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While running benchmarks for the GNN packages in a multinode environment, @jnke2016 and I found that calling Client.wait_for_workers was not working properly, causing a hang or crash when running a dask workflow. Currently, we have a workaround that uses a separate script (wait_for_workers.py) to wait for all workers prior to launching a workflow. This workaround should be eliminated in favor of fixing the bug and calling Client.wait_for_workers as intended by the dask API.
The text was updated successfully, but these errors were encountered:
Not sure it will be, sorry. The approach I had there was not considered appropriate long term. I'll see if I can dig up the current state of any discussions
While running benchmarks for the GNN packages in a multinode environment, @jnke2016 and I found that calling
Client.wait_for_workers
was not working properly, causing a hang or crash when running a dask workflow. Currently, we have a workaround that uses a separate script (wait_for_workers.py
) to wait for all workers prior to launching a workflow. This workaround should be eliminated in favor of fixing the bug and callingClient.wait_for_workers
as intended by the dask API.The text was updated successfully, but these errors were encountered: