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- dask.optimize on xarray objects · 5 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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690378323 | https://github.com/pydata/xarray/issues/3698#issuecomment-690378323 | https://api.github.com/repos/pydata/xarray/issues/3698 | MDEyOklzc3VlQ29tbWVudDY5MDM3ODMyMw== | TomAugspurger 1312546 | 2020-09-10T15:42:54Z | 2020-09-10T15:42:54Z | MEMBER | Thanks for confirming. I'll take another look at this today then. On Thu, Sep 10, 2020 at 10:30 AM Deepak Cherian notifications@github.com wrote:
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dask.optimize on xarray objects 550355524 | |
690367604 | https://github.com/pydata/xarray/issues/3698#issuecomment-690367604 | https://api.github.com/repos/pydata/xarray/issues/3698 | MDEyOklzc3VlQ29tbWVudDY5MDM2NzYwNA== | dcherian 2448579 | 2020-09-10T15:30:01Z | 2020-09-10T15:30:01Z | MEMBER | The numpy example is fixed but the dask rechunked example is still broken.
``` IndexError Traceback (most recent call last) <ipython-input-8-5663bc8bc82a> in <module> ----> 1 dask.optimize(xr.DataArray(a).chunk(5))[0].compute() ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xarray/core/dataarray.py in compute(self, kwargs) 838 """ 839 new = self.copy(deep=False) --> 840 return new.load(kwargs) 841 842 def persist(self, **kwargs) -> "DataArray": ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xarray/core/dataarray.py in load(self, kwargs) 812 dask.array.compute 813 """ --> 814 ds = self._to_temp_dataset().load(kwargs) 815 new = self._from_temp_dataset(ds) 816 self._variable = new._variable ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/xarray/core/dataset.py in load(self, kwargs) 656 657 # evaluate all the dask arrays simultaneously --> 658 evaluated_data = da.compute(*lazy_data.values(), kwargs) 659 660 for k, data in zip(lazy_data, evaluated_data): ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/base.py in compute(args, kwargs) 445 postcomputes.append(x.dask_postcompute()) 446 --> 447 results = schedule(dsk, keys, kwargs) 448 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)]) 449 ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs) 74 pools[thread][num_workers] = pool 75 ---> 76 results = get_async( 77 pool.apply_async, 78 len(pool._pool), ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs) 484 _execute_task(task, data) # Re-execute locally 485 else: --> 486 raise_exception(exc, tb) 487 res, worker_id = loads(res_info) 488 state["cache"][key] = res ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/local.py in reraise(exc, tb) 314 if exc.traceback is not tb: 315 raise exc.with_traceback(tb) --> 316 raise exc 317 318 ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception) 220 try: 221 task, data = loads(task_info) --> 222 result = _execute_task(task, data) 223 id = get_id() 224 result = dumps((result, id)) ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/core.py in _execute_task(arg, cache, dsk) 119 # temporaries by their reference count and can execute certain 120 # operations in-place. --> 121 return func(*(_execute_task(a, cache) for a in args)) 122 elif not ishashable(arg): 123 return arg ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/array/core.py in concatenate3(arrays) 4407 if not ndim: 4408 return arrays -> 4409 chunks = chunks_from_arrays(arrays) 4410 shape = tuple(map(sum, chunks)) 4411 ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/array/core.py in chunks_from_arrays(arrays) 4178 4179 while isinstance(arrays, (list, tuple)): -> 4180 result.append(tuple([shape(deepfirst(a))[dim] for a in arrays])) 4181 arrays = arrays[0] 4182 dim += 1 ~/miniconda3/envs/dcpy/lib/python3.8/site-packages/dask/array/core.py in <listcomp>(.0) 4178 4179 while isinstance(arrays, (list, tuple)): -> 4180 result.append(tuple([shape(deepfirst(a))[dim] for a in arrays])) 4181 arrays = arrays[0] 4182 dim += 1 IndexError: tuple index out of range ``` |
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dask.optimize on xarray objects 550355524 | |
689825648 | https://github.com/pydata/xarray/issues/3698#issuecomment-689825648 | https://api.github.com/repos/pydata/xarray/issues/3698 | MDEyOklzc3VlQ29tbWVudDY4OTgyNTY0OA== | dcherian 2448579 | 2020-09-09T21:14:16Z | 2020-09-09T21:14:16Z | MEMBER | I guess I can see that. Thanks Tom.
FYI the slicing behaviour is independent of chunk-size (matt's recommendation). |
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dask.optimize on xarray objects 550355524 | |
689808725 | https://github.com/pydata/xarray/issues/3698#issuecomment-689808725 | https://api.github.com/repos/pydata/xarray/issues/3698 | MDEyOklzc3VlQ29tbWVudDY4OTgwODcyNQ== | TomAugspurger 1312546 | 2020-09-09T20:38:39Z | 2020-09-09T20:38:39Z | MEMBER | FYI, @dcherian your recent PR to dask fixed this example. Playing around with chunk sizes, it seems to have fixed it even when the chunk size exceeds |
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dask.optimize on xarray objects 550355524 | |
592101136 | https://github.com/pydata/xarray/issues/3698#issuecomment-592101136 | https://api.github.com/repos/pydata/xarray/issues/3698 | MDEyOklzc3VlQ29tbWVudDU5MjEwMTEzNg== | TomAugspurger 1312546 | 2020-02-27T18:13:28Z | 2020-02-27T18:13:28Z | MEMBER | It looks like xarray is getting a bad task graph after the optimize. ```python In [1]: import xarray as xr import dask In [2]: import dask In [3]: a = dask.array.ones((10,5), chunks=(1,3)) ...: a = dask.optimize(a)[0] In [4]: da = xr.DataArray(a.compute()).chunk({"dim_0": 5}) ...: da = dask.optimize(da)[0] In [5]: dict(da.dask_graph()) Out[5]: {('xarray-<this-array>-e2865aa10d476e027154771611541f99', 1, 0): (<function _operator.getitem(a, b, /)>, 'xarray-<this-array>-e2865aa10d476e027154771611541f99', (slice(5, 10, None), slice(0, 5, None))), ('xarray-<this-array>-e2865aa10d476e027154771611541f99', 0, 0): (<function _operator.getitem(a, b, /)>, 'xarray-<this-array>-e2865aa10d476e027154771611541f99', (slice(0, 5, None), slice(0, 5, None)))} ``` Notice that are references to If we manually insert that, you'll see things work ```python In [9]: dsk['xarray-<this-array>-e2865aa10d476e027154771611541f99'] = da._to_temp_dataset()[xr.core.dataarray._THIS_ARRAY] In [11]: dask.get(dsk, keys=[('xarray-<this-array>-e2865aa10d476e027154771611541f99', 1, 0)]) Out[11]: (<xarray.DataArray \<this-array> (dim_0: 5, dim_1: 5)> dask.array<getitem, shape=(5, 5), dtype=float64, chunksize=(5, 5), chunktype=numpy.ndarray> Dimensions without coordinates: dim_0, dim_1,) ``` |
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dask.optimize on xarray objects 550355524 |
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