PostgreSQL for Data Science & AI Workloads
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This Docker image provides PostgreSQL with powerful extensions optimized for data science, analytics, and AI/ML workloads.
š„ Multi-Architecture Support: Available for AMD64 and ARM64 architectures.
This image comes with pre-installed PostgreSQL extensions for enhanced data processing, analytics, and AI use cases:
pgvector)TimescaleDB)pg_parquet from CrunchyData)AGE)jsquery)pg_partman)pg-hint-plan)pgaudit)pg_cron)pgq3)pgpcre)š Note: Building pg_parquet is resource-intensive and may take a long time.
docker pull rbehzadan/postgres:17.6
This automatically pulls the correct architecture (ARM64 or AMD64) based on your system.
docker run -d --name postgres \
-e POSTGRES_USER=admin \
-e POSTGRES_PASSWORD=secret \
-e POSTGRES_DB=mydb \
-p 5432:5432 \
rbehzadan/postgres:17.6
Using psql:
docker exec -it postgres psql -U admin -d mydb
Enable any installed extension inside PostgreSQL:
CREATE EXTENSION pg_parquet;
CREATE EXTENSION timescaledb;
CREATE EXTENSION pgvector;
CREATE EXTENSION age;
CREATE EXTENSION pgq3;
CREATE EXTENSION pgpcre;
Verify the container's health status:
docker inspect --format='{{json .State.Health}}' postgres
This image is available for the following architectures:
ā
AMD64 (linux/amd64)
ā
ARM64 (linux/arm64) (for ARM-based servers like AWS Graviton, Raspberry Pi, or Mac M1/M2 chips)
The correct version will be pulled automatically based on your system.
If you find a bug, want to add an extension, or optimize the build process, feel free to:
This project is licensed under the MIT License.
This image is optimized for data science, AI, and large-scale analytics. Whether you're building a feature store for ML, processing time-series data, or handling graph-based queries, this setup provides high-performance PostgreSQL with the right extensions pre-installed.
Content type
Image
Digest
sha256:151bec9b7ā¦
Size
181.8 MB
Last updated
11 months ago
docker pull rbehzadan/postgres:17.6