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11.5 -> 11.8 and clarify readme (#446)
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Signed-off-by: Erik Ordentlich <[email protected]>
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eordentlich committed Sep 12, 2023
1 parent 5fd3399 commit 0644590
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4 changes: 2 additions & 2 deletions ci/Dockerfile
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# limitations under the License.
#

ARG CUDA_VERSION=11.5.2
ARG CUDA_VERSION=11.8.0
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04

# Install packages to build spark-rapids-ml
Expand All @@ -38,5 +38,5 @@ RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86

# install cuML
ARG CUML_VER=23.08
RUN conda install -y -c rapidsai -c conda-forge -c nvidia cuml=$CUML_VER python=3.9 cuda-version=11.5 \
RUN conda install -y -c rapidsai -c conda-forge -c nvidia cuml=$CUML_VER python=3.9 cuda-version=11.8 \
&& conda clean --all -f -y
4 changes: 2 additions & 2 deletions docker/Dockerfile.python
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# limitations under the License.
#

ARG CUDA_VERSION=11.5.2
ARG CUDA_VERSION=11.8.0
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04

ARG CUML_VERSION=23.08
Expand All @@ -38,7 +38,7 @@ RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-py38_4.10.3-Linu

# install cuML

RUN conda install -y -c rapidsai -c conda-forge -c nvidia python=3.9 cudatoolkit=11.5 cuml=$CUML_VERSION \
RUN conda install -y -c rapidsai -c conda-forge -c nvidia python=3.9 cuda-version=11.8 cuml=$CUML_VERSION \
&& conda clean --all -f -y

# install python dependencies
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12 changes: 7 additions & 5 deletions python/README.md
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Expand Up @@ -6,11 +6,11 @@ This PySpark-compatible API leverages the RAPIDS cuML python API to provide GPU-

For simplicity, the following instructions just use Spark local mode, assuming a server with at least one GPU.

First, install RAPIDS cuML per [these instructions](https://rapids.ai/start.html).
First, install RAPIDS cuML per [these instructions](https://rapids.ai/start.html). Example for CUDA Toolkit 11.8:
```bash
conda create -n rapids-23.08 \
-c rapidsai -c conda-forge -c nvidia \
cuml=23.08 python=3.9 cudatoolkit=11.5
cuml=23.08 python=3.9 cuda-version=11.8
```

**Note**: while testing, we recommend using conda or docker to simplify installation and isolate your environment while experimenting. Once you have a working environment, you can then try installing directly, if necessary.
Expand All @@ -21,13 +21,15 @@ Once you have the conda environment, activate it and install the required packag
```bash
conda activate rapids-23.08

# for development access to notebooks, tests, and benchmarks
## for development access to notebooks, tests, and benchmarks
git clone --branch main https://github.com/NVIDIA/spark-rapids-ml.git
cd spark-rapids-ml/python
pip install -r requirements.txt
# install additional non-RAPIDS python dependencies for dev
pip install -r requirements_dev.txt
pip install -e .

# OPTIONAL: for package installation only
## OPTIONAL: for package installation only
# install additional non-RAPIDS python dependencies
pip install -r https://raw.githubusercontent.com/NVIDIA/spark-rapids-ml/main/python/requirements.txt
pip install spark-rapids-ml
```
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