{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":28755,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n# Use the kagglehub client library to attach Kaggle resources like competitions, datasets, and models to your session\n# Learn more about kagglehub: https://github.com/Kaggle/kagglehub/blob/main/README.md\n\nimport kagglehub\n# kagglehub.dataset_download('<owner>/<dataset-slug>')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-08-09T16:57:03.122899Z","iopub.execute_input":"2026-08-09T16:57:03.123249Z","iopub.status.idle":"2026-08-09T16:57:06.231954Z","shell.execute_reply.started":"2026-08-09T16:57:03.123205Z","shell.execute_reply":"2026-08-09T16:57:06.230649Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# P0 BRIDGE GPU TREE BENCHMARK — KAGGLE ONE-CELL RUNNER\n# 14 assets x 3 horizons; exact 639 causal features\n# ============================================================\nimport os, sys, json, shutil, base64, zipfile, io, subprocess, importlib.util\nfrom pathlib import Path\n\n# ---------- USER SWITCHES (edit only these if needed) ----------\nFAST_DEBUG = False                 # True = short model-fit smoke run; False = full benchmark\nENABLE_GPU_RF = False              # optional; requires working RAPIDS/cuML\nFAMILIES = [\"lightgbm\", \"xgboost\", \"catboost\"]\nRUN_LATENCY = True\nRUN_PLOTS = True\nCLEAN_OUTPUT_FIRST = True          # train from scratch; never mix old checkpoints\n\nDATA_DIR = Path(\"/kaggle/input/competitions/g-research-crypto-forecasting\")\nASSET_DETAILS = DATA_DIR / \"asset_details.csv\"\nOUTPUT_DIR = Path(\"/kaggle/working/p0_bridge_artifacts\")\nSOURCE_DIR = Path(\"/kaggle/working/p0_bridge_source\")\n\n# Competition files used by this benchmark.\nfor required in [DATA_DIR/\"train.csv\", DATA_DIR/\"supplemental_train.csv\", ASSET_DETAILS]:\n    if not required.exists():\n        raise FileNotFoundError(f\"Missing Kaggle input: {required}\")\n\n# Preserve Kaggle's existing packages; install only modules that are absent.\n# Strict GPU smoke tests inside the pipeline decide whether those builds are actually usable.\nrequired_modules = {\n    \"numpy\": \"numpy\", \"pandas\": \"pandas\", \"scipy\": \"scipy\", \"pyarrow\": \"pyarrow\",\n    \"matplotlib\": \"matplotlib\", \"joblib\": \"joblib\", \"lightgbm\": \"lightgbm\",\n    \"xgboost\": \"xgboost\", \"catboost\": \"catboost\",\n}\nmissing_packages = [pkg for mod, pkg in required_modules.items() if importlib.util.find_spec(mod) is None]\nif missing_packages:\n    print(\"Installing missing packages:\", missing_packages)\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *missing_packages])\n\nsubprocess.run([\"nvidia-smi\"], check=False)\n\n# Extract exactly the same source package shipped in the Local/Vast folder.\nPAYLOAD = \"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\"\nif SOURCE_DIR.exists():\n    shutil.rmtree(SOURCE_DIR)\nSOURCE_DIR.mkdir(parents=True, exist_ok=True)\nwith zipfile.ZipFile(io.BytesIO(base64.b64decode(PAYLOAD)), \"r\") as zf:\n    zf.extractall(SOURCE_DIR)\nsys.path.insert(0, str(SOURCE_DIR))\n\nif CLEAN_OUTPUT_FIRST and OUTPUT_DIR.exists():\n    shutil.rmtree(OUTPUT_DIR)\n\nfrom p0_bridge.config import BenchmarkConfig\nfrom p0_bridge.pipeline import run_benchmark\n\nconfig = BenchmarkConfig(\n    data_dir=DATA_DIR,\n    output_dir=OUTPUT_DIR,\n    asset_details_path=ASSET_DETAILS,\n    selected_families=FAMILIES,\n    enable_gpu_rf=ENABLE_GPU_RF,\n    require_gpu=True,\n    fast_debug=FAST_DEBUG,\n    run_latency=RUN_LATENCY,\n    run_plots=RUN_PLOTS,\n)\n\nsummary = run_benchmark(config)\narchive = shutil.make_archive(\"/kaggle/working/p0_bridge_artifacts\", \"zip\", OUTPUT_DIR)\nprint(\"\\nKAGGLE RUN FINISHED\")\nprint(json.dumps(summary, indent=2))\nprint(\"Artifacts folder:\", OUTPUT_DIR)\nprint(\"Artifacts zip   :\", archive)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}