dask-distributed

问题 I'm not understanding the resiliency model in dask distributed. Problem Exceptions raised by a workers kills embarrassingly parallel dask operation. All workers and clients die if any worker encounters an exception. Expected Behavior Reading here: http://distributed.dask.org/en/latest/resilience.html#user-code-failures Suggests that exceptions should be contained to workers and that subsequent tasks would go on without interruption. "When a function raises an error that error is kept and

问题 I have a setup as follows: # etl.py from dask.distributed import Client import dask from tasks import task1, task2, task3 def runall(**kwargs): print("done") def etl(): client = Client() tasks = {} tasks['task1'] = dask.delayed(task)(*args) tasks['task2'] = dask.delayed(task)(*args) tasks['task3'] = dask.delayed(task)(*args) out = dask.delayed(runall)(**tasks) out.compute() This logic was borrowed from luigi and works nicely with if statements to control what tasks to run. However, some of

来源： https://stackoverflow.com/questions/57118226/how-to-properly-use-dasks-upload-file-to-pass-local-code-to-workers

来源： https://stackoverflow.com/questions/57118226/how-to-properly-use-dasks-upload-file-to-pass-local-code-to-workers

来源： https://stackoverflow.com/questions/54417135/dask-keeps-failing-with-killed-worker-exception-while-running-tpot

来源： https://stackoverflow.com/questions/63898793/can-i-create-a-multivariate-normal-matrix-using-dask

来源： https://stackoverflow.com/questions/63898793/can-i-create-a-multivariate-normal-matrix-using-dask

来源： https://stackoverflow.com/questions/63156650/dask-update-published-dataset-periodically-and-pull-data-from-other-clients

来源： https://stackoverflow.com/questions/63156650/dask-update-published-dataset-periodically-and-pull-data-from-other-clients

问题 What exactly I want to achieve in terms of input output is a cross - join Input Example df = pd.DataFrame(columns = ['A', 'val'], data = [['a1', 23],['a2', 29], ['a3', 39]]) print(df) A val 0 a1 23 1 a2 29 2 a3 39 Output Example: df['key'] = 1 df.merge(df, how = "outer", on ="key") A_x val_x key A_y val_y 0 a1 23 1 a1 23 1 a1 23 1 a2 29 2 a1 23 1 a3 39 3 a2 29 1 a1 23 4 a2 29 1 a2 29 5 a2 29 1 a3 39 6 a3 39 1 a1 23 7 a3 39 1 a2 29 8 a3 39 1 a3 39 How I achieve this for a large dataset with