nvidia-docker
Build and run Docker containers leveraging NVIDIA GPUs (by NVIDIA)
dev-gpt
Your Virtual Development Team (by jina-ai)
nvidia-docker | dev-gpt | |
---|---|---|
53 | 25 | |
16,998 | 1,657 | |
- | 2.3% | |
0.0 | 9.2 | |
5 months ago | 9 months ago | |
Makefile | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nvidia-docker
Posts with mentions or reviews of nvidia-docker.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-08.
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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
dev-gpt
Posts with mentions or reviews of dev-gpt.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-21.
- I'll build your AI app for you free of charge(Yes, there's a catch).
-
What are the best AI tools you've ACTUALLY used?
Jina DevGPT: A Python package for GPT research.
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🌈 Rainbow Tweet Chrome Plugin (Python Backend)
I'm happy to announce Rainbow Tweet 🌈 - turn hate speech into fluffy rainbow speech! ✨ https://chrome.google.com/webstore/detail/rainbow-tweet/ckjindkmeaoifikfdcjlffmaajjfbddh?hl=en Just click on the icon of the extension, insert your openai-api-key and start turning your Twitter experience into a kaleidoscope of kindness, spreading warmth and positivity 🌈💕🦋🕊️🎈 Backend made by Dev-GPT: https://github.com/jina-ai/dev-gpt (using python and #langchain)
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20 years from now, the only people who will remember that you worked late are your kids
What do you think? Am I overreacting, or is it a valid point? Looking at repos like this is both astounding and scary at the same time: https://github.com/jina-ai/dev-gpt It is automizing the work of a junior developer.
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Fundamental LangChain Question
How you use it is up to you. There are hundreds of question answer clones with different data source adapters. Some projects that I found more interesting: https://github.com/GreyDGL/PentestGPT https://github.com/jina-ai/dev-gpt https://github.com/corca-ai/EVAL You can probably build something similar to smol developer with langchain too.
- Dev-GPT – your personal Junior Developer
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Use Python to Remove Passive Aggressiveness from Tweets (powered by Dev-GPT)
Online demo: https://gptdeploy-02e02e4150.wolf.jina.ai/playground/ Create something like this yourself: https://github.com/jina-ai/dev-gpt
- Guys, be honest with me. Is this AGI replacing me as a programmer?
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15 tips and tricks for writing better Python
I think the best trick is to use Dev-GPT and let GPT-4 write the code for you :D https://github.com/jina-ai/dev-gpt
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Hackathon Ideas? Gen AI
Look up langchain. Some example projects. https://github.com/corca-ai/EVAL https://github.com/jina-ai/dev-gpt https://github.com/GreyDGL/PentestGPT https://blog.langchain.dev/origin-web-browser/
What are some alternatives?
When comparing nvidia-docker and dev-gpt you can also consider the following projects:
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
chatGPTBox - Integrating ChatGPT into your browser deeply, everything you need is here
nvidia-container-runtime - NVIDIA container runtime
MetaGPT - 🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
Entware - Ultimate repo for embedded devices
phoenix - AI Observability & Evaluation
container-images
PentestGPT - A GPT-empowered penetration testing tool
Whisparr
dust - Amplify your team's potential with customizable and secure AI assistants.
docker-to-linux - Make bootable Linux disk image abusing Docker
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
nvidia-docker vs Pytorch
dev-gpt vs chatGPTBox
nvidia-docker vs nvidia-container-runtime
dev-gpt vs MetaGPT
nvidia-docker vs Entware
dev-gpt vs phoenix
nvidia-docker vs container-images
dev-gpt vs PentestGPT
nvidia-docker vs Whisparr
dev-gpt vs dust
nvidia-docker vs docker-to-linux
dev-gpt vs AgentGPT