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Describe the bug When Using Dask + UMAP to use multiple gpus, if a input array is np.array not cupy array, then dask error raises.
ValueError: could not broadcast input array from shape (7,1) into shape (7,)
If I cast the input array into cupy array, it runs without error.
Below is the code.
from dask_cuda import LocalCUDACluster from dask.distributed import Client import dask.array as da from cuml.manifold import UMAP from cuml.dask.manifold import UMAP as MNMG_UMAP import numpy as np import cupy if __name__ == "__main__": cluster = LocalCUDACluster(n_workers=2) client = Client(cluster) X = np.zeros((100, 10, 49), dtype=np.float32).reshape(100, -1) # X = cupy.asarray(X) print(X.shape, type(X)) local_model = UMAP(random_state=10, n_components=1) val = local_model.fit_transform(X) distributed_model = MNMG_UMAP(model=local_model) distributed_X = da.from_array(X, chunks=(7, -1)) embedding = distributed_model.transform(distributed_X) result = embedding.compute() client.close() cluster.close()
If I uncomment the "X = cupy.asarray(X)", then it runs without error.
The text was updated successfully, but these errors were encountered:
Thanks for the issue @nahaharo, this is useful feedback, it's something in the backlog, we will add it in the future, but no ETA currently.
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Describe the bug
When Using Dask + UMAP to use multiple gpus, if a input array is np.array not cupy array, then dask error raises.
ValueError: could not broadcast input array from shape (7,1) into shape (7,)
If I cast the input array into cupy array, it runs without error.
Below is the code.
If I uncomment the "X = cupy.asarray(X)", then it runs without error.
command: conda create -n rapids-24.04 -c rapidsai -c conda-forge -c nvidia rapids=24.04 python=3.11 cuda-version=12.2 h5py matplotlib
The text was updated successfully, but these errors were encountered: