Serverless ML in Your Own Cloud

# Define your model class with normal code
class MyModelClass: 
    def train(): 
          ..

    def predict(): 
          ..

    def save(): 
          ..

# Send your class to remote
RemoteClass = rh.module(MyModelClass).to(my_cluster)

# Instantiate, call an instance of the remote class
RemoteModel = RemoteClass(name = 'remote_model')
RemoteModel.train()
RemoteModel.save()

$ git add MyModelClass.py
$ git commit -m "Refactor the train method"
$ git push
$ echo "Develop code, not orchestrator pipelines!"

# Run distributed data processing over large datasets
process_data = rh.function(DaskPreprocess).to(rh_8x4_cpus)
process_data.distribute('dask')
process_data(s3_path)

# Execute a multinode PyTorch or Lightning training 
trainer = rh.module(PyTorchTraining).to(rh_8x4_gpus)
remote_trainer=trainer().distribute('pytorch')
remote_trainer.train()

# Use Ray Data for inference once the training is done   
batch_inference = rh.function(RayBatchInf).to(rh_4x1gpus)
batch_inference.distribute('ray')
batch_inference(s3_path)

# API route to fetch logs for a resource
@router.get(
    "/{uri}/logs",
    response_description="Resource logs retrieved",
)
@send_event
async def resource_logs_preview(...):
    ...

# API route to fetch cluster status and metrics
@router.get(
    "/{uri}/cluster/status",
    response_description="Cluster status retrieved",
)
@send_event
async def load_cluster_status(...):
    ...