Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add RAPIDS cuDF as a library that supports cuda_array_interface #1444

Merged
merged 3 commits into from
Apr 26, 2023
Merged

Add RAPIDS cuDF as a library that supports cuda_array_interface #1444

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
29d806f
Add RAPIDS cuDF as a library that supports cuda_array_interface
miguelusque Apr 20, 2023
20507c1
Merge branch 'branch-23.06' into patch-1
cjnolet Apr 25, 2023
1f27480
Merge branch 'branch-23.06' into patch-1
cjnolet Apr 25, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 6 additions & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -146,7 +146,7 @@ in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
output = pairwise_distance(in1, in2, metric="euclidean")
```

The `output` array in the above example is of type `raft.common.device_ndarray`, which supports [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html#cuda-array-interface-version-2) making it interoperable with other libraries like CuPy, Numba, and PyTorch that also support it. CuPy supports DLPack, which also enables zero-copy conversion from `raft.common.device_ndarray` to JAX and Tensorflow.
The `output` array in the above example is of type `raft.common.device_ndarray`, which supports [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html#cuda-array-interface-version-2) making it interoperable with other libraries like CuPy, Numba, PyTorch and RAPIDS cuDF that also support it. CuPy supports DLPack, which also enables zero-copy conversion from `raft.common.device_ndarray` to JAX and Tensorflow.

Below is an example of converting the output `pylibraft.device_ndarray` to a CuPy array:
```python
Expand All @@ -160,6 +160,11 @@ import torch
torch_tensor = torch.as_tensor(output, device='cuda')
```

Or converting to a RAPIDS cuDF dataframe:
```python
cudf_dataframe = cudf.DataFrame(output)
```

When the corresponding library has been installed and available in your environment, this conversion can also be done automatically by all RAFT compute APIs by setting a global configuration option:
```python
import pylibraft.config
Expand Down