Инструменты разработчика
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Смотреть руководство →postgresml — проект с открытым исходным кодом из категории «Инструменты разработчика» от postgresml (postgresml/postgresml на GitHub). У него 6 821 звезда на GitHub и 364 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 1 июля 2025.
PostgresML — machine learning inside PostgreSQL with GPU acceleration: train, embed and run LLMs in SQL.
Decide cloud versus self-hosted, and pick a first workload: in-database training, embeddings/RAG, or LLM calls from SQL.
Clone postgresml/postgresml from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
The fastest self-hosted path is the documented Docker image with a persistent volume; PostgresML Cloud skips installation entirely.
Connect with psql, run one in-database ML or embedding query on a sample table, and compare it with your current HTTP-serving approach.
Run machine learning and AI operations directly within PostgreSQL, keeping models and data together for privacy and simplicity.
Leverage GPU power for faster training computations and model inference inside the database.
Integrate and use state-of-the-art LLMs from Hugging Face in your SQL workflows.
Functions for chunking, embedding, ranking and transforming text, with vector search via pgvector integration.
A diverse set of classification and regression algorithms available in-database for classic ML tasks.
Positions inference as 8–40x faster than HTTP-based serving in its benchmarks, with horizontal scaling support.
Get the source from the official GitHub repository for postgresml.
git clone https://github.com/postgresml/postgresml.git
cd postgresmlStart the published image with its data volume and ports exactly as the README's self-hosted section shows.
docker run -it -v postgresml_data:/var/lib/postgresql -p 5433:5432 -p 8000:8000 ghcr.io/postgresml/postgresml:2.10.0 sudo -u postgresml psql -d postgresmlOnce connected, follow the README/docs examples to run an embedding, RAG or training query in SQL.
# Connect to the postgresml database and run a documented example query — see READMEThe PostgresML project, through the postgresml/postgresml repository, with the site and cloud at postgresml.org.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
Self-hosting is free under MIT with no API key; PostgresML Cloud is the paid managed option. GPU features need GPU hardware you provide.
A separate API means network hops, duplicated data pipelines and another service to secure. PostgresML moves training and inference into Postgres itself — simpler data flow and lower latency in its benchmarks, in exchange for running ML inside your database tier.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — connect with psql, run one in-database ML or embedding query on a sample table, and compare it with your current HTTP-serving approach.
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