nvidia-docker
AGiXT
nvidia-docker | AGiXT | |
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53 | 26 | |
16,998 | 2,456 | |
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0.0 | 9.9 | |
5 months ago | 2 days ago | |
Makefile | Python | |
Apache License 2.0 | MIT License |
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nvidia-docker
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What are the best AI tools you've ACTUALLY used?
Nvidia Docker on GitHub
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Plex setup through Docker + Nvidia card, but hardware acceleration stops working after some time
Here's where I found discussion regarding this https://github.com/NVIDIA/nvidia-docker/issues/1671
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Seeking Guidance on Leveraging Local Models and Optimizing GPU Utilization in containerized packages
I found the Faq, looks like Windows isn't supported which might indicate why I had this problem earlier. I might need to dual boot my machine if it won't work with WSL which I don't see mentioned in either page. WSL Cuda instructions found this I'll give it a try.
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Which GPU for HW transcoding in PMS: Intel Arc or Nvidia?
Arc has linux kernel support from 6.0, been using an A770 with tdarr for a few months. Super solid and no issues like the nvidia docker toolkit just losing the GPU. The workaround doesn't hold for long - https://github.com/NVIDIA/nvidia-docker/issues/1730 It is one of the reasons I went with Intel over waiting for a low end 40 series. The other was I that basically stole it for $199. So far Plex is the only thing that doesn't work with Arc and all the HW transcoding falls on the iGPU. Knowing how they prioritize things nobody wants, Arc support and AV1 transcoding will be added when 16th gen Intel CPUs are released.
- [D] Would a Tesla M40 provide cheap inference acceleration for self-hosted LLMs?
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Help! Accelerated-GPU with Cuda and CuPy
I use ubuntu with wsl2 under windows 11. I want to learn how to get GPU-accelerated computing working. My ASUS laptop has a NVIDIA graphics card as well as a built in Intel. Please ask me any needed questions and then tell me the steps I need to get this working? SOFTWARE DEVELOPER Thanks for sharing that information. Can you please confirm if you have already installed NVIDIA drivers on your Windows 11 system? If not, you'll need to install the appropriate NVIDIA drivers for your GPU in order to enable GPU acceleration. Additionally, what is your end goal is with GPU accelerated computing? yes I have the NVIDIA control panel installed. SOFTWARE DEVELOPER Great. To enable GPU acceleration in WSL2, you'll need to follow these steps: First, confirm you are running Windows 11 build 20145 or later, and have WSL2 installed and configured to use Ubuntu. Download the NVIDIA Linux driver for your GPU from the official NVIDIA Linux download page https://www.nvidia.com/Download/index.aspx. Install the latest NVIDIA driver, including the NVIDIA Docker plugin. You can follow instructions on the NVIDIA Docker plugin GitHub page (https://github.com/NVIDIA/nvidia-docker). Install the CUDA toolkit and cuDNN library. Once you've installed the NVIDIA driver successfully, you can download the CUDA toolkit and cuDNN library (if applicable) from the official NVIDIA CUDA download page https://developer.nvidia.com/cuda-downloads. Install the NVIDIA Container Toolkit. NVIDIA Container Toolkit is a nifty package that provides a set of tools for GPU-accelerated container deployments. You can find installation instructions and a getting started guide on the NVIDIA Container Toolkit GitHub page https://github.com/NVIDIA/nvidia-docker. Test the installation by running a TensorFlow or PyTorch script that uses the GPU for accelerated computing. Check if the GPU is recognized and being used in the output of the script. Once you've successfully followed these steps, you should have GPU-accelerated computing environments in WSL2. Hope that helps! Let me know if you have any other questions. yes to question #1. On #2, do I download the Game Ready driver or the Studio Driver. I don't play games as I'm a software devevloper but want accelerated graphics for development or data science purposes. SOFTWARE DEVELOPER Great to hear that you have WSL2 set up on your system. Regarding your question, you should download the Studio Driver version, as it's optimized for professional workloads, including data science and software development. The Game Ready version is optimized for gaming and might not always provide the best performance for non-gaming workloads. Once you have downloaded the Studio Driver for your GPU, follow the instructions provided by the driver installer to complete the installation process. Let me know if you have any further questions! Would you clarify #3? Is the NVIDIA Docker plugin included with the NVIDIA driver install? Or is it a separate install. I have Docker for Windows installed and working in WSL2. SOFTWARE DEVELOPER Sure, I'd be happy to clarify. The NVIDIA Docker plugin is an additional package that needs to be installed separately from the NVIDIA drivers. The plugin provides a convenient way for creating and running GPU-accelerated Docker containers in your system. To install the NVIDIA Docker plugin, follow the instructions on the NVIDIA Docker plugin GitHub page (https://github.com/NVIDIA/nvidia-docker). Note that the plugin is only compatible with Docker CE (Community Edition) or Docker EE (Enterprise Edition) on Linux-based systems, and does not work on Docker for Windows. Once you've installed the plugin, you can use it to run GPU-accelerated containers with Docker. It's worth noting that the NVIDIA Docker plugin requires access to your host system's NVIDIA GPU, so you'll need to ensure that your GPU has been correctly installed and recognized by your system. Hope that answers your question. Let me know if you have any additional questions!
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Plex Transcode (VC1 (HW) 1080p H264 (HW) 1080p) on Pixel 7 Pro
Im trying to determine how to troubleshoot & resolve the HW transcoding, but based on my testing Im assuming its some change to the NVIDIA toolkit https://github.com/NVIDIA/nvidia-docker
- jelyfin with nvidia acceleration stopped working
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Dockerize CUDA-Accelerated Applications
NVIDIA Container Toolkit
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Setting up a new unraid server with vgpu and plex docker transcodes
So I am in the initial planning stages of setting up a new unraid server. Looking at picking up an SC846 24bay 4u chassis. I've got a Gigabyte Aorus mb with an AMD 5950x, 32gb of ddr4 (adding more as needed) and an nvidia 3070ti. I plan on getting an LSI 8i for the drives and leaves room for expansion server plans. My goal is to have plex setup via docker and utilize the gpu transcoding to offload the cpu work. I also want to setup vms or a vm server to essentially also have a "gaming server" mainly for me and the kids. This means down the road I would be adding another GPU to split up with other users. Im trying to allow for a max of 4 people while also still allowing plex to transcode as needed. Now I know there's other ways to do this but I dont feel like splitting this up into multiple systems unless I have to. So really just trying to see if this might be possible. My worry is that in order to make the gpu available to the plex docker I have to setup an nvidia container. https://github.com/NVIDIA/nvidia-docker
AGiXT
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Conversational "memory loss"?
If you are more interested in AI assistants check out AGiXT. It has some really cool features but it is under heavy development. Not everything works jet and updates break sometimes already working functions. But it is still far better than babyAGI and other proof of concepts.
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
That's exactly my goal right now too! I have been trying to figure out how to use AGiXT agents to read and write to an "Adventurer's Log" text file to try to mimic a long term memory but honestly I'm not good enough with any of this to get it working yet. The idea I've got rn is that there'd be a DM agent which takes your input and then there'd be "memory" agents which would check text files such as "Adventurer's Log" and "Character Interactions/Relationships" to keep a contiguous understanding of what each character has done, who they've met, what they've been told/haven't been told by certain characters about their motivations. I'm sure there's someone *much* more talented than me working on this already, at this point I've sort of given up on the idea and I'm just waiting for someone to come out with a Tavern style interface where I can paste in world details and character details and just get going!
- AGiXT: A local automation platform with memories and SmartGPT-like prompting. Works with Ooba/LCPP/GPT4All, and more
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What are the best AI tools you've ACTUALLY used?
AGiXT: A Python package for AGI research.
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?Best LLM service for a tiny home server
Even if my (for example, privateGPT) LLM is glacially slow I'd still love to be able to say "Mr Holmes, have Mrs Doubtfire verb the data object in order to verb a product for me, please." (eg: analyse the wikipedia article on the peace of westfalia in order to ELI5 a short summary of it). Hopefully she'd crunch away at the data, and at my convenience, I could have her brief me on her conclusions. I'm sure folks here would do something more clever using AGiXT, or having the old girl prepare lesson-plans for Mycroft to deliver (I just think that sort of thing is world-changing-bonkers for anyone wanting to learn anything, perhaps for kids one day), but I'd have to work up to that.
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LlamaCPP and LangChain Agent Quality
Keep an eye on this project as well. https://github.com/Josh-XT/AGiXT
- Using the right prompt format makes responses so much better
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How big of a jump is 13B Vicuna Uncensored vs 30B Vicuna Uncensored?
File upload and automatic agents. It exists it is just buggy. They are working at an insane pace building it. It is practically broke 90% of the time. Maybe it's working better right now. I had success with v1.1.31 as well. https://github.com/Josh-xt/AGiXT
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Langchain, Langchain.js, vs AutoGPT for local agent development
Maybe you want to check out josh-xt/AGiXT it has its roots in langchain so you can see what the prompts look like and the code. They have made a lot of tools as well although you are going to have issues getting it to work. The newest version kinda works and version 1.1.31 I had the fast API backend working. Maybe you can help them out. They need more people to show them bugs. https://github.com/Josh-XT/AGiXT
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Is there an alternative to AgentGPT that I can run on my CPU with 32 GB of RAM?
https://github.com/Josh-XT/AGiXT I have tested this one and it is pretty much the same as AgentGPT, supports many providers + many local models (you can even make it work with oobabooga api which is pretty easy), don’t wait for insane results, the problem right now is context length with the local models, probably going to be an old issue in a few weeks we hope ;)
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
AgentOoba - An autonomous AI agent extension for Oobabooga's web ui
nvidia-container-runtime - NVIDIA container runtime
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
Entware - Ultimate repo for embedded devices
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
container-images
babyagi
Whisparr
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
docker-to-linux - Make bootable Linux disk image abusing Docker
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]