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When reading data from a CSV file, it is necessary to distribute the partitions across different GPUs. However, an error occurs when calling the to_device method.
df = dask_cudf.read_csv('./*.csv')
df = df.map_partitions(lambda part, i: part.to_device(i % 2))
KeyError: 'to_device'
During handling of the above exception, another exception occurred:
AttributeError: DataFrame object has no attribute to_device
Could you please help me understand what the error might be? Or is it necessary to use a different approach?
The text was updated successfully, but these errors were encountered:
With dask-cudf / dask.dataframe using the cudf backend, the in-memory dataframe objects are instances of cudf.DataFrame, which doesn't have a to_device method.
it is necessary to distribute the partitions across different GPUs
Could you share a bit more about why you want to do that explicitly? With the cudf backend, your tasks will automatically run on the device. Unless there's a strong reason not to, you might be better off letting dask choose which GPU to run particular tasks on.
When reading data from a CSV file, it is necessary to distribute the partitions across different GPUs. However, an error occurs when calling the to_device method.
df = dask_cudf.read_csv('./*.csv')
df = df.map_partitions(lambda part, i: part.to_device(i % 2))
KeyError: 'to_device'
During handling of the above exception, another exception occurred:
AttributeError: DataFrame object has no attribute to_device
Could you please help me understand what the error might be? Or is it necessary to use a different approach?
The text was updated successfully, but these errors were encountered: