Originally published June 2019

For most organizations undergoing a digital transformation, maintaining a good user experience on virtual desktops—an essential component of digital workplaces—is a challenge. Users naturally compare their new virtual desktop experience to their previous physical endpoint experience. As the user experience continues to gain importance in digital workplaces (see this blog for more information), it is essential that virtualized environments keep pace with growing demands for user experience improvements. 

This focus on the new user experience is being addressed by developers of modern-day operating systems and applications, who strive to meet the high expectations of their consumers. For example, the Windows 10 operating system, which plays a significant role in today's digital transformation initiatives, is more graphics-intensive than its predecessors. A study by Lakeside Software's SysTrack Community showed a 32 percent increase in graphics requirements when you move from Windows 7 to Windows 10. Microsoft Office applications (PowerPoint, Outlook, Excel, and so on), Skype for Business collaboration software, and all modern-day web browsers are designed to use more graphics acceleration in their newest releases. 

Dell EMC Ready Solutions for VDI with NVIDIA Tesla T4 GPU

Dell EMC Ready Solutions for VDI, coupled with NVIDIA GRID Virtual PC (GRID vPC) and Virtual Apps (GRID vApps) software, provides comprehensive graphics acceleration solutions for your desktop virtualization workloads. The core of the NVIDIA GRID software is NVIDIA vGPU technology. This technology creates virtual GPUs, which enables sharing of the underlying GPU hardware among multiple users or virtual desktops running concurrently on a single host.  This video compares the quality of a “CPU-only” VDI desktop with a VDI desktop powered by NVIDIA vGPU technology. 

The latest NVIDIA GPU offering that supports virtualization is the NVIDIA Tesla T4, which is a universal GPU that can cater to a variety of workloads. The Tesla T4 comes with a 16 GB DDR6 memory. It operates at 70 W, providing higher energy efficiency and lower operating costs than its predecessors, and has a single-slot PCIe form factor. You can configure up to six Tesla T4s in a single Dell EMC PowerEdge R740xd server, providing the highest density for GPU-accelerated VMs in a Dell EMC server. For more details about the NVIDIA Tesla T4 GPU, see the Tesla T4 for Virtualization Technology Brief.

                                                                     Image courtesy NVIDIA Corporation

                                             Figure 1.  NVIDIA vGPU technology stack

Tesla T4 vs. earlier Tesla GPU cards

Let's compare the NVIDIA Tesla T4 with other widely used cards—the NVIDIA Tesla P40 and the NVIDIA Tesla M10.

Tesla T4 vs. Tesla P40: 

• The Tesla T4 comes with a maximum framebuffer of 16 GB. In a PowerEdge R740xd server, T4 cards can provide up to 96 GB of memory (16 GB x 6 GPUs), compared to the maximum 72 GB provided by the P40 cards (24 GB x 3 GPUs). So, for higher user densities and cost efficiency, the Tesla T4 is a better option in VDI workloads. 
• You might have to sacrifice 3, 6, 12, and 24 GB profiles when using the T4, but 2 GB and 4 GB profiles, which are the most tested and configured profiles in VDI workloads, work well with the Tesla T4. However, NVIDIA Quadro vDWS use cases, which require higher memory per profile, are encouraged to use Tesla P40.

Tesla T4 vs. Tesla M10: 

• In the PowerEdge R740xd server, three Tesla M10 cards can give you the same 96 GB memory as six Tesla T4 cards in a PowerEdge R740xd server. However, when it comes to power consumption, the six Tesla T4 cards consume only 420 W (70 W x 6 GPUs), while the three Tesla M10 GPUs consume 675 W (225 W x 3 GPUs), a substantial difference of 255 W per server. When compared to the Tesla M10, the Tesla T4 provides power savings, reducing your data center operating costs.
• Tesla M10 cards support a 512 MB profile, which is not supported by the Tesla T4.  However, the 512 MB profile is not a viable option in today’s modern-day workplaces, where graphics-intensive Windows 10 operating systems, multi-monitors, and 4k monitors are prevalent.

The following table provides a summary of the Tesla T4, P40, and M10 cards.

Table 1.  Comparison of NVIDIA Tesla T4, P40 & M10

GPU sizing and support for mixed workloads

With multi-monitors and 4K monitors becoming a norm in the modern workplace, streaming high-resolution videos can saturate the encoding engine on the GPUs and increase the load on the CPUs, affecting the performance and scalability of VDI systems. Thus, it is important to size the GPUs based on the number of encoding streams and required frames per second (fps). The Tesla T4 comes with an enhanced NVIDIA NVENC encoder that can provide higher compression and better image quality in H.264 and H.265 (HEVC) video codecs. The Tesla T4 can encode 22 streams at 720 progressive scan (p) resolution, with simultaneous display in high-quality mode. On average, the Tesla T4 can also handle 10 streams at 1080p and 2–3 streams at Ultra HD (2160p) resolutions. Running in a low-latency mode, it can encode 37 streams at 720p resolution, 17–18 streams at 1080p resolution, and 4–5 streams in Ultra HD. 

VDI remote protocols such as VMware Blast Extreme can use NVIDIA GRID software and the Tesla T4 to encode video streams in H.265 and H.264 codecs, which can reduce the encoding latency and improve fps, providing a better user experience in digital workplaces. The new Tesla T4 NVENC encoder provides up to 25 percent bitrate savings for H.265 and up to 15 percent bitrate savings for H.264. Refer to this NVIDIA blog to learn more about the Tesla T4 NVENC encoding improvements. 

The Tesla T4 is well suited for use in a data center with mixed workloads. For example, it can run VDI workloads during the day and compute workloads at night. This concept, known as VDI by Day, HPC by Night, increases the productivity and  utilization of data center resources and reduces data center operating costs. 

Tesla T4 testing on Dell EMC VDI Ready Solution

At Dell EMC, our engineering team tested the NVIDIA Tesla T4 on our Ready Solutions VDI stack based on the Dell EMC VxRail hyperconverged infrastructure. The test bed environment was a 3-node VxRail V570F appliance cluster that was optimized for VDI workloads. The cluster was configured with 2^nd Generation Intel Xeon Scalable processors (Cascade Lake) and with NVIDIA Tesla T4 cards in one of the compute hosts. The environment included the following components:

• PowerEdge R740xd server
• Intel Xeon Gold 6248, 2 x 20-core, 2.5 GHz processors (Cascade Lake)
• NVIDIA Tesla T4 GPUs with 768 GB memory (12 x 64 GB @ 2,933 MHz) 
• VMware vSAN hybrid datastore using an SSD caching tier
• VMware ESXi 6.7 hypervisor
• VMware Horizon 7.7 VDI software layer

Dell EMC Engineering used the Power Worker workload from Login VSI for testing. You can find background information about Login VSI analysis at Login VSI Analyzing Results.

The GPU-enabled PowerEdge compute server hosted 96 VMs with a GRID vPC vGPU profile (T4-1B) of 1 GB memory each. The host was configured with six NVIDIA Tesla T4 cards, the maximum possible configuration for the NVIDIA Tesla T4 in a Dell PowerEdge R740xd server. 

With all VMs powered on, the host server recorded a steady-state average CPU utilization of approximately 95 percent and a steady-state average GPU utilization of approximately 34 percent. Login VSImax—the active number of sessions at the saturation point of the system—was not reached, which means the performance of the system was very good. Our standard threshold of 85 percent for average CPU utilization was relaxed for this testing to demonstrate the performance when graphics resources are fully utilized (96 profiles per host). You might get a better user experience with managing CPU at a threshold of 85 percent by decreasing user density or by using a higher-binned CPU. However, if your CPU is a previous generation Intel Xeon Scalable processor (Skylake), the recommendation is to use only up to four NVIDIA Tesla cards per PowerEdge R740xd server. With six T4 cards per PowerEdge R740xd server, the GPUs were connected to both x8 and x16 lanes. We found no issues using both x8 and x16 lanes and, as indicated by the Login VSI test results, system performance was very good.

Dell EMC Engineering performed similar tests with a Login VSI Multimedia Workload using 48 vGPU-enabled VMs on a GPU-enabled compute host, each having a Quadro vDWS-vGPU profile (T4-2Q) with a 2 GB frame buffer. With all VMs powered on, the average steady-state CPU utilization was approximately 48 percent, and the average steady-state GPU utilization was approximately 35 percent. The system performed well and the user experience was very good.

For more information about the test-bed environment configuration and additional resource utilization metrics, see the design and validation guides for VMware Horizon on VxRail and vSAN on our VDI Info Hub.

Summary

Just as Windows 10 and modern applications are incorporating more graphics to meet user expectations,  virtualized environments must keep pace with demands for an improved user experience. Dell EMC Ready Solutions for VDI, coupled with the NVIDIA Tesla T4 vGPU, are tested and validated solutions that provide the high-quality user experience that today’s workforce demands. Dell EMC Engineering used Login VSI’s Power Worker Workload and Multimedia Workload to test Ready Solutions for VDI with the Tesla T4, and observed very good results in both system performance and user experience. 

In the next blog, we will discuss the affect of memory speed on VDI user density based on testing done  by Dell EMC VDI engineering team. Stay tuned and we’d love to get your feedback!

What does it take to create an AI vision pipeline using modern tools on a Dell platform?
 
This blog describes how to implement object detection from a webcam video stream. The steps include:

• Install DeepStream software with a Docker container
• Process webcam Real Time Streaming Protocol (RTSP) output
• Detect objects (person, car, sign, bicycle) in each frame in near real time
• Draw bounding boxes with identifiers around the objects
• Stream the output using RTSP

NVIDIA Metropolis is an application framework with a set of developer tools that reside in a partner ecosystem. It features GPU-accelerated SDKs and tools to build, deploy, and scale AI-enabled video analytics and Internet of Things (IoT) applications optimally.

This blog focusses on NVIDIA DeepStream, which is one of the SDKs of the NVIDIA Metropolis stack. NVIDIA DeepStream SDK is a complete streaming analytics toolkit for AI-based multi-sensor processing, video, audio, and image understanding. Developers can use DeepStream SDK to create stream processing pipelines that incorporate neural networks and other complex processing tasks such as tracking, video encoding and decoding, IOT message brokers, and video rendering. DeepStream includes an open source Gstreamer project.

Metropolis-based components and solutions enable AI solutions that apply to a broad range of industries like manufacturing, retail, healthcare, and smart cities in the edge ecosystem.

The following figure shows the NVIDIA Metropolis framework:

The NVIDIA Metropolis framework consists of the following stages:

Generate─The stage in which images, video streams, and data originate. The data can be real-time data or synthetic data generated by using Synthetic Data Generation (SDG) tools. NVIDIA tools like NVIDIA Omniverse Replicator fit into this stage of the pipeline.

Train─The stage that uses the data from the Generate stage to feed into pretrained models and enables accelerated model tuning. Models developed from standard AI frameworks like TensorFlow and PyTorch are used in this stage and integrate into the Metropolis framework workflow. The NVIDIA Train, Adapt, and Optimize (TAO) toolkit is a low-code AI model development SDK that helps tune the pretrained models.

Build─The stage of the pipeline in which the core functionality of the Vision AI pipeline is performed. The Build stage of the pipeline includes the NVIDIA video storage toolkit, DeepStream, TensorRT, Triton, and Metropolis Microservices. The libraries and functions in these SDK components provide capabilities such as video codec, streaming analytics, inference optimization, runtime libraries, and inference services.

Deploy─The stage that deploys containerized AI solutions into the production environment at the edge or cloud. The deployment of containerized AI solutions uses industry-standard container orchestration technologies such as Kubernetes and Docker.

Test setup

The test setup includes the following hardware:

• Dell PowerEdge R740xd server with an NVIDIA A100 GPU
• Dell PowerEdge R750 server with an NVIDIA A16 GPU
• A 1080p webcam capable of streaming RTSP output and supporting H.264
• Client or laptop with VLC Media Player for viewing results

Note: Two servers are not required. We ran the demo on both servers to test different configurations. This hardware was available in the lab; we recommend the latest hardware for the best performance.

The test setup includes the following software:

• Ubuntu 20.04 server
• NVIDIA CUDA Toolkit and drivers
• Docker runtime

The following figure shows an example configuration:

Install NVIDIA CUDA

Enabling the CUDA toolkit on top of the base Ubuntu Linux operating system provides the necessary drivers and tools required to access the NVIDIA GPUs.  

The requirements for the CUDA toolkit installation include:

• A CUDA-capable GPU on the platform running the base Linux operating system
• A supported version of the GCC compiler and toolchain on the Linux operating system
• The CUDA Toolkit

1. Install the GCC compiler and other developer tool chains and libraries:

   ssudo apt-get update
   ssudo apt-get install build-essential

2. Verify that the installation is successful:

   gcc --version

3. Install the NVIDIA GPU CUDA toolkit and NVIDIA Container Toolkit:

   sudo sh NVIDIA-Linux-x86_64-515.76.run

   Note: For the PowerEdge system with an NVIDIA A16 GPU, the latest version of CUDA toolkit 12.2 did not function properly. After the installation, the nvidia-smi tool was unable to identify the GPU and activate the driver. Therefore, we chose an earlier version of the runfile (local installer) to install the CUDA toolkit package. We used CUDA Version 11.7 with driver version 515.76. The file used is NVIDIA-Linux-x86_64-515.76.run.
4. After installing the CUDA toolkit, see the nvidia-smi output for details about the GPU on the system:

   nvidia-smi

   

Install Docker Runtime

The following steps describe how to enable a Docker container runtime on top of the base operating system and enabling access to the GPUs from the container environment. With the release of Docker 19.03 and later, nvidia-docker2 packages are no longer required to access the NVIDIA GPUs from the Docker container environment as they are natively supported in Docker runtime.

Perform these steps in Ubuntu 20.04:

1. Update the apt package index and allow Advanced Packaging Tool (APT) to use a repository over HTTPS:

   sudo apt-get update
   ssudo apt-get install ca-certificates curl gnupg

2. Add Docker's official GPG key:

   sudo install -m 0755 -d /etc/apt/keyrings
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
   sudo chmod a+r /etc/apt/keyrings/docker.gpg

3. Set up the repository:

   sudo echo\
    "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
    "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
   sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

4. Update the apt package index:

   sudo apt-get update

5. Install the latest version of the Docker engine:

   sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

6. Verify that Docker is installed:

   sudo docker run hello-world

After the Docker engine is installed, install the NVIDIA Container Toolkit and enable the NVIDIA runtime to Docker runtime. This step makes the GPUs detectable to the Docker containers.  

1. Set up the package repository and the GPG key:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
       && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
       && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

After installing the repository sources, perform the following steps:

1. Update the repository list:

   sudo apt-get update

2. Install the NVIDIA Container Toolkit:

   sudo apt-get install -y nvidia-container-toolkit

3. Configure the Docker daemon to recognize the NVIDIA Container Runtime:

   sudo nvidia-ctk runtime configure --runtime=docker

4. Set the default runtime and then restart the Docker daemon to complete the installation:

   sudo systemctl restart docker

5. Verify that the GPUs are visible from inside a container:

   sudo docker run –rm –runtime=nvidia –gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi

   The following figure shows the NVIDIA SMI output:
   

Run the DeepStream Docker Container

To run the DeepStream Docker Container, perform the following steps:

1. Obtain the DeepStream docker container:

   sudo docker pull nvcr.io/nvidia/deepstream:6.2-devel

   At the time of this blog, the latest version is v6.2. Because the container is large, we recommend that you pull it down first before using it. It takes a few minutes to fully download all the container layers.
2. When the container is fully downloaded, run:

   sudo docker run --gpus all -it --rm -p 8554:8554 nvcr.io/nvidia/deepstream:6.2-devel

   This command instructs Docker to use any GPU it detects, run interactively, delete itself at termination, and open port 8554 for the RTSP output stream.
   
   When the command runs, the following output indicates that the Docker container is accessible and in interactive mode:

   root@9cfa2cfeb11b:/opt/nvidia/deepstream/deepstream-6.2#

Configure DeepStream inside a Docker Container

In the Docker container, make configuration changes so that the demo runs properly.

1. Install the required dependencies:

   /opt/nvidia/deepstream/deepstream/user_additional_install.sh

   The resulting output is long. The following example shows the beginning of the output of a successful installation:

   Get:1 file:/var/nv-tensorrt-local-repo-ubuntu2004-8.5.2-cuda-11.8  InRelease [1575 B]
   Get:1 file:/var/nv-tensorrt-local-repo-ubuntu2004-8.5.2-cuda-11.8  InRelease [1575 B]
   Hit:2 http://archive.ubuntu.com/ubuntu focal InRelease

   The following example shows the end of the output of a successful installation:

   Setting up libavfilter7:amd64 (7:4.2.7-0ubuntu0.1) ...
   Setting up libavresample-dev:amd64 (7:4.2.7-0ubuntu0.1) ... 
   Processing triggers for libc-bin (2.31-0ubuntu9.9) ...

   When we did not perform this step and tried to run the demo, we received the following error message, which is a common error reported on message boards:

   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.078: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstchromaprint.so': libavcodec.so.58: cannot open shared object file: No such file or directory
   
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.110: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpeg2dec.so': libmpeg2.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.111: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpeg2enc.so': libmpeg2encpp-2.1.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.112: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpg123.so': libmpg123.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.117: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstopenmpt.so': libmpg123.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:31.675: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_inferserver.so': libtritonserver.so: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:31.699: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_udp.so': librivermax.so.0: cannot open shared object file: No such file or directory
   ** ERROR: <create_udpsink_bin:644>: Failed to create 'sink_sub_bin_encoder1'
   ** ERROR: <create_udpsink_bin:719>: create_udpsink_bin failed
   ** ERROR: <create_sink_bin:828>: create_sink_bin failed
   ** ERROR: <create_processing_instance:884>: create_processing_instance failed
   ** ERROR: <create_pipeline:1485>: create_pipeline failed
   ** ERROR: <main:697>: Failed to create pipeline
   Quitting
   App run failed

2. Change directories and edit the configuration file:

   cd samples
   
   vim configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.txt

3. Find the following entries:

   [tiled-display]
   enable=1

4. Change enable=1 to enable=0.
   A nontiled display makes it easier to compare the before and after webcam video streams.
5. Find the following entries:

   [source0] 
   enable=1 
   #Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 
   type=3
   uri=file://../../streams/sample_1080p_h264.mp4 

6. Change:
   • type=3 to type=4
   • uri to uri=rtsp://192.168.10.210:554/s0
   Note: This URI is to the webcam that is streaming output.
7. Find the following entries:|

   [source1] 
   enable=1

8. Change enable=1 to enable=0.
9. Find the following entries:

   [sink0]
   enable=1

10. Change enable=1 to enable=0.
11. Find the following entries:

    [sink2] 
    enable=0 
    #Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 
    type=4
    #1=h264 2=h265 
    codec=1
    #encoder type 0=Hardware 1=Software 
    enc-type=0

12. Change:
    • enable=0 to enable=1
    •enc-type=0 to enc-type=1
    Note: The enc-type=1 entry changes the configuration to use software encoders instead of the hardware. We changed the entry because our demo system has an NVIDIA A100 GPU that has no hardware encoders. Ideally, keep this entry as enc-type=0 if hardware encoders are available. With the NVIDIA A16 GPU, we used enc-type=0 entry. The Video Encode and Decode GPU Support Matrix at https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new shows the GPU hardware and encoder support.
    
    If you do not change the enc-type=1 entry (software encoder), the following error message might be displayed:

    ERROR from sink_sub_bin_encoder1: Could not get/set settings from/on resource.
    Debug info: gstv4l2object.c(3511): gst_v4l2_object_set_format_full ():
    /GstPipeline:pipeline/GstBin:processing_bin_0/GstBin:sink_bin/GstBin:sink_sub_bin1/nvv4l2h264enc:sink_sub_bin_encoder1:
    Device is in streaming mode

13. Save the file and exit the editor.

Running the Demo

To run the demo:

1. In the container, start DeepStream to run with the new configuration. This command must be on one line.

   deepstream-app -c configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.txt

2. Find the following text in the warning messages that are displayed:

   *** DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test ***

   Even though the message indicates that DeepStream is bound to localhost, it is accessible remotely due to the Docker port command that was used earlier.
   After more text and warning messages are displayed, the following output indicates that the software has started and is processing video input from the webcam:

   Runtime commands: 
           h: Print this help
           q: Quit 
           p: Pause
           r: Resume
   
   **PERF:  FPS 0 (Avg) 
   **PERF:  0.00 (0.00) 
   ** INFO: <bus_callback:239>: Pipeline ready 
   
   ** ERROR: <cb_newpad3:510>: Failed to link depay loader to rtsp src 
   ** INFO: <bus_callback:225>: Pipeline running 
   
   **PERF:   30.89 (30.89) 
   **PERF:   30.00 (30.43)
   **PERF:   30.00 (30.28)

Viewing the Demo

To view the demo:

1. On a laptop, start the media player. We use VLC media player.
2. Click Media, and then in the dropdown list, select Open Network Stream… , as shown in the following figure:
   
3. Enter the IP address of the Linux system on which the container is running. 
   Note: The IP address in the following figure is an example. Use the appropriate IP address of your deployment.
4. Click Play.

In a few seconds, the webcam streams video that identifies objects with bounding boxes applied in near real time. This demo detects people, cars, signs, and bicycles. 

The following figure is an example that shows the video output of recognized objects:

Note: The model is not trained to detect animals and correctly detects people and cars.

Summary

In this blog, we reviewed the Metropolis DeepStream hardware configuration test setup, the software installation steps, and how to use DeepStream to create a common vision AI pipeline with a Dell server. We included detailed instructions so that you can gain a deeper understanding of the configuration and ease of use.

We hope you enjoyed following our DeepStream journey. 

Check back regularly for upcoming AI blogs. From Dell data center servers to rugged edge devices, Dell Technologies provides optimized solutions for running your AI workloads.