././. XR toolbox, Part 5: Running a native WRL7 appRunning a Native (WRL7) App.Check out Part 4 of the XR toolbox series:. IntroductionIf you haven’t checked out the earlier parts to the XR toolbox Series, then you can do so here:The purpose of this series is simple.
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Get users started with an IOS-XR setup on their laptop and incrementally enable them to try out the application-hosting infrastructure on IOS-XR.In this part, we explore how a user can build and deploy native WRL7 RPMs that they may host in the same process space as XR. What’s a native app?I go into some detail with respect to the IOS-XR application hosting architecture in the following blog:For reference, a part of the architecture is shown below. We focus on the green container in the figure from the original blog:This is the XR control plane LXC. XR processes (routing protocols, XR CLI etc.) are all housed in the blue region. We represent XR FIB within the same region to indicate that the XR control plane exclusively handles the data-plane programming and access to the real XR interfaces (Gig, Mgmt etc.)The gray region inside the control plane LXC represents the global-vrf network namespace in the XR linux environment. Today, IOS-XR only supports the mapping of global/default VRF in IOS-XR to the global-vrf network namespace in XR linux.To get into the XR linux shell (global-vrf network namespace), we have two possible techniques:.
From XR CLI: Issue the bash command to drop into the XR linux shell from the CLI. Over SSH using port 57722: Port 22 is used by XR SSH. To enable a user/tool to drop directly into the XR linux shell, we enable SSH over port 57722. Any reachable IP address of XR could be used for this purpose.Once in the XR linux shell, if we issue an ifconfig we should see all the interfaces (that are up/unshut) in the global/default VRF. AKSHSHAR-M-K0DS: akshshar$ git clone into 'vagrant-xrdocs'.remote: Counting objects: 204, done.remote: Compressing objects: 100% (17/17), done.remote: Total 204 (delta 4), reused 0 (delta 0), pack-reused 187Receiving objects: 100% (204/204), 27.84 KiB 0 bytes/s, done.Resolving deltas: 100% (74/74), done.Checking connectivity. Done.AKSHSHAR-M-K0DS: akshshar$AKSHSHAR-M-K0DS: akshshar$AKSHSHAR-M-K0DS: akshshar$ cd vagrant-xrdocs/native-app-topo-bootstrap/AKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$ pwd/Users/akshshar/vagrant-xrdocs/native-app-topo-bootstrapAKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$ lsVagrantfileconfigs scriptsAKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$Once you’re in the right directory, simply issue a vagrant up.
The.NET Framework version 2.0 (x64) improves scalability and performance with improved caching, application deployment and updating with ClickOnce, support for the broadest array of browsers and devices with ASP.NET 2.0 controls and services and 64-bit support. For more information on the.NET Framework 2.0 click here.
Localhost:$localhost:$ wget 14:57:13- iperf.fr. 194.158.119.186, 2001:860:f70a::2Connecting to iperf.fr 194.158.119.186 :443. Connected.HTTP request sent, awaiting response. 200 OKLength: 277702 (271K) application/x-gzipSaving to: 'iperf-2.0.9-source.tar.gz'100% 277,702 345KB/s in 0.8s2016-07-17 14:57:14 (345 KB/s) - 'iperf-2.0.9-source.tar.gz' saved 202localhost:$localhost:$localhost:$ lsiperf-2.0.9-source.tar.gzlocalhost:$Copy the source code tar ball into the expected location for rpmbuild: /usr/src/rpm/SOURCES/. AKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$ vagrant port rtrThe forwarded ports for the machine are listed below. Please note thatthese values may differ from values configured in the Vagrantfile if theprovider supports automatic port collision detection and resolution.22 (guest) = 2223 (host)57722 (guest) = 2222 (host)AKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$Get back into wrl7build and use HOST ip address = 10.0.2.2 with port 2222 to transfer the RPM to the router over the management network:The password for user vagrant on the router is “vagrant”. AKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$ vagrant port rtrThe forwarded ports for the machine are listed below.
AKSHSHAR-M-K0DS:native-app-topo-bootstrap akshshar$ vagrant ssh rtrLast login: Sun Jul 17 21: from 10.0.2.2xr-vmnode0RP0CPU0:$xr-vmnode0RP0CPU0:$ iperf -s -u-Server listening on UDP port 5001Receiving 1470 byte datagramsUDP buffer size: 64.0 MByte (default)-Yay! Iperf server is running natively in IOS-XR. Install iperf in devbox (ubuntu server)We will use devbox (ubuntu server) in the topology as an iperf client.
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Overclocking is our recommended option, as you get a great performance boost. A more expensive processor is always much more expensive.We offer two stages of overclocking with the guarantee of stability and reliability.Depending on the option we will include a more advanced cooling system.
We do not do extreme overclocking, which may damage the processor. Our overclocking is safe and does not affect CPU lifespan. Noise level also remains at the same level.Having experience in building workstations, we are experts in overclocking a computer. All the results are stress-tested to make sure that the system is 100% stable and reliable.
You can choose multiple operating systems. BIZON Z–Stack (includes Ubuntu 18.04 and latest deep learning frameworks)Recommended for Deep Learning. Preinstalled with the Most Powerful Deep Learning Software (including Tensorflow, Torch/PyTorch, Keras, Caffe 2.0 Caffe-nv, Theano, CUDA and cuDNN).We provide BIZON Z-Stack Tool with a user-friendly interface for easy installation and future upgrades. If a new version of any framework is released, you can upgrade with a click of a button and avoid complicated command lines.
Windows 10 ProOperating system for wide range of tasks. GPUs comparison:.
RTX 2080 8 GB: base line. (10.1 TFLOPS)Latest NVIDIA Turing Architecture. RTX 2080 Ti 11 GB: up to +20-30% performance (13.4 TFLOPS).Recommended for most use cases.2019–2020 Best GPU on the market in terms of price/performance for deep learning.
Latest NVIDIA Turing Architecture. Titan RTX 24 GB: up to +40% performance (16.2 TFLOPS)Recommended for training with large batch sizes and large networks.Latest NVIDIA Turing Architecture. TFLOPS numbers are for FP32 performance (the standard precision for deep learning training). GPUs comparison:. RTX 2080 8 GB: base line.
(10.1 TFLOPS)Latest NVIDIA Turing Architecture. RTX 2080 Ti 11 GB: up to +20-30% performance (13.4 TFLOPS).Recommended for most use cases.2019–2020 Best GPU on the market in terms of price/performance for deep learning. Latest NVIDIA Turing Architecture. Titan RTX 24 GB: up to +40% performance (16.2 TFLOPS)Recommended for training with large batch sizes and large networks.Latest NVIDIA Turing Architecture.
TFLOPS numbers are for FP32 performance (the standard precision for deep learning training). NVLink is a high-speed interconnect designed to allow multiple video cards (GPUs) to communicate directly with each other. We have seen up to +15% performance increase when using NVLink with 2 x NVIDIA RTX 2080 Ti (TensorFlow) compared to the same setup without NVLink. Each additional NVLink will add up to +5% performance. Example: For 4 GPUs you will get up to +20% performance.
Numbers are for reference only and may vary depending on many factors. One NVLink per 2 GPUs. NVLink is optional. Performance increase and NVLINK support depend on software support. Some application does not have NVLink support and you won't see any performance difference. Check with the developers of your application if they have implemented or plan to add NVLink support.
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