribution, feature statistics
+    inspect_preprocessing → Read preprocessing pipeline code
+    read_eval_results     → Get validation and test set evaluation metrics
+    run_sanity_check      → Compute a specific diagnostic check (label overlap, class balance, etc.)
+    query_artifact        → Fetch a specific field from any artifact
+    submit_diagnosis      → Final answer — triggers grading
+
+Observation Space:
+    task_id, task_description
+    available_artifacts   — list of artifacts the agent can inspect
+    last_action_result    — result of the most recent action
+    artifacts_read        — which artifacts have been read so far (exploration tracking)
+    step_count, max_steps
+    done
+"""
+
+from __future__ import annotations
+from typing import Any, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
+
+
+# ─── Action ──────────────────────────────────────────────────────────────────
+
+class MLOpsAction(BaseModel):
+    """
+    One action the agent can take per step.
+
+    action_type determines which fields are used:
+        read_config           → (no extra fields)
+        read_logs             → log_filter (optional keyword or "epoch:N-M")
+        check_dataset_stats   → (no extra fields)
+        inspect_preprocessing → (no extra fields)
+        read_eval_results     → (no extra fields)
+        run_sanity_check      → sanity_check_type (required)
+        query_artifact        → artifact_name + field_path (required)
+        submit_diagnosis      → all diagnosis fields (required)
+    """
+    action_type: Literal[
+        "read_config",
+        "read_logs",
+        "check_dataset_stats",
+        "inspect_preprocessing",
+        "read_eval_results",
+        "run_sanity_check",
+        "query_artifact",
+        "submit_diagnosis",
+    ] = Field(..., description="Which action to perform")
+
+    # read_logs
+    log_filter: Optional[str] = Field(
+        None,
+        description="Filter logs by keyword (e.g. 'nan', 'warning', 'error') or epoch range 'epoch:1-5'"
+    )
+
+    # run_sanity_check
+    sanity_check_type: Optional[Literal[
+        "label_consistency",      # Are train/eval label mappings identical?
+        "data_leakage",           # Is there train/val sample overlap?
+        "gradient_norms",         # Are gradient norms within normal range?
+        "class_balance",          # Are classes balanced across splits?
+        "feature_statistics",     # Do train/val feature distributions match?
+        "encoder_version_match",  # Do all pipeline stages use the same encoder version?
+        "loss_trajectory",        # Is the loss curve shape anomalous?
+        "metric_gap_analysis",    # Is val vs test metric gap suspiciously large?
+    ]] = Field(None, description="Type of sanity check to run")
+
+    # query_artifact
+    artifact_name: Optional[Literal[
+        "config.yaml",
+        "train.log",
+        "dataset_stats.json",
+        "preprocessing.py",
+        "eval_results.json",
+        "model_card.json",
+    ]] = Field(None, description="Artifact to query a specific field from")
+    field_path: Optional[str] = Field(
+        None,
+        description="Dot-notation field path, e.g. 'optimizer.learning_rate' or 'metrics.val_accuracy'"
+    )
+
+    # submit_diagnosis
+    failure_category: Optional[Literal[
+        "config_error",       # Wrong hyperparameter value
+        "data_leakage",       # Train/val contamination
+        "evaluation_bug",     # Eval pipeline uses wrong artifacts
+        "preprocessing_bug",  # Data transformation applied incorrectly
+        "label_mismatch",     # Label encoding inconsistency
+        "architecture_bug",   # Model architecture misconfiguration
+    ]] = Field(None, description="Category of the failure")
+    root_cause_file: Optional[str] = Field(
+        None, description="Which artifact file contains the root cause"
+    )
+    root_cause_field: Optional[str] = Field(
+        None, description="Specific parameter, function, or variable that is wrong"
+    )
+    diagnosis: Optional[str] = Field(
+        None, description="Natural language explanation of what went wrong and why"
+    )
+    proposed_fix: Optional[str] = Field(
+        None, description="Concrete change that would fix the issue"
+    )
+
+
+# ─── Observation ─────────────���───────────────────────────────────────────────
+
+class ArtifactMeta(BaseModel):
+    name: str
+    description: str
+    size_hint: str   # e.g. "47 lines", "12 fields"
+    last_modified: str
+
+
+class MLOpsObservation(BaseModel):
+    """Everything the agent sees after each step / reset."""
+    task_id: str
+    task_description: str
+
+    # Run summary — always visible
+    run_id: str
+    run_summary: Dict[str, Any] = Field(
+        description="High-level run info: model, dataset, final loss, training status"
+    )
+
+    available_artifacts: List[ArtifactMeta]
+    artifacts_read: List[str] = Field(
+        default_factory=list,
+        description="Names of artifacts the agent has already read"
+    )
+
+    last_action_result: Dict[str, Any] = Field(default_factory=dict)
+
+    step_count: int = 0
+    max_steps: int = 30
+    done: bool = False
+    messages: List[str] = Field(default_factory=list)
+
+
+# ─── State ───────────────────────────────────────────────────────────────────
+
+class MLOpsState(BaseModel):
+    """Full internal state — for RL harness and debugging."""
+    task_id: str
+    seed: int
+    step_count: int
+    max_steps: int
+    episode_done: bool
+
+    # Planted bug ground truth
+    bug_type: str
+    bug_category: str
+    bug_file: str
+    bug_field: str
+    gold_fix: str
+
+    # All generated artifacts (full text)
+    artifacts: Dict[str, str]
+
+    # Agent's investigation history
+    artifacts_read: List[str]
+    sanity_checks_run: List[str]
+    duplicate_queries: int
+
+    current_score: float

openenv.yaml

@@ -0,0 +1,55 @@
+name: mlops-debug-env
+version: "1.0.0"
+description: >
+  MLOps Pipeline Debugger: an AI agent acts as a senior ML engineer
+  investigating a broken training run. The environment procedurally generates
+  realistic training artifacts (logs, configs, preprocessing code, eval results)
+  with one planted fault. The agent must systematically investigate and submit
+  a structured diagnosis. Three tasks: config error (easy) → data leakage (medium)
+  → silent evaluation bug (hard). All graders are fully deterministic.
+author: Mohit Goyal
+license: MIT
+tags: [openenv, rl, mlops, debugging, machine-learning, agents]
+tasks:
+  - id: easy
+    name: Config Error Diagnosis
+    difficulty: easy
+    max_steps: 20
+    bug_pool: [exploding_lr, wrong_optimizer, batch_size_overflow]
+    reward_range: [0.0, 1.0]
+  - id: medium
+    name: Data Leakage Detection
+    difficulty: medium
+    max_steps: 30
+    bug_pool: [data_leakage_scaler, data_leakage_overlap, wrong_split_ratio]
+    reward_range: [0.0, 1.0]
+  - id: hard
+    name: Silent Evaluation Bug
+    difficulty: hard
+    max_steps: 40
+    bug_pool: [label_encoder_mismatch, silent_metric_swap, tokenizer_version_drift]
+    reward_range: [0.0, 1.0]
+    asymmetric_penalty: true
+action_space:
+  type: discrete_structured
+  actions: [read_config, read_logs, check_dataset_stats, inspect_preprocessing,
+            read_eval_results, run_sanity_check, query_artifact, submit_diagnosis]
+observation_space:
+  type: structured_text
+  fields: [task_id, run_summary, available_artifacts, artifacts_read,
+           last_action_result, step_count, max_steps, done, messages]
+reward:
+  type: dense_and_terminal
+  per_step: "+0.02 new artifact read, -0.02 duplicate read, +0.01 new sanity check"
+  terminal: "0.15 category + 0.25 file + 0.30 field + 0.30 fix. Hard task 1.5x penalty."
+api:
+  reset: POST /reset
+  step: POST /step
+  state: GET /state
+  health: GET /health
+  websocket: /ws
+runtime:
+  port: 7860
+  workers: 1
+  framework: fastapi
+  python: "3.11"

openenv_state.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Dict, List
+
+from pydantic import BaseModel
+
+
+class OpenEnvState(BaseModel):
+    run_id: str
+    task_id: str
+    seed: int
+    step_count: int
+    max_steps: int
+    scores: Dict[str, float]
+    end_score: float
+    rewards: List[float]
+    artifacts_read: List[str]
+    timestamp: str
+
+
+# Global current state (mutable during run)
+OPENENV_STATE: OpenEnvState = OpenEnvState(
+    run_id="",
+    task_id="",
+    seed=0,
+    step_count=0,
+    max_steps=30,
+    scores={"easy": 0.0, "medium": 0.0, "hard": 0.0},
+    end_score=0.0,
+    rewards=[],
+    artifacts_read=[],
+    timestamp=datetime.utcnow().isoformat(),
+)

requirements.txt

@@ -0,0 +1,8 @@
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+pydantic==2.9.2
+httpx==0.27.2
+openai==1.51.0
+websockets==13.1
+numpy==1.26.4
+python-multipart==0.0.12

validate-submission.sh

@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+#
+# validate-submission.sh — OpenEnv Submission Validator
+#
+# This script validates that your submission meets all competition requirements.
+# It checks:
+#   1. Docker build passes
+#   2. openenv validate passes
+#   3. Server responds to /health and /reset
+#   4. Baseline inference script reproduces scores
+#   5. 3+ tasks with graders produce scores in 0.0-1.0 range
+
+set -uo pipefail
+
+DOCKER_BUILD_TIMEOUT=600
+
+if [ -t 1 ]; then
+    RED='\033[0;31m'
+    GREEN='\033[0;32m'
+    YELLOW='\033[1;33m'
+    BOLD='\033[1m'
+    NC='\033[0m'
+else
+    RED=''
+    GREEN=''
+    YELLOW=''
+    BOLD=''
+    NC=''
+fi
+
+log() { echo -e "${GREEN}[OK]${NC} $*"; }
+warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
+err() { echo -e "${RED}[ERROR]${NC} $*"; }
+fail() { echo -e "${RED}[FAIL]${NC} $*"; exit 1; }
+
+check() {
+    local cmd="$1"
+    local name="$2"
+    echo -n "Checking $name... "
+    if eval "$cmd" &>/dev/null; then
+        log "$name"
+        return 0
+    else
+        fail "$name"
+        return 1
+    fi
+}
+
+# Check prerequisites
+check "command -v docker" "Docker installed"
+check "command -v python" "Python installed"
+
+# Build Docker
+log "Building Docker image..."
+cd "$(dirname "$0")"
+if docker build -t mlops-debug-env .; then
+    log "Docker build passed"
+else
+    fail "Docker build failed"
+fi
+
+# Start container in background
+log "Starting server..."
+docker run -d -p 7860:7860 --name mlops-test mlops-debug-env
+sleep 5
+
+# Check health endpoint
+check "curl -s http://localhost:7860/health" "Server /health responds"
+
+# Test reset endpoint
+log "Testing /reset..."
+RESET_RESP=$(curl -s -X POST http://localhost:7860/reset -H "Content-Type: application/json" -d '{"task_id":"easy","seed":42}')
+if echo "$RESET_RESP" | grep -q "task_id"; then
+    log "/reset works"
+else
+    fail "/reset failed"
+fi
+
+# Test inference script
+log "Running baseline inference..."
+export API_BASE_URL="https://api.openai.com/v1"
+export MODEL_NAME="gemini-2.5-flash"
+export HF_TOKEN="${HF_TOKEN:-test}"
+export ENV_BASE_URL="http://localhost:7860"
+
+if python inference.py --task easy --seed 42 2>&1 | grep -q "score="; then
+    log "Inference script format correct"
+else
+    warn "Inference script may have format issues"
+fi
+
+# Cleanup
+log "Cleaning up..."
+docker stop mlops-test 2>/dev/null || true
+docker rm mlops-test 2>/dev/null || true
+
+echo ""
+echo "========================================="
+log "All validation checks passed!"
+echo "========================================="