Instantly create and run a Kaggle kernel from any Jupyter notebook (local file or URL).
$ pip install kernel-run --upgrade $ kernel-run path/to/notebook.ipynb Kernel created successfully: https://www.kaggle.com/aakashns/kr-notebook/edit $ kernel-run http://cs231n.stanford.edu/notebooks/pytorch_tutorial.ipynb Kernel created successfully: https://www.kaggle.com/aakashns/kr-pytorch-tutorial/edit
kernel-run uploads the Jupyter notebook to a private kernel in your Kaggle account, and launches a browser window so you can start editing/executing the code immediately.
pip install kernel-run --upgrade
The above command install a command-line tool called
kernel-run which can be invoked from the terminal/command prompt.
Note: To allow
kaggle-run to upload the notebook to your Kaggle account, you need to download the Kaggle API credentials file
kaggle.json. To download the
- Go to
- Log in and go to your account page
- Click the "Create New API Token" button in the "API" section
- Move the downloaded
kaggle.jsonfile to the folder
CLI Usage & Options
kernel-run command on your terminal/command prompt with a Jupyter notebook's path (or URL) as the argument:
$ kernel-run path/to/notebook.ipynb Kernel created successfully: https://www.kaggle.com/aakashns/kr-notebook/edit $ kernel-run http://cs231n.stanford.edu/notebooks/pytorch_tutorial.ipynb Kernel created successfully: https://www.kaggle.com/aakashns/kr-pytorch-tutorial/edit
There are various options you can configure. Run
kernel-run -h to see the options:
usage: kernel-run notebook_path_or_url [-h] [--public] [--new] [--no-browser] [--strip-output] [--prefix PREFIX] positional arguments: notebook_path_or_url Path/URL of the Jupyter notebook optional arguments: -h, --help show this help message and exit --public Create a public kernel --new Create a new kernel, if a kernel with the same name exists --no-browser Don't open a browser window automatically --strip-output Clear output cells before uploading notebook (useful for large files) --prefix PREFIX Prefix added to kernel title to easy identification (defaults to 'kr/')
You can also use the library form a Python script or Jupyter notebook. It can be imported as
from kernel_run import create_kernel create_kernel('path/to/notebook.ipynb', public=True, no_browser=True) # Kernel created successfully: https://www.kaggle.com/aakashns/kr-notebook/edit
The arguments to
create_kernel are identical to the CLI options:
def create_kernel(path_or_url, public=False, no_browser=False, new=False, strip_output=False, prefix='kr/', creds_path=None): """Instantly create and run a Kaggle kernel from a Jupyter notebook (local file or URL) Arguments: path_or_url (string): Path/URL to the Jupyter notebook public (bool, optional): If true, creates a public kernel. A private kernel is created by default. no_browser (bool, optional): If true, does not attempt to automatically open a browser tab to edit the created Kernel new (bool, optional): If true, creates a new Kernel by adding a random 5-letter string at the end of the title prefix (string, optional): A prefix added to the Kernel title, to indicate that the Kernel was created using kernel-run creds_path (string, optional): Path to the 'kaggle.json' credentials file (defaults to '~/.kaggle/kaggle.json') strip_output (bool, optional): Clear output cells before uploading notebook. """
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