gloo | The Feature-rich , Kubernetes-native , Next-Generation API | REST library

You can use the k8sutil repo, see the apply example:

I'm adding here the solution of @ptrblck written in the PyTorch discussion forum.

Here're two quotes.

The statement:

[...] the allocated memory get doubled when torch.distributed.Reducer is instantiated in the constructor of DistributedDataParallel

And the answer:

[...] the Reducer will create gradient buckets for each parameter, so that the memory usage after wrapping the model into DDP will be 2x model_parameter_size. Note that the parameter size of a model is often much smaller than the activation size so that this memory increase might or might not be significant

So, from here we can see the reason why the memory footprint sometimes doubles.

When you call fit() you can pass this parameter job_name=yourJobName

Please add returnStdout: true to your script command which will return exact output. Otherwise it will return status code which 0. Please add returnStdout: true like this so your command should be like this,

There are the following errors:

• The self.ui.vispy_widget(canvas.native) command does not make sense, the idea is to use vispy_widget as a container for the native vispy widget that can be placed through a layout.

• The choice of the .ui form is used to determine the base class, in your case you should use QDialog instead of QMainWindow.

• If you already set the Ui_Dialog in the widget then it is unnecessary to implement the same in if __name__ == "__main__":.

• You must import the math module.

you can iterate over files in bash

first make sure that it only hits the folders that you want

You have to use the wso2 Microgateway toolkit(Microgateway CLI tool) to generate the API artifacts. The responsibility of the toolkit is to create a Microgateway project and after that generate an executable jar file for the Microgateway runtime. There is no other way to generate the Microgateway project and artifacts without CLI. To manage generated Microgateway projects you can use some version control system like Git and create some automation for the deployment.

Following the suggestions of @djhoese and @RolfofSaxony, I have updated the code. The code now successfully adds an arrow upon clicking the button.

The only annotation that you are supposed to use with Gloo is kubernetes.io/ingress.class: gloo which is the standard way to mark an Ingress object as handled by a specific Ingress controller. This requirement will go away if you add the ability for Gloo to be the default Ingress controller for your cluster. Also, according to the documentation:

If you need more advanced routing capabilities, we encourage you to use Gloo VirtualServices by installing as glooctl install gateway.

Gloo Gateway uses Kubernetes Custom Resources instead of Ingress Objects as the only way to configure Ingress’ beyond their basic routing spec is to use lots of vendor-specific Kubernetes Annotations to your Kubernetes manifests.

So you are supposed to use VirtualService in order to achieve your goal. You can see the example below: