fastkaggle
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## Install
Either:
pip install fastkaggle
or:
mamba install -c fastai fastkaggle
(or replace `mamba` with `conda` if you don’t mind it taking much longer
to run…)
## How to use
### Competition
This little library is where I’ll be putting snippets of stuff which are
useful on Kaggle. Functionality includes the following:
It defines
[`iskaggle`](https://fastai.github.io/fastkaggle/core.html#iskaggle)
which is `True` if you’re running on Kaggle:
``` python
'Kaggle' if iskaggle else 'Not Kaggle'
```
'Not Kaggle'
It provides a
[`setup_comp`](https://fastai.github.io/fastkaggle/core.html#setup_comp)
function which gets a path to the data for a competition, downloading it
if needed, and also installs any modules that might be missing or out of
data if running on Kaggle:
``` python
setup_comp('titanic')
```
Path('titanic')
There’s also
[`push_notebook`](https://fastai.github.io/fastkaggle/core.html#push_notebook)
to push a notebook to Kaggle Notebooks, and
[`import_kaggle`](https://fastai.github.io/fastkaggle/core.html#import_kaggle)
to use the Kaggle API (even when you’re on Kaggle!) See the
`fastkaggle.core` docs for details.
### Datasets
This section is designed to make uploading pip libraries to kaggle
datasets easy. There’s 2 primary high level functions to be used. First
we can define our kaggle username and the local path we want to use to
store datasets when we create them.
<div>
> **Usage tip**
>
> The purpose of this is to create datasets that can be used in no
> internet inference competitions to install libraries using
> `pip install -Uqq library --no-index --find-links=file:///kaggle/input/your_dataset/`
</div>
``` python
lib_path = Path('/root/kaggle_datasets')
username = 'isaacflath'
```
#### List of Libraries
We can take a list of libraries and upload them as seperate datasets.
For example the below will create a `library-fastcore` and
`library-timm` dataset. If they already exist, it will push a new
version if there is a more recent version available.
``` python
libs = ['fastcore','timm']
create_libs_datasets(libs,lib_path,username)
```
Processing fastcore as library-fastcore at /root/kaggle_datasets/library-fastcore
-----Downloading or Creating Dataset
-----Checking dataset version against pip
-----Kaggle dataset already up to date 1.5.16 to 1.5.16
Processing timm as library-timm at /root/kaggle_datasets/library-timm
-----Downloading or Creating Dataset
-----Checking dataset version against pip
-----Kaggle dataset already up to date 0.6.7 to 0.6.7
Complete
This creates datasets in kaggle with the needed files.
#### requirements.txt
We can also create a singular dataset with multiple libraries based on a
`requirements.txt` file for the project. If there are any different
files it will push a new version.
``` python
create_requirements_dataset('test_files/requirements.txt',lib_path,'libraries-pawpularity', username)
```
Processing libraries-pawpularity at /root/kaggle_datasets/libraries-pawpularity
-----Downloading or Creating Dataset
Data package template written to: /root/kaggle_datasets/libraries-pawpularity/dataset-metadata.json
-----Checking dataset version against pip
-----Updating libraries-pawpularity in Kaggle
Complete
This creats a dataset in kaggle with the needed files.
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"description": "fastkaggle\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## Install\n\nEither:\n\n pip install fastkaggle\n\nor:\n\n mamba install -c fastai fastkaggle\n\n(or replace `mamba` with `conda` if you don\u2019t mind it taking much longer\nto run\u2026)\n\n## How to use\n\n### Competition\n\nThis little library is where I\u2019ll be putting snippets of stuff which are\nuseful on Kaggle. Functionality includes the following:\n\nIt defines\n[`iskaggle`](https://fastai.github.io/fastkaggle/core.html#iskaggle)\nwhich is `True` if you\u2019re running on Kaggle:\n\n``` python\n'Kaggle' if iskaggle else 'Not Kaggle'\n```\n\n 'Not Kaggle'\n\nIt provides a\n[`setup_comp`](https://fastai.github.io/fastkaggle/core.html#setup_comp)\nfunction which gets a path to the data for a competition, downloading it\nif needed, and also installs any modules that might be missing or out of\ndata if running on Kaggle:\n\n``` python\nsetup_comp('titanic')\n```\n\n Path('titanic')\n\nThere\u2019s also\n[`push_notebook`](https://fastai.github.io/fastkaggle/core.html#push_notebook)\nto push a notebook to Kaggle Notebooks, and\n[`import_kaggle`](https://fastai.github.io/fastkaggle/core.html#import_kaggle)\nto use the Kaggle API (even when you\u2019re on Kaggle!) See the\n`fastkaggle.core` docs for details.\n\n### Datasets\n\nThis section is designed to make uploading pip libraries to kaggle\ndatasets easy. There\u2019s 2 primary high level functions to be used. First\nwe can define our kaggle username and the local path we want to use to\nstore datasets when we create them.\n\n<div>\n\n> **Usage tip**\n>\n> The purpose of this is to create datasets that can be used in no\n> internet inference competitions to install libraries using\n> `pip install -Uqq library --no-index --find-links=file:///kaggle/input/your_dataset/`\n\n</div>\n\n``` python\nlib_path = Path('/root/kaggle_datasets')\nusername = 'isaacflath'\n```\n\n#### List of Libraries\n\nWe can take a list of libraries and upload them as seperate datasets.\nFor example the below will create a `library-fastcore` and\n`library-timm` dataset. If they already exist, it will push a new\nversion if there is a more recent version available.\n\n``` python\nlibs = ['fastcore','timm']\ncreate_libs_datasets(libs,lib_path,username)\n```\n\n Processing fastcore as library-fastcore at /root/kaggle_datasets/library-fastcore\n -----Downloading or Creating Dataset\n -----Checking dataset version against pip\n -----Kaggle dataset already up to date 1.5.16 to 1.5.16\n Processing timm as library-timm at /root/kaggle_datasets/library-timm\n -----Downloading or Creating Dataset\n -----Checking dataset version against pip\n -----Kaggle dataset already up to date 0.6.7 to 0.6.7\n Complete\n\nThis creates datasets in kaggle with the needed files.\n\n\n\n#### requirements.txt\n\nWe can also create a singular dataset with multiple libraries based on a\n`requirements.txt` file for the project. If there are any different\nfiles it will push a new version.\n\n``` python\ncreate_requirements_dataset('test_files/requirements.txt',lib_path,'libraries-pawpularity', username)\n```\n\n Processing libraries-pawpularity at /root/kaggle_datasets/libraries-pawpularity\n -----Downloading or Creating Dataset\n Data package template written to: /root/kaggle_datasets/libraries-pawpularity/dataset-metadata.json\n -----Checking dataset version against pip\n -----Updating libraries-pawpularity in Kaggle\n Complete\n\nThis creats a dataset in kaggle with the needed files.\n\n\n\n\n",
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