| # Kernelbot Data Processing Skills | |
| This document describes how to extract and process submission data from the Kernelbot database. | |
| ## Database Connection | |
| The production database is hosted on Heroku. **NEVER run write operations (INSERT, UPDATE, DELETE) on this database.** | |
| ```bash | |
| # Get DATABASE_URL from Heroku | |
| heroku config:get DATABASE_URL --app discord-cluster-manager | |
| ``` | |
| ## Database Schema | |
| The relevant tables are in the `leaderboard` schema: | |
| | Table | Description | | |
| |-------|-------------| | |
| | `leaderboard.leaderboard` | Problem definitions (id, name, deadline, task, description) | | |
| | `leaderboard.submission` | User submissions (id, leaderboard_id, user_id, code_id, submission_time, status) | | |
| | `leaderboard.runs` | Execution results (submission_id, score, passed, mode, runner, result) | | |
| | `leaderboard.user_info` | User details (id, user_name) | | |
| | `leaderboard.gpu_type` | GPU types per problem (leaderboard_id, gpu_type) | | |
| | `leaderboard.code_files` | Actual submission code content (old_code text, code bytea) | | |
| ## Key Problem IDs | |
| ### NVFP4 Problems | |
| - **595**: nvfp4_gemv | |
| - **597**: nvfp4_gemm | |
| - **598**: nvfp4_dual_gemm | |
| - **730**: nvfp4_group_gemm (not released yet) | |
| ### AMD Problems | |
| - **398**: amd-identity | |
| - **399**: amd-fp8-mm | |
| - **430**: amd-mixture-of-experts | |
| - **463**: amd-mla-decode | |
| - **563**: amd-all2all | |
| - **564**: amd-gemm-rs | |
| - **565**: amd-ag-gemm | |
| ## Run Modes | |
| | Mode | Description | Has Score? | | |
| |------|-------------|------------| | |
| | `test` | Correctness tests | No | | |
| | `benchmark` | Performance benchmarks (internal) | No | | |
| | `leaderboard` | Official leaderboard runs | **Yes** | | |
| | `profile.0-3` | Profiling runs | No | | |
| **Important:** | |
| - Use `mode = 'leaderboard'` when joining runs to get scores. | |
| - **Lower scores are better** (scores are execution time in seconds). | |
| ## SQL Queries | |
| All SQL queries are in `queries.sql`. Key queries: | |
| - List all problems | |
| - Check submission counts | |
| - Export deduplicated submissions with code | |
| - Get top N submissions | |
| - Get user progression over time | |
| ## Adding Support for a New Problem | |
| ### Step 1: Find the Problem ID | |
| Use the "LIST ALL PROBLEMS" query from `queries.sql`. | |
| ### Step 2: Check Submission Counts | |
| Use the "CHECK SUBMISSION COUNTS" query from `queries.sql`. | |
| ### Step 3: Export Deduplicated Submissions | |
| Use the "EXPORT DEDUPLICATED SUBMISSIONS WITH CODE" query from `queries.sql`. | |
| ```python | |
| import pandas as pd | |
| import psycopg2 | |
| DATABASE_URL = "..." # from heroku config:get | |
| conn = psycopg2.connect(DATABASE_URL) | |
| # Read query from queries.sql and modify problem IDs as needed | |
| with open('queries.sql') as f: | |
| # Find and use the export query section | |
| pass | |
| df = pd.read_sql(query, conn) | |
| df.to_parquet('new_problem_submissions.parquet', index=False) | |
| ``` | |
| ### Step 4: Verify Data Quality | |
| ```python | |
| from analyze_submissions import load_submissions, leaderboard_summary | |
| df = load_submissions('new_problem_submissions.parquet') | |
| print(leaderboard_summary(df)) | |
| ``` | |
| ## Accessing Submission Code | |
| The parquet files include the full code content for each submission: | |
| ```python | |
| from analyze_submissions import load_submissions | |
| df = load_submissions() | |
| # Get a specific user's best submission | |
| user_subs = df[(df['user_name'] == 'gau.nernst') & (df['problem_name'] == 'nvfp4_gemv')] | |
| best = user_subs.sort_values('score').head(1) | |
| # Access the code | |
| code = best['code'].values[0] | |
| print(code) | |
| ``` | |
| ## Helper Functions | |
| Use `analyze_submissions.py`: | |
| ```python | |
| from analyze_submissions import ( | |
| load_submissions, # Load parquet file | |
| author_progression, # See user's submissions over time | |
| top_contestants, # Get leaderboard rankings | |
| leaderboard_summary, # Summary stats per problem | |
| user_stats, # Stats for a specific user | |
| format_score # Format score with units (us, ms, s) | |
| ) | |
| ``` | |
| ## Environment Setup | |
| ```bash | |
| uv venv .venv | |
| source .venv/bin/activate | |
| uv pip install pandas pyarrow psycopg2-binary | |
| ``` | |
| ## Files | |
| | File | Description | | |
| |------|-------------| | |
| | `nvidia_nvfp4_submissions.parquet` | Deduplicated NVIDIA NVFP4 submissions with code (~1.4 GB) | | |
| | `queries.sql` | All SQL queries for data extraction | | |
| | `scripts/nvfp4/analyze_submissions.py` | Helper functions library | | |
| | `scripts/nvfp4/get_fastest_submission.py` | Print user's fastest submission | | |
| | `scripts/nvfp4/query_submissions.py` | List submission IDs or query specific ID | | |
| ## Review Checklist Before Pushing | |
| 1. Verify submission counts match expectations | |
| 2. Check for any anomalies in scores (negative, extremely large, etc.) | |
| 3. Confirm deduplication worked correctly | |
| 4. Test helper functions work with the new data | |
| 5. Run `python scripts/nvfp4/query_submissions.py` to verify | |