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> AWS GPU Instances: A Beginner's Guide [...] Here are the different types of AWS GPU instances and their use cases The section goes on to teach Amazon-specifi

by lucb1e 3y ago

> AWS GPU Instances: A Beginner's Guide [...] Here are the different types of AWS GPU instances and their use cases The section goes on to teach Amazon-specific terminology and products. A "bare minimum everyone must know" guide should not include vendor-specific guidance. I had this in school with Microsoft already, with never a mention of Linux because they already paid for Windows Server licenses for all of us... Edit: and speaking of inclusivity, the screenshots-of-text have their alt text set to "Alt text". Very useful. It doesn't need to be verbatim copies, but it could at least summarize in a few words what you're meant to get from the terminal screenshot to help people that use screen readers. Since this comment floated to the top, I want to also say that I didn't mean for this to dominate the conversation! The guide may not be perfect, but it helped me by showing how to run arbitrary code on my GPU. A few years ago I also looked into it, but came away thinking it's dark magic that I can't make use of. The practical examples in both high- and low-level languages are useful Another edit: cool, this comment went from all the way at the top to all the way at the bottom, without losing a single vote. I agree it shouldn't be the very top thing, but this moderation also feels weird

mikehollinger 3y ago
Agreed. This isn’t actually that useful of a guide in the first place. Tbh the most basic question is: “are you innovating inside the AI box or outside the AI box?” If inside - this guide doesn’t really share anything practical. Like if you’re going to be tinkering with a core algorithm and trying to optimize it, understanding BLAS and cuBLAS or whatever AMD / Apple / Google equivalent, then understanding what pandas, torch, numpy and a variety of other tools are doing for you, then being able to wield these effectively makes more sense. If outside the box - understanding how to spot the signs of inefficient use of resource - whether that’s network, storage, accelerator, cpu, or memory, and then reasoning through how to reduce that bottleneck. Like - I’m certain we will see this in the near future, but off the top of my head the innocent but incorrect things people do: 1. Sending single requests, instead of batching 2. Using a synchronous programming model when asynchronous is probably better 3. Sending data across a compute boundary unnecessarily 4. Sending too much data 5. Assuming all accelerators are the same. That T4 gpu is cheaper than an H100 for a reason. 6. Ignoring bandwidth limitations 7. Ignoring access patterns
StableAlkyne 3y ago
Are there any surveys of just how many Windows Servers boxes exist? Even when I was working at an Azure-only shop, I've never actually seen anyone use Windows Server. Lots of CentOS (before IBM ruined it) and other Unixes, but never a Windows Server.
- lucb1e 3y ago
  We come across them all the time when doing internal network pentests (most organizations use AD for managing their fleet of end-user systems), and occasionally external tests as well. Stackoverflow is a site that comes to mind as being known for running their production systems on Windows Server. It's useful to have experienced, but I do take issue with exclusively (or primarily) focusing on one ecosystem as a mostly-publicly-funded school.
  - StableAlkyne 3y ago
    Huh, TIL StackOverflow is on Windows Server
deleted 3y ago
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by lucb1e 3y ago

mikehollinger 3y ago

Agreed. This isn’t actually that useful of a guide in the first place. Tbh the most basic question is: “are you innovating inside the AI box or outside the AI box?” If inside - this guide doesn’t really share anything practical. Like if you’re going to be tinkering with a core algorithm and trying to optimize it, understanding BLAS and cuBLAS or whatever AMD / Apple / Google equivalent, then understanding what pandas, torch, numpy and a variety of other tools are doing for you, then being able to wield these effectively makes more sense. If outside the box - understanding how to spot the signs of inefficient use of resource - whether that’s network, storage, accelerator, cpu, or memory, and then reasoning through how to reduce that bottleneck. Like - I’m certain we will see this in the near future, but off the top of my head the innocent but incorrect things people do: 1. Sending single requests, instead of batching 2. Using a synchronous programming model when asynchronous is probably better 3. Sending data across a compute boundary unnecessarily 4. Sending too much data 5. Assuming all accelerators are the same. That T4 gpu is cheaper than an H100 for a reason. 6. Ignoring bandwidth limitations 7. Ignoring access patterns

StableAlkyne 3y ago

Are there any surveys of just how many Windows Servers boxes exist? Even when I was working at an Azure-only shop, I've never actually seen anyone use Windows Server. Lots of CentOS (before IBM ruined it) and other Unixes, but never a Windows Server.

lucb1e 3y ago
We come across them all the time when doing internal network pentests (most organizations use AD for managing their fleet of end-user systems), and occasionally external tests as well. Stackoverflow is a site that comes to mind as being known for running their production systems on Windows Server. It's useful to have experienced, but I do take issue with exclusively (or primarily) focusing on one ecosystem as a mostly-publicly-funded school.
- StableAlkyne 3y ago
  Huh, TIL StackOverflow is on Windows Server

deleted 3y ago

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