# Dataloader for JAX
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
<a href="https://static.pepy.tech/badge/jax-dataloader"><img src="https://static.pepy.tech/badge/jax-dataloader" alt="Downloads"></a>
## Overview
`jax_dataloader` brings *pytorch-like* dataloader API to `jax`. It
supports
- **4 datasets to download and pre-process data**:
- [jax dataset](https://birkhoffg.github.io/jax-dataloader/dataset/)
- [huggingface datasets](https://github.com/huggingface/datasets)
- [pytorch
Dataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)
- [tensorflow dataset](www.tensorflow.org/datasets)
- **3 backends to iteratively load batches**:
- [jax
dataloader](https://birkhoffg.github.io/jax-dataloader/core.html#jax-dataloader)
- [pytorch
dataloader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)
- [tensorflow dataset](www.tensorflow.org/datasets)
A minimum `jax-dataloader` example:
``` python
import jax_dataloader as jdl
dataloader = jdl.DataLoader(
dataset, # Can be a jdl.Dataset or pytorch or huggingface dataset
backend='jax', # Use 'jax' for loading data (also supports `pytorch`)
)
batch = next(iter(dataloader)) # iterate next batch
```
## Installation
The latest `jax-dataloader` release can directly be installed from PyPI:
``` sh
pip install jax-dataloader
```
or install directly from the repository:
``` sh
pip install git+https://github.com/BirkhoffG/jax-dataloader.git
```
<div>
> **Note**
>
> We keep `jax-dataloader`’s dependencies minimum, which only install
> `jax`-related dependencies, and `plum-dispatch` for backend
> dispatching. If you wish to use integration of `pytorch`, huggingface
> `datasets`, or `tensorflow`, we recommend manually install those
> dependencies.
>
> You can also run `pip install jax-dataloader[all]` to install
> everything (not recommended).
</div>
## Usage
[`jax_dataloader.core.DataLoader`](https://birkhoffg.github.io/jax-dataloader/core.html#dataloader)
follows similar API as the pytorch dataloader.
- The `dataset` should be an object of the subclass of
`jax_dataloader.core.Dataset` or `torch.utils.data.Dataset` or (the
huggingface) `datasets.Dataset` or `tf.data.Dataset`.
- The `backend` should be one of `"jax"` or `"pytorch"` or
`"tensorflow"`. This argument specifies which backend dataloader to
load batches.
Note that not every dataset is compatible with every backend. See the
compatibility table below:
| | `jdl.Dataset` | `torch_data.Dataset` | `tf.data.Dataset` | `datasets.Dataset` |
|:---------------|:--------------|:---------------------|:------------------|:-------------------|
| `"jax"` | ✅ | ❌ | ❌ | ✅ |
| `"pytorch"` | ✅ | ✅ | ❌ | ✅ |
| `"tensorflow"` | ✅ | ❌ | ✅ | ✅ |
### Using [`ArrayDataset`](https://birkhoffg.github.io/jax-dataloader/dataset.html#arraydataset)
The `jax_dataloader.core.ArrayDataset` is an easy way to wrap multiple
`jax.numpy.array` into one Dataset. For example, we can create an
[`ArrayDataset`](https://birkhoffg.github.io/jax-dataloader/dataset.html#arraydataset)
as follows:
``` python
# Create features `X` and labels `y`
X = jnp.arange(100).reshape(10, 10)
y = jnp.arange(10)
# Create an `ArrayDataset`
arr_ds = jdl.ArrayDataset(X, y)
```
This `arr_ds` can be loaded by *every* backends.
``` python
# Create a `DataLoader` from the `ArrayDataset` via jax backend
dataloader = jdl.DataLoader(arr_ds, 'jax', batch_size=5, shuffle=True)
# Or we can use the pytorch backend
dataloader = jdl.DataLoader(arr_ds, 'pytorch', batch_size=5, shuffle=True)
```
### Using Huggingface Datasets
The huggingface [datasets](https://github.com/huggingface/datasets) is a
morden library for downloading, pre-processing, and sharing datasets.
`jax_dataloader` supports directly passing the huggingface datasets.
``` python
from datasets import load_dataset
```
For example, We load the `"squad"` dataset from `datasets`:
``` python
hf_ds = load_dataset("squad")
```
Then, we can use `jax_dataloader` to load batches of `hf_ds`.
``` python
# Create a `DataLoader` from the `datasets.Dataset` via jax backend
dataloader = jdl.DataLoader(hf_ds['train'], 'jax', batch_size=5, shuffle=True)
# Or we can use the pytorch backend
dataloader = jdl.DataLoader(hf_ds['train'], 'pytorch', batch_size=5, shuffle=True)
```
### Using Pytorch Datasets
The [pytorch Dataset](https://pytorch.org/docs/stable/data.html) and its
ecosystems (e.g.,
[torchvision](https://pytorch.org/vision/stable/index.html),
[torchtext](https://pytorch.org/text/stable/index.html),
[torchaudio](https://pytorch.org/audio/stable/index.html)) supports many
built-in datasets. `jax_dataloader` supports directly passing the
pytorch Dataset.
<div>
> **Note**
>
> Unfortuantely, the [pytorch
> Dataset](https://pytorch.org/docs/stable/data.html) can only work with
> `backend=pytorch`. See the belowing example.
</div>
``` python
from torchvision.datasets import MNIST
import numpy as np
```
We load the MNIST dataset from `torchvision`. The `ToNumpy` object
transforms images to `numpy.array`.
``` python
class ToNumpy(object):
def __call__(self, pic):
return np.array(pic, dtype=float)
```
``` python
pt_ds = MNIST('/tmp/mnist/', download=True, transform=ToNumpy(), train=False)
```
This `pt_ds` can **only** be loaded via `"pytorch"` dataloaders.
``` python
dataloader = jdl.DataLoader(pt_ds, 'pytorch', batch_size=5, shuffle=True)
```
### Using Tensowflow Datasets
`jax_dataloader` supports directly passing the [tensorflow
datasets](www.tensorflow.org/datasets).
``` python
import tensorflow_datasets as tfds
import tensorflow as tf
```
For instance, we can load the MNIST dataset from `tensorflow_datasets`
``` python
tf_ds = tfds.load('mnist', split='test', as_supervised=True)
```
and use `jax_dataloader` for iterating the dataset.
``` python
dataloader = jdl.DataLoader(tf_ds, 'tensorflow', batch_size=5, shuffle=True)
```
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"description": "# Dataloader for JAX\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n\n\n\n \n<a href=\"https://static.pepy.tech/badge/jax-dataloader\"><img src=\"https://static.pepy.tech/badge/jax-dataloader\" alt=\"Downloads\"></a>\n\n## Overview\n\n`jax_dataloader` brings *pytorch-like* dataloader API to `jax`. It\nsupports\n\n- **4 datasets to download and pre-process data**:\n - [jax dataset](https://birkhoffg.github.io/jax-dataloader/dataset/)\n - [huggingface datasets](https://github.com/huggingface/datasets)\n - [pytorch\n Dataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)\n - [tensorflow dataset](www.tensorflow.org/datasets)\n- **3 backends to iteratively load batches**:\n - [jax\n dataloader](https://birkhoffg.github.io/jax-dataloader/core.html#jax-dataloader)\n - [pytorch\n dataloader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)\n - [tensorflow dataset](www.tensorflow.org/datasets)\n\nA minimum `jax-dataloader` example:\n\n``` python\nimport jax_dataloader as jdl\n\ndataloader = jdl.DataLoader(\n dataset, # Can be a jdl.Dataset or pytorch or huggingface dataset\n backend='jax', # Use 'jax' for loading data (also supports `pytorch`)\n)\n\nbatch = next(iter(dataloader)) # iterate next batch\n```\n\n## Installation\n\nThe latest `jax-dataloader` release can directly be installed from PyPI:\n\n``` sh\npip install jax-dataloader\n```\n\nor install directly from the repository:\n\n``` sh\npip install git+https://github.com/BirkhoffG/jax-dataloader.git\n```\n\n<div>\n\n> **Note**\n>\n> We keep `jax-dataloader`\u2019s dependencies minimum, which only install\n> `jax`-related dependencies, and `plum-dispatch` for backend\n> dispatching. If you wish to use integration of `pytorch`, huggingface\n> `datasets`, or `tensorflow`, we recommend manually install those\n> dependencies.\n>\n> You can also run `pip install jax-dataloader[all]` to install\n> everything (not recommended).\n\n</div>\n\n## Usage\n\n[`jax_dataloader.core.DataLoader`](https://birkhoffg.github.io/jax-dataloader/core.html#dataloader)\nfollows similar API as the pytorch dataloader.\n\n- The `dataset` should be an object of the subclass of\n `jax_dataloader.core.Dataset` or `torch.utils.data.Dataset` or (the\n huggingface) `datasets.Dataset` or `tf.data.Dataset`.\n- The `backend` should be one of `\"jax\"` or `\"pytorch\"` or\n `\"tensorflow\"`. This argument specifies which backend dataloader to\n load batches.\n\nNote that not every dataset is compatible with every backend. See the\ncompatibility table below:\n\n| | `jdl.Dataset` | `torch_data.Dataset` | `tf.data.Dataset` | `datasets.Dataset` |\n|:---------------|:--------------|:---------------------|:------------------|:-------------------|\n| `\"jax\"` | \u2705 | \u274c | \u274c | \u2705 |\n| `\"pytorch\"` | \u2705 | \u2705 | \u274c | \u2705 |\n| `\"tensorflow\"` | \u2705 | \u274c | \u2705 | \u2705 |\n\n### Using [`ArrayDataset`](https://birkhoffg.github.io/jax-dataloader/dataset.html#arraydataset)\n\nThe `jax_dataloader.core.ArrayDataset` is an easy way to wrap multiple\n`jax.numpy.array` into one Dataset. For example, we can create an\n[`ArrayDataset`](https://birkhoffg.github.io/jax-dataloader/dataset.html#arraydataset)\nas follows:\n\n``` python\n# Create features `X` and labels `y`\nX = jnp.arange(100).reshape(10, 10)\ny = jnp.arange(10)\n# Create an `ArrayDataset`\narr_ds = jdl.ArrayDataset(X, y)\n```\n\nThis `arr_ds` can be loaded by *every* backends.\n\n``` python\n# Create a `DataLoader` from the `ArrayDataset` via jax backend\ndataloader = jdl.DataLoader(arr_ds, 'jax', batch_size=5, shuffle=True)\n# Or we can use the pytorch backend\ndataloader = jdl.DataLoader(arr_ds, 'pytorch', batch_size=5, shuffle=True)\n```\n\n### Using Huggingface Datasets\n\nThe huggingface [datasets](https://github.com/huggingface/datasets) is a\nmorden library for downloading, pre-processing, and sharing datasets.\n`jax_dataloader` supports directly passing the huggingface datasets.\n\n``` python\nfrom datasets import load_dataset\n```\n\nFor example, We load the `\"squad\"` dataset from `datasets`:\n\n``` python\nhf_ds = load_dataset(\"squad\")\n```\n\nThen, we can use `jax_dataloader` to load batches of `hf_ds`.\n\n``` python\n# Create a `DataLoader` from the `datasets.Dataset` via jax backend\ndataloader = jdl.DataLoader(hf_ds['train'], 'jax', batch_size=5, shuffle=True)\n# Or we can use the pytorch backend\ndataloader = jdl.DataLoader(hf_ds['train'], 'pytorch', batch_size=5, shuffle=True)\n```\n\n### Using Pytorch Datasets\n\nThe [pytorch Dataset](https://pytorch.org/docs/stable/data.html) and its\necosystems (e.g.,\n[torchvision](https://pytorch.org/vision/stable/index.html),\n[torchtext](https://pytorch.org/text/stable/index.html),\n[torchaudio](https://pytorch.org/audio/stable/index.html)) supports many\nbuilt-in datasets. `jax_dataloader` supports directly passing the\npytorch Dataset.\n\n<div>\n\n> **Note**\n>\n> Unfortuantely, the [pytorch\n> Dataset](https://pytorch.org/docs/stable/data.html) can only work with\n> `backend=pytorch`. See the belowing example.\n\n</div>\n\n``` python\nfrom torchvision.datasets import MNIST\nimport numpy as np\n```\n\nWe load the MNIST dataset from `torchvision`. The `ToNumpy` object\ntransforms images to `numpy.array`.\n\n``` python\nclass ToNumpy(object):\n def __call__(self, pic):\n return np.array(pic, dtype=float)\n```\n\n``` python\npt_ds = MNIST('/tmp/mnist/', download=True, transform=ToNumpy(), train=False)\n```\n\nThis `pt_ds` can **only** be loaded via `\"pytorch\"` dataloaders.\n\n``` python\ndataloader = jdl.DataLoader(pt_ds, 'pytorch', batch_size=5, shuffle=True)\n```\n\n### Using Tensowflow Datasets\n\n`jax_dataloader` supports directly passing the [tensorflow\ndatasets](www.tensorflow.org/datasets).\n\n``` python\nimport tensorflow_datasets as tfds\nimport tensorflow as tf\n```\n\nFor instance, we can load the MNIST dataset from `tensorflow_datasets`\n\n``` python\ntf_ds = tfds.load('mnist', split='test', as_supervised=True)\n```\n\nand use `jax_dataloader` for iterating the dataset.\n\n``` python\ndataloader = jdl.DataLoader(tf_ds, 'tensorflow', batch_size=5, shuffle=True)\n```\n",
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