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https://github.com/jovianhq/kernel-run
Run any Jupyter notebook instantly using Kaggle kernels
https://github.com/jovianhq/kernel-run
command-line-tool jupyter-notebook kaggle machine-learning
Last synced: about 2 months ago
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Run any Jupyter notebook instantly using Kaggle kernels
- Host: GitHub
- URL: https://github.com/jovianhq/kernel-run
- Owner: JovianHQ
- Created: 2019-05-18T22:09:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-11T09:25:52.000Z (over 4 years ago)
- Last Synced: 2024-10-16T02:25:37.316Z (2 months ago)
- Topics: command-line-tool, jupyter-notebook, kaggle, machine-learning
- Language: Python
- Homepage:
- Size: 28.3 KB
- Stars: 63
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# `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`](https://github.com/Kaggle/kaggle-api#api-credentials). To download the `kaggle.json` file:
1. Go to https://kaggle.com
2. Log in and go to your account page
3. Click the "Create New API Token" button in the "API" section
4. Move the downloaded `kaggle.json` file 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`.
```python
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:
```python
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.