Use an NVIDIA Container Image to verify container access to NVIDIA GPUs

Verify that your container can communicate with the GPU and the type of  NVidia GPUs your docker containers can access using one of NVIDIA's container images.

docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

 Sample Output

The containers on this server have access to a single Titan RTX card


Source

Revision 

Created 2024/07 




Comments

Post a Comment

Popular posts from this blog

Installing the RNDIS driver on Windows 11 to use USB Raspberry Pi as network attached

Image
I do a lot of my development and configuration via ssh into my Raspberry Pi Zero over the RNDIS connection. Some models of the Raspberry PIs can be configured with  gadget drivers  that let the Raspberry pi emulate different devices when plugged into computers via USB. My favorite gadget  is the network profile that makes a Raspberry Pi look like an RNDIS-attached network device.  All types of network services travel over an RNDIS device without knowing it is a USB hardware connection. A Raspberry Pi shows up as a Remote NDIS (RNDIS) device when you plug the Pi into a PC or Mac via a USB cable. The gadget  in the Windows Device Manager picture shows this RNDIS Gadget connectivity between a Windows machine and a Raspberry Pi. The Problem Windows 11 and Windows 10 no longer auto-installs the RNDIS driver that makes magic happen. Windows recognizes that the Raspberry Pi is some type of generic USB COM device. Manually running W indows Update  or  Upd...
Read more

Understanding your WSL2 RAM and swap - Changing the default 50%-25%

Image
The Windows Subsystem for Linux operates as a virtual machine that can dynamically grow the amount of RAM to a maximum set at startup time.  Microsoft sets a default maximum RAM available to 50% of the physical memory and a swap-space that is 1/4 of the maximum WSL RAM.  You can scale those numbers up or down to allocate more or less RAM to the Linux instance. The first drawing shows the default WSL memory and swap space sizing. The images below show a developer machine that is running a dev environment in WSL2 and Docker Desktop.  Docker Desktop has two of its own WSL modules that need to be accounted for.  You can see that the memory would actually be oversubscribed, 3 x 50% if every VM used its maximum memory.  The actual amount of memory used is significantly smaller allowing every piece to fit. Click to Enlarge The second drawing shows the memory allocation on my 64GB laptop. WSL Linux defaul...
Read more

Get AI code assist VSCode with local LLMs using LM Studio and the Continue.dev extension - Windows

Image
This is about running VSCode AI code assist locally replacing Copilot or some other service.  You may run local models to guarantee none of your code ends up on external servers. Or, you may not want to maintain an ongoing AI subscription. We are going to use  LM  Studio and VS Code.  This was tested on Windows 11 with an RTX 3060 TI with 8GB of VRAM. 8GB really limits the number and size of the models we can use. LM Studio's simple hosting model of 1 LLM and an embedding works for us in this situation.  You want a big card. 8GB is a tiny card. Related blog articles and videos Several related blogs and videos that cover VSCode and local LLMs Blog  Get AI code assist VSCode with local LLMs using Ollama and the Continue.dev extension - Mac Get AI code assist VSCode with local LLMs using LM Studio and the Continue.dev extension - Windows Rocking an older Titan RTX 24GB as my local AI Code assist on Windows 11, Ollama and VS Code YouTube Video  Using loc...
Read more