diff --git a/source/guides/clear/compatible-kernels.rst b/source/guides/clear/compatible-kernels.rst index 70874745..5882459b 100644 --- a/source/guides/clear/compatible-kernels.rst +++ b/source/guides/clear/compatible-kernels.rst @@ -12,7 +12,7 @@ Bare metal only Kernel native The *kernel-native* bundle focuses on the bare metal platforms. It is - optimized for fast booting and performs best on the Intel® architectures + optimized for fast booting and performs best on the Intel® Architecture Processors described on the :ref:`supported hardware list`. The optimization patches are found in our `Linux`_ GitHub\* repo. @@ -24,8 +24,8 @@ Also compatible with VMs Kernel LTS The *kernel-lts* bundle focuses on the bare metal platforms but uses the latest :abbr:`LTS (Long Term Support)` Linux kernel. It is optimized for - fast booting and performs best on the Intel® architectures described on the - :ref:`supported hardware list`. Additionally, this + fast booting and performs best on the Intel® Architecture Processors described + on the :ref:`supported hardware list`. Additionally, this kernel includes the VirtualBox\* kernel modules, see our :ref:`instructions on using Virtualbox` for more information. The optimization patches are found in our `Linux-LTS`_ GitHub @@ -37,8 +37,8 @@ VM only Kernel KVM The *kernel-kvm* bundle focuses on the Linux :abbr:`KVM (Kernel-based Virtual Machine)`. It is optimized for fast - booting and performs best on Virtual Machines running on the Intel® - architectures described on the + booting and performs best on Virtual Machines running on the Intel® Architecture + Processors described on the :ref:`supported hardware list`. Use this kernel when running |CL| as the guest OS on top of *qemu/kvm*. Use this kernel with **cloud orchestrators** using *qemu/kvm* internally as their **hypervisor** @@ -49,7 +49,7 @@ Kernel KVM Kernel Hyper-V\* The *kernel-hyperv* bundle focuses on running Linux on Microsoft\* Hyper-V. It is optimized for fast booting and performs best on Virtual - Machines running on the Intel® architectures described on the + Machines running on the Intel® Architecture Processors described on the :ref:`supported hardware list`. Use this kernel when running |CL| as the guest OS of **Cloud Instances** in projects such as Microsoft `Azure`_\*. This kernel can be used in a @@ -57,6 +57,8 @@ Kernel Hyper-V\* for more information. The optimization patches are found in our `Linux-HyperV`_ GitHub repo. +*Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* + .. _Linux: https://github.com/clearlinux-pkgs/linux .. _Linux-LTS: https://github.com/clearlinux-pkgs/linux-lts .. _Linux-KVM: https://github.com/clearlinux-pkgs/linux-kvm diff --git a/source/guides/clear/security.rst b/source/guides/clear/security.rst index c78fb6d3..0f125b24 100644 --- a/source/guides/clear/security.rst +++ b/source/guides/clear/security.rst @@ -138,6 +138,8 @@ some examples: * `Tallow`_, a lightweight service which monitors and blocks suspicious SSH login patterns, is installed with the :command:`openssh-server` bundle. +*Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* + .. _`Security for software update in Clear Linux* OS`: https://clearlinux.org/blogs/security-software-update-clear-linux-os-intel-architecture .. _`Recent GNU* C library improvements`: https://clearlinux.org/blogs/recent-gnu-c-library-improvements .. _`rolling release`: https://en.wikipedia.org/wiki/Rolling_release diff --git a/source/guides/maintenance/cpu-performance.rst b/source/guides/maintenance/cpu-performance.rst index 5a78fc2b..828a0f00 100644 --- a/source/guides/maintenance/cpu-performance.rst +++ b/source/guides/maintenance/cpu-performance.rst @@ -203,11 +203,10 @@ Better thermal control and performance can be achieved by providing platform specific configuration to :command:`thermald`. `Linux DPTF Extract Utility`_ is a companion tool to :command:`thermald`, -This tool uses Intel® -:abbr:`DPTF (Dynamic Platform and Thermal Framework)` technology and -can convert to the :file:`thermal_conf.xml` configuration format used by -:command:`thermald`. Closed-source projects, like this one, cannot be packaged -as a bundle in |CL|, so you must install it manually: +This tool uses Intel® Dynamic Platform and Thermal Framework (Intel® DPTF) +technology and can convert to the :file:`thermal_conf.xml` configuration format +used by :command:`thermald`. Closed-source projects, like this one, cannot be +packaged as a bundle in |CL|, so you must install it manually: #. Make sure your machine's BIOS has DPTF feature and is enabled. It will usually be in the :guilabel:`Advanced` or :guilabel:`Advanced>Power` section of the BIOS. @@ -248,15 +247,13 @@ The following output means the configuration has already been applied: thermald[*]: [WARN]Using generated /etc/thermald/thermal-conf.xml.auto -.. admonition:: Disclaimer +*Intel® Turbo Boost Technology requires a PC with a processor with Intel Turbo +Boost Technology capability. Intel Turbo Boost Technology performance varies +depending on hardware, software and overall system configuration. Check with +your PC manufacturer on whether your system delivers Intel Turbo Boost Technology. +For more information, see http://www.intel.com/technology/turboboost* - Intel® Turbo Boost Technology requires a PC with a processor with Intel - Turbo Boost Technology capability. Intel Turbo Boost Technology performance - varies depending on hardware, software and overall system configuration. - Check with your PC manufacturer on whether your system delivers Intel Turbo - Boost Technology. For more information, see http://www.intel.com/technology/turboboost - - Intel SpeedStep is a trademark of Intel Corporation or its subsidiaries. +*Intel, Intel SpeedStep, and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* .. _`Intel P-state driver`: https://www.kernel.org/doc/Documentation/cpu-freq/intel-pstate.txt diff --git a/source/guides/stacks/dars.rst b/source/guides/stacks/dars.rst index b9dc910c..1a1fcbf3 100644 --- a/source/guides/stacks/dars.rst +++ b/source/guides/stacks/dars.rst @@ -24,7 +24,8 @@ The Data Analytics Reference Stack provides two pre-built Docker images, available on `Docker Hub`_: * A |CL|-derived `DARS with OpenBlas`_ stack optimized for `OpenBLAS`_ -* A |CL|-derived `DARS with Intel® MKL`_ stack optimized for `MKL`_ (Intel® Math Kernel Library) +* A |CL|-derived `DARS with Intel® MKL`_ stack optimized for + `Intel® Math Kernel Library`_ (Intel® MKL) We recommend you view the latest component versions for each image in the :file:`releasenote` found in the `Data Analytics Reference Stack`_ GitHub\* @@ -98,7 +99,7 @@ If you choose to build your own DARS container images, you can customize them as To construct images with |CL|, start with a |CL| development platform that has the :command:`containers-basic-dev` bundle installed. Learn more about bundles and installing them by using :ref:`swupd-guide`. -#. The `Data Analytics Reference Stack`_ is part of the Intel® stacks GitHub\* repository. Clone the :file:`stacks` repository. +#. The `Data Analytics Reference Stack`_ is part of the Intel stacks GitHub\* repository. Clone the :file:`stacks` repository. .. code-block:: bash @@ -671,6 +672,7 @@ This exception can be disregarded because DARS does not use hadoop.hive.shims. H #. There is an exception message `Exception in thread "Thread-3" java.lang.ExceptionInInitializerError at org.apache.hadoop.hive.conf.HiveConf` This is related to the same issue with |CL| and JDK11 noted above, and does not affect DARS for the same reason. +*Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* .. _Data Analytics Reference Stack: https://github.com/intel/stacks/tree/master/dars/clearlinux @@ -678,7 +680,7 @@ This exception can be disregarded because DARS does not use hadoop.hive.shims. H .. _OpenBLAS: http://www.openblas.net/ -.. _MKL: https://software.intel.com/en-us/mkl +.. _Intel® Math Kernel Library: https://software.intel.com/en-us/mkl .. _CentOS: https://www.centos.org/ diff --git a/source/guides/stacks/dbrs.rst b/source/guides/stacks/dbrs.rst index d012072c..ac3c5f90 100644 --- a/source/guides/stacks/dbrs.rst +++ b/source/guides/stacks/dbrs.rst @@ -14,7 +14,7 @@ Overview ******** The Database Reference Stack is integrated, highly-performant, open source, -and optimized for 2nd generation Intel® Xeon® Scalable Processors and Intel® +and optimized for 2nd generation Intel® Xeon® Scalable processors and Intel® Optane™ persistent memory. This open source community release is part of an effort to ensure developers have easy access to the features and functionality of Intel Platforms. @@ -52,10 +52,10 @@ The release announcement for each release provides more detail about the stack f Hardware Requirements ********************* -* Intel® Xeon Scalable Platform with Intel® C620 chipset series -* 2nd Gen Intel® Xeon Scalable processor CPU (Intel® Optane™ PMM-enabled stepping) Provides cache & memory control. Intel® Optane™ PMem works only on systems powered by 2nd Generation Intel® Xeon® Platinum or Gold processors. +* Intel Xeon Scalable platform with Intel® C620 series chipset +* 2nd Gen Intel Xeon Scalable processor CPU (Intel® Optane™ PMem-enabled stepping). Provides cache & memory control. Intel Optane PMem works only on systems powered by 2nd Generation Intel® Xeon® Platinum or Intel® Xeon® Gold processors. * BIOS with Reference Code -* Intel®Optane™ PMem +* Intel Optane PMem Hardware configuration used in stacks development ================================================= @@ -64,12 +64,12 @@ Hardware configuration used in stacks development * BIOS with Reference Code * BIOS ID: SE5C620.86B.0D.01.0438.032620191658 * BMC Firmware: 1.94.6b42b91d - * Intel® Optane™ PMemFirmware: 1.2.0.5310 -* 2x Intel® Xeon Platinum 8268 Processor -* Intel® SSD DC S5600 Series 960GB 2.5in SATA Drive + * Intel Optane PMem Firmware: 1.2.0.5310 +* 2x Intel Xeon Platinum 8268 Processor +* Intel® SSD Data Center Family S5600 Series 960GB 2.5in SATA Drive * 64 GB RAM - Distributed in 4x 16 GB DDR4 DIMM's -* 2x Intel® Optane™ PMem 256GB Module -* 1-1-1 Layout 8 Optane™ : 1 RAM ratio +* 2x Intel Optane PMem 256GB Module +* 1-1-1 Layout 8 Intel Optane : 1 RAM ratio .. list-table:: **Table 1. IMC** @@ -104,7 +104,7 @@ Firmware configuration When updating DCPMM Firmware, all DCPMM parts must be in the same mode (you cannot mix 1LM and 2LM parts). -The latest firmware download for the Intel® Server System S2600WF Family is available at the `Intel Download Center`_ +The latest firmware download for the Intel® Server Board S2600WF Family is available at the `Intel Download Center`_ Firmware Update Steps ===================== @@ -117,7 +117,7 @@ Firmware Update Steps #. After update BMC firmware, system BIOS, ME firmware,FD, FRUSDR, system will reboot automatically. -If Intel® Optane™ PMem is installed, run startup.nsh a second time after the first reboot to upgrade Intel® Optane™ PMem Firmware: +If Intel Optane PMem is installed, run startup.nsh a second time after the first reboot to upgrade Intel Optane PMem Firmware: * Boot to EFI shell. * Input "fsx(x:0,1,...):" to enter into your usb disk @@ -134,9 +134,9 @@ Online Resources Before going through the configuration steps, we strongly recommend visiting the following resources and wikis to have a broader understanding of what is being done -* `Quick Start Guide`_ Configure Intel® Optane™ PMem Modules on Linux +* `Quick Start Guide`_ Configure Intel Optane PMem Modules on Linux * `Managing NVDIMMs`_ -* `Configure, Manage, and Profile`_ Intel® Optane™ PMem Modules +* `Configure, Manage, and Profile`_ Intel Optane PMem Modules Optane™ DIMM Configuration ========================== @@ -569,7 +569,7 @@ Eventually all the given nodes will be shown as running using :command:`kubectl Running DBRS with Redis *********************** -The Redis stack application is enabled for a multinode Kubernetes environment using Intel® Optane™ DCPMM PMem DIMMs in fsdax mode for storage. +The Redis stack application is enabled for a multinode Kubernetes environment using Intel Optane DCPMM PMem DIMMs in fsdax mode for storage. The source code used for this application can be found in the `Github repository`_ @@ -584,7 +584,7 @@ The following examples will use the `Docker image with Redis`_. You can also bu Single node =========== -Prior to starting the container, you will need to have the Intel® Optane™ DCPMM module in fsdax with a file system and mounted in `/mnt/dax0` as shown above. +Prior to starting the container, you will need to have the Intel Optane DCPMM module in fsdax with a file system and mounted in `/mnt/dax0` as shown above. Use the following to start the container, replacing ${DOCKER_IMAGE} with the name of the image you are using. @@ -664,6 +664,8 @@ where: For more information please refer to this `blog post`_ from `Memcached`_ +*Intel, Xeon, Intel Optane, and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* + .. _Intel Download Center: https://downloadcenter.intel.com/download/28695/Intel-Server-Board-S2600WF-Family-BIOS-and-Firmware-Update-Package-for-UEFI diff --git a/source/guides/stacks/dlrs-inference.rst b/source/guides/stacks/dlrs-inference.rst index fdcb9feb..eebcf15d 100644 --- a/source/guides/stacks/dlrs-inference.rst +++ b/source/guides/stacks/dlrs-inference.rst @@ -19,7 +19,7 @@ Overview The solution covered here requires the following software components: -* `Deep Learning Reference Stack`_ which is a |CL-ATTR| based Docker\* container providing deep learning frameworks and is optimized for Intel Xeon Scalable platforms. +* `Deep Learning Reference Stack`_ which is a |CL-ATTR| based Docker\* container providing deep learning frameworks and is optimized for Intel® Xeon® Scalable processors. * `Kubeflow`_ is the machine learning toolkit for Kubernetes that helps with deployment of Seldon Core and Istio components. * `Seldon Core`_ is a software platform for deploying machine learning models. We use the DLRS container to serve the OpenVino\* framework for inference with the Seldon Core. * The OpenVino Model Server is included in DLRS and provides the OpenVino framework for inference. From OpenVino, the `OpenVino Toolkit`_ provides improved neural network performance on a variety of Intel processors. For this guide, we converted pre-trained Caffe models into the `Intermediate Representation(IR)`_ of ResNet50 with the OpenVino toolkit. @@ -85,7 +85,7 @@ Although this guide assumes a |CL| host system, it has also been validated with Recommended Hardware ==================== -We validated this guide on an `Intel Cascade Lake`_ server and this is recommended to get optimal performance and take advantage of the built in Intel® Deep Learning Boost functionality. +We validated this guide on a server with a `2nd Generation Intel Xeon Scalable processor`_, formerly Cascade Lake, and this is recommended to get optimal performance and take advantage of the built in Intel® Deep Learning Boost (Intel® DL Boost) functionality. Required Software ================= @@ -917,7 +917,7 @@ To find out how to assign the cores and memory properly run :command:`numactl -H 1: 21 10 -In this case, the tests are run on Intel(R) Xeon(R) Platinum 6260L with 2 sockets(nodes) and 24 cores (CPUs) on each socket. +In this case, the tests are run on Intel® Xeon® Platinum 6260L processor with 2 sockets(nodes) and 24 cores (CPUs) on each socket. Running the inference serving the application with :command:`numactl --membind=0 --cpubind=0-3` forces the system to use 0,1,2,3 cores and memory located on the same socket (0). To use all available cores there is a need to create more service deployments assigned to the remaining cores. The `ai-inferencing` repository contains an example deployment script with 2 cores per instance assignment. @@ -1029,7 +1029,7 @@ The test performed on a 2 node cluster with 48 cores per node showed that there KMP_BLOCKTIME=1 - +*Intel, Xeon, and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* .. _Deep Learning Reference Stack: https://clearlinux.org/stacks/deep-learning @@ -1040,7 +1040,7 @@ The test performed on a 2 node cluster with 48 cores per node showed that there .. _Istio: https://istio.io/ .. _Source-to-Image: https://github.com/openshift/source-to-image .. _Min.io: https://min.io/ -.. _Intel Cascade Lake: https://www.intel.com/content/www/us/en/design/products-and-solutions/processors-and-chipsets/cascade-lake/2nd-gen-intel-xeon-scalable-processors.html +.. _2nd Generation Intel Xeon Scalable processor: https://www.intel.com/content/www/us/en/design/products-and-solutions/processors-and-chipsets/cascade-lake/2nd-gen-intel-xeon-scalable-processors.html .. _Docker 18.09: https://kubernetes.io/docs/setup/production-environment/container-runtimes/ .. _Kubernetes 1.15.3: https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/ .. _gsutil: https://cloud.google.com/storage/docs/gsutil_install#linux diff --git a/source/guides/stacks/dlrs.rst b/source/guides/stacks/dlrs.rst index 2fb2fe19..4387ee2b 100644 --- a/source/guides/stacks/dlrs.rst +++ b/source/guides/stacks/dlrs.rst @@ -14,7 +14,7 @@ Overview ******** We created the Deep Learning Reference Stack to help AI developers deliver -the best experience on Intel® Architecture. This stack reduces complexity +the best experience on Intel® architecture. This stack reduces complexity common with deep learning software components, provides flexibility for customized solutions, and enables you to quickly prototype and deploy Deep Learning workloads. Use this guide to run benchmarking workloads on your @@ -28,16 +28,18 @@ The latest release of the Deep Learning Reference Stack (`DLRS V6.0`_ ) supports * Transformers* which is a state-of-the-art Natural Language Processing (NLP) library for TensorFlow 2.0 and PyTorch * Flair*, a PyTorch NLP framework * OpenVINO™ model server version 2020.1, delivering improved neural network performance on Intel processors, helping unlock cost-effective, real-time vision applications. -* Intel® Deep Learning Boost (Intel® DL Boost) with AVX-512 Vector Neural Network Instruction (Intel® AVX-512 VNNI), designed to accelerate deep neural network-based algorithms. +* Intel® Deep Learning Boost (Intel® DL Boost) with Intel® Advanced Vector + Extensions 512 (Intel® AVX-512) Vector Neural Network Instruction , designed to + accelerate deep neural network-based algorithms. * Deep Learning Compilers (TVM* 0.6), an end-to-end compiler stack. .. important:: - To take advantage of the Intel® AVX-512 and VNNI functionality (including the Intel® oneAPI Deep Neural Network Library (oneDNN), found at `oneDNN`_. with the Deep Learning Reference Stack, you must use the following hardware: + To take advantage of the Intel AVX-512 and VNNI functionality (including the Intel® oneAPI Deep Neural Network Library (oneDNN), found at `oneDNN`_, with the Deep Learning Reference Stack, you must use the following hardware: - * Intel® AVX-512 images require an Intel® Xeon® Scalable processor - * VNNI requires a 2nd generation Intel® Xeon® Scalable processor + * Intel AVX-512 images require an Intel® Xeon® Scalable processor + * VNNI requires a 2nd generation Intel Xeon Scalable processor Releases @@ -194,7 +196,7 @@ TensorFlow. TensorFlow benchmarks. If you are using an FP32 based model, it can be converted to an int8 model - using `Intel® quantization tools`_. + using `Intel® AI Quantization Tools for TensorFlow`_. PyTorch single and multi-node benchmarks **************************************** @@ -237,8 +239,8 @@ TensorFlow Training (TFJob) with Kubeflow and DLRS .. warning:: - If you choose the Intel® oneDNN image, your platform - must support the Intel® AVX-512 instruction set. Otherwise, an + If you choose the Intel oneDNN image, your platform + must support the Intel AVX-512 instruction set. Otherwise, an *illegal instruction* error may appear, and you won’t be able to complete this guide. A `TFJob`_ is Kubeflow's custom resource used to run TensorFlow training jobs on Kubernetes. This example shows how to use a TFJob within the DLRS container. @@ -1059,7 +1061,7 @@ Related topics * `Jupyter Notebook`_ -OpenVINO is a trademark of Intel Corporation or its subsidiaries +*Intel, OpenVINO, Xeon, and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* .. _TensorFlow: https://www.tensorflow.org/ @@ -1132,7 +1134,7 @@ OpenVINO is a trademark of Intel Corporation or its subsidiaries .. _Distributed TensorFlow: https://www.tensorflow.org/deploy/distributed .. _TFJobs: https://www.kubeflow.org/docs/components/tftraining/ -.. _Intel® quantization tools: https://github.com/IntelAI/tools/blob/master/tensorflow_quantization/README.md#quantization-tools +.. _Intel® AI Quantization Tools for TensorFlow: https://github.com/IntelAI/tools/blob/master/tensorflow_quantization/README.md#quantization-tools .. _OpenCV open model zoo: https://github.com/opencv/open_model_zoo diff --git a/source/guides/stacks/greengrass.rst b/source/guides/stacks/greengrass.rst index 38b7828b..4feb21c6 100644 --- a/source/guides/stacks/greengrass.rst +++ b/source/guides/stacks/greengrass.rst @@ -6,7 +6,7 @@ Enable AWS Greengrass\* and OpenVINO™ toolkit This guide explains how to enable AWS Greengrass\* and OpenVINO™ toolkit. Specifically, the guide demonstrates how to: -* Set up the Intel® edge device with |CL-ATTR| +* Set up the Intel edge device with |CL-ATTR| * Install the OpenVINO™ toolkit and Amazon Web Services\* (AWS\*) Greengrass\* software stacks * Use AWS Greengrass\* and AWS Lambda\* to deploy the FaaS samples from @@ -20,19 +20,19 @@ Overview ******** Hardware accelerated Function-as-a-Service (FaaS) enables cloud developers to -deploy inference functionalities [1] on Intel® IoT edge devices with +deploy inference functionalities [1] on Intel IoT edge devices with accelerators (CPU, Integrated GPU, Intel® FPGA, and Intel® Movidius™ technology). These functions provide a great developer experience and seamless migration of visual analytics from cloud to edge in a secure manner using a containerized environment. Hardware-accelerated FaaS provides the best-in-class performance by accessing optimized deep learning libraries on -Intel® IoT edge devices with accelerators. +Intel IoT edge devices with accelerators. Supported platforms ******************* * Operating System: |CL| latest release -* Hardware: Intel® core platforms (that support inference on CPU only) +* Hardware: Intel® Core™ processors (that support inference on CPU only) Sample description ================== @@ -111,7 +111,7 @@ for the OpenVINO software stack: Learn more about how to :ref:`swupd-guide`. The :command:`computer-vision-basic` bundle installs the OpenVINO™ toolkit, -and the sample models optimized for Intel® edge platforms. +and the sample models optimized for Intel edge platforms. .. _convert-dl-models: @@ -130,7 +130,7 @@ download the BVLC AlexNet model files `bvlc_alexnet.caffemodel`_ and :file:`/usr/share/openvino/models`. Any custom pre-trained classification models can be used with the classification sample. -For object detection, the sample models optimized for Intel® edge platforms +For object detection, the sample models optimized for Intel edge platforms are included with the computer-vision-basic bundle installation at :file:`/usr/share/openvino/models`. These models are provided as an example; you may also use a custom SSD model with the Greengrass object detection sample. @@ -181,7 +181,7 @@ In these examples: Configure AWS Greengrass group ****************************** -For each Intel® edge platform, you must create a new AWS Greengrass group +For each Intel edge platform, you must create a new AWS Greengrass group and install AWS Greengrass core software to establish the connection between cloud and edge. @@ -278,7 +278,8 @@ configuring the Lambda function for AWS Greengrass. - Value * - PARAM_MODEL_XML - /, where is user specified and - contains IR.xml, the Intermediate Representation file from Intel® Model Optimizer. + contains IR.xml, the Intermediate Representation file from the + OpenVINO™ Model Optimizer. For this guide, should be set to '/usr/share/openvino/models' or one of its subdirectories. * - PARAM_INPUT_SOURCE @@ -396,6 +397,10 @@ References #. AWS Lambda: https://aws.amazon.com/lambda/ #. AWS Kinesis: https://aws.amazon.com/kinesis/ + +*Intel, OpenVINO, and the Intel logo are trademarks of Intel Corporation or its subsidiaries.* + + .. _Edge-Analytics-FaaS: https://github.com/intel/Edge-Analytics-FaaS/tree/v1.0/AWS%20Greengrass .. _bvlc_alexnet.caffemodel: http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel diff --git a/source/guides/stacks/mers.rst b/source/guides/stacks/mers.rst index 8a9a91e8..d1ac448e 100644 --- a/source/guides/stacks/mers.rst +++ b/source/guides/stacks/mers.rst @@ -82,9 +82,9 @@ image and aims to support the latest |CL| version. |MERS| provides the following libraries and drivers: .. list-table:: - :widths: auto + :widths: 15 85 - * - SVT-HEVC* + * - SVT-HEVC - Scalable Video Technology for HEVC encoding, also known as H.265 * - SVT-AV1 - Scalable Video Technology for AV1 encoding @@ -103,8 +103,8 @@ image and aims to support the latest |CL| version. * - gmmlib - `Intel® Graphics Memory Management Library `_ provides device specific and buffer - management for the Intel® Graphics Compute Runtime for OpenCL(TM) and - the Intel® Media Driver for VAAPI. + management for the Intel® Graphics Compute Runtime for oneAPI Level Zero + and OpenCL™ Driver and the Intel Media Driver for VAAPI. Components of the |MERS| include: @@ -130,7 +130,7 @@ Components of the |MERS| include: .. note:: - The |MERS| is validated on 11th generation Intel® Processor Graphics and + The |MERS| is validated on 11th generation Intel Processor Graphics and newer. Older generations should work but are not tested against. .. note:: @@ -559,5 +559,6 @@ following the same steps in this tutorial by substituting the image name with *sysstacks/mers-clearlinux:aom*. -**Intel, Xeon, OpenVINO, and the Intel logo are trademarks of Intel -Corporation or its subsidiaries.** +*Intel, Xeon, OpenVINO, and the Intel logo are trademarks of Intel +Corporation or its subsidiaries. OpenCL and the OpenCL logo are trademarks of +Apple Inc. used by permission by Khronos.*