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Migrate doc files to Markdown #1522

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<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Git vs HTTP paradigm

The `huggingface_hub` library is a library for interacting with the Hugging Face Hub, which is a
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# Interact with Discussions and Pull Requests

The `huggingface_hub` library provides a Python interface to interact with Pull Requests and Discussions on the Hub.
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# Download files from the Hub

The `huggingface_hub` library provides functions to download files from the repositories
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# Interact with the Hub through the Filesystem API

In addition to the [`HfApi`], the `huggingface_hub` library provides [`HfFileSystem`], a pythonic [fsspec-compatible](https://filesystem-spec.readthedocs.io/en/latest/) file interface to the Hugging Face Hub. The [`HfFileSystem`] builds of top of the [`HfApi`] and offers typical filesystem style operations like `cp`, `mv`, `ls`, `du`, `glob`, `get_file`, and `put_file`.
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# Run Inference on servers

Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive,
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# Integrate any ML framework with the Hub

The Hugging Face Hub makes hosting and sharing models with the community easy. It supports
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# Manage `huggingface_hub` cache-system

## Understand caching
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# Manage your Space

In this guide, we will see how to manage your Space runtime
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# Create and share Model Cards

The `huggingface_hub` library provides a Python interface to create, share, and update Model Cards.
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# How-to guides

In this section, you will find practical guides to help you achieve a specific goal.
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# Create and manage a repository

The Hugging Face Hub is a collection of git repositories. [Git](https://git-scm.com/) is a widely used tool in software
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# Search the Hub

In this tutorial, you will learn how to search models, datasets and spaces on the Hub using `huggingface_hub`.
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# Upload files to the Hub

Sharing your files and work is an important aspect of the Hub. The `huggingface_hub` offers several options for uploading your files to the Hub. You can use these functions independently or integrate them into your library, making it more convenient for your users to interact with the Hub. This guide will show you how to push files:
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# Webhooks Server

Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to
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# 🤗 Hub client library

The `huggingface_hub` library allows you to interact with the [Hugging Face
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# Installation

Before you start, you will need to setup your environment by installing the appropriate packages.
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# Cache-system reference

The caching system was updated in v0.8.0 to become the central cache-system shared
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# Repository Cards

The huggingface_hub library provides a Python interface to create, share, and update Model/Dataset Cards.
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# Interacting with Discussions and Pull Requests

Check the [`HfApi`] documentation page for the reference of methods enabling
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# Environment variables

`huggingface_hub` can be configured using environment variables.
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# Downloading files

## Download a single file
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# HfApi Client

Below is the documentation for the `HfApi` class, which serves as a Python wrapper for the Hugging Face Hub's API.
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# Filesystem API

The `HfFileSystem` class provides a pythonic file interface to the Hugging Face Hub based on [`fssepc`](https://filesystem-spec.readthedocs.io/en/latest/).
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# Inference

Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive,
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# Login and logout

The `huggingface_hub` library allows users to programmatically login and logout the
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# Mixins & serialization methods

## Mixins
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# Overview

This section contains an exhaustive and technical description of `huggingface_hub` classes and methods.
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# Managing local and online repositories

The `Repository` class is a helper class that wraps `git` and `git-lfs` commands. It provides tooling adapted
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# Managing your Space runtime

Check the [`HfApi`] documentation page for the reference of methods to manage your Space on the Hub.
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# TensorBoard logger

TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing
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# Utilities

## Configure logging
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# Webhooks Server

Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to
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# Quickstart

The [Hugging Face Hub](https://huggingface.co/) is the go-to place for sharing machine learning
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