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capitalized all Nvidia and remove A2 from 21.06, add to 21.10
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Signed-off-by: liyuan <[email protected]>
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nvliyuan committed Oct 26, 2021
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2 changes: 1 addition & 1 deletion docs/FAQ.md
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Expand Up @@ -35,7 +35,7 @@ release.

### What hardware is supported?

The plugin is tested and supported on V100, T4, A10, A30 and A100 datacenter GPUs. It is possible
The plugin is tested and supported on V100, T4, A2, A10, A30 and A100 datacenter GPUs. It is possible
to run the plugin on GeForce desktop hardware with Volta or better architectures. GeForce hardware
does not support [CUDA forward
compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#forward-compatibility-title),
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30 changes: 15 additions & 15 deletions docs/download.md
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Expand Up @@ -23,13 +23,13 @@ Hardware Requirements:

The plugin is tested on the following architectures:

GPU Architecture: NVIDIA V100, T4 and A10/A30/A100 GPUs
GPU Architecture: NVIDIA V100, T4 and A2/A10/A30/A100 GPUs

Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8

CUDA & Nvidia Drivers*: 11.0-11.4 & v450.80.02+
CUDA & NVIDIA Drivers*: 11.0-11.4 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, 3.2.0, Cloudera CDP 7.1.6, 7.1.7, Databricks 7.3 ML LTS or 8.2 ML Runtime, and GCP Dataproc 2.0

Expand All @@ -47,7 +47,7 @@ for your hardware's minimum driver version.

This package is built against CUDA 11.2 and has [CUDA forward
compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html) enabled. It is tested
on V100, T4, A30 and A100 GPUs with CUDA 11.0-11.4. For those using other types of GPUs which
on V100, T4, A2, A10, A30 and A100 GPUs with CUDA 11.0-11.4. For those using other types of GPUs which
do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will
need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

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OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8

CUDA & Nvidia Drivers*: 11.0-11.4 & v450.80.02+
CUDA & NVIDIA Drivers*: 11.0-11.4 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, Cloudera CDP 7.1.6, 7.1.7, Databricks 7.3 ML LTS or 8.2 ML Runtime, and GCP Dataproc 2.0

Expand Down Expand Up @@ -184,7 +184,7 @@ Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 8

CUDA & Nvidia Drivers*: 11.0 or 11.2 & v450.80.02+
CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, Cloudera CDP 7.1.7, and GCP Dataproc 2.0

Expand Down Expand Up @@ -224,7 +224,7 @@ Hardware Requirements:

The plugin is tested on the following architectures:

GPU Architecture: NVIDIA V100, T4 and A2/A10/A30/A100 GPUs
GPU Architecture: NVIDIA V100, T4 and A10/A30/A100 GPUs

Software Requirements:

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OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS8

CUDA & Nvidia Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+
CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0

Expand Down Expand Up @@ -344,7 +344,7 @@ Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & Nvidia Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+
CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0

Expand All @@ -364,7 +364,7 @@ The list of all supported operations is provided [here](supported_ops.md).
For a detailed list of changes, please refer to the
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md).

**_Note:_** Using Nvidia driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
**_Note:_** Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy
compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer
driver. This issue is resolved in the 0.5.0 and higher releases.
Expand All @@ -386,7 +386,7 @@ Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & Nvidia Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+
CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0

Expand Down Expand Up @@ -418,7 +418,7 @@ The list of all supported operations is provided [here](supported_ops.md).
For a detailed list of changes, please refer to the
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md).

**_Note:_** Using Nvidia driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
**_Note:_** Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy
compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer
driver. This issue is resolved in the 0.5.0 and higher releases.
Expand All @@ -440,7 +440,7 @@ Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & Nvidia Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+
CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0

Expand Down Expand Up @@ -469,7 +469,7 @@ The list of all supported operations is provided [here](supported_ops.md).
For a detailed list of changes, please refer to the
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md).

**_Note:_** Using Nvidia driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
**_Note:_** Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy
compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer
driver. This issue is resolved in the 0.5.0 and higher releases.
Expand All @@ -491,7 +491,7 @@ Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & Nvidia Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+
CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1

Expand Down Expand Up @@ -523,7 +523,7 @@ The list of all supported operations is provided
For a detailed list of changes, please refer to the
[CHANGELOG](https://github.com/NVIDIA/spark-rapids/blob/main/CHANGELOG.md).

**_Note:_** Using Nvidia driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
**_Note:_** Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the
CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy
compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer
driver. This issue is resolved in the 0.5.0 and higher releases.
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2 changes: 1 addition & 1 deletion docs/get-started/getting-started-aws-emr.md
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Expand Up @@ -26,7 +26,7 @@ documentation](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-i

## Configure and Launch AWS EMR with GPU Nodes

The following steps are based on the AWS EMR document ["Using the Nvidia Spark-RAPIDS Accelerator
The following steps are based on the AWS EMR document ["Using the NVIDIA Spark-RAPIDS Accelerator
for Spark"](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-rapids.html)

### Launch an EMR Cluster using AWS CLI
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2 changes: 1 addition & 1 deletion sql-plugin/src/main/scala/com/nvidia/spark/ExclusiveModeGpuDiscoveryPlugin.scala
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Expand Up @@ -25,7 +25,7 @@ import org.apache.spark.api.resource.ResourceDiscoveryPlugin
import org.apache.spark.resource.{ResourceInformation, ResourceRequest}

/**
* A Spark Resource Discovery Plugin that relies on the Nvidia GPUs being in PROCESS_EXCLUSIVE
* A Spark Resource Discovery Plugin that relies on the NVIDIA GPUs being in PROCESS_EXCLUSIVE
* mode so that it can discover free GPUs.
* This plugin iterates through all the GPUs on the node and tries to initialize a CUDA context
* on each one. When the GPUs are in process exclusive mode this
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