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[DOC] Update docs for 23.10.0 release [skip ci] (#9427)
This PR updates download docs for 23.10.0 release. Signed-off-by: Suraj Aralihalli [email protected]
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--- | ||
Below are archived releases for RAPIDS Accelerator for Apache Spark. | ||
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## Release v23.08.2 | ||
### Hardware Requirements: | ||
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The plugin is tested on the following architectures: | ||
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GPU Models: NVIDIA P100, V100, T4, A10/A100, L4 and H100 GPUs | ||
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### Software Requirements: | ||
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OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8 | ||
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NVIDIA Driver*: R470+ | ||
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Runtime: | ||
Scala 2.12 | ||
Python, Java Virtual Machine (JVM) compatible with your spark-version. | ||
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* Check the Spark documentation for Python and Java version compatibility with your specific | ||
Spark version. For instance, visit `https://spark.apache.org/docs/3.4.1` for Spark 3.4.1. | ||
Please be aware that we do not currently support Spark builds with Scala 2.13. | ||
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Supported Spark versions: | ||
Apache Spark 3.1.1, 3.1.2, 3.1.3 | ||
Apache Spark 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.2.4 | ||
Apache Spark 3.3.0, 3.3.1, 3.3.2 | ||
Apache Spark 3.4.0, 3.4.1 | ||
Apache Spark 3.5.0 | ||
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Supported Databricks runtime versions for Azure and AWS: | ||
Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1) | ||
Databricks 11.3 ML LTS (GPU, Scala 2.12, Spark 3.3.0) | ||
Databricks 12.2 ML LTS (GPU, Scala 2.12, Spark 3.3.2) | ||
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Supported Dataproc versions: | ||
GCP Dataproc 2.0 | ||
GCP Dataproc 2.1 | ||
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*Some hardware may have a minimum driver version greater than R470. Check the GPU spec sheet | ||
for your hardware's minimum driver version. | ||
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*For Cloudera and EMR support, please refer to the | ||
[Distributions](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#which-distributions-are-supported) section of the FAQ. | ||
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### Download v23.08.2 | ||
* Download the [RAPIDS | ||
Accelerator for Apache Spark 23.08.2 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar) | ||
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This package is built against CUDA 11.8. It is tested on V100, T4, A10, A100, L4 and H100 GPUs with | ||
CUDA 11.8 through CUDA 12.0. | ||
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Note that v23.08.1 is deprecated. | ||
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### Verify signature | ||
* Download the [RAPIDS Accelerator for Apache Spark 23.08.2 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar) | ||
and [RAPIDS Accelerator for Apache Spark 23.08.2 jars.asc](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar.asc) | ||
* Download the [PUB_KEY](https://keys.openpgp.org/[email protected]). | ||
* Import the public key: `gpg --import PUB_KEY` | ||
* Verify the signature: `gpg --verify rapids-4-spark_2.12-23.08.2.jar.asc rapids-4-spark_2.12-23.08.2.jar` | ||
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The output of signature verify: | ||
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gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <[email protected]>" | ||
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### Release Notes | ||
New functionality and performance improvements for this release include: | ||
* Compatibility with Databricks AWS & Azure 12.2 ML LTS. | ||
* Enhanced stability and support for ORC and Parquet. | ||
* Reduction of out-of-memory (OOM) occurrences. | ||
* Corner case evaluation for data formats, operators and expressions | ||
* Qualification and Profiling tool: | ||
* Profiling tool now supports Azure Databricks and AWS Databricks. | ||
* Qualification tool can provide advice on unaccelerated operations. | ||
* Improve user experience through CLI design. | ||
* Qualification tool provides configuration and migration recommendations for Dataproc and EMR. | ||
* Fixes Databricks build issues from the previous 23.08 release. | ||
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For a detailed list of changes, please refer to the | ||
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md). | ||
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## Release v23.08.1 | ||
### Hardware Requirements: | ||
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@@ -18,7 +18,7 @@ cuDF jar, that is either preinstalled in the Spark classpath on all nodes or sub | |
that uses the RAPIDS Accelerator For Apache Spark. See the [getting-started | ||
guide](https://nvidia.github.io/spark-rapids/Getting-Started/) for more details. | ||
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## Release v23.08.2 | ||
## Release v23.10.0 | ||
### Hardware Requirements: | ||
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The plugin is tested on the following architectures: | ||
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Please be aware that we do not currently support Spark builds with Scala 2.13. | ||
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Supported Spark versions: | ||
Apache Spark 3.1.1, 3.1.2, 3.1.3 | ||
Apache Spark 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.2.4 | ||
Apache Spark 3.3.0, 3.3.1, 3.3.2 | ||
Apache Spark 3.3.0, 3.3.1, 3.3.2, 3.3.3 | ||
Apache Spark 3.4.0, 3.4.1 | ||
Apache Spark 3.5.0 | ||
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*For Cloudera and EMR support, please refer to the | ||
[Distributions](https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html#which-distributions-are-supported) section of the FAQ. | ||
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### Download v23.08.2 | ||
#### RAPIDS Accelerator's Support Policy for Apache Spark | ||
The RAPIDS Accelerator maintains support for Apache Spark versions available for download from [Apache Spark](https://spark.apache.org/downloads.html) | ||
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### Download v23.10.0 | ||
* Download the [RAPIDS | ||
Accelerator for Apache Spark 23.08.2 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar) | ||
Accelerator for Apache Spark 23.10.0 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.10.0/rapids-4-spark_2.12-23.10.0.jar) | ||
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This package is built against CUDA 11.8. It is tested on V100, T4, A10, A100, L4 and H100 GPUs with | ||
CUDA 11.8 through CUDA 12.0. | ||
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Note that v23.08.0 is deprecated. | ||
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### Verify signature | ||
* Download the [RAPIDS Accelerator for Apache Spark 23.08.2 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar) | ||
and [RAPIDS Accelerator for Apache Spark 23.08.2 jars.asc](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.08.2/rapids-4-spark_2.12-23.08.2.jar.asc) | ||
* Download the [RAPIDS Accelerator for Apache Spark 23.10.0 jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.10.0/rapids-4-spark_2.12-23.10.0.jar) | ||
and [RAPIDS Accelerator for Apache Spark 23.10.0 jars.asc](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.10.0/rapids-4-spark_2.12-23.10.0.jar.asc) | ||
* Download the [PUB_KEY](https://keys.openpgp.org/[email protected]). | ||
* Import the public key: `gpg --import PUB_KEY` | ||
* Verify the signature: `gpg --verify rapids-4-spark_2.12-23.08.2.jar.asc rapids-4-spark_2.12-23.08.2.jar` | ||
* Verify the signature: `gpg --verify rapids-4-spark_2.12-23.10.0.jar.asc rapids-4-spark_2.12-23.10.0.jar` | ||
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The output if signature verify: | ||
The output of signature verify: | ||
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gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <[email protected]>" | ||
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### Release Notes | ||
New functionality and performance improvements for this release include: | ||
* Compatibility with Databricks AWS & Azure 12.2 ML LTS. | ||
* Enhanced stability and support for ORC and Parquet. | ||
* Reduction of out-of-memory (OOM) occurrences. | ||
* Corner case evaluation for data formats, operators and expressions | ||
* Introduced support for Spark 3.5.0. | ||
* Improved memory management for better control in YARN and K8s on CSP. | ||
* Strengthened Parquet and ORC tests for enhanced stability and support. | ||
* Reduce GPU out-of-memory (OOM) occurrences. | ||
* Enhanced driver log with actionable insights. | ||
* Qualification and Profiling tool: | ||
* Profiling tool now supports Azure Databricks and AWS Databricks. | ||
* Qualification tool can provide advice on unaccelerated operations. | ||
* Improve user experience through CLI design. | ||
* Qualification tool provides configuration and migration recommendations for Dataproc and EMR. | ||
* Fixes Databricks build issues from the previous 23.08 release. | ||
* Enhanced user experience with the availability of the 'ascli' tool for qualification and | ||
profiling across all platforms. | ||
* The qualification tool now accommodates CPU-fallback transitions and broadens the speedup factor coverage. | ||
* Extended diagnostic support for user tools to cover EMR, Databricks AWS, and Databricks Azure. | ||
* Introduced support for cluster configuration recommendations in the profiling tool for supported platforms. | ||
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For a detailed list of changes, please refer to the | ||
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md). | ||
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