Once you understand what an Executor is, you may want to wrap it into a container so you can isolate its dependencies and make it ready to run in the cloud or Kubernetes.


The recommended way to containerize an Executor is to leverage Executor Hub to ensure your Executor can run as a container. It handles auto-provisioning, building, version control, etc:

jina hub new

# work on the Executor

jina hub push .

The image building happens on the cloud, and once done the image is available immediately for anyone to use.

You can also build a Docker image yourself and use it like any other Executor. There are some requirements on how this image needs to be built:

  • Jina must be installed inside the image.

  • The Jina CLI command to start the Executor must be the default entrypoint.


To understand how a container image for an Executor is built, you need a basic understanding of Docker, both of how to write a Dockerfile, and how to build a Docker image.

You need Docker installed locally to reproduce the example below.

Install Jina in the Docker image#

Jina must be installed inside the Docker image. This can be achieved in one of two ways:

  • Use a Jina based image as the base image in your Dockerfile. This ensures that everything needed for Jina to run the Executor is installed.

FROM jinaai/jina:3-py38-perf
  • Install Jina like any other Python package. You can do this by specifying Jina in requirements.txt, or by including the pip install jina command as part of the image building process.

RUN pip install jina

Set Jina Executor CLI as entrypoint#

Jina executes docker run with extra arguments under the hood. This means that Jina assumes that whatever runs inside the container also runs like it would in a regular OS process. Therefore, ensure that the basic entrypoint of the image calls jina executor CLI command.

ENTRYPOINT ["jina", "executor", "--uses", "config.yml"]


We strongly encourage you to name the Executor YAML as config.yml, otherwise using your containerized Executor with Kubernetes requires an extra step. When using to_kubernetes_yaml() or to_docker_compose_yaml(), Jina adds --uses config.yml in the entrypoint. To change that you need to manually edit the generated files.

Example: Dockerized Executor#

Here we show how to build a basic Executor with a dependency on another external package.

Write the Executor#

You can define your soon-to-be-dockerized Executor exactly like any other Executor.

We do this here in the file:

import torch  # Our Executor has dependency on torch
from jina import Executor, requests
from docarray import DocList
from docarray.documents import TextDoc

class ContainerizedEncoder(Executor):
    def foo(self, docs: DocList[TextDoc], **kwargs) -> DocList[TextDoc]:
        for doc in docs:
            doc.text = 'This Document is embedded by ContainerizedEncoder'
            doc.embedding = torch.randn(10)
        return docs

Write the Executor YAML file#

The YAML configuration, as a minimal working example, is required to point to the file containing the Executor.

More YAML options

To see what else can be configured using Jina’s YAML interface, see here.

This is necessary for the Executor to be put inside the Docker image, and we can define such a configuration in config.yml:

jtype: ContainerizedEncoder

Write requirements.txt#

In our case, our Executor has only one requirement besides Jina: torch.

Specify a single requirement in requirements.txt:


Write the Dockerfile#

The last step is to write a Dockerfile, which has to do little more than launching the Executor via the Jina CLI:

FROM jinaai/jina:3-py38-perf

# make sure the files are copied into the image
COPY . /executor_root/

WORKDIR /executor_root

RUN pip install -r requirements.txt

ENTRYPOINT ["jina", "executor", "--uses", "config.yml"]

Build the image#

At this point we have a folder structure that looks like this:

└── requirements.txt
└── config.yml
└── Dockerfile

We just need to build the image:

docker build -t my_containerized_executor .

Once the build is successful, you should see the following output when you run docker images:

REPOSITORY                       TAG                IMAGE ID       CREATED          SIZE
my_containerized_executor        latest             5cead0161cb5   13 seconds ago   2.21GB

Use the containerized Executor#

The containerized Executor can be used like any other, the only difference being the ‘docker’ prefix in the uses parameter:

from jina import Deployment
from docarray import DocList
from docarray.documents import TextDoc

dep = Deployment(uses='docker://my_containerized_executor')

with dep:
    returned_docs ='/', inputs=DocList[TextDoc]([TextDoc()]), return_type=DocList[TextDoc])

for doc in returned_docs:
    print(f'Document returned with text: "{doc.text}"')
    print(f'Document embedding of shape {doc.embedding.shape}')
Document returned with text: "This Document is embedded by ContainerizedEncoder"
Document embedding of shape torch.Size([10])