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Dataset Grouper - Scalable Dataset Pipelines for Group-Structured Learning

PyPI version

Dataset Grouper is a library for creating, writing, and iterating over datasets with group-level structure. It is primarily intended for creating large-scale datasets for federated learning research.

Installation

We recommend installing via PyPI. Please check the PyPI page for up-to-date version requirements, including python version requirements.

pip install --upgrade pip
pip install dataset-grouper

Getting Started

Below is a simple starting example, that partitions MNIST across 10 clients by label.

First we import some necessary packages, including Apache Beam, which will be used to run the Dataset Grouper's pipelines.

import apache_beam as beam
import dataset_grouper as dsgp
import tensorflow_datasets as tfds

Next, we download and prepare the MNIST dataset.

dataset_builder = tfds.builder('mnist')
dataset_builder.download_and_prepare(...)

We now write a function that assigns each MNIST example a client identifier (generally a bytes object). In this case, we will partition examples according to their label, but you can use much more interesting partition functions as well.

def label_partition(x):
  label = x['label'].numpy()
  return str(label).encode('utf-8')

Finally, we build a pipeline that will partition MNIST according to this function, and run it using Beam's Direct Runner.

mnist_pipeline = dsgp.tfds_to_tfrecords(
    dataset_builder=dataset_builder,
    split='test',
    get_key_fn=label_partition,
    file_path_prefix=...
)
with beam.Pipeline() as root:
  mnist_pipeline(root)

This will save a version of MNIST that has been partitioned according to labels to a TFRecord format. We can also load it to iterate over client datasets.

partitioned_dataset = dsgp.PartitionedDataset(
  file_pattern=...,
  tfds_features='mnist')

for group_dataset in partitioned_dataset.build_group_stream():
  pass

Generally, PartitionedDataset.build_group_stream() is a tf.data.Dataset that yields datasets, each of which is contains all the examples held by one group. If you'd like to use these datasets with NumPy, you can simply do:

group_dataset_numpy = group_dataset.as_numpy_iterator()

What Else?

The example above is primarily for educational purposes. MNIST is a relatively small dataset, and can generally fit entirely into memory. For more interesting examples, check out the examples folder.

Dataset Grouper is intended more for large-scale datasets, especially those datasets that do not fit into memory. For these datasets, we recommend using more sophisticated Beam runners, in order to partition the data in a distributed fashion.

Disclaimers

This is not an officially supported Google product.

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're interested in learning more about responsible AI practices, please see Google AI's Responsible AI Practices.

Dataset Grouper is Apache 2.0 licensed. See the LICENSE file.