Merge branch 'branch-24.08' into raft-cpu-miniforge

.github/CODEOWNERS

@@ -1,2 +1,2 @@
-#ops code owners
-*       @rapidsai/ops-codeowners
+# packaging code owners
+* @rapidsai/packaging-codeowners

CONTRIBUTING.md

@@ -6,9 +6,9 @@ Building the images requires `docker` `>=18.09` with [`buildkit`](https://docs.d
 
 ## Building
 
-To build the `notebooks` image with default arguments: `docker buildx build --pull -f Dockerfile -t rapidsai/rapidsai-notebooks context/`
+To build the `notebooks` image with default arguments: `docker buildx build --pull -f Dockerfile -t rapidsai/notebooks context/`
 
-To build just the `base` image with default arguments: `docker buildx build --pull -f Dockerfile -t rapidsai/rapidsai --target=base context/`
+To build just the `base` image with default arguments: `docker buildx build --pull -f Dockerfile -t rapidsai/base --target=base context/`
 
 ### Build arguments
 

Dockerfile

@@ -6,8 +6,8 @@ ARG LINUX_DISTRO=ubuntu
 ARG LINUX_DISTRO_VER=22.04
 ARG LINUX_VER=${LINUX_DISTRO}${LINUX_DISTRO_VER}
 
-ARG RAPIDS_VER=24.06
-ARG DASK_SQL_VER=2024.3.0
+ARG RAPIDS_VER=24.08
+ARG DASK_SQL_VER=2024.5.0
 
 # Gather dependency information
 FROM rapidsai/ci-conda:latest AS dependencies
@@ -127,7 +127,7 @@ LABEL com.nvidia.workbench.application.jupyterlab.webapp.url-cmd="jupyter lab li
 LABEL com.nvidia.workbench.cuda-version="$CUDA_VER"
 LABEL com.nvidia.workbench.description="RAPIDS with CUDA ${CUDA_VER}"
 LABEL com.nvidia.workbench.entrypoint-script="/home/rapids/entrypoint.sh"
-LABEL com.nvidia.workbench.image-version="24.04.00"
+LABEL com.nvidia.workbench.image-version="24.08.00"
 LABEL com.nvidia.workbench.labels="cuda${CUDA_VER}"
 LABEL com.nvidia.workbench.name="RAPIDS with CUDA ${CUDA_VER}"
 LABEL com.nvidia.workbench.os-distro-release="$LINUX_DISTRO_VER"

README.md

@@ -5,17 +5,17 @@ This repository contains the end-user docker images for [RAPIDS](https://rapids.
 
 ## Image types
 
-There are two image types: `base` ([`rapidsai/rapidsai`](https://hub.docker.com/r/rapidsai/rapidsai)) and `notebooks` ([`rapidsai/rapidsai-notebooks`](https://hub.docker.com/r/rapidsai/rapidsai-notebooks)).
+There are two image types: `base` ([`rapidsai/base`](https://hub.docker.com/r/rapidsai/base)) and `notebooks` ([`rapidsai/notebooks`](https://hub.docker.com/r/rapidsai/notebooks)).
 
 ### Base image
 
-This image can be found here: https://hub.docker.com/r/rapidsai/rapidsai
+This image can be found here: https://hub.docker.com/r/rapidsai/base
 
 It contains the basic installation of RAPIDS and [`dask-sql`](https://github.com/dask-contrib/dask-sql). By default it starts an `ipython` REPL.
 
 ### Notebooks image
 
-This image can be found here: https://hub.docker.com/r/rapidsai/rapidsai-notebooks
+This image can be found here: https://hub.docker.com/r/rapidsai/notebooks
 
 It extends the `base` images to include RAPIDS notebooks and a [`jupyterlab` server](https://jupyter.org/) which starts automatically.
 

context/raft-ann-bench/get_datasets.sh

@@ -5,10 +5,10 @@ set -eo pipefail
 
 export CONDA_PREFIX=/opt/conda
 
-python -m raft-ann-bench.get_dataset --dataset deep-image-96-angular --normalize --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset fashion-mnist-784-euclidean --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset glove-50-angular --normalize --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset glove-100-angular --normalize --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset mnist-784-euclidean --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset nytimes-256-angular --normalize --dataset-path /home/rapids/preloaded_datasets
-python -m raft-ann-bench.get_dataset --dataset sift-128-euclidean --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset deep-image-96-angular --normalize --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset fashion-mnist-784-euclidean --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset glove-50-angular --normalize --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset glove-100-angular --normalize --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset mnist-784-euclidean --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset nytimes-256-angular --normalize --dataset-path /home/rapids/preloaded_datasets
+python -m raft_ann_bench.get_dataset --dataset sift-128-euclidean --dataset-path /home/rapids/preloaded_datasets

context/raft-ann-bench/run_benchmark.sh

@@ -21,7 +21,7 @@ export RUN_ARGS=$3
 export PLOT_ARGS=$4
 
 # (1) prepare dataset.
-python -m raft-ann-bench.get_dataset ${DATASET_ARG} ${GET_DATASET_ARGS} --dataset-path /data/benchmarks/datasets
+python -m raft_ann_bench.get_dataset ${DATASET_ARG} ${GET_DATASET_ARGS} --dataset-path /data/benchmarks/datasets
 
 if [[ "$DATASET_ARG" == *"angular"* ]]; then
   export DATASET_ARG=${DATASET_ARG/angular/inner}
@@ -30,10 +30,10 @@ if [[ "$DATASET_ARG" == *"angular"* ]]; then
 fi
 
 # (2) build and search index
-python -m raft-ann-bench.run  ${DATASET_ARG} --dataset-path /data/benchmarks/datasets --force ${RUN_ARGS}
+python -m raft_ann_bench.run  ${DATASET_ARG} --dataset-path /data/benchmarks/datasets --force ${RUN_ARGS}
 
 # (3) export data
-python -m raft-ann-bench.data_export  ${DATASET_ARG} --dataset-path /data/benchmarks/datasets
+python -m raft_ann_bench.data_export  ${DATASET_ARG} --dataset-path /data/benchmarks/datasets
 
 # Extract the algorithms from the run command to use in the plot command
 ALGOS=$(grep -oP "algorithms\s+\K(\w+,?\w+)" <<< "$RUN_ARGS")
@@ -44,4 +44,4 @@ fi
 # (4) plot results
 mkdir -p /data/benchmarks/datasets/result
 cd /data/benchmarks/datasets/result
-python -m raft-ann-bench.plot  ${DATASET_ARG} ${ALGOS} ${PLOT_ARGS} --dataset-path /data/benchmarks/datasets --build --search
+python -m raft_ann_bench.plot  ${DATASET_ARG} ${ALGOS} ${PLOT_ARGS} --dataset-path /data/benchmarks/datasets --build --search

context/raft-ann-bench/run_benchmarks_preloaded_datasets.sh

@@ -27,7 +27,7 @@ case $DATASET_ARG in
     export DATASET_PATH=/home/rapids/preloaded_datasets ;;
    *)
     export DATASET_PATH=/data/benchmarks/datasets
-    python -m raft-ann-bench.get_dataset ${DATASET_ARG} ${GET_DATASET_ARGS} --dataset-path $DATASET_PATH ;;
+    python -m raft_ann_bench.get_dataset ${DATASET_ARG} ${GET_DATASET_ARGS} --dataset-path $DATASET_PATH ;;
 esac
 
 if [[ "$DATASET_ARG" == *"angular"* ]]; then
@@ -37,10 +37,10 @@ if [[ "$DATASET_ARG" == *"angular"* ]]; then
 fi
 
 # (2) build and search index
-python -m raft-ann-bench.run  ${DATASET_ARG} --dataset-path $DATASET_PATH ${RUN_ARGS}
+python -m raft_ann_bench.run  ${DATASET_ARG} --dataset-path $DATASET_PATH ${RUN_ARGS}
 
 # (3) export data
-python -m raft-ann-bench.data_export  ${DATASET_ARG} --dataset-path $DATASET_PATH
+python -m raft_ann_bench.data_export  ${DATASET_ARG} --dataset-path $DATASET_PATH
 
 # Extract the algorithms from the run command to use in the plot command
 ALGOS=$(grep -oP "algorithms\s+\K(\w+,?\w+)" <<< "$RUN_ARGS")
@@ -51,4 +51,4 @@ fi
 # (4) plot results
 mkdir -p $DATASET_PATH/result
 cd $DATASET_PATH/result
-python -m raft-ann-bench.plot  ${DATASET_ARG} ${ALGOS} ${PLOT_ARGS} --dataset-path $DATASET_PATH
+python -m raft_ann_bench.plot  ${DATASET_ARG} ${ALGOS} ${PLOT_ARGS} --dataset-path $DATASET_PATH

dockerhub-readme.md

@@ -9,12 +9,13 @@ The RAPIDS suite of software libraries gives you the freedom to execute end-to-e
 **NOTE:** Review our [system requirements](https://docs.rapids.ai/install#system-req) to ensure you have a compatible system!
 
 
-### Current Version - RAPIDS v24.06
+### Current Version - RAPIDS v24.08
 
 RAPIDS Libraries included in the images:
 - `cuDF`
 - `cuML`
 - `cuGraph`
+- `cuVS`
 - `RMM`
 - `RAFT`
 - `cuSpatial`
@@ -39,7 +40,7 @@ There are two types:
 
 The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
 ```
-24.06-cuda11.8-py3.11
+24.08-cuda11.8-py3.11
  ^        ^      ^
  |        |      Python version
  |        |
@@ -48,7 +49,7 @@ The tag naming scheme for RAPIDS images incorporates key platform details into t
  RAPIDS version
 ```
 
-**Note: Nightly builds of the images have the RAPIDS version appended with an `a` (ie `24.06a-cuda11.8-py3.11`)**
+**Note: Nightly builds of the images have the RAPIDS version appended with an `a` (ie `24.08a-cuda11.8-py3.11`)**
 
 ## Usage
 
@@ -81,7 +82,7 @@ $ docker run \
     -e EXTRA_CONDA_PACKAGES="jq" \
     -e EXTRA_PIP_PACKAGES="beautifulsoup4" \
     -p 8888:8888 \
-    rapidsai/notebooks:24.06-cuda11.8-py3.11
+    rapidsai/notebooks:24.08-cuda11.8-py3.11
 ```
 
 ### Bind Mounts
@@ -106,7 +107,7 @@ $ docker run \
     --gpus all \
     -shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
     -v $(pwd)/environment.yml:/home/rapids/environment.yml \
-    rapidsai/base:24.06-cuda11.8-py3.11
+    rapidsai/base:24.08-cuda11.8-py3.11
 ```
 
 ### Use JupyterLab to Explore the Notebooks

matrix.yaml

@@ -7,4 +7,4 @@ PYTHON_VER:
   - "3.10"
   - "3.11"
 DASK_SQL_VER:
-  - "2024.3.0"
+  - "2024.5.0"

raft-ann-bench/README.md

@@ -36,7 +36,7 @@ export DATA_FOLDER=path/to/store/results/and/data
 docker run --gpus all --rm -it \
     -v $DATA_FOLDER:/home/rapids/benchmarks  \
     -u $(id -u) \
-    rapidsai/raft-ann-bench:24.06a-cuda11.8-py3.11 \
+    rapidsai/raft-ann-bench:24.08a-cuda11.8-py3.11 \
     "--dataset deep-image-96-angular" \
     "--normalize" \
     "--algorithms raft_cagra" \
@@ -50,7 +50,7 @@ export DATA_FOLDER=path/to/store/results/and/data # <- Results and datasets will
 docker run --gpus all --rm -it \
     -v $DATA_FOLDER:/home/rapids/benchmarks  \ # <- local folder to store datasets and results
     -u $(id -u) \ # <- this flag allows the container to use the host user for permissions
-    rapidsai/raft-ann-bench:24.06a-cuda11.8-py3.11 \ # <- image to use, either `raft-ann-bench` or `raft-ann-bench-datasets`
+    rapidsai/raft-ann-bench:24.08a-cuda11.8-py3.11 \ # <- image to use, either `raft-ann-bench` or `raft-ann-bench-datasets`
     "--dataset deep-image-96-angular" \ # <- dataset name
     "--normalize" \ # <- whether to normalize the dataset, leave string empty ("") to not normalize.
     "--algorithms raft_cagra" \ # <- what algorithm(s) to use as a ; separated list, as well as any other argument to pass to `raft_ann_benchmarks.run`
@@ -63,7 +63,7 @@ export DATA_FOLDER=path/to/store/results/and/data
 docker run  all --rm -it \
     -v $DATA_FOLDER:/home/rapids/benchmarks  \
     -u $(id -u) \ # <- this flag allows the container to use the host user for permissions
-    rapidsai/raft-ann-bench-cpu:24.06a-py3.11 \
+    rapidsai/raft-ann-bench-cpu:24.08a-py3.11 \
      "--dataset deep-image-96-angular" \
      "--normalize" \
      "--algorithms raft_cagra" \
@@ -77,7 +77,7 @@ export DATA_FOLDER=path/to/store/results/and/data
 docker run --gpus all --rm -it \
     -v $DATA_FOLDER:/home/rapids/benchmarks  \
     -u $(id -u) \
-    rapidsai/raft-ann-bench:24.06a-cuda11.8-py3.11 \
+    rapidsai/raft-ann-bench:24.08a-cuda11.8-py3.11 \
     --entrypoint /bin/bash
 ```
 

raft-ann-bench/cpu/Dockerfile

@@ -1,7 +1,7 @@
 # syntax=docker/dockerfile:1
 
 ARG PYTHON_VER=3.11
-ARG RAPIDS_VER=24.06
+ARG RAPIDS_VER=24.08
 
 FROM condaforge/miniforge3:24.3.0-0 as raft-ann-bench-cpu
 ARG RAPIDS_VER

raft-ann-bench/gpu/Dockerfile

@@ -4,7 +4,7 @@ ARG CUDA_VER=12.0.1
 ARG PYTHON_VER=3.11
 ARG LINUX_VER=ubuntu22.04
 
-ARG RAPIDS_VER=24.06
+ARG RAPIDS_VER=24.08
 
 FROM rapidsai/miniforge-cuda:cuda${CUDA_VER}-base-${LINUX_VER}-py${PYTHON_VER} as raft-ann-bench
 ARG CUDA_VER