Container Images
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.
System Info
Beam provides a variety of hardware with varying versions of software and drivers. If your application is sensitive to certain versions, consider the following:
Container OS:
- Ubuntu 22.04
GPU Drivers
GPU | Driver Version |
---|---|
A10G | 535.161.07 (CUDA 12.3) or 550.127.05 (CUDA 12.4) |
A100-40 | 535.129.03 (CUDA 12.2) |
RTX4090 | 550.127.05 (CUDA 12.4) |
T4 | 550.120 (CUDA 12.4) or 550.127.05 (CUDA 12.4) |
Bring Your Own Dockerfile
You can build images based on your own Dockerfile.
The from_dockerfile()
command accepts a path to a valid Dockerfile:
Conda 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.
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.
Public Docker Registries
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
.
Beam only supports Debian-based images. In addition, make sure your image is built for the correct x86 architecture.
Private Docker Registries
Beam supports importing images from the following private registries: AWS ECR, Google Artifact Registry, Docker Hub, and NVCR.
Private registries require credentials, and you can pass the credentials to Beam in two ways: as a dictionary, or exported from your shell so Beam can automatically lookup the values.
Passing Credentials as a Dictionary
You can provide the values for the registry as a dictionary directly, like this:
Passing Credentials from your Environment
Alternatively, you can export your credentials in your shell and pass the environment variable names to base_image_creds
as a list:
AWS ECR
To use a private image from Amazon ECR, export your AWS environment variables. Then configure the Image object with those environment variables.
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.
GCP Artifact Registry
To use a private image from Google Artifact Registry, export your access token.
Then configure the Image object to use the environment variable.
NVIDIA GPU Cloud (NGC)
To use a private image from NVIDIA GPU Cloud, export your API key.
Then configure the Image object to use the environment variable.
Docker Hub
To use a private image from Docker Hub, export your Docker Hub credentials.
Then configure the Image object with those environment variables.
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:
Adding Python Packages
You can add Python packages to the runtime in the python_packages
field:
python_version
is provided.Alternatively, you can pass in a path to a requirements.txt
file:
Passing Secrets
You can pass secrets to the image build by using the with_secrets
command:
Using Environment Variables
If your environment requires certain environment variables set, you can do so using the env_vars
parameter:
You can also use the following syntax, if you prefer:
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