Shawn Yang
Shawn Yang
**Describe the bug** When executing a blockwise operations in mars which have many setitem/getitem nodes, mars will take about 1 minutes, which is too long. **To Reproduce** To help us...
**Describe the bug** When access task status for ray DAG mode in mars dashboard, got incorrect task status. Following task is finished, the graph should be green instead of blank:...
**Describe the bug** Mars client can't fetch large data, otherwise tornado will throw `Content-Length too long` exception **To Reproduce** To help us reproducing this bug, please provide information below: 1....
# Background Large-scale distributed computing systems may fail due to various reasons, including network problems, machine failures, and process restarts. Network failures can cause nodes and workers to fail to...
# Background When building chunk graph, Mars chunks won't be added to graph if some chunks not used by downstream, because mars build graph back to first. This will make...
**Describe the bug** When running test case `DEBUG_OSCAR=1 pytest -v -s mars/deploy/oscar/tests/test_ray.py::test_ownership_when_scale_in`, it hangs occasionally. **To Reproduce** To help us reproducing this bug, please provide information below: 1. Your Python...
**Is your feature request related to a problem? Please describe.** `Ray.put` with owner will issue an sync rpc to owner, when there are many obejcts to put, `Ray.put` will become...
Currently `RayExecutionContext.get_chunk_meta` is not supported, which will make any operands relied on this API failed on tiling, such as when call `DataFrame.groupby`: ``` df = md.DataFrame(mt.random.rand(300, 4, chunk_size=100), columns=list("abcd")) df["a"],...
**Describe the bug** When debugging mars in pycharm, following exception is throwed:  Although it didn't affect program execution, but this error log is very confusing, and should be removed....
**Describe the bug** When running following code in nodebook cell and cancel in the middle and re-execute it again, mars will throw timeout error:: ``` urldf = df.groupby(["id"])["trd_longitude","trd_latitude","id"].apply(lambda x: x.sum()).reset_index().execute()...