tfds-nightly

library4.9.9.dev202510250044pypypi✓ verified 86d ago

tensorflow/datasets is a library of datasets ready to use with TensorFlow. The `tfds-nightly` package provides daily releases, offering the latest features and bug fixes, often before they are available in the stable `tensorflow-datasets` release. It provides a vast collection of datasets for machine learning pipelines, supporting various frameworks beyond TensorFlow, including JAX and PyTorch.

ai-ml data

pip install tfds-nightly

github ↗package ↗

INSTALL

IMPORT

SIG · TFDS-NIGHTLY

tfds-nightly

ai-mlpythonv4.9.9.dev202510250044

Install

27.1s avg

Import

1853ms

Disk

693MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v4.9.9.dev202510250044 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.940 runs

timeout

glibc

py 3.10–3.940 runs

installs and imports cleanly · install 27.1s · import 1.853s · 294MB

693MB installed

● package 693MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

tfds

✓ import tensorflow_datasets as tfds

This quickstart demonstrates how to install `tfds-nightly`, import `tensorflow_datasets`, load a common dataset like MNIST, and iterate over a few examples. It configures a data directory and retrieves dataset information.

import tensorflow_datasets as tfds import os # Set TFDS data directory (optional, but good practice for caching) os.environ['TFDS_DATA_DIR'] = '/tmp/tfds_data' # Load a dataset (e.g., MNIST) ds, info = tfds.load( 'mnist', split='train', shuffle_files=True, as_supervised=True, # Returns (image, label) tuples with_info=True ) print(f"Dataset info: {info.description}") print(f"Number of training examples: {info.splits['train'].num_examples}") # Iterate over a few examples for image, label in ds.take(1): print(f"Image shape: {image.shape}, Label: {label}")

Debug

Known issues

breakingNightly builds often include API changes and experimental features that may be unstable or subject to further modification before a stable release. Code written against a `tfds-nightly` version might break with subsequent nightly updates.

fix

Regularly check the GitHub changelog and be prepared to update code to match API changes. Pin `tfds-nightly` to a specific nightly version if stability is crucial, but acknowledge that this defeats the purpose of 'nightly'.

affects: All nightly versions

breakingThe handling of `None` values when processing Hugging Face datasets (e.g., via `HuggingfaceDatasetBuilder`) changed from defaulting to `0`/`0.0` for int/float features to using NumPy's `-inf`. This can silently alter data or cause downstream errors if your code expected the old default behavior for missing values.

fix

Explicitly handle `None` values during dataset creation or consumption, e.g., by filtering, mapping, or using `tfds.features.Optional` if applicable, rather than relying on implicit defaults.

affects: v4.9.3 and later nightly builds

gotchaUsing `tfds build` for Beam-based datasets requires `apache-beam` to be installed, but `tfds-nightly` does not always automatically install it as a direct dependency. There have also been specific `apache-beam` version compatibility pins (`<2.65.0` in v4.9.9) that might cause issues.

fix

Manually install `apache-beam` if you plan to use `tfds build` or other Beam-dependent functionalities: `pip install apache-beam`. Check the `tfds` changelog or documentation for any specific version requirements for `apache-beam`.

affects: v4.9.9 and potentially other versions when using Beam

gotchaThere was a bug where the `resource` module, which is not available on Windows, caused a `ModuleNotFoundError` when importing `tensorflow_datasets`. While fixed in later versions, similar platform-specific dependency issues can arise in nightly builds.

fix

Ensure you are on the latest `tfds-nightly` version. If encountering `ModuleNotFoundError` for platform-specific modules on non-Linux OS, check relevant GitHub issues for patches or workarounds.

affects: Around v4.9.2-v4.9.3 (fixed in subsequent patches)

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'tensorflow_datasets'

The `tensorflow-datasets` or `tfds-nightly` package is not installed in the current Python environment, or the environment is not correctly activated.

fix

Install the package: `pip install tfds-nightly` (or `pip install tensorflow-datasets` for stable). If using a virtual environment, ensure it is activated.

ValueError: None values not supported.

This error can occur when TensorFlow operations encounter `None` values in tensors where they are not expected, particularly after changes in `tfds`'s handling of missing data or if input data contains unexpected `None`s.

fix

Inspect your dataset and data processing pipeline for `None` values. For Hugging Face datasets, be aware of the v4.9.3 change in `None` handling. Filter out `None`s, provide explicit default values, or use `tfds.features.Optional` where appropriate.

NonMatchingChecksumError: Checksum mismatch for downloaded file...

The downloaded file (or a file on the local disk) does not match the expected checksum, indicating a potential corruption, an update to the source data, or a local file system issue.

fix

Delete the corrupted file from the `downloads` folder and try again. If the upstream data has genuinely changed, the dataset builder needs to be updated. For custom datasets, use `tfds build --register_checksums` to update the checksum.

TypeError: Unknown resource path: : MultiplexedPath

This is an error that can occur when `tfds build` is run, potentially related to file system access or an internal path resolution issue within the library.

fix

Ensure your environment is clean and all dependencies are up-to-date (`pip install --upgrade tfds-nightly`). If the problem persists, consult the TensorFlow Datasets GitHub issues for specific workarounds or related bugs.

Upgrade

Version history

4.9.9.dev202510250044latest on PyPI · released Oct 25, 2025

Audit

Dependencies

tensorflowoptionalWhile TFDS can be used framework-agnostically and in a 'TensorFlow-less' manner for reading, full functionality (especially for building datasets or using `tf.data.Dataset` objects) often relies on TensorFlow.

apache-beamoptionalRequired for generating large datasets in a distributed manner, particularly when using the `tfds build` CLI for Beam-based datasets.

array_recordoptionalNeeded for the 'TensorFlow-less' data loading path, providing efficient random access to dataset records.

Agent activity

10 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

githubhttps://github.com/tensorflow/datasets ↗

packagehttps://pypi.org/project/tfds-nightly/ ↗