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The ray config tab shows you the current autoscaler configuration.
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You can also see it is infeasible to create ``Actor2`` because it requires 4 GPUs which
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is bigger than the total gpus available in this cluster (2 GPUs).
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Debugging ObjectStoreFullError and Memory Leak
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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You can view information for Ray objects in the memory tab. It is useful to debug memory leaks, especially `ObjectStoreFullError`.
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Note that this is the same information as displayed in the `ray memory command <https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory>`_. For details about the information contained in the table, please see the `ray memory` documentation.
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Inspect Memory Usage
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~~~~~~~~~~~~~~~~~~~~
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You can detect local memory anomalies through the Logical View tab. If NumObjectIdsInScope,
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**Pause Collection**: A button to stop/continue updating Ray memory tables.
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**IP Address**: Node IP Address where a Ray object is pinned.
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**Pid**: ID of a process where a Ray object is being used.
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**Type**: Type of a process. It is either a driver or worker.
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**Object ID**: Object ID of a Ray object.
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**Object Size** Object Size of a Ray object in bytes.
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**Reference Type**: Reference types of Ray objects. Checkout the `ray memory command <https://docs.ray.io/en/latest/memory-management.html#debugging-using-ray-memory>`_ to learn each reference type.
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**Call Site**: Call site where this Ray object is referenced.
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Ray Config
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~~~~~~~~~~~~
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If you are using the autoscaler, this Configuration defined at ``cluster.yaml`` is shown.
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