dvc-studio-client
Small library to post data from DVC/DVCLive to Iterative Studio
Decision gist · record as of 2026-08-14
Yes, if you use DVC Studio and need to post metrics or access model registry data from Python code. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it safe to add. Not necessary if you only use DVC Studio's web UI or don't track experiments with it.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; DVC Studio backend endpoint and authentication credentials needed for actual metric posting.
- Low friction install with only three runtime dependencies (dulwich, requests, voluptuous).
- Actively maintained with a recent release 74 days ago and current commit activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
last release 2026-06-01 (74 days) · last repo commit 2026-08-10 · 10 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,253,619 downloads/mo, #3,187 on PyPI
Alternatives
Verify before relying
pip install dvc-studio-client
from dvc_studio_client import post_live_metrics
post_live_metrics(api_url, metrics_dict, headers)- Whether get_download_uris, post_live_metrics, and get_access_token are the complete public API or if there are additional functions.
- Whether the package works offline or always requires network connectivity to DVC Studio.
- Performance characteristics when posting large volumes of metrics or handling network failures.
What it is and what it does
dvc-studio-client is a lightweight Python library that bridges local machine learning workflows to DVC Studio, Iterative's experiment tracking and model management platform. It provides three main capabilities: retrieving download URIs for registered models, posting live experiment metrics to a Studio backend, and handling OAuth-style authentication for client applications.
The package is designed as a thin integration layer, not a standalone ML platform—it assumes you already have a DVC Studio instance or account and need to programmatically send data to it. It depends on dulwich for Git operations, requests for HTTP communication, and voluptuous for configuration validation. The library targets modern Python versions (3.9 through 3.14) and is actively maintained by Iterative, the team behind DVC.
Use it for
- Post live metrics from DVCLive experiments to DVC Studio during training runs for real-time monitoring.
- Retrieve download URIs for models stored in DVC Studio's model registry for deployment pipelines.
- Authenticate a custom application or CI/CD workflow to interact with DVC Studio's API.
- Integrate experiment tracking into non-DVC ML workflows that still use DVC Studio as a central hub.
- Automate metric collection and upload from distributed training jobs to a centralized Studio instance.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use DVC Studio and need to post metrics or access model registry data from Python code.
The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it safe to add. Not necessary if you only use DVC Studio's web UI or don't track experiments with it.
Install
dvc-studio-client on PyPI
Before you install
Low friction install with only three runtime dependencies (dulwich, requests, voluptuous). Actively maintained with a recent release 74 days ago and current commit activity.
Requires Python 3.9 or later; DVC Studio backend endpoint and authentication credentials needed for actual metric posting.
License in practice
Apache-2.0 permissive license allows free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
Quickstart
pip install dvc-studio-client
from dvc_studio_client import post_live_metrics
post_live_metrics(api_url, metrics_dict, headers)Verify before relying
- Whether get_download_uris, post_live_metrics, and get_access_token are the complete public API or if there are additional functions.
- Whether the package works offline or always requires network connectivity to DVC Studio.
- Performance characteristics when posting large volumes of metrics or handling network failures.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdulwichrequestsvoluptuous |
| Maintenance | Actively maintained 74 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,253,619 / month, #3,187 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 1 - PlanningProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: dvc_studio_client-0.23.0-py3-none-any.whl
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See also dvc · dvclive · dvc-render · dvc-data · neptune · comet-ml · azureml-mlflow · trackio · sagemaker-experiments · iterative-telemetry