kaggle-kernel-runner

name: kaggle-kernel-runner description: > Push, monitor, debug, and iterate on Kaggle kernel notebooks using the kaggle CLI. Covers: nbformat notebook generation, kernel-metadata.json configuration (GPU/dataset), push→poll→debug→fix→repush cycle, papermill timeout avoidance, and output download. Use when the user wants to run a notebook on Kaggle cloud GPUs and iteratively fix errors until it succeeds. agent_created: true

Kaggle Kernel Runner

Iterative workflow for pushing Python notebooks to Kaggle, monitoring execution, debugging errors, and downloading results.

Core Commands

# Push notebook to Kaggle
kaggle kernels push -p <directory>

# Check status
kaggle kernels status <owner>/<kernel-slug>

# View logs (JSON-lines format with stream_name, time, data fields)
kaggle kernels logs <owner>/<kernel-slug>

# Download all output files
kaggle kernels output <owner>/<kernel-slug> -p <output-dir>

Workflow: Push → Poll → Debug → Fix → Repush

1. Project Structure

my-kernel/
├── kernel-metadata.json    # Kaggle kernel configuration
├── my-kernel.ipynb         # Generated notebook
└── gen_notebook.py         # Python script that generates the .ipynb

2. kernel-metadata.json Template

{
  "id": "<username>/<kernel-slug>",
  "title": "<kernel-title>",
  "code_file": "<kernel-slug>.ipynb",
  "language": "python",
  "kernel_type": "notebook",
  "is_private": true,
  "enable_gpu": true,
  "enable_internet": true,
  "machine_shape": "NvidiaTeslaT4",
  "dataset_sources": ["<username>/<dataset-slug>"],
  "docker_image": "docker.io/kaggle/python-gpu:latest"
}

GPU options for machine_shape:

• "NvidiaTeslaT4" — 2x T4 GPUs (16GB each), recommended for most tasks
• "NvidiaL4x4" — 4x L4 GPUs (only for competitions)
• Omit field for CPU-only

Important: "dataset_sources" array attaches Kaggle datasets as read-only input at /kaggle/input/<dataset-slug>/.

3. Generating Notebooks with nbformat

import nbformat as nbf

nb = nbf.v4.new_notebook()

cell1 = nbf.v4.new_code_cell(
    "import subprocess, sys, os\n"
    "# Install deps\n"
    "subprocess.run([sys.executable, '-m', 'pip', 'install', '-q', 'package'], check=True)\n"
)

cell2 = nbf.v4.new_code_cell("print('Hello from Kaggle!')")

nb.cells = [cell1, cell2]
nb.metadata["kernelspec"] = {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
}

with open("my-kernel.ipynb", "w") as f:
    nbf.write(nb, f)

4. Poll Status Loop

# Check status (returns KernelWorkerStatus.QUEUED/RUNNING/COMPLETE/ERROR)
kaggle kernels status owner/kernel-slug

# Tail logs while running
kaggle kernels logs owner/kernel-slug 2>&1 | tail -c 3000

5. Log Parsing

Logs are JSON-lines with leading [ on first line and , prefix on subsequent lines:

import json
with open('kernel.log', 'r', errors='replace') as f:
    for line in f:
        line = line.strip()
        if not line.startswith(','): continue
        try:
            d = json.loads(line[1:])
            t = d.get('time', 0)
            data = d.get('data', '').strip()
            stream = d.get('stream_name', 'stdout')
            if data:
                print(f'[{t:8.2f}s] [{stream}] {data[:200]}')
        except: pass

6. Download Output

# Download all output files (videos, models, etc.)
kaggle kernels output owner/kernel-slug -p ./output/

# Only files in /kaggle/working/ are saved as output

Critical Pitfalls

Papermill IOPub Timeout

Problem: Kaggle uses papermill which kills the kernel if no output is produced for ~4 seconds (IOPub timeout).

Solution: For long-running training loops:

from stable_baselines3 import PPO
model = PPO("CnnPolicy", env, verbose=1)
model.learn(
    total_timesteps=1_000_000,
    progress_bar=False,     # CRITICAL: disable tqdm (triggers timeout)
    log_interval=10         # print every 10 iterations to keep papermill alive
)

Dataset Auto-Extraction (.zip files)

Problem: Kaggle automatically extracts .zip, .tar.gz, .tar, .7z files when uploaded as datasets. If you upload multiple model .zip files, they all get extracted into the same directory and overwrite each other.

Solution: Rename to a non-standard extension before uploading:

# Rename .zip -> .sb3 (or any custom extension)
for f in *.zip; do mv "$f" "${f%.zip}.sb3"; done

# Upload dataset
kaggle datasets version -p dataset-upload/ -m "description"

# In notebook, copy back to .zip:
import shutil
for src in glob.glob('/kaggle/input/**/*.sb3', recursive=True):
    dst = os.path.join(WORK_DIR, os.path.basename(src).replace('.sb3', '.zip'))
    shutil.copy2(src, dst)

Dataset Upload Structure

dataset-upload/
├── dataset-metadata.json   # Required metadata
├── file1.sb3               # Files at root level (NO subdirectories!)
├── file2.sb3
└── file3.sb3

dataset-metadata.json:

{
  "id": "<username>/<dataset-slug>",
  "title": "<dataset-title>",
  "licenses": [{"name": "other"}]
}

Commands:

# Create new dataset
kaggle datasets create -p dataset-upload/

# Update existing dataset (new version)
kaggle datasets version -p dataset-upload/ -m "version message"

# With subdirectories (zips them):
kaggle datasets version -p dataset-upload/ --dir-mode zip -m "msg"

Important: Flat file structure is preferred. Subdirectories get skipped unless --dir-mode zip is used.

Kaggle CLI Auth Inside Kernels

Problem: kaggle kernels output and other API calls don't work inside a Kaggle kernel because there's no CLI authentication.

Solution: Use datasets as the transfer mechanism between kernels instead of the API.

GPU Detection

import torch
print(f'GPU count: {torch.cuda.device_count()}')
for i in range(torch.cuda.device_count()):
    print(f'  GPU {i}: {torch.cuda.get_device_name(i)}')
# With NvidiaTeslaT4: shows 2x Tesla T4 GPUs

Web UI vs CLI Created Kernels

"Notebook not found" Push Error

Problem: kaggle kernels push fails with "Kernel push error: Notebook not found" even though kaggle kernels pull works.

Cause: Kernel was created via Kaggle web UI but never "committed" (saved/run). The push API can't find uncommitted web-created kernels.

Solution:

1. Go to the kernel page on Kaggle web
2. Click "Save & Run All" (or "Save Version")
3. Wait for it to save
4. THEN use kaggle kernels pull -m + modify + kaggle kernels push

Alternative: Push to a new kernel slug that doesn't exist yet — the API creates it automatically:

{ "id": "username/my-new-kernel-slug", ... }

Correct Push Workflow for Existing Kernels

# 1. Pull with metadata (gets id_no and correct format)
kaggle kernels pull owner/kernel-slug -p . -m

# 2. Replace the .ipynb with your generated notebook
python gen_notebook.py

# 3. Push back
kaggle kernels push -p .

Competition Data Access

competition_sources Not Mounting

Problem: "competition_sources": ["comp-slug"] in metadata but /kaggle/input/comp-slug/ doesn't exist at runtime.

Common causes:

• User hasn't clicked "Join Competition" button on Kaggle web
• Competition rules not accepted
• For Playground Series: need to join even if rules are simple

Solution — use kagglehub as fallback (requires enable_internet: true):

import os
DATA_DIR = '/kaggle/input/playground-series-s6e6'
if not os.path.exists(DATA_DIR):
    print('Data not mounted, using kagglehub...', flush=True)
    import kagglehub
    DATA_DIR = kagglehub.competition_download('playground-series-s6e6')
    print(f'Downloaded to: {DATA_DIR}', flush=True)

train = pd.read_csv(DATA_DIR + '/train.csv')

NEVER Use subprocess kaggle CLI Inside Kernel

Problem: subprocess.run(['kaggle', 'competitions', 'download', ...]) hangs indefinitely inside Kaggle kernels (no credentials, no tty).

Solution: Use kagglehub library instead (pre-installed, auto-authenticated):

import kagglehub
# Download competition data
path = kagglehub.competition_download('competition-slug')
# Download dataset
path = kagglehub.dataset_download('owner/dataset-slug')

Debugging Checklist

1. Kernel fails immediately → Check kaggle kernels logs, look for import errors or pip failures
2. Kernel times out → Likely papermill IOPub timeout. Add progress_bar=False and log_interval=N
3. Files not found in /kaggle/input/ → Verify dataset_sources/competition_sources in metadata; user must join competition; use kagglehub fallback
4. Output empty → Only files in /kaggle/working/ are saved. Check paths.
5. Dataset files auto-extracted → Rename to .sb3 or other non-standard extension before upload
6. GPU not detected → Check machine_shape is "NvidiaTeslaT4" (not "gpuT4x2" or similar)
7. Push fails "Notebook not found" → Kernel was web-created but never saved/run. Save from web UI first, or push to a new slug.
8. Kernel RUNNING forever, no logs → Likely subprocess.run(['kaggle', ...]) hanging. Use kagglehub instead.