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bump all 23.10 version references to 23.12
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Signed-off-by: Erik Ordentlich <[email protected]>
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eordentlich committed Jan 3, 2024
1 parent 6269357 commit 26fe4b8
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Showing 15 changed files with 21 additions and 21 deletions.
2 changes: 1 addition & 1 deletion docker/Dockerfile.pip
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Expand Up @@ -18,7 +18,7 @@ ARG CUDA_VERSION=11.8.0
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04

ARG PYSPARK_VERSION=3.3.1
ARG RAPIDS_VERSION=23.10.0
ARG RAPIDS_VERSION=23.12.0
ARG ARCH=amd64
#ARG ARCH=arm64
# Install packages to build spark-rapids-ml
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2 changes: 1 addition & 1 deletion docker/Dockerfile.python
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Expand Up @@ -17,7 +17,7 @@
ARG CUDA_VERSION=11.8.0
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04

ARG CUML_VERSION=23.10
ARG CUML_VERSION=23.12

# Install packages to build spark-rapids-ml
RUN apt update -y \
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4 changes: 2 additions & 2 deletions jvm/README.md
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Expand Up @@ -94,8 +94,8 @@ repository, usually in your `~/.m2/repository`.
Add the artifact jar to the Spark, for example:
```bash
ML_JAR="target/rapids-4-spark-ml_2.12-23.10.0-SNAPSHOT.jar"
PLUGIN_JAR="~/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.10.0/rapids-4-spark_2.12-23.10.0.jar"
ML_JAR="target/rapids-4-spark-ml_2.12-23.12.0-SNAPSHOT.jar"
PLUGIN_JAR="~/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.12.0/rapids-4-spark_2.12-23.12.1.jar"
$SPARK_HOME/bin/spark-shell --master $SPARK_MASTER \
--driver-memory 20G \
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2 changes: 1 addition & 1 deletion notebooks/aws-emr/init-bootstrap-action.sh
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Expand Up @@ -8,7 +8,7 @@ sudo chmod a+rwx -R /sys/fs/cgroup/devices
sudo yum install -y gcc openssl-devel bzip2-devel libffi-devel tar gzip wget make mysql-devel
sudo bash -c "wget https://www.python.org/ftp/python/3.9.9/Python-3.9.9.tgz && tar xzf Python-3.9.9.tgz && cd Python-3.9.9 && ./configure --enable-optimizations && make altinstall"

RAPIDS_VERSION=23.10.0
RAPIDS_VERSION=23.12.0

# install scikit-learn
sudo /usr/local/bin/pip3.9 install scikit-learn
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2 changes: 1 addition & 1 deletion notebooks/databricks/README.md
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Expand Up @@ -44,7 +44,7 @@ If you already have a Databricks account, you can run the example notebooks on a
spark.task.resource.gpu.amount 1
spark.databricks.delta.preview.enabled true
spark.python.worker.reuse true
spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-23.10.0.jar:/databricks/spark/python
spark.executorEnv.PYTHONPATH /databricks/jars/rapids-4-spark_2.12-23.12.1.jar:/databricks/spark/python
spark.sql.execution.arrow.maxRecordsPerBatch 100000
spark.rapids.memory.gpu.minAllocFraction 0.0001
spark.plugins com.nvidia.spark.SQLPlugin
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4 changes: 2 additions & 2 deletions notebooks/databricks/init-pip-cuda-11.8.sh
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Expand Up @@ -4,8 +4,8 @@ SPARK_RAPIDS_ML_ZIP=/dbfs/path/to/zip/file
# IMPORTANT: specify RAPIDS_VERSION fully 23.10.0 and not 23.10
# also in general, RAPIDS_VERSION (python) fields should omit any leading 0 in month/minor field (i.e. 23.8.0 and not 23.08.0)
# while SPARK_RAPIDS_VERSION (jar) should have leading 0 in month/minor (e.g. 23.08.2 and not 23.8.2)
RAPIDS_VERSION=23.10.0
SPARK_RAPIDS_VERSION=23.10.0
RAPIDS_VERSION=23.12.0
SPARK_RAPIDS_VERSION=23.12.1

curl -L https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/${SPARK_RAPIDS_VERSION}/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}-cuda11.jar -o /databricks/jars/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar

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2 changes: 1 addition & 1 deletion notebooks/dataproc/README.md
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Expand Up @@ -29,7 +29,7 @@ If you already have a Dataproc account, you can run the example notebooks on a D
- Create a cluster with at least two single-gpu workers. **Note**: in addition to the initialization script from above, this also uses the standard [initialization actions](https://github.com/GoogleCloudDataproc/initialization-actions) for installing the GPU drivers and RAPIDS:
```
export CUDA_VERSION=11.8
export RAPIDS_VERSION=23.10.0
export RAPIDS_VERSION=23.12.0
gcloud dataproc clusters create $USER-spark-rapids-ml \
--image-version=2.1-ubuntu \
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2 changes: 1 addition & 1 deletion notebooks/dataproc/spark_rapids_ml.sh
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@@ -1,6 +1,6 @@
#!/bin/bash

RAPIDS_VERSION=23.10.0
RAPIDS_VERSION=23.12.0

# patch existing packages
mamba install "llvmlite<0.40,>=0.39.0dev0" "numba>=0.56.2"
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2 changes: 1 addition & 1 deletion notebooks/umap.ipynb
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Expand Up @@ -652,7 +652,7 @@
"import os\n",
"import requests\n",
"\n",
"SPARK_RAPIDS_VERSION = \"23.08.1\"\n",
"SPARK_RAPIDS_VERSION = \"23.12.1\"\n",
"cuda_version = \"12\"\n",
"rapids_jar = f\"rapids-4-spark_2.12-{SPARK_RAPIDS_VERSION}.jar\"\n",
"\n",
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6 changes: 3 additions & 3 deletions python/README.md
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Expand Up @@ -8,9 +8,9 @@ For simplicity, the following instructions just use Spark local mode, assuming a

First, install RAPIDS cuML per [these instructions](https://rapids.ai/start.html). Example for CUDA Toolkit 11.8:
```bash
conda create -n rapids-23.10 \
conda create -n rapids-23.12 \
-c rapidsai -c conda-forge -c nvidia \
cuml=23.10 python=3.9 cuda-version=11.8
cuml=23.12 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 @@ -19,7 +19,7 @@ conda create -n rapids-23.10 \

Once you have the conda environment, activate it and install the required packages.
```bash
conda activate rapids-23.10
conda activate rapids-23.12

## for development access to notebooks, tests, and benchmarks
git clone --branch main https://github.com/NVIDIA/spark-rapids-ml.git
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2 changes: 1 addition & 1 deletion python/benchmark/databricks/gpu_cluster_spec.sh
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Expand Up @@ -9,7 +9,7 @@ cat <<EOF
"spark.task.cpus": "1",
"spark.databricks.delta.preview.enabled": "true",
"spark.python.worker.reuse": "true",
"spark.executorEnv.PYTHONPATH": "/databricks/jars/rapids-4-spark_2.12-23.10.0.jar:/databricks/spark/python",
"spark.executorEnv.PYTHONPATH": "/databricks/jars/rapids-4-spark_2.12-23.12.1.jar:/databricks/spark/python",
"spark.sql.files.minPartitionNum": "2",
"spark.sql.execution.arrow.maxRecordsPerBatch": "10000",
"spark.executor.cores": "8",
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6 changes: 3 additions & 3 deletions python/benchmark/databricks/init-pip-cuda-11.8.sh
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Expand Up @@ -4,9 +4,9 @@ SPARK_RAPIDS_ML_ZIP=/dbfs/path/to/spark-rapids-ml.zip
BENCHMARK_ZIP=/dbfs/path/to/benchmark.zip
# IMPORTANT: specify rapids fully 23.10.0 and not 23.10
# also, in general, RAPIDS_VERSION (python) fields should omit any leading 0 in month/minor field (i.e. 23.8.0 and not 23.08.0)
# while SPARK_RAPIDS_VERSION (jar) should have leading 0 in month/minor (e.g. 23.08.2 and not 23.8.1)
RAPIDS_VERSION=23.10.0
SPARK_RAPIDS_VERSION=23.10.0
# while SPARK_RAPIDS_VERSION (jar) should have leading 0 in month/minor (e.g. 23.08.2 and not 23.8.2)
RAPIDS_VERSION=23.12.0
SPARK_RAPIDS_VERSION=23.12.1

curl -L https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/${SPARK_RAPIDS_VERSION}/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}-cuda11.jar -o /databricks/jars/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar

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2 changes: 1 addition & 1 deletion python/benchmark/dataproc/init_benchmark.sh
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Expand Up @@ -8,7 +8,7 @@ function get_metadata_attribute() {
/usr/share/google/get_metadata_value "attributes/${attribute_name}" || echo -n "${default_value}"
}

RAPIDS_VERSION=$(get_metadata_attribute rapids-version 23.10.0)
RAPIDS_VERSION=$(get_metadata_attribute rapids-version 23.12.0)

# patch existing packages
mamba install "llvmlite<0.40,>=0.39.0dev0" "numba>=0.56.2"
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2 changes: 1 addition & 1 deletion python/pyproject.toml
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@@ -1,6 +1,6 @@
[project]
name = "spark-rapids-ml"
version = "23.10.0"
version = "23.12.0"
authors = [
{ name="Jinfeng Li", email="[email protected]" },
{ name="Bobby Wang", email="[email protected]" },
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2 changes: 1 addition & 1 deletion python/run_benchmark.sh
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Expand Up @@ -99,7 +99,7 @@ EOF

if [[ $cluster_type == "gpu_etl" ]]
then
SPARK_RAPIDS_VERSION=23.10.0
SPARK_RAPIDS_VERSION=23.12.1
rapids_jar=${rapids_jar:-rapids-4-spark_2.12-$SPARK_RAPIDS_VERSION.jar}
if [ ! -f $rapids_jar ]; then
echo "downloading spark rapids jar"
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