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/bin/sh: iptables: not found

This means the grafana/grafana-oss:latest default doesn't include the iptables command.

You could install it with apk add --no-cache iptables ip6tables, see Running (and debugging) iptables inside a Docker container.

A quick experiment as next:

Turn off ON ERROR RESUME NEXT everywhere except in your outermost script. So something like:

I think you are mixing some points between microservices and osgi-based applications. With microservices you usually have a independent process running each microservice which can be deployed in the same o other machines. Because of that you can scale as you said and gain benefits. But the communication model is not process to process. It has to use a different approach and its highly recommended that you use a standard integration mechanism, you can use REST, you can use Json RPC, SOAP, or queues or topics to use a event-driven communication. By this mechanisms you invoke the 'other' service operations as you do in osgi, but you are just using a different interface, instead of a local invocation you do a remote invocation.

Service discovery is something that you can do with just Virtual IP's accessing other services through a common dns name and a load balancer, or using kubernetes DNS, if you go for kubernetes as platform. You could use also a central configuration service or let each service register itself in a central registry. There are already plenty different flavours of solutions to tackle this complexity.

Also more importantly, you will have to be aware of your new complexities, but some you already have.

• Contract versioning and design
• Synchronous or asynchronous communication between services.
• How to deal with security in the boundary of the services / Do i even need security in most of my services or i just need information about the user identity.
• Increased maintenance cost and redundant side code for common features (here quarkus helps you a lot with its extensions and also you have microprofile compatibility).
• ...

Deciding to go with microservices is not an easy decision and not one that should be taken in a single step. My recommendation is that you analyse your application domain and try to check if your design is ok to go with microservices (in terms of separation of concenrs and model cohesion) and extract small parts of your osgi platform into microservices, otherwise you mostly will be force to make changes in your service interfaces which would be more difficult to do due to the service to service contract dependency than change a method and some invocations.

The short answer is it depends, but a more nuance answer is depends what you want to do with the component.

As base knowledge, when a KFP pipeline is compiled, it's actually a series of different YAMLs that are launched by Argo Workflows. All of these needs to be container based to run on Kubernetes, even if the container itself has all python.

A function to Python Container Op is a quick way to get started with Kubeflow Pipelines. It was designed to model after Airflow's python-native DSL. It will take your python function and run it within a defined Python container. You're right it's easier to encapsulate all your work within the same Git folder. This set up is great for teams just getting started with KFP and don't mind some boilerplate to get going quickly.

Components really become powerful when your team needs to share work, or you have an enterprise ML platform that is creating template logic of how to run specific jobs in a pipeline. The components can be separately versioned and built to use on any of your clusters in the same way (underlying container should be stored in docker hub or ECR, if you're on AWS). There are inputs/outputs to prescribe how the run will execute using the component. You can imagine a team in Uber might use a KFP to pull data for number of drivers in a certain zone. The inputs to the component could be Geo coordinate box and also time of day of when to load the data. The component saves the data to S3, which then is loaded to your model for training. Without the component, there would be quite a bit of boiler plate that would need to copy the code across multiple pipelines and users.

I'm a former PM at AWS for SageMaker and open source ML integrations, and this is sharing from my experience looking at enterprise set ups.

By default, the 5.0 tag gives you Windows Nano Server when targeting Windows containers. This SKU of Windows is trimmed down and does not contain gdiplus.dll. What you would want instead is Windows Server Core which does have that DLL. This is documented here: https://github.com/dotnet/dotnet-docker/blob/main/documentation/scenarios/using-system-drawing-common.md.

Based on the other tags I see you referencing, it seems that you're targeting Windows Server 2019 to use for your container. Luckily, .NET does provide .NET 5.0 images for Windows Server Core 2019. The tag to use for that is 5.0-windowsservercore-ltsc2019. You can find this listed at https://hub.docker.com/_/microsoft-dotnet-runtime. That would be the recommended tag to use to resolve this issue.

I question whether you're actually using Windows Server 2016 as a host environment. No version of Windows Server is capable of running containers using a Windows version newer than the host version. This Windows container version compatibility matrix is described here: https://docs.microsoft.com/en-us/virtualization/windowscontainers/deploy-containers/version-compatibility?tabs=windows-server-2016%2Cwindows-10-20H2.

I'm also a little confused by the content of your Dockerfile because you keep listing two successive FROM instructions. For example, you posted this:

It's never a good idea to reuse MACs on the same network.

The reason is that network switches will remember the MAC address and will direct traffic to the target port even when machine/VM is powered off. Usually, a switch will forget the MAC after a while if it is not reachable, but this time can depend on arbitrary settings from switch manfacturer or network administrator.

So with tha same MAC you can be lucky or not, but even if it appears to work it will still be non-deterministic and can break at any time.

According to this video it's only available in US East 1 currently.

https://youtu.be/rA5l82vypXc

Finally, thanks to another user I have understood the problem. The problem I had is I am not passing an object, actually it is a dictionary, it's not the same thing. If I want to access the properties of my dictionary here is what I need to do: {% for object, props in object_list.items %} instead of {% for object in object_list %}.

In this way I can access in my for loop the property 'prog' as I wanted.

Here is how it looks like now that it is working: views.py:

I raised an issue, and it's not possible this way. The suggested workaround is to create a layer which parses a string into a list: