ngcsdk
NVIDIA GPU Cloud SDK
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive license, and is purpose-built for NGC integration. Install it if you need programmatic access to NVIDIA GPU Cloud resources from Python. Verify that your use case aligns with the 19 runtime dependencies (particularly docker and boto3) before committing to production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10 and a valid NGC API key (obtainable from https://ngc.nvidia.com/setup).
- Low friction installation with a pure-wheel distribution.
- Actively maintained with a recent release.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most integration scenarios.
last release 2026-08-04 (10 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 270,932 downloads/mo, #8,228 on PyPI
Alternatives
Verify before relying
pip install ngcsdk
from ngcsdk import Client
clt = Client()
clt.configure(api_key='your-api-key', org_name='nvidia', team_name='no-team')
model = clt.registry.model.info('nvidia/nemotron')- Whether the 19 runtime dependencies (including docker, boto3, aiohttp) are all required for basic use or if many are optional.
- Performance characteristics and rate limits when querying the NGC registry.
- Whether telemetry extras are commonly needed or only for advanced monitoring scenarios.
What it is and what it does
NGC-SDK is NVIDIA's official Python client for the NVIDIA GPU Cloud platform. It provides authenticated access to NGC's model registry, allowing you to query, list, and retrieve metadata about models and other resources hosted in the cloud. The SDK handles API authentication via API keys, manages token caching, and returns results as both Python objects and JSON, making it straightforward to integrate NGC resources into Python applications and workflows.
The package is built on a foundation of common HTTP and cloud libraries (aiohttp, requests, boto3, docker) to support both synchronous and asynchronous operations, as well as container and storage interactions. It requires Python 3.10 or later and is actively maintained, with recent releases indicating ongoing support for current Python versions.
Use it for
- Query and retrieve model metadata from the NGC registry in Python applications.
- Automate access to NVIDIA's pre-trained models and resources for ML workflows.
- Integrate NGC resources into CI/CD pipelines or batch processing jobs.
- Build internal tools that need authenticated programmatic access to NGC.
- Debug NGC API interactions using built-in logging and configuration inspection.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive license, and is purpose-built for NGC integration. Install it if you need programmatic access to NVIDIA GPU Cloud resources from Python. Verify that your use case aligns with the 19 runtime dependencies (particularly docker and boto3) before committing to production.
Install
ngcsdk on PyPI
Before you install
Low friction installation with a pure-wheel distribution. Actively maintained with a recent release. Requires Python 3.10 or later and pulls in 19 runtime dependencies including aiohttp, boto3, docker, and requests for cloud and container operations.
Requires Python >= 3.10 and a valid NGC API key (obtainable from https://ngc.nvidia.com/setup).
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most integration scenarios.
Quickstart
pip install ngcsdk
from ngcsdk import Client
clt = Client()
clt.configure(api_key='your-api-key', org_name='nvidia', team_name='no-team')
model = clt.registry.model.info('nvidia/nemotron')Verify before relying
- Whether the 19 runtime dependencies (including docker, boto3, aiohttp) are all required for basic use or if many are optional.
- Performance characteristics and rate limits when querying the NGC registry.
- Whether telemetry extras are commonly needed or only for advanced monitoring scenarios.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 19 packagesaiofilesaiohttpboto3botocorecertificryptographydockerisodatepackagingpolling2prettytablepsutilpython-dateutilrequests-toolbeltrequestsrichshortuuidurllib3validators |
| Maintenance | Actively maintained 10 days since the last release |
| First released | |
| Downloads | 270,932 / month, #8,228 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: ngcsdk-4.34.10-py3-none-any.whl
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See also cuda-toolkit · langchain-nvidia-ai-endpoints · nvidia-cuda-cupti · cuda-core · cuda-python · nccl4py · nvidia-cuda-cupti-cu12 · py3nvml · nvidia-cuda-crt · cupti-python