Avoid controlling endless loop with exception in loadAnonymousClasses, e.g. by extracting loading class to the method:

  private boolean tryLoadClass(String innerClassName) {
        try {
            parent.loadClass(innerClassName);
        } catch (ClassNotFoundException ex) {
            return false;
        }
        return true;
    }

https://sonarcloud.io/project

🚀 Feature Request

ENUMs for the catalyst-based conventions.
As an example, I will use scope.

Motivation

We have a lot of callbacks that used scope and maybe some other parameter that has only in catalyst-based meaning.
And every time we need to do something like that:

if isinstance(scope, str) and scope in [
            "on_batch_end",
            "on_batch_start",
  

Our coverage badge is a bit misleading showing coverage below 90%. This is due to us not collecting coverage in a few places. Also, we simply have a few modules which are only there for debugging and/or historical reasons

The most relevant parts (scheduler, worker, etc.) do have quite good coverage. I believe the <90% batch doesn't reflect well on the project and the wrong configuration creates

It seems that the number of joining clients (not the num of computing clients) is fixed in fedml_api/data_preprocessing/**/data_loader and cannot be changed except CIFAR10 datasets.

Here I mean that it seems the total clients is decided by the datasets, rather the input from run_fedavg_distributed_pytorch.sh.

https://github.com/FedML-AI/FedML/blob/3d9fda8d149c95f25ec4898e31df76f035a33b5d/fed

Summary

What change needs making?
Example script on how to use created secret successfully.
I have tried myself using the following code from test
It creates a secret successfully but fails to echo the secrets value.

Use Cases

When would you use this?
when I n

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing