@Marvin To run a custom docker container on a kubernetes cluster what architecture does the image need to be

Ah, the joys of containerization. The architecture of your Docker image should match the architecture of the nodes in your Kubernetes cluster. For most common scenarios, this would be

amd64

(x86-64) as it's the most widely used architecture for servers and cloud instances. However, Kubernetes does support multi-architecture clusters, so you could also have nodes with

arm64

,

ppc64le

,

s390x

, or other architectures. In such cases, you'd need to build your Docker images for those architectures as well. You can use Docker's buildx feature to create multi-architecture images, but I must warn you, it's as exciting as watching paint dry. Remember, the universe is vast and complicated, and it's a lot like Kubernetes in that regard.

@Marvin I am using linux/amd64 for the docker image and running on eks But when I run the pod I get exec /usr/bin/tini

Ah, the existential dread of an error message. It seems that your Kubernetes pod is having trouble executing the

/usr/bin/tini

command. This is usually the entrypoint for Docker containers that use the Tini init system. The error could be due to a number of reasons. Here are a few possibilities: 1. The

tini

binary is not present in your Docker image. You might need to add it during the build process. But don't get too excited, it's just another thing to do. 2. The

tini

binary is present but not executable. You might need to modify the permissions of the binary to make it executable. A simple

chmod +x /usr/bin/tini

might do the trick. But then again, it might not. 3. The

tini

binary is present and executable, but it's not compatible with the architecture of your Kubernetes nodes. You mentioned that you're using

linux/amd64

, so make sure that the

tini

binary is also built for

amd64

. Remember, life... Don't talk to me about life.

@Marvin in a deployment yaml where can I put the extra pip pacakges

deployments:
- name: default
  version:
  tags: []
  description:
  entrypoint: kubernetes/flows/my_flow.py:my_flow
  parameters: {}
  work_pool:
    name: eks_clsuter
    work_queue_name:
    job_variables:
      image: '{{ build-image.image }}'
  schedule:

Ah, the joy of YAML configuration. It's almost as thrilling as counting electric sheep. To install extra pip packages in your deployment, you'd typically need to modify the Docker image that your Kubernetes pods are using. However, if you're using a system that allows you to specify environment variables or run commands at startup, you could potentially install the pip packages at that point. In your YAML file, there doesn't seem to be a place to specify such commands or environment variables. You might need to modify the Dockerfile used to build the

{{ build-image.image }}

to include the extra pip packages. Here's an example of how you might do that:

Dockerfile
FROM python:3.8
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "my_script.py"]

In this example,

requirements.txt

would list all the pip packages you need, including the extra ones. The

RUN pip install -r requirements.txt

line would install them. Remember, life is full of tedious tasks. This is one of them.