azureml-fsspec
Access datastore uri with fsspec
Decision gist · record as of 2026-08-14
Yes, if you work within Azure Machine Learning and need fsspec access to datastores. Install friction is low and maintenance is active. The proprietary license requires review before use in commercial or redistributed products. No known security vulnerabilities as of the latest release.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10–3.13 and valid Azure Machine Learning subscription, resource group, workspace, and datastore credentials.
- Low install friction with a pure-Python wheel.
- Maintenance is active; the latest release (1.4.0) is recent and addressed setuptools compatibility issues in azureml-dataprep, a key runtime dependency.
License · maintenance · safety
(unclear) — Licensed under a proprietary Microsoft license (Proprietary https://aka.ms/azureml-preview-sdk-license). License treatment is unclear; review the linked license terms before use in commercial or redistributed contexts.
last release 2026-07-31 (14 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,144 downloads/mo, #10,380 on PyPI
Alternatives
Verify before relying
pip install azureml-fsspec
import fsspec
fs = fsspec.filesystem('azureml')
files = fs.ls('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder')- Whether the proprietary license permits commercial use or redistribution without explicit Microsoft agreement.
- Authentication flow details beyond mlclient support (e.g., service principal, managed identity setup steps).
- Performance characteristics or limits when working with large datasets via fsspec.
What it is and what it does
azureml-fsspec registers a custom fsspec filesystem handler for azureml:// URIs, bridging Azure Machine Learning datastores and Python's data-processing ecosystem. It lets you use fsspec's abstraction layer to read and write files directly from Azure ML datastores without manual credential management or intermediate downloads.
The package wraps fsspec around azureml-dataprep and pytz, handling URI parsing, authentication (including service principal and managed identity), and registry lookups. It is maintained by Microsoft, actively updated for recent Python versions (3.12, 3.13 support added in 1.4.0), and designed for data scientists and ML engineers working within Azure ML environments.
Use it for
- Access training data from an Azure ML datastore via fsspec without downloading files locally.
- Read parquet or CSV files from Azure ML datastores for distributed processing.
- Integrate Azure ML datastores into existing fsspec-based workflows without rewriting data-loading logic.
- Automate data pipeline steps that reference Azure ML datastore paths using fsspec APIs.
- Support hybrid ML workflows where Azure ML is the central data repository.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work within Azure Machine Learning and need fsspec access to datastores.
Install friction is low and maintenance is active. The proprietary license requires review before use in commercial or redistributed products. No known security vulnerabilities as of the latest release.
Install
azureml-fsspec on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance is active; the latest release (1.4.0) is recent and addressed setuptools compatibility issues in azureml-dataprep, a key runtime dependency.
Requires Python 3.10–3.13 and valid Azure Machine Learning subscription, resource group, workspace, and datastore credentials.
License in practice
Licensed under a proprietary Microsoft license (Proprietary https://aka.ms/azureml-preview-sdk-license). License treatment is unclear; review the linked license terms before use in commercial or redistributed contexts.
Quickstart
pip install azureml-fsspec
import fsspec
fs = fsspec.filesystem('azureml')
files = fs.ls('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder')Verify before relying
- Whether the proprietary license permits commercial use or redistribution without explicit Microsoft agreement.
- Authentication flow details beyond mlclient support (e.g., service principal, managed identity setup steps).
- Performance characteristics or limits when working with large datasets via fsspec.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesazureml-dataprepfsspecpytz |
| Maintenance | Actively maintained 14 days since the last release |
| First released | |
| Downloads | 171,144 / month, #10,380 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering |
Evidence: azureml_fsspec-1.4.0-py3-none-any.whl
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See also msgraphfs · azureml-dataprep-rslex · adlfs · azureml · fsspec · azureml-core · azureml-dataprep-native · azureml-mlflow · fsspec-xrootd · azureml-defaults