Juice-Labs
Juice Community Version Public Release (by Juice-Labs)
server
The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)
Juice-Labs | server | |
---|---|---|
20 | 24 | |
387 | 7,356 | |
2.3% | 2.7% | |
8.7 | 9.5 | |
4 months ago | 3 days ago | |
Go | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
Juice-Labs
Posts with mentions or reviews of Juice-Labs.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-07.
- GPU-over-IP for LLM inference?
- GTA 5 running in Qemu without PCI Passthrough using Juicy Labs
-
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
-
EGPU ALTERNATIVE?
I recently discovered juicelabs.co but I have not yet tested it. Maybe worth a look.
-
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
-
Using Cloud-GPU as an eGPU?
check out https://www.juicelabs.co/
-
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
-
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
-
ClosedAI strikes again
Even then you can always use Juice. https://www.juicelabs.co/
-
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
server
Posts with mentions or reviews of server.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-08.
- FLaNK Weekly 08 Jan 2024
- Is there any open source app to load a model and expose API like OpenAI?
- "A matching Triton is not available"
-
best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?
- Triton Inference Server - Backend
-
Single RTX 3080 or two RTX 3060s for deep learning inference?
For inference of CNNs, memory should really not be an issue. If it is a software engineering problem, not a hardware issue. FP16 or Int8 for weights is fine and weight size won’t increase due to the high resolution. And during inference memory used for hidden layer tensors can be reused as soon as the last consumer layer has been processed. You likely using something that is designed for training for inference and that blows up the memory requirement, or if you are using TensorRT or something like that, you need to be careful to avoid that every tasks loads their own copy of the library code into the GPU. Maybe look at https://github.com/triton-inference-server/server
-
Machine Learning Inference Server in Rust?
I am looking for something like [Triton Inference Server](https://github.com/triton-inference-server/server) or [TFX Serving](https://www.tensorflow.org/tfx/guide/serving), but in Rust. I came across [Orkon](https://github.com/vertexclique/orkhon) which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](https://github.com/vaaaaanquish/Awesome-Rust-MachineLearning)
-
Multi-model serving options
You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
-
I mean,.. we COULD just make our own lol
[1] https://docs.nvidia.com/launchpad/ai/chatbot/latest/chatbot-triton-overview.html[2] https://github.com/triton-inference-server/server[3] https://neptune.ai/blog/deploying-ml-models-on-gpu-with-kyle-morris[4] https://thechief.io/c/editorial/comparison-cloud-gpu-providers/[5] https://geekflare.com/best-cloud-gpu-platforms/
-
Why TensorFlow for Python is dying a slow death
"TensorFlow has the better deployment infrastructure"
Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).
For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.
Triton Server is very impressive and I'm always surprised to see how relatively niche it is.
[0] - https://github.com/triton-inference-server/server