issue_comments: 988359778
This data as json
html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
https://github.com/pydata/xarray/issues/4406#issuecomment-988359778 | https://api.github.com/repos/pydata/xarray/issues/4406 | 988359778 | IC_kwDOAMm_X8466Sxi | 13684161 | 2021-12-08T00:05:24Z | 2021-12-08T00:06:22Z | NONE | I am having a similar issue as well. Using latest versions of dask, xarray, distributed, fsspec, and gcsfs. I use h5netcdf backend because it is the only one that works with fsspec's binary stream, reading from cloud. My workflow consists of: 1. Start dask client with 1 process per CPU, and 2 threads each. This is because it doesn't scale up reading from the cloud with threads. 2. Opening 12x monthly climate data (hourly sampled) using xarray.open_mfdataset 3. Using reasonable dask chunks in the open function 4. Take monthly average across time axis, and write to local NetCDF. 5. Repeate 2-4 for different years. It is a hit or miss. It hangs towards the middle or end of a year. Next time I run it, it doesn't. Once it hangs, and I hit stop, in the traceback it is stuck at await of threading lock. Any ideas how to avoid this? Things I tried: 1. Use processes only, 1 thread per worker 2. lock=True, lock=False on open_mfdataset 3. Dask scheduler as: spawn and forkserver 4. Different (but recent) versions of all the libraries |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
694112301 |