{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/6"}],"enrichment":{"capability":"Python SDK for building machine learning projects, datasets, models, and inference pipelines on the Matrice.ai platform, with support for training, deployment, and monitoring workflows.","skillfed_tags":["ml-platform-sdk","inference-deployment","project-management"],"use_cases":["Create and manage ML projects end-to-end, from data import through model training and deployment on Matrice.ai.","Automate annotation workflows by creating tasks, assigning labelers and reviewers, and tracking completion.","Export trained models in multiple formats and deploy them to FastAPI or Triton inference servers.","Build and manage inference pipelines that combine multiple applications and handle real-time camera streams.","Monitor model drift in production deployments and track inference metrics over time.","Run local tests and benchmarks on models before cloud deployment using provided test utilities."],"what_it_does":"Matrice is a Python SDK that wraps the Matrice.ai platform's backend services, letting you programmatically manage the full lifecycle of machine learning projects\u2014from creation through model training, export, and deployment. It provides high-level abstractions that handle session management, RPC calls, and response parsing via matrice_common, plus utilities for local testing, metrics calculation, and streaming automation.\n\nThe package is designed for developers building ML workflows: you can create projects, import data from local or cloud sources, submit training jobs with custom configurations, export trained models in multiple formats, deploy to FastAPI or Triton, create inference pipelines, and monitor drift. It also includes helpers for Docker environment setup, local test harnesses, and performance benchmarking. Optional dependencies extend functionality for data processing and streaming, but core project and model management work with just matrice_common.","worth_installing":"Yes, if you are working within the Matrice.ai ecosystem. The SDK is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and installs with minimal friction. It is the standard way to programmatically manage projects and workflows on the platform. If you are not already using Matrice.ai, it will not be useful\u2014it is a platform-specific client library, not a general ML framework."},"id":"matrice","links":{"html":"https://skillfed.io/packages/matrice","md":"https://skillfed.io/packages/matrice.md","pypi":"https://pypi.org/project/matrice/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-29","license_spdx":null,"license_treatment":"permissive","name":"matrice","python_support":"supports_current","summary":"Common server utilities for Matrice.ai services"},"popularity":{"monthly_downloads":2150374,"position":3250,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.9"}