Applications on Beam are run inside containers. A container is a lightweight VM that packages a set of software packages required by your application.

Containers are based on container images which are instructions for how a container should be built.

Because you are building a custom application, it is likely that your application depends on some custom software to run.

You can customize the container image used to run your Beam application with the Image parameter.

Beam containers have two defaults to be aware of:

Default Container OS: Ubuntu 20.04

Default CUDA: CUDA 12.3

Using Existing Docker Images

You can import existing images from remote Docker registries, like Docker Hub, Google Artifact Registry, ECR, GitHub Container Registry, Nvidia and more.

Just supply a base_image argument to Image.

from beam import endpoint, Image

image = (
    Image(
        base_image="docker.io/nvidia/cuda:12.3.1-runtime-ubuntu20.04",
        python_version="python3.9",
    )
    .add_commands(["apt-get update -y", "apt-get install neovim -y"])
    .add_python_packages(["torch"])
)


@endpoint(image=image)
def handler():
    import torch

    return {"torch_version": torch.__version__}

Beam only supports Debian-based images. In addition, make sure your image is built for the correct x86 architecture.

Adding Shell Commands

You can also run any shell commands you want in the environment before it starts up. Just pass them into the commands field in your app definition.

Below, we’ll customize our image with requests and some shell commands:

from beam import endpoint, Image


image = (
    Image(python_version="python3.9")
    .add_commands(["apt-get update", "pip install beautifulsoup4"])
    .add_python_packages(["requests"])
)

@endpoint(cpu=1, memory="16Gi", gpu="T4", image=image)
def handler():
    return {}

Adding Python Packages

You can add Python packages to the runtime in the python_packages field:

from beam import Image


Image(python_version="python3.9").add_python_packages(["requests"])
Beam will default to Python 3.8 if no python_version is provided.

Alternatively, you can pass in a path to a requirements.txt file:

from beam import Image


Image(python_version="python3.9", python_packages="requirements.txt")

Using Anaconda Environments

Beam supports using Anaconda environments via micromamba. To get started, you can chain the micromamba method to your Image definition and then specify packages and channels via the add_micromamba_packages method.

from beam import Image


image = (
    Image(python_version="python3.11")
    .micromamba()
    .add_micromamba_packages(packages=["pandas", "numpy"], channels=["conda-forge"])
    .add_python_packages(packages=["huggingface-hub[cli]"])
    .add_commands(commands=["micromamba run -n beta9 huggingface-cli download gpt2 config.json"])
)

You can still use pip to install additional packages in the conda environment and you can run shell commands too.

If you need to run a shell command inside the conda environment, you should prepend the command with micromamba run -n beta9 as shown above.

Using Private Registries

Beam supports importing images from private AWS ECR registries.

You can authenticate with either your static AWS credentials or an AWS STS token. If you use the AWS STS token, your AWS_SESSION_TOKEN key must also be set.

You can either pass the credentials as a dictionary, or export them from your shell and Beam will automatically lookup the values.

Passing Credentials as a Dictionary

You can provide the values for the registry as a dictionary directly, like this:

image = Image(
    base_image="111111111111.dkr.ecr.us-east-1.amazonaws.com/my-app:latest",
    base_image_creds={
      "AWS_ACCESS_KEY_ID": "xxxx",
      "AWS_SECRET_ACCESS_KEY": "xxxx"
      "AWS_REGION": "xxxx"
    },
)

Passing Credentials from your Environment

Alternatively, you can export your AWS credentials in your shell and pass the environment variable names to base_image_creds as a list:

image = Image(
    base_image="111111111111.dkr.ecr.us-east-1.amazonaws.com/my-app:latest",
    base_image_creds=[
        "AWS_ACCESS_KEY_ID",
        "AWS_SECRET_ACCESS_KEY",
        "AWS_SESSION_TOKEN",
        "AWS_REGION",
    ],
)

@endpoint(image=image)
def squared(i: int = 0) -> int:
    return i**2