From 526ebfac779926616df740d5075a799a43fd4a85 Mon Sep 17 00:00:00 2001
From: Adam Narozniak <51029327+adam-narozniak@users.noreply.github.com>
Date: Wed, 20 Sep 2023 10:58:49 +0200
Subject: [PATCH] Add FDS tutorial docs (#2375)

---
 datasets/doc/source/tutorial-quickstart.rst | 72 +++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 datasets/doc/source/tutorial-quickstart.rst

diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
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+Quickstart
+==========
+
+Run Flower Datasets as fast as possible by learning only the essentials.
+
+Install Federated Datasets
+--------------------------
+Run on the command line::
+
+  python -m pip install flwr-datasets[vision]
+
+Install the ML framework
+------------------------
+TensorFlow::
+
+  pip install tensorflow
+
+PyTorch::
+
+  pip install torch torchvision
+
+Choose the dataset
+------------------
+Choose the dataset by going to Hugging Face `Datasets Hub <https://huggingface.co/datasets>`_ and searching for your
+dataset by name. Note that the name is case sensitive, so make sure to pass the correct name as the `dataset` parameter
+to `FederatedDataset`.
+
+Partition the dataset
+---------------------
+::
+
+  from flwr_datasets import FederatedDataset
+
+  fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+  partition = fds.load_partition(0, "train")
+  centralized_dataset = fds.load_full("test")
+
+Now you're ready to go. You have ten partitions created from the train split of the MNIST dataset and the test split
+for the centralized evaluation. We will convert the type of the dataset from Hugging Face's Dataset type to the one
+supported by your framework.
+
+Conversion
+----------
+For more detailed instructions, go to :doc:`how-to`.
+
+PyTorch DataLoader
+^^^^^^^^^^^^^^^^^^
+Transform the Dataset directly into the DataLoader::
+
+  from torch.utils.data import DataLoader
+  from torchvision.transforms import ToTensor
+
+  transforms = ToTensor()
+  partition_torch = partition.map(
+        lambda img: {"img": transforms(img)}, input_columns="img"
+    ).with_format("torch")
+  dataloader = DataLoader(partition_torch, batch_size=64)
+
+NumPy
+^^^^^
+NumPy can be used as input to the TensorFlow model and is very straightforward::
+
+   partition_np = partition.with_format("numpy")
+   X_train, y_train = partition_np["img"], partition_np["label"]
+
+TensorFlow Dataset
+^^^^^^^^^^^^^^^^^^
+Transformation to TensorFlow Dataset is a one-liner::
+
+  tf_dataset = partition.to_tf_dataset(columns="img", label_cols="label", batch_size=64,
+                                     shuffle=True)
+