Images

This guide walks you through how to define the environment your Modal Functions run in.

These environments are called containers. Containers are like light-weight virtual machines — container engines use operating system tricks to isolate programs from each other (“containing” them), making them work as though they were running on their own hardware with their own filesystem. This makes execution environments more reproducible, for example by preventing accidental cross-contamination of environments on the same machine. For added security, Modal runs containers using the sandboxed gVisor container runtime.

Containers are started up from a stored “snapshot” of their filesystem state called an image. Producing the image for a container is called building the image.

By default, Modal Functions are executed in a Debian Linux container with a basic Python installation of the same minor version v3.x as your local Python interpreter.

To make your Apps and Functions useful, you will probably need some third party system packages or Python libraries. Modal provides a number of options to customize your container images at different levels of abstraction and granularity, from high-level convenience methods like pip_install through wrappers of core container image build features like RUN and ENV to full on “bring-your-own-Dockerfile”. We’ll cover each of these in this guide, along with tips and tricks for building Images effectively when using each tool.

The typical flow for defining an image in Modal is method chaining starting from a base image, like this:

import modal

image = (
    modal.Image.debian_slim(python_version="3.10")
    .apt_install("git")
    .pip_install("torch==2.6.0")
    .env({"HALT_AND_CATCH_FIRE": "0"})
    .run_commands("git clone https://github.com/modal-labs/agi && echo 'ready to go!'")
)

In addition to being Pythonic and clean, this also matches the onion-like layerwise build process of container images.

Add Python packages with `pip_install`

The simplest and most common container modification is to add some third party Python package, like pandas.

You can add Python packages to the environment by passing all the packages you need to the pip_install method of an image.

You can include typical Python dependency version specifiers, like "torch <= 2.0", in the arguments. But we recommend pinning dependencies tightly, like "torch == 1.9.1", to improve the reproducibility and robustness of your builds.

import modal

datascience_image = (
    modal.Image.debian_slim(python_version="3.10")
    .pip_install("pandas==2.2.0", "numpy")
)


@app.function(image=datascience_image)
def my_function():
    import pandas as pd
    import numpy as np

    df = pd.DataFrame()
    ...

Note that because you can define a different environment for each and every Modal Function if you so choose, you don’t need to worry about virtual environment management. Containers make for much better separation of concerns!

If you want to run a specific version of Python remotely rather than just matching the one you’re running locally, provide the python_version as a string when constructing the base image, like we did above.

Add local files with `add_local_dir` and `add_local_file`

If you want to forward files from your local system, you can do that using the image.add_local_dir and image.add_local_file image builder methods.

image = modal.Image.debian_slim().add_local_dir("/user/erikbern/.aws", remote_path="/root/.aws")

By default, these files are added to your container as it starts up rather than introducing a new image layer. This means that the redeployment after making changes is really quick, but also means you can’t run additional build steps after. You can specify a copy=True argument to the add_local_ methods to instead force the files to be included in a built image.

Adding local Python modules

There is a convenience method for the special case of adding local Python modules to the container: Image.add_local_python_source

The difference from add_local_dir is that add_local_python_source takes module names as arguments instead of a file system path and looks up the local package’s or module’s location via Python’s importing mechanism. The files are then added to directories that make them importable in containers in the same way as they are locally.

This is mostly intended for pure Python auxiliary modules that are part of your project and that your code imports, whereas third party packages should be installed via Image.pip_install() or similar.

import modal

app = modal.App()

image_with_module = modal.Image.debian_slim().add_local_python_source("my_local_module")

@app.function(image=image_with_module)
def f():
    import my_local_module  # this will now work in containers
    my_local_module.do_stuff()

What if I have different Python packages locally and remotely?

You might want to use packages inside your Modal code that you don’t have on your local computer. In the example above, we build a container that uses pandas. But if we don’t have pandas locally, on the computer launching the Modal job, we can’t put import pandas at the top of the script, since it would cause an ImportError.

The easiest solution to this is to put import pandas in the function body instead, as you can see above. This means that pandas is only imported when running inside the remote Modal container, which has pandas installed.

Be careful about what you return from Modal Functions that have different packages installed than the ones you have locally! Modal Functions return Python objects, like pandas.DataFrames, and if your local machine doesn’t have pandas installed, it won’t be able to handle a pandas object (the error message you see will mention serialization/deserialization).

If you have a lot of functions and a lot of Python packages, you might want to keep the imports in the global scope so that every function can use the same imports. In that case, you can use the imports() context manager:

import modal

pandas_image = modal.Image.debian_slim().pip_install("pandas", "numpy")


with pandas_image.imports():
    import pandas as pd
    import numpy as np


@app.function(image=pandas_image)
def my_function():
    df = pd.DataFrame()

Because these imports happen before a new container processes its first input, you can combine this decorator with memory snapshots to improve cold start performance for Functions that frequently scale from zero.

Run shell commands with `.run_commands`

You can also supply shell commands that should be executed when building the container image.

You might use this to preload custom assets, like model parameters, so that they don’t need to be retrieved when Functions start up:

import modal

image_with_model = (
    modal.Image.debian_slim().apt_install("curl").run_commands(
        "curl -O https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalcatface.xml",
    )
)


@app.function(image=image_with_model)
def find_cats():
    content = open("/haarcascade_frontalcatface.xml").read()
    ...

You can also use this command to install Python packages. For example, you can use it to install packages with uv, which can be substantially faster than pip:

import modal

image = (
    modal.Image.debian_slim()
    .pip_install("uv")
    .run_commands("uv pip install --system --compile-bytecode torch")
)

Note that it is important to pass --compile-bytecode when using uv on Modal. Unlike pip, uv does not produce Python bytecode (the contents of the .pyc files in those __pycache__ folders you may have noticed in your Python projects) by default when packages are installed. On a serverless platform like Modal, skipping that work at installation time means it instead has to be done every time a container starts.

Run a Python function during your build with `.run_function`

Instead of using shell commands, you can also run a Python function as an image build step using the Image.run_function method. For example, you can use this to download model parameters from Hugging Face into your Image:

import os
import modal

def download_models() -> None:
    import diffusers

    model_name = "segmind/small-sd"
    pipe = diffusers.StableDiffusionPipeline.from_pretrained(
        model_name, use_auth_token=os.environ["HF_TOKEN"]
    )
    pipe.save_pretrained("/model")


image = (
    modal.Image.debian_slim()
        .pip_install("diffusers[torch]", "transformers", "ftfy", "accelerate")
        .run_function(download_models, secrets=[modal.Secret.from_name("huggingface-secret")])
)

Any kwargs accepted by @app.function (Volumes, and specifications of resources like GPUs) can be supplied here.

Essentially, this is equivalent to running a Modal Function and snapshotting the resulting filesystem as an image.

Whenever you change other features of your image, like the base image or the version of a Python package, the image will automatically be rebuilt the next time it is used. This is a bit more complicated when changing the contents of functions. See the reference documentation for details.

Attach GPUs during setup

If a step in the setup of your container image should be run on an instance with a GPU (e.g., so that a package can query the GPU to set compilation flags), pass a desired GPU type when defining that step:

import modal

image = (
    modal.Image.debian_slim()
    .pip_install("bitsandbytes", gpu="H100")
)

Use `mamba` instead of `pip` with `micromamba_install`

pip installs Python packages, but some Python workloads require the coordinated installation of system packages as well. The mamba package manager can install both. Modal provides a pre-built Micromamba base image that makes it easy to work with micromamba:

import modal

app = modal.App("bayes-pgm")

numpyro_pymc_image = (
    modal.Image.micromamba()
    .micromamba_install("pymc==5.10.4", "numpyro==0.13.2", channels=["conda-forge"])
)


@app.function(image=numpyro_pymc_image)
def sample():
    import pymc as pm
    import numpyro as np

    print(f"Running on PyMC v{pm.__version__} with JAX/numpyro v{np.__version__} backend")
    ...

Use an existing container image with `.from_registry`

You don’t always need to start from scratch! Public registries like Docker Hub have many pre-built container images for common software packages.

You can use any public image in your function using Image.from_registry, so long as:

Python 3.9 or later is installed on the $PATH as python
pip is installed correctly
The image is built for the linux/amd64 platform
The image has a valid ENTRYPOINT

import modal

sklearn_image = modal.Image.from_registry("huanjason/scikit-learn")


@app.function(image=sklearn_image)
def fit_knn():
    from sklearn.neighbors import KNeighborsClassifier
    ...

If an existing image does not have either python or pip set up properly, you can still use it. Just provide a version number as the add_python argument to install a reproducible standalone build of Python:

import modal

image1 = modal.Image.from_registry("ubuntu:22.04", add_python="3.11")
image2 = modal.Image.from_registry("gisops/valhalla:latest", add_python="3.11")

The from_registry method can load images from all public registries, such as Nvidia’s nvcr.io, AWS ECR, and GitHub’s ghcr.io.

We also support access to private AWS ECR and GCP Artifact Registry images.

Bring your own image definition with `.from_dockerfile`

Sometimes, you might be already have a container image defined in a Dockerfile.

You can define an Image with a Dockerfile using Image.from_dockerfile. It takes a path to an existing Dockerfile.

For instance, we might write a Dockerfile that adds scikit-learn to the official Python image:

FROM python:3.9
RUN pip install sklearn

and then define a Modal Image with it:

import modal

dockerfile_image = modal.Image.from_dockerfile("Dockerfile")


@app.function(image=dockerfile_image)
def fit():
    import sklearn
    ...

Note that you can still do method chaining to extend this image!

Dockerfile command compatibility

Since Modal doesn’t use Docker to build containers, we have our own implementation of the Dockerfile specification. Most Dockerfiles should work out of the box, but there are some differences to be aware of.

First, a few minor Dockerfile commands and flags have not been implemented yet. Please reach out to us if your use case requires any of these.

Next, there are some command-specific things that may be useful when porting a Dockerfile to Modal.

`ENTRYPOINT`

While the ENTRYPOINT command is supported, there is an additional constraint to the entrypoint script provided: it must also exec the arguments passed to it at some point. This is so that Modal’s own Python entrypoint can run after your own. Most entrypoint scripts in Docker containers are wrappers over other scripts, so this is likely already the case.

If you wish to write your own entrypoint script, you can use the following as a template:

#!/usr/bin/env bash

# Your custom startup commands here.

exec "$@" # Runs the command passed to the entrypoint script.

If the above file is saved as /usr/bin/my_entrypoint.sh in your container, then you can register it as an entrypoint with ENTRYPOINT ["/usr/bin/my_entrypoint.sh"] in your Dockerfile, or with entrypoint as an Image build step.

import modal

image = (
    modal.Image.debian_slim()
    .pip_install("foo")
    .entrypoint(["/usr/bin/my_entrypoint.sh"])
)

`ENV`

We currently don’t support default values in interpolations, such as ${VAR:-default}

Image caching and rebuilds

Modal uses the definition of an Image to determine whether it needs to be rebuilt. If the definition hasn’t changed since the last time you ran or deployed your App, the previous version will be pulled from the cache.

Images are cached per layer (i.e., per Image method call), and breaking the cache on a single layer will cause cascading rebuilds for all subsequent layers. You can shorten iteration cycles by defining frequently-changing layers last so that the cached version of all other layers can be used.

In some cases, you may want to force an Image to rebuild, even if the definition hasn’t changed. You can do this by adding the force_build=True argument to any of the Image building methods.

import modal

image = (
    modal.Image.debian_slim()
    .apt_install("git")
    .pip_install("slack-sdk", force_build=True)
    .run_commands("echo hi")
)

As in other cases where a layer’s definition changes, both the pip_install and run_commands layers will rebuild, but the apt_install will not. Remember to remove force_build=True after you’ve rebuilt the Image, or it will rebuild every time you run your code.

Alternatively, you can set the MODAL_FORCE_BUILD environment variable (e.g. MODAL_FORCE_BUILD=1 modal run ...) to rebuild all images attached to your App. But note that when you rebuild a base layer, the cache will be invalidated for all Images that depend on it, and they will rebuild the next time you run or deploy any App that uses that base.

Image builder updates

Because changes to base images will cause cascading rebuilds, Modal is conservative about updating the base definitions that we provide. But many things are baked into these definitions, like the specific versions of the Image OS, the included Python, and the Modal client dependencies.

We provide a separate mechanism for keeping base images up-to-date without causing unpredictable rebuilds: the “Image Builder Version”. This is a workspace level-configuration that will be used for every Image built in your workspace. We release a new Image Builder Version every few months but allow you to update your workspace’s configuration when convenient. After updating, your next deployment will take longer, because your Images will rebuild. You may also encounter problems, especially if your Image definition does not pin the version of the third-party libraries that it installs (as your new Image will get the latest version of these libraries, which may contain breaking changes).

You can set the Image Builder Version for your workspace by going to your workspace settings. This page also documents the important updates in each version.

Images

Add Python packages with pip_install

Add local files with add_local_dir and add_local_file