kernel-run 🔥 🚀
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.
Installation
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 kaggle.json file:
- Go to https://kaggle.com
- 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~/.kaggle/
CLI Usage & Options
Run the 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/')
Python API
You can also use the library form a Python script or Jupyter notebook. It can be imported as kernel_run.
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/editThe 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.
"""Credits
Developed with love by the Jovian team ( https://www.jvn.io )! Contributions welcome.