Fubin Zhang zfb132

Goal

Install working tensorflow or pytorch via standard conda environment workflow.

Basic Setup : Install `pytorch` in a fresh conda environment

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

What is NUMA (Non-Uniformed Memory Access)

Non-Uniform Memory Access (NUMA) is one of the computer memory design methods used in multiprocessor systems, and the time to access the memory varies depending on the relative position between the memory and the processor. In the NUMA architecture, when a processor accesses its local memory, it is faster than when it accesses the remote memory. Remote memory refers to memory that is connected to another processor, and local memory refers to memory that is connected to its own processor. In other words, it is a technology to increase memory access efficiency while using multiple processors on one motherboard. When a specific processor runs out of memory, it monopolizes the bus by itself, so other processors have to play. , and designate 'access only here', and call it a NUMA node.

1. Check Nodes

lspci | grep -i nvidia
  
01:00.0 VGA compatible controller: NVIDIA Corporation TU106 [GeForce RTX 2060 12GB] (rev a1)

仅为 GitHub 设置代理

git 代理

设置 git config --global http.https://github.com.proxy socks5://127.0.0.1:1086
设置完成后, ~/.gitconfig 文件中会增加以下条目:

[http "https://github.com"]
    proxy = socks5://127.0.0.1:1086

方法一：
为 PowerShell 设置代理，命令如下：

$Env:http_proxy="http://127.0.0.1:7890";
$Env:https_proxy="http://127.0.0.1:7890";

这是临时命令，重新代理终端需要重新输入
如果想要永久设置代理，建议使用自定义配置，使每次代理 PowerShell 窗口时，运行如上命令：

在 PowerShell 窗口中运行如下指令：

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare [email protected]:usi-systems/easytrace.git

	- make sure sriov is enabled in your motherboard bios
	- don't use the nvidia gpu as a display (i use asrock rack boards and the crappy on board graphics for local display, which i actually don't use unless there's a problem).
	- install nvidia drivers on the host: ./NVIDIA-Linux-x86_64-xxx.xxx.run
	- blacklist nvidia and nouvaeu kernel drivers on the host, reboot
	- pass the devices through to the LXC before installing drivers, details below (prob doesn't matter, but i do it in this order)
	- install THE SAME nvidia drivers on the LXC: ./NVIDIA-Linux-x86_64-xxx.xxx.run --no-kernel-module

	below is an example with 4 gpus

	find the device info you'll need for passthrough: