azureml-dataset-runtime
The package is to coordinate dependencies within AzureML packages. This package is internal, and is not intended to be used directly.
Decision gist · record as of 2026-08-14
No—do not install this package directly. It is explicitly marked as internal to the Azure ML SDK and exists only to coordinate dependencies for other AzureML components. Install it only if another AzureML package requires it as a transitive dependency. Direct installation offers no value and may conflict with how the broader SDK expects it to be managed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- This package is explicitly internal to the Azure ML SDK and not intended for direct use—it exists to coordinate dependencies within the broader AzureML ecosystem.
- Active maintenance with a recent release (14 days ago).
- Low install friction with only three runtime dependencies: azureml-dataprep, pyarrow, and numpy.
License · maintenance · safety
(unclear) — License treatment is unclear—the raw license URL points to a Microsoft legal page but no SPDX identifier is available, making it difficult to assess compatibility with your project's license requirements without manual review.
last release 2026-07-31 (14 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 357,385 downloads/mo, #7,269 on PyPI
Alternatives
Verify before relying
pip install azureml-dataset-runtime
import azureml_dataset_runtime
# This package is internal; direct usage is not recommended.- Whether direct installation outside the AzureML SDK ecosystem is supported or recommended.
- Exact Python version compatibility (requires_python is unspecified in metadata).
- What the Microsoft license URL (https://aka.ms/azureml-sdk-license) permits in terms of redistribution and modification.
What it is and what it does
azureml-dataset-runtime is an internal coordination package within the Azure Machine Learning SDK for Python. Its sole purpose is to manage and align dependencies used by other AzureML components that handle dataset operations. It is not designed for direct user installation or consumption.
The package depends on azureml-dataprep, pyarrow, and numpy to provide the underlying data handling capabilities that the broader AzureML ecosystem relies on. Because it is explicitly marked as internal, installing it directly is not recommended—it should only be pulled in as a transitive dependency when you install other AzureML packages that require it.
Use it for
- Automatically installed as a transitive dependency when using other Azure ML SDK packages for dataset handling.
- Ensuring consistent versions of pyarrow and numpy across AzureML dataset operations within an organization.
- Supporting Azure ML pipelines that process datasets without needing to manage dataset runtime dependencies separately.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—do not install this package directly.
It is explicitly marked as internal to the Azure ML SDK and exists only to coordinate dependencies for other AzureML components. Install it only if another AzureML package requires it as a transitive dependency. Direct installation offers no value and may conflict with how the broader SDK expects it to be managed.
Install
azureml-dataset-runtime on PyPI
Before you install
Active maintenance with a recent release (14 days ago). Low install friction with only three runtime dependencies: azureml-dataprep, pyarrow, and numpy.
This package is explicitly internal to the Azure ML SDK and not intended for direct use—it exists to coordinate dependencies within the broader AzureML ecosystem.
License in practice
License treatment is unclear—the raw license URL points to a Microsoft legal page but no SPDX identifier is available, making it difficult to assess compatibility with your project's license requirements without manual review.
Quickstart
pip install azureml-dataset-runtime
import azureml_dataset_runtime
# This package is internal; direct usage is not recommended.
Verify before relying
- Whether direct installation outside the AzureML SDK ecosystem is supported or recommended.
- Exact Python version compatibility (requires_python is unspecified in metadata).
- What the Microsoft license URL (https://aka.ms/azureml-sdk-license) permits in terms of redistribution and modification.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesazureml-datapreppyarrownumpy |
| Maintenance | Actively maintained 14 days since the last release |
| First released | |
| Downloads | 357,385 / month, #7,269 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: azureml_dataset_runtime-1.62.0.post1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “azure machine learning dataset runtime”
- azureml-dataset-runtimeCoordinates internal Azure Machine Learning SDK dependencies for…
- azureml-defaultsazureml-defaults is a metapackage that bundles Azure Machine Learning…
- azuremlConnects to Azure Machine Learning Studio workspaces to download,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also azureml-defaults · azureml · azure-ml-component · azureml-ai-monitoring · azureml-core · azureml-train · azureml-dataprep · azureml-mlflow · azureml-pipeline-core · azureml-automl-core