Introduction to Data Loaders on CPU with JAX

Introduction to Data Loaders on CPU with JAX#

Open in Colab

This tutorial explores different data loading strategies for using JAX on a single CPU. While JAX doesn’t include a built-in data loader, it seamlessly integrates with popular data loading libraries, including:

In this tutorial, you’ll learn how to efficiently load data using these libraries for a simple image classification task based on the MNIST dataset.

Compared to GPU or multi-device setups, CPU-based data loading is straightforward as it avoids challenges like GPU memory management and data synchronization across devices. This makes it ideal for smaller-scale tasks or scenarios where data resides exclusively on the CPU.

If you’re looking for GPU-specific data loading advice, see Data Loaders on GPU.

If you’re looking for a multi-device data loading strategy, see Data Loaders on Multi-Device Setups.

Setting JAX to Use CPU Only#

First, you’ll restrict JAX to use only the CPU, even if a GPU is available. This ensures consistency and allows you to focus on CPU-based data loading.

import os
os.environ['JAX_PLATFORM_NAME'] = 'cpu'

Import JAX API

import jax
import jax.numpy as jnp
from jax import random, grad, jit, vmap

CPU Setup Verification#

jax.devices()

[CpuDevice(id=0)]

Setting Hyperparameters and Initializing Parameters#

You’ll define hyperparameters for your model and data loading, including layer sizes, learning rate, batch size, and the data directory. You’ll also initialize the weights and biases for a fully-connected neural network.

# A helper function to randomly initialize weights and biases
# for a dense neural network layer
def random_layer_params(m, n, key, scale=1e-2):
  w_key, b_key = random.split(key)
  return scale * random.normal(w_key, (n, m)), scale * random.normal(b_key, (n,))

# Function to initialize network parameters for all layers based on defined sizes
def init_network_params(sizes, key):
  keys = random.split(key, len(sizes))
  return [random_layer_params(m, n, k) for m, n, k in zip(sizes[:-1], sizes[1:], keys)]

layer_sizes = [784, 512, 512, 10]  # Layers of the network
step_size = 0.01                   # Learning rate for optimization
num_epochs = 8                     # Number of training epochs
batch_size = 128                   # Batch size for training
n_targets = 10                     # Number of classes (digits 0-9)
num_pixels = 28 * 28               # Input size (MNIST images are 28x28 pixels)
data_dir = '/tmp/mnist_dataset'    # Directory for storing the dataset

# Initialize network parameters using the defined layer sizes and a random seed
params = init_network_params(layer_sizes, random.PRNGKey(0))

Model Prediction with Auto-Batching#

In this section, you’ll define the predict function for your neural network. This function computes the output of the network for a single input image.

To efficiently process multiple images simultaneously, you’ll use vmap, which allows you to vectorize the predict function and apply it across a batch of inputs. This technique, called auto-batching, improves computational efficiency by leveraging hardware acceleration.

from jax.scipy.special import logsumexp

def relu(x):
  return jnp.maximum(0, x)

def predict(params, image):
  # per-example prediction
  activations = image
  for w, b in params[:-1]:
    outputs = jnp.dot(w, activations) + b
    activations = relu(outputs)

  final_w, final_b = params[-1]
  logits = jnp.dot(final_w, activations) + final_b
  return logits - logsumexp(logits)

# Make a batched version of the `predict` function
batched_predict = vmap(predict, in_axes=(None, 0))

Utility and Loss Functions#

You’ll now define utility functions for:

  • One-hot encoding: Converts class indices to binary vectors.

  • Accuracy calculation: Measures the performance of the model on the dataset.

  • Loss computation: Calculates the difference between predictions and targets.

To optimize performance:

  • grad is used to compute gradients of the loss function with respect to network parameters.

  • jit compiles the update function, enabling faster execution by leveraging JAX’s XLA compilation.

import time

def one_hot(x, k, dtype=jnp.float32):
  """Create a one-hot encoding of x of size k."""
  return jnp.array(x[:, None] == jnp.arange(k), dtype)

def accuracy(params, images, targets):
  """Calculate the accuracy of predictions."""
  target_class = jnp.argmax(targets, axis=1)
  predicted_class = jnp.argmax(batched_predict(params, images), axis=1)
  return jnp.mean(predicted_class == target_class)

def loss(params, images, targets):
  """Calculate the loss between predictions and targets."""
  preds = batched_predict(params, images)
  return -jnp.mean(preds * targets)

@jit
def update(params, x, y):
  """Update the network parameters using gradient descent."""
  grads = grad(loss)(params, x, y)
  return [(w - step_size * dw, b - step_size * db)
          for (w, b), (dw, db) in zip(params, grads)]

def reshape_and_one_hot(x, y):
    """Reshape and one-hot encode the inputs."""
    x = jnp.reshape(x, (len(x), num_pixels))
    y = one_hot(y, n_targets)
    return x, y

def train_model(num_epochs, params, training_generator, data_loader_type='streamed'):
    """Train the model for a given number of epochs."""
    for epoch in range(num_epochs):
        start_time = time.time()
        for x, y in training_generator() if data_loader_type == 'streamed' else training_generator:
            x, y = reshape_and_one_hot(x, y)
            params = update(params, x, y)

        print(f"Epoch {epoch + 1} in {time.time() - start_time:.2f} sec: "
              f"Train Accuracy: {accuracy(params, train_images, train_labels):.4f}, "
              f"Test Accuracy: {accuracy(params, test_images, test_labels):.4f}")

Loading Data with PyTorch DataLoader#

This section shows how to load the MNIST dataset using PyTorch’s DataLoader, convert the data to NumPy arrays, and apply transformations to flatten and cast images.

!pip install torch torchvision

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import numpy as np
from jax.tree_util import tree_map
from torch.utils import data
from torchvision.datasets import MNIST

def numpy_collate(batch):
  """Convert a batch of PyTorch data to NumPy arrays."""
  return tree_map(np.asarray, data.default_collate(batch))

class NumpyLoader(data.DataLoader):
    """Custom DataLoader to return NumPy arrays from a PyTorch Dataset."""
    def __init__(self, dataset, batch_size=1, shuffle=False, **kwargs):
        super().__init__(dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=numpy_collate, **kwargs)

class FlattenAndCast(object):
  """Transform class to flatten and cast images to float32."""
  def __call__(self, pic):
    return np.ravel(np.array(pic, dtype=jnp.float32))

Load Dataset with Transformations#

Standardize the data by flattening the images, casting them to float32, and ensuring consistent data types.

mnist_dataset = MNIST(data_dir, download=True, transform=FlattenAndCast())

Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 403: Forbidden

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /tmp/mnist_dataset/MNIST/raw/train-images-idx3-ubyte.gz
Extracting /tmp/mnist_dataset/MNIST/raw/train-images-idx3-ubyte.gz to /tmp/mnist_dataset/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 403: Forbidden

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /tmp/mnist_dataset/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting /tmp/mnist_dataset/MNIST/raw/train-labels-idx1-ubyte.gz to /tmp/mnist_dataset/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 403: Forbidden

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /tmp/mnist_dataset/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting /tmp/mnist_dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to /tmp/mnist_dataset/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 403: Forbidden

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to /tmp/mnist_dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting /tmp/mnist_dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to /tmp/mnist_dataset/MNIST/raw

100%|██████████| 9.91M/9.91M [00:00<00:00, 49.4MB/s]
100%|██████████| 28.9k/28.9k [00:00<00:00, 2.09MB/s]
100%|██████████| 1.65M/1.65M [00:00<00:00, 13.3MB/s]
100%|██████████| 4.54k/4.54k [00:00<00:00, 8.81MB/s]

Full Training Dataset for Accuracy Checks#

Convert the entire training dataset to JAX arrays.

train_images = jnp.array(mnist_dataset.data.numpy().reshape(len(mnist_dataset.data), -1), dtype=jnp.float32)
train_labels = one_hot(np.array(mnist_dataset.targets), n_targets)

Get Full Test Dataset#

Load and process the full test dataset.

mnist_dataset_test = MNIST(data_dir, download=True, train=False)
test_images = jnp.array(mnist_dataset_test.data.numpy().reshape(len(mnist_dataset_test.data), -1), dtype=jnp.float32)
test_labels = one_hot(np.array(mnist_dataset_test.targets), n_targets)

print('Train:', train_images.shape, train_labels.shape)
print('Test:', test_images.shape, test_labels.shape)

Train: (60000, 784) (60000, 10)
Test: (10000, 784) (10000, 10)

Training Data Generator#

Define a generator function using PyTorch’s DataLoader for batch training. Setting num_workers > 0 enables multi-process data loading, which can accelerate data loading for larger datasets or intensive preprocessing tasks. Experiment with different values to find the optimal setting for your hardware and workload.

Note: When setting num_workers > 0, you may see the following RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. This warning can be safely ignored since data loaders do not use JAX within the forked processes.

def pytorch_training_generator(mnist_dataset):
    return NumpyLoader(mnist_dataset, batch_size=batch_size, num_workers=0)

Training Loop (PyTorch DataLoader)#

The training loop uses the PyTorch DataLoader to iterate through batches and update model parameters.

train_model(num_epochs, params, pytorch_training_generator(mnist_dataset), data_loader_type='iterable')

Epoch 1 in 28.93 sec: Train Accuracy: 0.9158, Test Accuracy: 0.9196
Epoch 2 in 8.33 sec: Train Accuracy: 0.9372, Test Accuracy: 0.9384
Epoch 3 in 6.99 sec: Train Accuracy: 0.9492, Test Accuracy: 0.9468
Epoch 4 in 7.01 sec: Train Accuracy: 0.9569, Test Accuracy: 0.9532
Epoch 5 in 8.17 sec: Train Accuracy: 0.9630, Test Accuracy: 0.9579
Epoch 6 in 8.27 sec: Train Accuracy: 0.9674, Test Accuracy: 0.9615
Epoch 7 in 8.32 sec: Train Accuracy: 0.9708, Test Accuracy: 0.9650
Epoch 8 in 8.07 sec: Train Accuracy: 0.9737, Test Accuracy: 0.9671

Loading Data with TensorFlow Datasets (TFDS)#

This section demonstrates how to load the MNIST dataset using TFDS, fetch the full dataset for evaluation, and define a training generator for batch processing. GPU usage is explicitly disabled for TensorFlow.

import tensorflow_datasets as tfds
import tensorflow as tf

# Ensuring CPU-Only Execution, disable any GPU usage(if applicable) for TF
tf.config.set_visible_devices([], device_type='GPU')

Fetch Full Dataset for Evaluation#

Load the dataset with tfds.load, convert it to NumPy arrays, and process it for evaluation.

# tfds.load returns tf.Tensors (or tf.data.Datasets if batch_size != -1)
mnist_data, info = tfds.load(name="mnist", batch_size=-1, data_dir=data_dir, with_info=True)
mnist_data = tfds.as_numpy(mnist_data)
train_data, test_data = mnist_data['train'], mnist_data['test']

# Full train set
train_images, train_labels = train_data['image'], train_data['label']
train_images = jnp.reshape(train_images, (len(train_images), num_pixels))
train_labels = one_hot(train_labels, n_targets)

# Full test set
test_images, test_labels = test_data['image'], test_data['label']
test_images = jnp.reshape(test_images, (len(test_images), num_pixels))
test_labels = one_hot(test_labels, n_targets)

Downloading and preparing dataset 11.06 MiB (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /tmp/mnist_dataset/mnist/3.0.1...
Dataset mnist downloaded and prepared to /tmp/mnist_dataset/mnist/3.0.1. Subsequent calls will reuse this data.

print('Train:', train_images.shape, train_labels.shape)
print('Test:', test_images.shape, test_labels.shape)

Train: (60000, 784) (60000, 10)
Test: (10000, 784) (10000, 10)

Define the Training Generator#

Create a generator function to yield batches of data for training.

def training_generator():
  # as_supervised=True gives us the (image, label) as a tuple instead of a dict
  ds = tfds.load(name='mnist', split='train', as_supervised=True, data_dir=data_dir)
  # You can build up an arbitrary tf.data input pipeline
  ds = ds.batch(batch_size).prefetch(1)
  # tfds.dataset_as_numpy converts the tf.data.Dataset into an iterable of NumPy arrays
  return tfds.as_numpy(ds)

Training Loop (TFDS)#

Use the training generator in a custom training loop.

train_model(num_epochs, params, training_generator)

Epoch 1 in 8.46 sec: Train Accuracy: 0.9252, Test Accuracy: 0.9270
Epoch 2 in 7.79 sec: Train Accuracy: 0.9429, Test Accuracy: 0.9412
Epoch 3 in 9.84 sec: Train Accuracy: 0.9533, Test Accuracy: 0.9514
Epoch 4 in 9.47 sec: Train Accuracy: 0.9602, Test Accuracy: 0.9551
Epoch 5 in 9.32 sec: Train Accuracy: 0.9652, Test Accuracy: 0.9602
Epoch 6 in 9.30 sec: Train Accuracy: 0.9692, Test Accuracy: 0.9630
Epoch 7 in 9.24 sec: Train Accuracy: 0.9726, Test Accuracy: 0.9655
Epoch 8 in 8.00 sec: Train Accuracy: 0.9755, Test Accuracy: 0.9667

Loading Data with Grain#

This section demonstrates how to load MNIST data using Grain, a data-loading library. You’ll define a custom dataset class for Grain and set up a Grain DataLoader for efficient training.

Install Grain

!pip install grain

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Import Required Libraries (import MNIST dataset from torchvision)

import numpy as np
import grain.python as pygrain
from torchvision.datasets import MNIST

Define Dataset Class#

Create a custom dataset class to load MNIST data for Grain.

class Dataset:
    def __init__(self, data_dir, train=True):
        self.data_dir = data_dir
        self.train = train
        self.load_data()

    def load_data(self):
        self.dataset = MNIST(self.data_dir, download=True, train=self.train)

    def __len__(self):
        return len(self.dataset)

    def __getitem__(self, index):
        img, label = self.dataset[index]
        return np.ravel(np.array(img, dtype=np.float32)), label

Initialize the Dataset#

mnist_dataset = Dataset(data_dir)

Get the full train and test dataset#

# Convert training data to JAX arrays and encode labels as one-hot vectors
train_images = jnp.array([mnist_dataset[i][0] for i in range(len(mnist_dataset))], dtype=jnp.float32)
train_labels = one_hot(np.array([mnist_dataset[i][1] for i in range(len(mnist_dataset))]), n_targets)

# Load test dataset and process it
mnist_dataset_test = MNIST(data_dir, download=True, train=False)
test_images = jnp.array([np.ravel(np.array(mnist_dataset_test[i][0], dtype=np.float32)) for i in range(len(mnist_dataset_test))], dtype=jnp.float32)
test_labels = one_hot(np.array([mnist_dataset_test[i][1] for i in range(len(mnist_dataset_test))]), n_targets)

print("Train:", train_images.shape, train_labels.shape)
print("Test:", test_images.shape, test_labels.shape)

Train: (60000, 784) (60000, 10)
Test: (10000, 784) (10000, 10)

Initialize PyGrain DataLoader#

Set up a PyGrain DataLoader for sequential batch sampling.

sampler = pygrain.SequentialSampler(
    num_records=len(mnist_dataset),
    shard_options=pygrain.NoSharding()) # Single-device, no sharding

def pygrain_training_generator():
    """Grain DataLoader generator for training."""
    return pygrain.DataLoader(
        data_source=mnist_dataset,
        sampler=sampler,
        operations=[pygrain.Batch(batch_size)],
    )

Training Loop (Grain)#

Run the training loop using the Grain DataLoader.

train_model(num_epochs, params, pygrain_training_generator)

Epoch 1 in 15.39 sec: Train Accuracy: 0.9158, Test Accuracy: 0.9196
Epoch 2 in 15.27 sec: Train Accuracy: 0.9372, Test Accuracy: 0.9384
Epoch 3 in 12.61 sec: Train Accuracy: 0.9492, Test Accuracy: 0.9468
Epoch 4 in 12.62 sec: Train Accuracy: 0.9569, Test Accuracy: 0.9532
Epoch 5 in 12.39 sec: Train Accuracy: 0.9630, Test Accuracy: 0.9579
Epoch 6 in 12.19 sec: Train Accuracy: 0.9674, Test Accuracy: 0.9615
Epoch 7 in 12.56 sec: Train Accuracy: 0.9708, Test Accuracy: 0.9650
Epoch 8 in 13.04 sec: Train Accuracy: 0.9737, Test Accuracy: 0.9671

Loading Data with Hugging Face#

This section demonstrates loading MNIST data using the Hugging Face datasets library. You’ll format the dataset for JAX compatibility, prepare flattened images and one-hot-encoded labels, and define a training generator.

Install the Hugging Face datasets library.

!pip install datasets

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Import Library

from datasets import load_dataset

Load and Format MNIST Dataset#

Load the MNIST dataset from Hugging Face and format it as numpy arrays for quick access or jax to get JAX arrays.

mnist_dataset = load_dataset("mnist").with_format("numpy")

/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: 
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
  warnings.warn(

Extract images and labels#

Get image shape and flatten for model input

train_images = mnist_dataset["train"]["image"]
train_labels = mnist_dataset["train"]["label"]
test_images = mnist_dataset["test"]["image"]
test_labels = mnist_dataset["test"]["label"]

# Flatten images and one-hot encode labels
image_shape = train_images.shape[1:]
num_features = image_shape[0] * image_shape[1]

train_images = train_images.reshape(-1, num_features)
test_images = test_images.reshape(-1, num_features)

train_labels = one_hot(train_labels, n_targets)
test_labels = one_hot(test_labels, n_targets)

print('Train:', train_images.shape, train_labels.shape)
print('Test:', test_images.shape, test_labels.shape)

Train: (60000, 784) (60000, 10)
Test: (10000, 784) (10000, 10)

Define Training Generator#

Set up a generator to yield batches of images and labels for training.

def hf_training_generator():
    """Yield batches for training."""
    for batch in mnist_dataset["train"].iter(batch_size):
        x, y = batch["image"], batch["label"]
        yield x, y

Training Loop (Hugging Face Datasets)#

Run the training loop using the Hugging Face training generator.

train_model(num_epochs, params, hf_training_generator)

Epoch 1 in 9.77 sec: Train Accuracy: 0.9158, Test Accuracy: 0.9196
Epoch 2 in 9.94 sec: Train Accuracy: 0.9372, Test Accuracy: 0.9384
Epoch 3 in 9.44 sec: Train Accuracy: 0.9492, Test Accuracy: 0.9468
Epoch 4 in 9.48 sec: Train Accuracy: 0.9569, Test Accuracy: 0.9532
Epoch 5 in 9.41 sec: Train Accuracy: 0.9630, Test Accuracy: 0.9579
Epoch 6 in 9.98 sec: Train Accuracy: 0.9674, Test Accuracy: 0.9615
Epoch 7 in 12.19 sec: Train Accuracy: 0.9708, Test Accuracy: 0.9650
Epoch 8 in 10.91 sec: Train Accuracy: 0.9737, Test Accuracy: 0.9671

Summary#

This notebook has introduced efficient strategies for data loading on a CPU with JAX, demonstrating how to integrate popular libraries like PyTorch DataLoader, TensorFlow Datasets, Grain, and Hugging Face Datasets. Each library offers distinct advantages, enabling you to streamline the data loading process for machine learning tasks. By understanding the strengths of these methods, you can select the approach that best suits your project’s specific requirements.

Contents