darts Docker & Self-Hosted Deployment

GitHub

A python library for user-friendly forecasting and anomaly detection on time series.

9,569 stars Python Python 12 files · ~57,339 tokens #anomaly-detection#data-science#deep-learning#forecasting#machine-learning#python#time-series
1. Pull Image (DOCKERHUB) docker pull unit8co/darts:latest
2. Docker Run (1-Click Launch) docker run -d --name darts --restart always unit8co/darts:latest
3. Podman Run podman run -d --name darts --restart always unit8co/darts:latest
4. Skopeo Inspect (Zero-Pull Manifest) skopeo inspect docker://docker.io/unit8co/darts:latest
5. ORAS Pull (OCI Artifact) oras pull unit8co/darts:latest
Dockerfile 
# Use a specific Python version for better reproducibility
FROM python:3.13-slim

# Set work directory
WORKDIR /app

# Install uv from official Astral source
# Using the standalone installer for reliability
COPY --from=ghcr.io/astral-sh/uv:0.11.7 /uv /usr/local/bin/uv

# Copy source code
COPY darts/ /app/darts/
# Copy only the files explicitly needed for installation
COPY pyproject.toml README.md /app/

# Install dependencies using uv
# --no-dev would install only core dependencies, but we want dev-all for the full environment
RUN uv sync --group dev-all

# Copy examples
COPY examples/ /app/examples/

# Set Python path so imports work correctly
ENV PATH="/app/.venv/bin:$PATH"

# Build and run instructions:
# docker build . -t darts:latest
# docker run -it -v $(pwd)/:/app/ darts:latest bash
#
# For Jupyter:
# docker run -it -p 8888:8888 darts:latest bash
# Then inside: uv run jupyter lab --ip 0.0.0.0 --no-browser --allow-root