.. _fault-tolerance-tasks: .. _task-fault-tolerance: Task Fault Tolerance ==================== Tasks can fail due to application-level errors, e.g., Python-level exceptions, or system-level failures, e.g., a machine fails. Here, we describe the mechanisms that an application developer can use to recover from these errors. Catching application-level failures ----------------------------------- Ray surfaces application-level failures as Python-level exceptions. When a task on a remote worker or actor fails due to a Python-level exception, Ray wraps the original exception in a ``RayTaskError`` and stores this as the task's return value. This wrapped exception will be thrown to any worker that tries to get the result, either by calling ``ray.get`` or if the worker is executing another task that depends on the object. If the user's exception type can be subclassed, the raised exception is an instance of both ``RayTaskError`` and the user's exception type so the user can try-catch either of them. Otherwise, the wrapped exception is just ``RayTaskError`` and the actual user's exception type can be accessed via the ``cause`` field of the ``RayTaskError``. .. literalinclude:: ../doc_code/task_exceptions.py :language: python :start-after: __task_exceptions_begin__ :end-before: __task_exceptions_end__ Example code of catching the user exception type when the exception type can be subclassed: .. literalinclude:: ../doc_code/task_exceptions.py :language: python :start-after: __catch_user_exceptions_begin__ :end-before: __catch_user_exceptions_end__ Example code of accessing the user exception type when the exception type can *not* be subclassed: .. literalinclude:: ../doc_code/task_exceptions.py :language: python :start-after: __catch_user_final_exceptions_begin__ :end-before: __catch_user_final_exceptions_end__ Use `ray list tasks` from :ref:`State API CLI ` to query task exit details: .. code-block:: bash # This API is only available when you download Ray via `pip install "ray[default]"` ray list tasks .. code-block:: bash ======== List: 2023-05-26 10:32:00.962610 ======== Stats: ------------------------------ Total: 3 Table: ------------------------------ TASK_ID ATTEMPT_NUMBER NAME STATE JOB_ID ACTOR_ID TYPE FUNC_OR_CLASS_NAME PARENT_TASK_ID NODE_ID WORKER_ID ERROR_TYPE 0 16310a0f0a45af5cffffffffffffffffffffffff01000000 0 f FAILED 01000000 NORMAL_TASK f ffffffffffffffffffffffffffffffffffffffff01000000 767bd47b72efb83f33dda1b661621cce9b969b4ef00788140ecca8ad b39e3c523629ab6976556bd46be5dbfbf319f0fce79a664122eb39a9 TASK_EXECUTION_EXCEPTION 1 c2668a65bda616c1ffffffffffffffffffffffff01000000 0 g FAILED 01000000 NORMAL_TASK g ffffffffffffffffffffffffffffffffffffffff01000000 767bd47b72efb83f33dda1b661621cce9b969b4ef00788140ecca8ad b39e3c523629ab6976556bd46be5dbfbf319f0fce79a664122eb39a9 TASK_EXECUTION_EXCEPTION 2 c8ef45ccd0112571ffffffffffffffffffffffff01000000 0 f FAILED 01000000 NORMAL_TASK f ffffffffffffffffffffffffffffffffffffffff01000000 767bd47b72efb83f33dda1b661621cce9b969b4ef00788140ecca8ad b39e3c523629ab6976556bd46be5dbfbf319f0fce79a664122eb39a9 TASK_EXECUTION_EXCEPTION .. _task-retries: Retrying failed tasks --------------------- When a worker is executing a task, if the worker dies unexpectedly, either because the process crashed or because the machine failed, Ray will rerun the task until either the task succeeds or the maximum number of retries is exceeded. The default number of retries is 3 and can be overridden by specifying ``max_retries`` in the ``@ray.remote`` decorator. Specifying -1 allows infinite retries, and 0 disables retries. To override the default number of retries for all tasks submitted, set the OS environment variable ``RAY_TASK_MAX_RETRIES``. e.g., by passing this to your driver script or by using :ref:`runtime environments`. You can experiment with this behavior by running the following code. .. literalinclude:: ../doc_code/tasks_fault_tolerance.py :language: python :start-after: __tasks_fault_tolerance_retries_begin__ :end-before: __tasks_fault_tolerance_retries_end__ When a task returns a result in the Ray object store, it is possible for the resulting object to be lost **after** the original task has already finished. In these cases, Ray will also try to automatically recover the object by re-executing the tasks that created the object. This can be configured through the same ``max_retries`` option described here. See :ref:`object fault tolerance ` for more information. By default, Ray will **not** retry tasks upon exceptions thrown by application code. However, you may control whether application-level errors are retried, and even **which** application-level errors are retried, via the ``retry_exceptions`` argument. This is ``False`` by default. To enable retries upon application-level errors, set ``retry_exceptions=True`` to retry upon any exception, or pass a list of retryable exceptions. An example is shown below. .. literalinclude:: ../doc_code/tasks_fault_tolerance.py :language: python :start-after: __tasks_fault_tolerance_retries_exception_begin__ :end-before: __tasks_fault_tolerance_retries_exception_end__ Use `ray list tasks -f task_id=\` from :ref:`State API CLI ` to see task attempts failures and retries: .. code-block:: bash # This API is only available when you download Ray via `pip install "ray[default]"` ray list tasks -f task_id=16310a0f0a45af5cffffffffffffffffffffffff01000000 .. code-block:: bash ======== List: 2023-05-26 10:38:08.809127 ======== Stats: ------------------------------ Total: 2 Table: ------------------------------ TASK_ID ATTEMPT_NUMBER NAME STATE JOB_ID ACTOR_ID TYPE FUNC_OR_CLASS_NAME PARENT_TASK_ID NODE_ID WORKER_ID ERROR_TYPE 0 16310a0f0a45af5cffffffffffffffffffffffff01000000 0 potentially_fail FAILED 01000000 NORMAL_TASK potentially_fail ffffffffffffffffffffffffffffffffffffffff01000000 94909e0958e38d10d668aa84ed4143d0bf2c23139ae1a8b8d6ef8d9d b36d22dbf47235872ad460526deaf35c178c7df06cee5aa9299a9255 WORKER_DIED 1 16310a0f0a45af5cffffffffffffffffffffffff01000000 1 potentially_fail FINISHED 01000000 NORMAL_TASK potentially_fail ffffffffffffffffffffffffffffffffffffffff01000000 94909e0958e38d10d668aa84ed4143d0bf2c23139ae1a8b8d6ef8d9d 22df7f2a9c68f3db27498f2f435cc18582de991fbcaf49ce0094ddb0 Cancelling misbehaving tasks ---------------------------- If a task is hanging, you may want to cancel the task to continue to make progress. You can do this by calling ``ray.cancel`` on an ``ObjectRef`` returned by the task. By default, this will send a KeyboardInterrupt to the task's worker if it is mid-execution. Passing ``force=True`` to ``ray.cancel`` will force-exit the worker. See :func:`the API reference ` for ``ray.cancel`` for more details. Note that currently, Ray will not automatically retry tasks that have been cancelled. Sometimes, application-level code may cause memory leaks on a worker after repeated task executions, e.g., due to bugs in third-party libraries. To make progress in these cases, you can set the ``max_calls`` option in a task's ``@ray.remote`` decorator. Once a worker has executed this many invocations of the given remote function, it will automatically exit. By default, ``max_calls`` is set to infinity.