tensorflow-io-gcs-filesystem
TensorFlow IO
Decision gist · record as of 2026-08-14
Yes, if you are building TensorFlow pipelines that consume data from Google Cloud Storage and want to avoid local downloads. Install only if your TensorFlow version matches the compatibility table (0.37.1 requires TensorFlow 2.16.x). No known vulnerabilities. Medium install friction due to compiled wheels, but straightforward once the correct TensorFlow version is in place.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow 2.16.x installed; Python 3.7–3.12 supported; GCS credentials must be configured in your environment.
- Medium install friction due to platform-specific compiled wheels (cp39–cp312 across macOS and Linux architectures).
- Actively maintained with recent commits; compatible with TensorFlow 2.16.x per version table.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
last release 2024-07-01 (774 days) · last repo commit 2026-06-25 · 739 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,480,961 downloads/mo, #1,904 on PyPI
Alternatives
Verify before relying
pip install tensorflow-io-gcs-filesystem
# Read dataset directly from GCS URLs
dataset_url = "https://storage.googleapis.com/cvdf-datasets/mnist/"
d_train = tfio.IODataset.from_mnist(
dataset_url + "train-images-idx3-ubyte.gz",
dataset_url + "train-labels-idx1-ubyte.gz"
)
d_train = d_train.batch(32)- Whether this package is a standalone filesystem plugin or requires the full tensorflow-io package as a peer dependency.
- Exact GCS authentication method expected (Application Default Credentials, service account key, etc.).
- Performance characteristics when reading large datasets from GCS vs. local storage.
- Whether automatic decompression of gzipped files is supported by this filesystem component.
What it is and what it does
tensorflow-io-gcs-filesystem is a TensorFlow I/O extension that adds Google Cloud Storage filesystem support to TensorFlow. It allows you to read and write data directly from GCS buckets within TensorFlow data pipelines, eliminating the need to download datasets locally before processing. The package integrates with tf.data.Dataset and Keras workflows to stream data from cloud storage.
The package is distributed as platform-specific compiled wheels for Python 3.7–3.12 on macOS (x86_64 and ARM64) and Linux (x86_64 and aarch64). It has no runtime dependencies beyond TensorFlow itself and is actively maintained, with the latest release (0.37.1) compatible with TensorFlow 2.16.x. Installation requires matching your TensorFlow version to the compatibility table provided in the documentation.
Use it for
- Load training datasets directly from GCS URLs in machine learning workflows without downloading to disk first.
- Stream large datasets from cloud storage in data pipelines for memory-efficient processing.
- Access public datasets hosted on GCS via HTTP/HTTPS URLs in TensorFlow code.
- Build data preprocessing workflows that read raw data from GCS and write processed results back to cloud storage.
- Integrate GCS file access into distributed TensorFlow training on Google Cloud Platform infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building TensorFlow pipelines that consume data from Google Cloud Storage and want to avoid local downloads.
Install only if your TensorFlow version matches the compatibility table (0.37.1 requires TensorFlow 2.16.x). No known vulnerabilities. Medium install friction due to compiled wheels, but straightforward once the correct TensorFlow version is in place.
Install
tensorflow-io-gcs-filesystem on PyPI
Before you install
Medium install friction due to platform-specific compiled wheels (cp39–cp312 across macOS and Linux architectures). Actively maintained with recent commits; compatible with TensorFlow 2.16.x per version table.
Requires TensorFlow 2.16.x installed; Python 3.7–3.12 supported; GCS credentials must be configured in your environment.
License in practice
Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install tensorflow-io-gcs-filesystem
# Read dataset directly from GCS URLs
dataset_url = "https://storage.googleapis.com/cvdf-datasets/mnist/"
d_train = tfio.IODataset.from_mnist(
dataset_url + "train-images-idx3-ubyte.gz",
dataset_url + "train-labels-idx1-ubyte.gz"
)
d_train = d_train.batch(32)Verify before relying
- Whether this package is a standalone filesystem plugin or requires the full tensorflow-io package as a peer dependency.
- Exact GCS authentication method expected (Application Default Credentials, service account key, etc.).
- Performance characteristics when reading large datasets from GCS vs. local storage.
- Whether automatic decompression of gzipped files is supported by this filesystem component.
Package facts
| License | permissive license permissive |
| Python support | Capped below the current Python release <3.13,>=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 774 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,480,961 / month, #1,904 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 :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-macosx_10_14_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-macosx_12_0_arm64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-macosx_10_14_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-macosx_12_0_arm64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp312-cp312-macosx_10_14_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp312-cp312-macosx_12_0_arm64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp39-cp39-macosx_10_14_x86_64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp39-cp39-macosx_12_0_arm64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_io_gcs_filesystem-0.37.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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See also tensorflow-io · boostedblob · tensorflow-transform · gcsfs · azure-datalake-store · tensorflow-datasets · tfds-nightly · tensorflow-cpu · tensorflow-data-validation · gcloud-aio-storage