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Starting an interactive Ray Cluster

  • This example demonstrates how to create, and connect to a Practicus AI Ray cluster, and execute simple Ray operations.

Note on shared drives

  • Practicus AI distributed clusters require a shared drive accessible by multiple workers, such as Practicus AI ~/my or ~/shared folders.
  • If you do not have access to ~/my or ~/shared folders, please check the auto-scaled examples which does not need such drives, but are limited in functionality.
worker_size = None
worker_count = None
worker_image = "practicus-ray"

assert worker_size, "Please enter your worker_size."
assert worker_count, "Please enter your worker_count."
assert worker_image, "Please enter your worker_image."

import practicuscore as prt

# Let's define the distributed features
distributed_config = prt.DistJobConfig(
    job_type=prt.DistJobType.ray,
    worker_count=worker_count,
)

# Let's define worker features of the cluster
worker_config = prt.WorkerConfig(
    # Please note that Ray requires a specific worker image
    worker_image=worker_image,
    worker_size=worker_size,
    distributed_config=distributed_config,
)

# Creating the coordinator (master) worker
# will also create the cluster.
coordinator_worker = prt.create_worker(
    worker_config=worker_config,
)

# Since this is an interactive Ray cluster,
#  let's login to execute some code.

notebook_url = coordinator_worker.open_notebook()

print("Page did not open? You can open this url manually:", notebook_url)

Please continue experimenting on the new browser tab

by opening the next example in this directory

# Done experimenting? Let's terminate the coordinator
#  which will also terminate the cluster.
coordinator_worker.terminate()

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