tensorflow-datasets
tensorflow/datasets is a library of datasets ready to use with TensorFlow.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, and low install friction. It solves a real problem—standardized dataset access for TensorFlow workflows—with a permissive license. Install it if you work with TensorFlow and need quick access to public datasets; skip it only if you manage datasets entirely through custom pipelines.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and TensorFlow installed separately.
- Low install friction with a pure-Python wheel.
- Active maintenance (last commit 2026-07-29) and 18 runtime dependencies including numpy, pyarrow, and tensorflow-metadata.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license. Users are responsible for determining their own permission to use each underlying dataset; the library itself is freely usable and modifiable.
last release 2026-05-08 (98 days) · last repo commit 2026-07-29 · 4,581 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,764,589 downloads/mo, #3,579 on PyPI
Alternatives
Verify before relying
pip install tensorflow-datasets
import tensorflow_datasets as tfds
ds = tfds.load('mnist', split='train', as_supervised=True, shuffle_files=True)
ds = ds.shuffle(1000).batch(128).prefetch(10)- Whether all advertised datasets in the catalog are currently available and maintained.
- Performance characteristics and download speeds for large datasets.
- Compatibility with specific TensorFlow versions beyond the Python requirement.
What it is and what it does
TensorFlow Datasets is a library that provides standardized access to many public datasets, automatically handling download and preparation into tf.data.Dataset objects ready for training pipelines. It wraps datasets from various sources and exposes them through a simple API, letting you load data like MNIST or other benchmarks with a single function call, then chain standard TensorFlow operations like shuffle, batch, and prefetch.
The library emphasizes simplicity for standard use cases, reproducibility (all users get the same examples in the same order), and performance by following TensorFlow best practices. It has 18 runtime dependencies including numpy, pyarrow, and tensorflow-metadata, and requires Python 3.10 or later. The package is actively maintained and carries no known security vulnerabilities.
Use it for
- Load benchmark datasets like MNIST or CIFAR for quick prototyping and model evaluation.
- Build reproducible input pipelines for training by ensuring deterministic dataset ordering across runs.
- Access a curated catalog of public datasets without manually downloading or preprocessing files.
- Integrate datasets into tf.data pipelines with standard operations like batching and prefetching.
- Prepare datasets for distributed training by leveraging TFDS's performance-optimized loading.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, and low install friction. It solves a real problem—standardized dataset access for TensorFlow workflows—with a permissive license. Install it if you work with TensorFlow and need quick access to public datasets; skip it only if you manage datasets entirely through custom pipelines.
Install
tensorflow-datasets on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance (last commit 2026-07-29) and 18 runtime dependencies including numpy, pyarrow, and tensorflow-metadata. Requires Python 3.10 or later.
Requires Python 3.10 or later and TensorFlow installed separately.
License in practice
Apache 2.0 permissive license. Users are responsible for determining their own permission to use each underlying dataset; the library itself is freely usable and modifiable.
Quickstart
pip install tensorflow-datasets
import tensorflow_datasets as tfds
ds = tfds.load('mnist', split='train', as_supervised=True, shuffle_files=True)
ds = ds.shuffle(1000).batch(128).prefetch(10)Verify before relying
- Whether all advertised datasets in the catalog are currently available and maintained.
- Performance characteristics and download speeds for large datasets.
- Compatibility with specific TensorFlow versions beyond the Python requirement.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesabsl-pyarray_recorddm-treeetilsimmutabledictnumpypromiseprotobufpsutilpyarrowrequestssimple_parsingtensorflow-metadatatermcolortomltqdmwraptimportlib_resources |
| Maintenance | Actively maintained 98 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,764,589 / month, #3,579 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: tensorflow_datasets-4.9.10-py3-none-any.whl
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See also cinemagoer · opendatalab · tfds-nightly · seqio · tensorflow-metadata · tensorboard-data-server · seqio-nightly · tensorflow-io · datasets · tflite