Updated Documentation for contributing and previewing

README.md

@@ -1,30 +1,47 @@
-# hub-docs
+# Hub Documentation
 
-This repository regroups documentation and information that is hosted on the Hugging Face website.
+Welcome to the documentation repository for Hugging Face Hub. This repository contains the documentation and information hosted on the Hugging Face website.
 
-You can access the Hugging Face Hub documentation in the `docs` folder at [hf.co/docs/hub](https://hf.co/docs/hub).
+## Accessing Documentation
 
-For some related components, check out the [Hugging Face Hub JS repository](https://github.com/huggingface/huggingface.js)
-- Utilities to interact with the Hub: [huggingface/huggingface.js/packages/hub](https://github.com/huggingface/huggingface.js/tree/main/packages/hub)
-- Hub Widgets: [huggingface/huggingface.js/packages/widgets](https://github.com/huggingface/huggingface.js/tree/main/packages/widgets)
-- Hub Tasks (as visible on the page [hf.co/tasks](https://hf.co/tasks)):  [huggingface/huggingface.js/packages/tasks](https://github.com/huggingface/huggingface.js/tree/main/packages/tasks)
+You can find the Hugging Face Hub documentation in the `docs` folder. For direct access, visit: [hf.co/docs/hub](https://huggingface.co/docs/hub/index).
 
-### How to contribute to the docs
+## Related Components
 
-Just add/edit the Markdown files, commit them, and create a PR.
-Then the CI bot will build the preview page and provide a url for you to look at the result!
+Explore these related components for more utilities and features:
 
-For simple edits, you don't need a local build environment.
+- **Hugging Face Hub JS Repository:**
+  - [Utilities to Interact with the Hub](https://github.com/huggingface/huggingface.js/tree/main/packages/hub): Contains utilities to interact with the Hugging Face Hub.
+  - [Hub Widgets](https://github.com/huggingface/huggingface.js/tree/main/packages/widgets): Includes widgets for the Hub.
+  - [Hub Tasks](https://github.com/huggingface/huggingface.js/tree/main/packages/tasks): Provides information on tasks visible on [hf.co/tasks](https://huggingface.co/tasks).
 
-### Previewing locally
+## Contributing to the Documentation
 
-```bash
-# install doc-builder (if not done already)
-pip install hf-doc-builder
+To contribute:
 
-# you may also need to install some extra dependencies
-pip install black watchdog
+1. **Edit/Add Markdown Files:** Make changes directly to the Markdown files in this repository.
+2. **Commit Changes:** Commit your changes and create a Pull Request (PR).
+3. **CI Bot Preview:** After creating a PR, the CI bot will build a preview of your changes. You will receive a URL to review the result.
 
-# run `doc-builder preview` cmd
-doc-builder preview hub {YOUR_PATH}/hub-docs/docs/hub/ --not_python_module
-```
+For straightforward edits, you do not need a local build environment.
+
+## Previewing Documentation Locally
+
+To preview the documentation changes on your local machine, follow these steps:
+
+1. **Install Doc-Builder:**
+   ```bash
+   pip install hf-doc-builder
+   ```
+
+2. **Install Additional Dependencies (if needed):**
+   ```bash
+   pip install black watchdog
+   ```
+
+3. **Run the Preview Command:**
+   ```bash
+   doc-builder preview hub {YOUR_PATH}/hub-docs/docs/hub/ --not_python_module
+   ```
+
+Replace `{YOUR_PATH}` with the path to the cloned repository on your local machine.

docs/hub/datasets-dask.md

@@ -1,47 +1,62 @@
-# Dask
+# Dask Integration with Hugging Face
 
-[Dask](https://github.com/dask/dask) is a parallel and distributed computing library that scales the existing Python and PyData ecosystem.
-Since it uses [fsspec](https://filesystem-spec.readthedocs.io) to read and write remote data, you can use the Hugging Face paths ([`hf://`](/docs/huggingface_hub/guides/hf_file_system#integrations)) to read and write data on the Hub:
+[Dask](https://github.com/dask/dask) is a powerful parallel and distributed computing library that scales the existing Python and PyData ecosystem. By leveraging [fsspec](https://filesystem-spec.readthedocs.io/en/latest/), Dask can seamlessly interact with remote data sources, including the Hugging Face Hub. This allows you to read and write datasets directly from the Hub using Hugging Face paths (`hf://`).
 
-First you need to [Login with your Hugging Face account](/docs/huggingface_hub/quick-start#login), for example using:
+## Prerequisites
 
-```
-huggingface-cli login
-```
+Before you can use Hugging Face paths with Dask, you need to:
 
-Then you can [Create a dataset repository](/docs/huggingface_hub/quick-start#create-a-repository), for example using:
+1. **Login to your Hugging Face account:**  
+   Authenticate your session by logging in using the Hugging Face CLI:
+   ```bash
+   huggingface-cli login
+   ```
 
-```python
-from huggingface_hub import HfApi
+2. **Create a dataset repository:**  
+   You can create a new dataset repository on the Hugging Face Hub using the `HfApi` class:
+   ```python
+   from huggingface_hub import HfApi
 
-HfApi().create_repo(repo_id="username/my_dataset", repo_type="dataset")
-```
+   HfApi().create_repo(repo_id="username/my_dataset", repo_type="dataset")
+   ```
+
+## Writing Data to the Hub
 
-Finally, you can use [Hugging Face paths](/docs/huggingface_hub/guides/hf_file_system#integrations) in Dask:
+Once your environment is set up, you can easily write Dask DataFrames to the Hugging Face Hub. For instance, to store your dataset in Parquet format:
 
 ```python
 import dask.dataframe as dd
 
+# Writing the entire dataset to a single location
 df.to_parquet("hf://datasets/username/my_dataset")
 
-# or write in separate directories if the dataset has train/validation/test splits
+# Writing data to separate directories for train/validation/test splits
 df_train.to_parquet("hf://datasets/username/my_dataset/train")
 df_valid.to_parquet("hf://datasets/username/my_dataset/validation")
-df_test .to_parquet("hf://datasets/username/my_dataset/test")
+df_test.to_parquet("hf://datasets/username/my_dataset/test")
 ```
 
-This creates a dataset repository `username/my_dataset` containing your Dask dataset in Parquet format.
-You can reload it later:
+This will create a dataset repository `username/my_dataset` containing your data in Parquet format, which can be accessed later.
+
+## Reading Data from the Hub
+
+You can reload your dataset from the Hugging Face Hub just as easily:
 
 ```python
 import dask.dataframe as dd
 
+# Reading the entire dataset
 df = dd.read_parquet("hf://datasets/username/my_dataset")
 
-# or read from separate directories if the dataset has train/validation/test splits
+# Reading data from separate directories for train/validation/test splits
 df_train = dd.read_parquet("hf://datasets/username/my_dataset/train")
 df_valid = dd.read_parquet("hf://datasets/username/my_dataset/validation")
-df_test  = dd.read_parquet("hf://datasets/username/my_dataset/test")
+df_test = dd.read_parquet("hf://datasets/username/my_dataset/test")
 ```
 
-For more information on the Hugging Face paths and how they are implemented, please refer to the [the client library's documentation on the HfFileSystem](/docs/huggingface_hub/guides/hf_file_system).
+This allows you to seamlessly integrate your Dask workflows with datasets stored on the Hugging Face Hub.
+
+## Further Information
+
+For more detailed information on using Hugging Face paths and their implementation, refer to the [Hugging Face File System documentation](https://huggingface.co/docs/huggingface_hub/en/guides/hf_file_system).
+