Juice-Labs
model_analyzer
| Juice-Labs | model_analyzer | |
|---|---|---|
| 20 | 2 | |
| 387 | 376 | |
| 2.3% | 4.3% | |
| 8.7 | 8.2 | |
| 4 months ago | about 18 hours ago | |
| Go | Python | |
| MIT License | Apache License 2.0 |
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Juice-Labs
- GPU-over-IP for LLM inference?
- GTA 5 running in Qemu without PCI Passthrough using Juicy Labs
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This looks very cool: GPU-over-IP with Juice. You can attach GPU to non GPU nodes, share GPU across multiple users and applications, bring GPU to your data (vs bringing your data to the GPU) - all with just software.
The website https://www.juicelabs.co/ they have an community version as well https://github.com/Juice-Labs/Juice-Labs
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EGPU ALTERNATIVE?
I recently discovered juicelabs.co but I have not yet tested it. Maybe worth a look.
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Why I think 3D artists should get an eGPU for rendering, even if they have a desktop [How stuff works + Idea]
Or you could even use a remote GPU like Juice GPU
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Using Cloud-GPU as an eGPU?
check out https://www.juicelabs.co/
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Looking for a Bitfusion replacement? I think I may have found something really cool... Juice - which not only supports CUDA but all the graphical APIs
So our lab had been using Bitfusion until recently for a large number of VM deployments. With Bitfusion support coming to an end, we were talking about solutions and did some Googleing around GPU-over-IP and stumbled across these guys: www.juicelabs.co
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is it possible to install Automatic1111 and manage it like locally, but using a shared gpu service such as runpod.io/endpoints?
The Juice may help passing gpu over IP, I haven't tried it yet though
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ClosedAI strikes again
Even then you can always use Juice. https://www.juicelabs.co/
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Multiple inference, single remote GPU of Stable Diffusion
The functionality to do this today is available via our community edition here: https://github.com/Juice-Labs/Juice-Labs/wiki
model_analyzer
- [P] Benchmarking some PyTorch Inference Servers
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Show HN: Software for Remote GPU-over-IP
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
What are some alternatives?
Easy-GPU-P - A Project dedicated to making GPU Partitioning on Windows easier!
kserve - Standardized Serverless ML Inference Platform on Kubernetes
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
vgpu_unlock - Unlock vGPU functionality for consumer grade GPUs.
nebuly - The user analytics platform for LLMs
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
ml-stable-diffusion - Stable Diffusion with Core ML on Apple Silicon