Build Your Pod into a Docker Image


The objective of this tutorial is to serve as a guide for Pod image usage. Users can use Pod images in several ways:

  • Run with Docker (docker run)

    • docker run jinahub/pod.encoder.dummy_mwu_encoder:0.0.6-1.0.2 --port-in 55555 --port-out 55556
  • Flow API

    • from jina.flow import Flow
      f = (Flow()
          .add(name='my-encoder', uses='docker://jinahub/pod.encoder.dummy_mwu_encoder:0.0.6-1.0.2', port_in=55555, port_out=55556)
          .add(name='my-indexer', uses='indexer.yml'))
  • Jina CLI

    • jina pod --uses docker://jinahub/pod.encoder.dummy_mwu_encoder:0.0.6-1.0.2 --port-in 55555 --port-out 55556
  • Conventional local usage with uses argument

    • jina pod --uses hub/example/dummy_mwu_encoder.yml --port-in 55555 --port-out 55556

More information about the usage can be found here.


So you have implemented an Executor and you would want to reuse it in another Jina application or share it with people around the world.

You might also want to offer people a ready-to-use interface without the hassle of repeating the pitfalls you faced.

The best way to do this is to pack everything (Python file, YAML config, pre-trained data, dependencies) into a container image and use Jina as the entry point. You can also annotate your image with some meta information to facilitate the search, archive and classification.

Here is a list of motivating reasons for building a Pod image:

  • You want to use one of the built-in Executor (e.g. PyTorch-based) and you don’t want to install PyTorch dependencies on the host.

  • You modify or write a new Executor and want to reuse it in another project, without touching Jina’s core.

  • You customize the driver and want to reuse it in another project, without touching Jina’s core.

  • You have a self-built library optimized for your architecture (e.g. tensorflow/numpy on GPU/CPU/x64/arm64), and you want this specific Pod to benefit from it.

  • Your Executor requires certain Linux headers that can only be installed via apt or yum, but you don’t have sudo on the host.

  • Your Executor relies on a pretrained model, you want to include this 100MB file into the image so that people don’t need to download it again.

  • You use Kubernetes or Docker Swarm and this orchestration framework requires each microservice to run as a Docker container.

  • You are using Jina on the cloud and you want to deploy an immutable Pod and version control it.

  • You have figured out a set of parameters that works best for an Executor, you want to write it down in a YAML config and share it to others.

  • You are debugging, doing try-and-error on exploring new packages, and you don’t want ruin your local dev environments.

What files should be in the Pod image?

Typically, the following files are packed into the container image.

File Descriptions
Dockerfile describes the dependency setup and expose the entry point;
build.args metadata of the image, author, tags, etc. help the Hub to index and classify your image
*.py describes the Executor logic written in Python, if applicable;
*.yml a YAML file describes the Executor arguments and configs, if you want users to use your config;
Other data files may be required to run the Executor, e.g. pre-trained model, fine-tuned model, home-made data.

Except Dockerfile, all other options to build a valid Pod image depending on your case. build.args is only required when you want to upload your image to Jina Hub.

Step-by-Step Example

In this example, we consider the scenario where we create a new Executor and want to reuse it in another project, without tweaking any code in jina-ai/jina.

1. Code your Executor and write its config

We write a new dummy encoder named MWUEncoder in which encodes any input into a random 3-dimensional vector. This encoder has a dummy parameter greetings which prints a greeting message on start and on every encode.

from typing import Any

import numpy as np

from jina.executors.encoders import BaseEncoder

class MWUEncoder(BaseEncoder):

    def __init__(self, greetings: str, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._greetings = greetings
        self.logger.success(f'look at me! {greetings}')

    def encode(self, data: Any, *args, **kwargs) -> Any:'{self._greetings} {data}')
        return np.random.random([data.shape[0], 3])

In mwu_encoder.yml, the metas.py_modules helps Jina to load the class of this Executor from

  greetings: im from internal yaml!
  name: my-mwu-encoder
  workspace: ./

The documentation of the YAML syntax can be found at here.

2. Write a 3-line Dockerfile

The Dockerfile in this example is a simple three-line snippet.

FROM jinaai/jina

ADD *.py mwu_encoder.yml ./

ENTRYPOINT ["jina", "pod", "--uses", "mwu_encoder.yml"]

Let’s try to understand them one by one.

FROM jinaai/jina

In the first line, we choose jinaai/jina as the base image, which corresponds to the latest master of jina-ai/jina. You are free to use others as well, e.g. tensorflow/tensorflow:nightly-gpu-jupyter.

In practice, whether to use Jina base image depends on the dependencies you would like to introduce. For example, someone provides a hard-to-compile package as a Docker image, which is much harder than compiling/installing Jina itself. In this case, you may want to use this image as the base image to save some troubles. Nevertheless, don’t forget to install Python >=3.7 (if not included) and Jina afterwards, e.g.

FROM awesome-gpu-optimized-kit

RUN pip install jina --no-cache-dir --compile

The ways of installing Jina can be found here.

In this example, our dummy MWUEncoder only requires Jina and does not need any third-party framework. Thus, jinaai/jina is used.

ADD *.py mwu_encoder.yml ./

The second step is to add all necessary files to the image. Typically, Python codes, YAML config and some data files.

In this example, our dummy MWUEncoder does not require any extra data files.

ENTRYPOINT ["jina", "pod", "--uses", "mwu_encoder.yml"]

The last step is to specify the entrypoint of this image, which is usually via jina pod.

In this example, we set mwu_encoder.yml as a default YAML config. So, if the user later runs

docker run jinaai/hub.examples.mwu_encoder

This is equivalent to running:

jina pod --uses hub/example/mwu_encoder.yml

Any key-value arguments followed after docker run jinaai/hub.examples.mwu_encoder will be passed to jina pod. For example,

docker run jinaai/hub.examples.mwu_encoder --port-in 55555 --port-out 55556

This is the same as running:

jina pod --uses hub/example/mwu_encoder.yml --port-in 55555 --port-out 55556

One can also override the internal YAML config by specifying an out-of-Docker external YAML config via:

docker run -v $(pwd)/hub/example/mwu_encoder_ext.yml:/ext.yml jinaai/hub.examples.mwu_encoder --uses /ext.yml

3. Build the Pod image

You can build the Pod image now via docker build:

cd hub/example
docker build -t jinaai/hub.examples.mwu_encoder .

Depending on whether you want to use the latest Jina image, you may first pull it via docker pull jinaai/jina before the build. For the sake of immutability, docker build will not automatically pull the latest image for you.

Congratulations! You can now re-use this Pod image however and wherever you want.