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rapids-bot merged 2 commits into from
Jun 25, 2025
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refactor(cuda11): remove cuda11-only conda channels, cleanup docs #1046

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884415e
refactor(cuda11): remove cuda11-only conda channels, cleanup docs
gforsyth Jun 24, 2025
063d148
fix(pytest): disable pytest module warnings that break the test suite
gforsyth Jun 24, 2025
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11 changes: 3 additions & 8 deletions README.md
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Expand Up @@ -90,17 +90,12 @@ cuVS comes with pre-built packages that can be installed through [conda](https:/
It is recommended to use [mamba](https://conda.github.io/conda-libmamba-solver/user-guide/) to install the desired packages. The following command will install the Python package. You can substitute `cuvs` for any of the packages in the table above:

```bash
conda install -c conda-forge -c nvidia -c rapidsai cuvs
conda install -c rapidsai -c conda-forge cuvs
```

The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.

For CUDA 11 packages:
```bash
pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com
````

And CUDA 12 packages:
For CUDA 12 packages:
```bash
pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
```
Expand All @@ -109,7 +104,7 @@ pip install cuvs-cu12 --extra-index-url=https://pypi.nvidia.com
If installing a version that has not yet been released, the `rapidsai` channel can be replaced with `rapidsai-nightly`:

```bash
conda install -c conda-forge -c nvidia -c rapidsai-nightly cuvs=25.08
conda install -c rapidsai-nightly -c conda-forge cuvs=25.08
```

cuVS also has `pip` wheel packages that can be installed. Please see the [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build/) for more information on installing the available cuVS packages and building from source.
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2 changes: 0 additions & 2 deletions conda/environments/all_cuda-128_arch-aarch64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- breathe>=4.35.0
- c-compiler
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2 changes: 0 additions & 2 deletions conda/environments/all_cuda-128_arch-x86_64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- breathe>=4.35.0
- c-compiler
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2 changes: 0 additions & 2 deletions conda/environments/bench_ann_cuda-128_arch-aarch64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- benchmark>=1.8.2
- c-compiler
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2 changes: 0 additions & 2 deletions conda/environments/bench_ann_cuda-128_arch-x86_64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- benchmark>=1.8.2
- c-compiler
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2 changes: 0 additions & 2 deletions conda/environments/go_cuda-128_arch-aarch64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- c-compiler
- clang-tools==20.1.4
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2 changes: 0 additions & 2 deletions conda/environments/go_cuda-128_arch-x86_64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- c-compiler
- clang-tools==20.1.4
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2 changes: 0 additions & 2 deletions conda/environments/rust_cuda-129_arch-aarch64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- c-compiler
- clang-tools==20.1.4
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2 changes: 0 additions & 2 deletions conda/environments/rust_cuda-129_arch-x86_64.yaml
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Expand Up @@ -3,9 +3,7 @@
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
- c-compiler
- clang-tools==20.1.4
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2 changes: 0 additions & 2 deletions dependencies.yaml
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Expand Up @@ -212,9 +212,7 @@ files:
channels:
- rapidsai
- rapidsai-nightly
- dask/label/dev
- conda-forge
- nvidia
dependencies:
build:
common:
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12 changes: 3 additions & 9 deletions docs/source/build.rst
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Expand Up @@ -43,27 +43,21 @@ C/C++ Package

.. code-block:: bash

conda install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.8
conda install -c rapidsai -c conda-forge libcuvs cuda-version=12.8

Python Package
~~~~~~~~~~~~~~

.. code-block:: bash

conda install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.8
conda install -c rapidsai -c conda-forge cuvs cuda-version=12.8

Python through Pip
^^^^^^^^^^^^^^^^^^

The cuVS Python package can also be `installed through pip <https://docs.rapids.ai/install#pip>`_.

For CUDA 11 packages:

.. code-block:: bash

pip install cuvs-cu11 --extra-index-url=https://pypi.nvidia.com

And CUDA 12 packages:
For CUDA 12 packages:

.. code-block:: bash

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10 changes: 5 additions & 5 deletions docs/source/cuvs_bench/index.rst
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Expand Up @@ -65,12 +65,12 @@ Conda
conda activate cuvs_benchmarks

# to install GPU package:
conda install -c rapidsai -c conda-forge -c nvidia cuvs-bench=<rapids_version> cuda-version=11.8*
conda install -c rapidsai -c conda-forge cuvs-bench=<rapids_version> cuda-version=12.9*

# to install CPU package for usage in CPU-only systems:
conda install -c rapidsai -c conda-forge cuvs-bench-cpu

The channel `rapidsai` can easily be substituted `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.
The channel `rapidsai` can easily be substituted with `rapidsai-nightly` if nightly benchmarks are desired. The CPU package currently allows to run the HNSW benchmarks.

Please see the :doc:`build instructions <build>` to build the benchmarks from source.

Expand Down Expand Up @@ -236,7 +236,7 @@ For GPU-enabled systems, the `DATA_FOLDER` variable should be a local folder whe
export DATA_FOLDER=path/to/store/datasets/and/results
docker run --gpus all --rm -it -u $(id -u) \
-v $DATA_FOLDER:/data/benchmarks \
rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10 \
rapidsai/cuvs-bench:25.06-cuda12.8-py3.13 \
"--dataset deep-image-96-angular" \
"--normalize" \
"--algorithms cuvs_cagra,cuvs_ivf_pq --batch-size 10 -k 10" \
Expand All @@ -249,7 +249,7 @@ Usage of the above command is as follows:
* - Argument
- Description

* - `rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10`
* - `rapidsai/cuvs-bench:25.06-cuda12.8-py3.13`
- Image to use. Can be either `cuvs-bench` or `cuvs-bench-datasets`

* - `"--dataset deep-image-96-angular"`
Expand Down Expand Up @@ -296,7 +296,7 @@ All of the `cuvs-bench` images contain the Conda packages, so they can be used d
--entrypoint /bin/bash \
--workdir /data/benchmarks \
-v $DATA_FOLDER:/data/benchmarks \
rapidsai/cuvs-bench:24.10a-cuda11.8-py3.10
rapidsai/cuvs-bench:25.06-cuda12.8-py3.13

This will drop you into a command line in the container, with the `cuvs-bench` python package ready to use, as described in the [Running the benchmarks](#running-the-benchmarks) section above:

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2 changes: 1 addition & 1 deletion notebooks/cuvs_hpo_example.ipynb
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Expand Up @@ -21,7 +21,7 @@
"outputs": [],
"source": [
"#Install Required Packages\n",
"%mamba install -c conda-forge -c nvidia -c rapidsai-nightly cuvs optuna -y\n",
"%mamba install -c rapidsai-nightly -c conda-forge cuvs optuna -y\n",
"%pip install cupy"
]
},
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4 changes: 3 additions & 1 deletion python/cuvs/.coveragerc
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@@ -1,3 +1,5 @@
# Configuration file for Python coverage tests
[run]
source = pylibraft
source = cuvs
disable_warnings = module-not-measured
no-data-collected
2 changes: 2 additions & 0 deletions python/cuvs_bench/.coveragerc
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@@ -1,3 +1,5 @@
# Configuration file for Python coverage tests
[run]
source = cuvs_bench
disable_warnings = module-not-measured
no-data-collected