🚀 Feature Request

The evaluate_loader method for Python API. Similar to .train and .predict_loader

Motivation

What happened:

When creating a LocalCluster object the comm is started on a random high port, even if there are no other clusters running.

What you expected to happen:

Should use port 8786.

Minimal Complete Verifiable Example:

$ conda create -n dask-lc-test -c conda-forge -y python=3.8 ipython dask distributed
$ conda activate dask-lc-test

The `d

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

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

Summary

What happened/what you expected to happen?

We expect the container use the existing volume defined inside the workflow. Such as:
volumes-existing.yaml

example for testing:

import os

import couler.argo as couler
from couler.argo_submitter import ArgoSubmitter
from couler.core.t