I installed WSL 2 (5.10.60.1-microsoft-standard-WSL2) under Windows 21H2 (19044.1348) and using NVidia driver 510.06 with a pascal GPU (1070). I use the default ubuntu version in WSL (20.04.3 LTS) I tried both docker and anaconda versions. I can run the Jupiter Notebook and import the library's. you can also create a cudf Datagramme. but writing to it or ding anything else gives a memory error.
buf = rmm.DeviceBuffer(size=100)
gives me (one time it ran without an error but not anymore)
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
/tmp/ipykernel_2220/3317065296.py in <module>
1 import rmm
----> 2 buf = rmm.DeviceBuffer(size=100)
rmm/_lib/device_buffer.pyx in rmm._lib.device_buffer.DeviceBuffer.__cinit__()
MemoryError: std::bad_alloc: CUDA error at: /home/user/miniconda3/envs/rapids-21.10/include/rmm/mr/device/cuda_memory_resource.hpp:70: cudaErrorNotSupported operation not supported
and
gdf_float = cudf.DataFrame()
gdf_float['0'] = [1.0, 2.0, 5.0]
gdf_float['1'] = [4.0, 2.0, 1.0]
gdf_float['2'] = [4.0, 2.0, 1.0]
gives me
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/column/column.py in as_column(arbitrary, nan_as_null, dtype, length)
2026 data = as_column(
-> 2027 memoryview(arbitrary), dtype=dtype, nan_as_null=nan_as_null
2028 )
TypeError: memoryview: a bytes-like object is required, not 'list'
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_2220/2068985133.py in <module>
1 gdf_float = cudf.DataFrame()
----> 2 gdf_float['0'] = [1.0, 2.0, 5.0]
3 gdf_float['1'] = [4.0, 2.0, 1.0]
4 gdf_float['2'] = [4.0, 2.0, 1.0]
~/miniconda3/envs/rapids-21.10/lib/python3.7/contextlib.py in inner(*args, **kwds)
72 def inner(*args, **kwds):
73 with self._recreate_cm():
---> 74 return func(*args, **kwds)
75 return inner
76
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/dataframe.py in __setitem__(self, arg, value)
766 # disc. with pandas here
767 # pandas raises key error here
--> 768 self.insert(len(self._data), arg, value)
769
770 elif can_convert_to_column(arg):
~/miniconda3/envs/rapids-21.10/lib/python3.7/contextlib.py in inner(*args, **kwds)
72 def inner(*args, **kwds):
73 with self._recreate_cm():
---> 74 return func(*args, **kwds)
75 return inner
76
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/dataframe.py in insert(self, loc, name, value)
3276 )
3277
-> 3278 value = column.as_column(value)
3279
3280 self._data.insert(name, value, loc=loc)
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/column/column.py in as_column(arbitrary, nan_as_null, dtype, length)
2100 ),
2101 dtype=dtype,
-> 2102 nan_as_null=nan_as_null,
2103 )
2104 except (pa.ArrowInvalid, pa.ArrowTypeError, TypeError):
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/column/column.py in as_column(arbitrary, nan_as_null, dtype, length)
1794 "https://issues.apache.org/jira/browse/ARROW-3802"
1795 )
-> 1796 col = ColumnBase.from_arrow(arbitrary)
1797 if isinstance(arbitrary, pa.NullArray):
1798 if type(dtype) == str and dtype == "empty":
~/miniconda3/envs/rapids-21.10/lib/python3.7/site-packages/cudf/core/column/column.py in from_arrow(cls, array)
305 return cudf.core.column.Decimal64Column.from_arrow(array)
306
--> 307 result = libcudf.interop.from_arrow(data, data.column_names)[0]["None"]
308
309 result = result._with_type_metadata(
cudf/_lib/interop.pyx in cudf._lib.interop.from_arrow()
RuntimeError: CUDA error encountered at: ../src/bitmask/null_mask.cu:93: 801 cudaErrorNotSupported operation not supported
If this is relevant my System Memorys is always close to full (16GB) with Vmmem cooping around 10GB my Graphics memory only is at 1,4/8GB
Sadly, RAPIDS on WSL2 only runs on Pascal GPUs with RAPIDS 21.08, but not 21.10 or later. Please try 21.08. It was still experimental with those versions, so YMMV.