I’m running into an issue with my dockerfile + flyte + ray integration (🧵 ).

timestamp: 2025-06-05 22:36:50,156
level: ERROR
message: !! Begin Unknown System Error Captured by Flyte !!

Traceback (most recent call last):
  File "/usr/local/lib/python3.11/site-packages/flytekit/bin/entrypoint.py", line 179, in dispatch_execute
    outputs = task_def.dispatch_execute(ctx, idl_input_literals)
    
  File "/usr/local/lib/python3.11/site-packages/flytekit/core/base_task.py", line 728, in dispatch_execute
    new_user_params = self.pre_execute(ctx.user_space_params)
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/flytekitplugins/ray/task.py", line 81, in pre_execute
    ray.init(**init_params)
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
    return func(*args, **kwargs)
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/ray/_private/worker.py", line 1780, in init
    _global_node = ray._private.node.Node()
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/ray/_private/node.py", line 365, in __init__
    self.start_ray_processes()
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/ray/_private/node.py", line 1500, in start_ray_processes
    ) = ray._private.services.determine_plasma_store_config()
    
  File "/home/flytekit/.local/lib/python3.11/site-packages/ray/_private/services.py", line 2198, in determine_plasma_store_config
    raise ValueError(

ValueError: Attempting to cap object store memory usage at 46216396 bytes, 
           but the minimum allowed is 78643200 bytes.

timestamp: 2025-06-05 22:36:50,157
level: ERROR
message: !! End Error Captured by Flyte !!

This is my ray task

@task(
    container_image=CONTAINER_IMAGE,
    task_config=RayJobConfig(
        # Configure the Ray cluster for this task
        head_node_config=HeadNodeConfig(
            ray_start_params={
                "log-color": "True",
                "object-store-memory": "100_000_000",
            },
        ),
        worker_node_config=[
            WorkerNodeConfig(
                ray_start_params={
                    "object-store-memory": "100_000_000",
                },
                replicas=2,
                group_name="worker-group",
            )
        ],  # 2 Ray workers
        shutdown_after_job_finishes=True,  # Clean up Ray cluster after task
        ttl_seconds_after_finished=120,  # Keep cluster for 2 mins after completion for debugging
    ),
    requests=Resources(cpu="1", mem="1Gi"),  # Resources for the task pod
    limits=Resources(cpu="1", mem="1Gi"),  # Maximum resources
)

The error being thrown here comes from the Ray library itself so I would start with looking at the source code from the call stack in ray.

Resolved, my particular issue was I didn’t want to spin up my own ray cluster but I wanted to connect to my existing one.

I see. I believe that connecting to an existing only works as local execution at the moment. If you run the ray task remotely it spins up kuberay CRDs which will stand up a cluster.