7. FaaS Docker Image

Dataloop enables you to deploy in the FaaS module a custom docker image, to enrich your application with literallyanything required for your project. Deploying a docker image is as easy as providing the Docker image path whendeploying the service:

service = package.deploy(service_name='my-service',

or if you want to update an existing service:

service = dl.services.get('service-id-or-name')
service.runtime.runner_image = 'python:3.8'

7.1. Our Docker Image

We have our list of docker images publicly available in DockerhubYou can see the env of each docker on the dockerfile here

7.2. Public Docker Images

You can use any public docker image, and on runtime, the Dataloop agent will install:

  1. Package requirements

  2. dtlpy package (version as defined on the service)

  3. dtlpy-agent (same version as the SDK)

For example, using docker.io/python:3.9.13 will run the function with Python 3.9.

7.3. Build Your Own Docker Image

If you want other environment or need to add some apt-get installation, you can create any docker image and use it directly.You will need to set the HOME directory to /tmp and install the python packages with –user (or as USER 1000).For instance:

FROM dockerhub.io/dataloopai/dtlpy-agent:latest.gpu.cuda11.5.py3.8.opencv  
RUN apt update && apt install -y zip ffmpeg  
USER 1000  
ENV HOME=/tmp  
RUN pip3 install --user \  
    dtlpy==1.54.10 \  
    dtlpy-agent==1.54.10 \  
    torch \  
    torchvision \  
    imgaug \  

7.4. Using Private Docker Registry

Using private images is NOT supported yet!

To connect a private registry, you’ll need to add the credentials as an Organization Secret and use the secret in the runtime configuration:

service = package.deploy(service_name='my-service',