aistore
Client-side APIs to access and utilize clusters, buckets, and objects on AIStore.
Decision gist · record as of 2026-08-14
Yes, if you are working with AIStore clusters or need distributed object storage integration in Python. The package is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. Most valuable for machine learning and high-performance computing teams.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running AIStore cluster; Python 3.8 or later.
- Low install friction with a pure-Python wheel and 13 well-established runtime dependencies.
- Active maintenance with recent commits and a 1912-star repository indicates ongoing development.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
last release 2026-05-20 (86 days) · last repo commit 2026-08-14 · 1,912 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 149,433 downloads/mo, #10,992 on PyPI
Alternatives
Verify before relying
pip install aistore
from aistore.sdk import Client
client = Client()
buckets = client.list_buckets()- Whether PyTorch integration requires PyTorch as a separate install or is optional within aistore.
- Specific performance characteristics or throughput limits for typical workloads.
- Whether Botocore patch is automatically applied or requires manual activation.
- Default endpoint configuration and connection requirements for client initialization.
What it is and what it does
AIStore is a Python SDK for interacting with AIStore clusters—a distributed object storage system designed for petascale AI and machine learning workloads. The package provides client-side APIs to create and manage buckets, upload and retrieve objects, and perform ETL operations on stored data. It includes native PyTorch integration for efficient data loading in training pipelines and a Botocore patch for S3-compatible access patterns.
The SDK depends on 13 common libraries (requests, pydantic, msgspec, tenacity, and others) to handle HTTP communication, data validation, serialization, and retry logic. It targets developers working with large-scale distributed storage, machine learning infrastructure, and high-performance computing environments where object storage efficiency matters.
Use it for
- Load training data from AIStore clusters directly into PyTorch for distributed ML training.
- Manage and query petascale object storage buckets and objects programmatically from Python.
- Perform ETL operations on objects stored in AIStore clusters with built-in retry logic.
- Access AIStore via S3-compatible APIs using the included Botocore patch for existing workflows.
- Build data pipelines that fetch and process objects from distributed AIStore clusters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with AIStore clusters or need distributed object storage integration in Python.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. Most valuable for machine learning and high-performance computing teams.
Install
aistore on PyPI
Before you install
Low install friction with a pure-Python wheel and 13 well-established runtime dependencies. Active maintenance with recent commits and a 1912-star repository indicates ongoing development.
Requires a running AIStore cluster; Python 3.8 or later.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
Quickstart
pip install aistore
from aistore.sdk import Client
client = Client()
buckets = client.list_buckets()Verify before relying
- Whether PyTorch integration requires PyTorch as a separate install or is optional within aistore.
- Specific performance characteristics or throughput limits for typical workloads.
- Whether Botocore patch is automatically applied or requires manual activation.
- Default endpoint configuration and connection requirements for client initialization.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagesbraceexpandcloudpicklehumanizemsgspecoverridespackagingpydanticpython-dateutilpyyamlrequeststenacityurllib3xxhash |
| Maintenance | Actively maintained 86 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 149,433 / month, #10,992 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 :: MIT LicenseProgramming Language :: Python :: 3 :: OnlyTopic :: Scientific/Engineering |
Evidence: aistore-1.25.0-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 › “aistore python client”
- aistoreProvides Python client APIs and PyTorch integration to access and…
- multi-storage-clientUnified Python client providing a generic interface to read, write,…
- codewords-clientA Python client library for the Codewords API with built-in FastAPI…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also multi-storage-client · amazon-dax-client · torch · megatron-fsdp · raydp · s3torchconnectorclient · s3torchconnector · gcloud-aio-storage · nvidia-cufile-cu12 · webdataset