uv/docs/guides/integration/docker.md

Using uv in Docker

Running in Docker

A Docker image is published with a built version of uv available. To run a uv command in a container:

$ docker run ghcr.io/astral-sh/uv --help

Installing uv

uv can be installed by copying from the official Docker image:

FROM python:3.12-slim-bookworm
COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv

Or, with the installer:

FROM python:3.12-slim-bookworm

# The installer requires curl (and certificates) to download the release archive
RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates

# Download the latest installer
ADD https://astral.sh/uv/install.sh /uv-installer.sh

# Run the installer then remove it
RUN sh /uv-installer.sh && rm /uv-installer.sh

# Ensure the installed binary is on the `PATH`
ENV PATH="/root/.cargo/bin/:$PATH"

Note this requires curl to be available.

In either case, it is best practice to pin to a specific uv version, e.g., with:

COPY --from=ghcr.io/astral-sh/uv:0.3.3 /uv /bin/uv

Or, with the installer:

ADD https://astral.sh/uv/0.3.3/install.sh /uv-installer.sh

Installing a project

If you're using uv to manage your project, you can copy it into the image and install it:

# Copy the project into the image
ADD . /app
WORKDIR /app

# Sync the project into a new environment, using the frozen lockfile
RUN uv sync --frozen

Once the project is installed, you can either activate the virtual environment:

# Use the virtual environment automatically
ENV VIRTUAL_ENV=/app/.venv
# Place executables in the environment at the front of the path
ENV PATH="/app/.venv/bin:$PATH"

Or, you can use uv run to run commands in the environment:

RUN uv run some_script.py

And, to start your application by default:

# Presuming there is a `my_app` command provided by the project
CMD ["uv", "run", "my_app"]

Using installed tools

To use installed tools, ensure the tool bin directory is on the path:

ENV PATH=/root/.local/bin:$PATH
RUN uv tool install cowsay

$ docker run -it $(docker build -q .) /bin/bash -c "cowsay -t hello"
  _____
| hello |
  =====
     \
      \
        ^__^
        (oo)\_______
        (__)\       )\/\
            ||----w |
            ||     ||

To determine the tool bin directory, run uv tool dir --bin in the container.

Using the pip interface

Installing a package

The system Python environment is safe to use this context, since a container is already isolated. The --system flag can be used to install in the system environment:

RUN uv pip install --system ruff

To use the system Python environment by default, set the UV_SYSTEM_PYTHON variable:

ENV UV_SYSTEM_PYTHON=1

Alternatively, a virtual environment can be created and activated:

RUN uv venv /opt/venv
# Use the virtual environment automatically
ENV VIRTUAL_ENV=/opt/venv
# Place entry points in the environment at the front of the path
ENV PATH="/opt/venv/bin:$PATH"

When using a virtual environment, the --system flag should be omitted from uv invocations:

RUN uv pip install ruff

Installing requirements

To install requirements files, copy them into the container:

COPY requirements.txt .
RUN uv pip install -r requirements.txt

Installing a project

When installing a project alongside requirements, it is prudent to separate copying the requirements from the rest of the source code. This allows the dependencies of the project (which do not change often) to be cached separately from the project itself (which changes very frequently).

COPY pyproject.toml .
RUN uv pip install -r pyproject.toml
COPY . .
RUN uv pip install -e .

Optimizations

Using uv temporarily

If uv isn't needed in the final image, the binary can be mounted in each invocation:

RUN --mount=from=uv,source=/uv,target=/bin/uv \
    uv pip install --system ruff

Compiling bytecode

Compiling Python source files to bytecode is typically desirable for production images as it tends to improve startup time (at the cost of increased installation time).

To enable bytecode compilation, use the --compile-bytecode flag:

RUN uv sync --compile-bytecode

Alternatively, you can set the UV_COMPILE_BYTECODE environment variable to ensure that all commands within the Dockerfile compile bytecode:

ENV UV_COMPILE_BYTECODE=1

Caching

A cache mount can be used to improve performance across builds:

RUN --mount=type=cache,target=/root/.cache/uv \
 ./uv pip install -r requirements.txt -->

Note the cache directory's location can be determined with the uv cache dir command. Alternatively, the cache can be set to a constant location:

ENV UV_CACHE_DIR=/opt/uv-cache/

If not mounting the cache, image size can be reduced with --no-cache flag.

Intermediate layers

If you're using uv to manage your project, you can improve build times by moving your transitive dependency installation into its own layer via the --no-install options.

uv sync --no-install-project will install the dependencies of the project but not the project itself. Since the project changes frequently, but its dependencies are generally static, this can be a big time saver.

# Install uv
FROM python:3.12-slim
COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv

# Copy the lockfile and `pyproject.toml` into the image
ADD uv.lock /app/uv.lock
ADD pyproject.toml /app/pyproject.toml

# Install dependencies
WORKDIR /app
RUN uv sync --frozen --no-install-project

# Copy the project into the image
ADD . /app

# Sync the project
RUN uv sync --frozen

Note that the pyproject.toml is required to identify the project root and name, but the project contents are not copied into the image until the final uv sync command.

!!! tip

If you're using a [workspace](../../concepts/workspaces.md), then use the
`--no-install-workspace` flag which excludes the project _and_ any workspace members.

If you want to remove specific packages from the sync, use `--no-install-package <name>`.