ray

After training a model, we should expose utilities so that the user can load their best checkpoint in memory.

1. Trainer.load_checkpoint(path) to load an arbitrary checkpoint.
2. trainer.best_checkpoint to load trainer.best_checkpoint_path into memory.

Describe the bug

Failed to execute Series.drop_duplicates.

In [75]: a = md.DataFrame(np.random.rand(10, 2), columns=['a', 'b'], chunk_size=2)                  

In [76]: a['a'].drop_duplicates().execute()               

Overview

As a CFP user, I would like to split a dataset (e.g., np array, pandas dataframe) into smaller objects that can then be fed into other nodes/pipeline. This is especially useful when we have compute intensive tasks and would like to parallelize it eas

Search before asking

• I had searched in the issues and found no similar feature requirement.

Description

Currently, we only lint & test ray-operator, since we merge service code here, we should also update CI to support these submodules.

https://github.com/ray-project/kuberay/blob/6f87ca64c107cd51d3ab955faf4be198e0094536/.git