tuner | Online tuner based on web audio api | Audio Utils library

If both the models need to be jointly optimized, you could run a SageMaker HPO job in script mode and define both the models in the script. Or you could run two HPO jobs, optimize each model, and then create the Pipeline Model. There is no native support for doing an HPO job on a PipelineModel.

I work at AWS and my opinions are my own.

Thanks for opening the issue, I added a +1. In the meantime, you can use alternative SDKs to train Random Cut Forest and set test channel distribution to FullyReplicated.

For example, those SDKs should give you this control:

• AWS CLI create_training_job
• boto3 create_training_job
• SageMaker Python SDK Estimator to which you pass the RCF docker image in image_uri parameter

The problem here is that rtl_433 never exits, it just keeps printing lines for every message it receives.

This is a problem because curl is going to wait until it receives an EOF (end of file) marker to know when it has received all payload it needs to send to in the HTTP request, but of course it never gets this. The EOF would be automatically added when rtl_433 exited.

Your best bet is probable to move from a one line script to a proper bash command reads from rtl_433 a line at a time and then runs sed on that line before passing it to curl.

e.g.

Turning linking off is the lazy answer. The point of using the linker is to remove "dead" code and minimize the size of the program. The size of a mobile app is more important to many users.

The linker used in Xamarin.Android is going to remove code it thinks you are not using. This is especially true when using reflection. To fix this you would need to tell the compiler you're using the type.

A popular solution is to use a LinkerPleaseInclude.cs file and make stub methods using the type and properties so the compiler thinks you're using them. You will find many examples on the Internet.

What your linker settings? Please change it to None, then release it, if you will get the same exception,then please share Android option screenshot that contains all the settings when release. No Dex compiler was specified, select D8 Dex compiler in the Android project properties => android options => dex compiler D8 Code :

d8

In my case I tracked the issue down to test -x not working. The /usr/bin/R script uses that to check if the /usr/lib/R folder exists.

Searching for solutions to this sent me to various bug reports in Ubuntu but I managed to resolve the issue by upgrading docker version. My Docker host is Centos not Ubuntu like in most bug reports, however my container is based on Ubuntu.

Things worked again after upgrading Docker to the following packages:

You have to provide an input_shape to the first layer. In your case this is tf.keras.layers.Bidirectional() and not tf.keras.layers.LSTM.

Two alternatives are:

• First call your model with actual data
• Call model.build(input_shape) before calling model.compile()

The reason is the lines where you say:

The error is giving you the key to solve the problem. You need to match the names of the keys produced by fit_tuner with the ones provided to the RandomSearch function. Try substituting 'val_accuracy' for 'val_acc' in the RandomSearch function.