{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%reload_ext autoreload\n%autoreload 2"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "from fastai.gen_doc.gen_notebooks import *\nfrom pathlib import Path"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### To update this notebook"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "Run `tools/sgen_notebooks.py"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "Or run below:  \nYou need to make sure to refresh right after"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "import glob\nfor f in Path().glob('*.ipynb'):\n    generate_missing_metadata(f)"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Metadata generated below"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "update_nb_metadata('callbacks.tracking.ipynb',\n    summary='Callbacks that take decisions depending on the evolution of metrics during training',\n    title='callbacks.tracking')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('torch_core.ipynb',\n    summary='Basic functions using pytorch',\n    title='torch_core')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('gen_doc.convert2html.ipynb',\n    summary='Converting the documentation notebooks to HTML pages',\n    title='gen_doc.convert2html')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('metrics.ipynb',\n    summary='Useful metrics for training',\n    title='metrics')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.fp16.ipynb',\n    summary='Training in mixed precision implementation',\n    title='callbacks.fp16')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.general_sched.ipynb',\n    summary='Implementation of a flexible training API',\n    title='callbacks.general_sched')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('text.ipynb',\n    keywords='fastai',\n    summary='Application to NLP, including ULMFiT fine-tuning',\n    title='text')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callback.ipynb',\n    summary='Implementation of the callback system',\n    title='callback')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('tabular.models.ipynb',\n    keywords='fastai',\n    summary='Model for training tabular/structured data',\n    title='tabular.models')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.mixup.ipynb',\n    summary='Implementation of mixup',\n    title='callbacks.mixup')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('applications.ipynb',\n    summary='Types of problems you can apply the fastai library to',\n    title='applications')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.data.ipynb',\n    summary='Basic dataset for computer vision and helper function to get a DataBunch',\n    title='vision.data')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('overview.ipynb',\n    summary='Overview of the core modules',\n    title='overview')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('training.ipynb',\n    keywords='fastai',\n    summary='Overview of fastai training modules, including Learner, metrics, and callbacks',\n    title='training')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('text.transform.ipynb',\n    summary='NLP data processing; tokenizes text and creates vocab indexes',\n    title='text.transform')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "# do not overwrite this notebook, or changes may get lost!\n# update_nb_metadata('jekyll_metadata.ipynb')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('collab.ipynb',\n    summary='Application to collaborative filtering',\n    title='collab')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('text.learner.ipynb',\n    summary='Easy access of language models and ULMFiT',\n    title='text.learner')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('gen_doc.nbdoc.ipynb',\n    summary='Helper function to build the documentation',\n    title='gen_doc.nbdoc')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.learner.ipynb',\n    summary='`Learner` support for computer vision',\n    title='vision.learner')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('core.ipynb',\n    summary='Basic helper functions for the fastai library',\n    title='core')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('fastai_typing.ipynb',\n    keywords='fastai',\n    summary='Type annotations names',\n    title='fastai_typing')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('gen_doc.gen_notebooks.ipynb',\n    summary='Generation of documentation notebook skeletons from python module',\n    title='gen_doc.gen_notebooks')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('basic_train.ipynb',\n    summary='Learner class and training loop',\n    title='basic_train')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('gen_doc.ipynb',\n    keywords='fastai',\n    summary='Documentation modules overview',\n    title='gen_doc')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.rnn.ipynb',\n    summary='Implementation of a callback for RNN training',\n    title='callbacks.rnn')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.one_cycle.ipynb',\n    summary='Implementation of the 1cycle policy',\n    title='callbacks.one_cycle')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.ipynb',\n    summary='Application to Computer Vision',\n    title='vision')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.transform.ipynb',\n    summary='List of transforms for data augmentation in CV',\n    title='vision.transform')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.lr_finder.ipynb',\n    summary='Implementation of the LR Range test from Leslie Smith',\n    title='callbacks.lr_finder')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('text.data.ipynb',\n    summary='Basic dataset for NLP tasks and helper functions to create a DataBunch',\n    title='text.data')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('text.models.ipynb',\n    summary='Implementation of the AWD-LSTM and the RNN models',\n    title='text.models')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('tabular.data.ipynb',\n    summary='Base class to deal with tabular data and get a DataBunch',\n    title='tabular.data')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.ipynb',\n    keywords='fastai',\n    summary='Callbacks implemented in the fastai library',\n    title='callbacks')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('train.ipynb',\n    summary='Extensions to Learner that easily implement Callback',\n    title='train')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.hooks.ipynb',\n    summary='Implement callbacks using hooks',\n    title='callbacks.hooks')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.image.ipynb',\n    summary='Image class, variants and internal data augmentation pipeline',\n    title='vision.image')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.models.unet.ipynb',\n    summary='Dynamic Unet that can use any pretrained model as a backbone.',\n    title='vision.models.unet')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('vision.models.ipynb',\n    keywords='fastai',\n    summary='Overview of the models used for CV in fastai',\n    title='vision.models')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('tabular.transform.ipynb',\n    summary='Transforms to clean and preprocess tabular data',\n    title='tabular.transform')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('index.ipynb',\n    keywords='fastai',\n    toc='false', \n    title='Welcome to fastai')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('layers.ipynb',\n    summary='Provides essential functions to building and modifying `Model` architectures.',\n    title='layers')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('tabular.ipynb',\n    keywords='fastai',\n    summary='Application to tabular/structured data',\n    title='tabular')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('basic_data.ipynb',\n    summary='Basic classes to contain the data for model training.',\n    title='basic_data')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('datasets.ipynb')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('tmp.ipynb',\n    keywords='fastai')"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": "update_nb_metadata('callbacks.tracking.ipynb')"
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "hide_input": false,
    "trusted": true
   },
   "source": "update_nb_metadata('data_block.ipynb',\n    keywords='fastai',\n    summary='The data block API',\n    title='data_block')",
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}