serving
llama_cpp.rb
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| serving | llama_cpp.rb | |
|---|---|---|
| 12 | 2 | |
| 6,071 | 129 | |
| 0.2% | - | |
| 9.8 | 9.6 | |
| 3 days ago | 8 days ago | |
| C++ | C++ | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
serving
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Llama.cpp: Full CUDA GPU Acceleration
Yet another TEDIOUS BATTLE: Python vs. C++/C stack.
This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".
NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp
[1] https://github.com/tensorflow/serving
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[D] How do OpenAI and other companies manage to have real-time inference on model with billions of parameters over an API?
I mean, probably - it's written in C++ https://github.com/tensorflow/serving
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Should I wait for the M2 Macbook Pro?
We’re looking into that solution at the moment, the issue I’m referring to is related to this https://github.com/tensorflow/serving/issues/1948 we’ll know if the plug-in approach works for our uses soon but haven’t started looking into implementing it yet
- TF Serving has been unavailable for 9 days so far due to outdated GPG key
- TF Serving has been unavailable for 8 days
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Would you use maturin for ML model serving?
Which ML framework do you use? Tensorflow has https://github.com/tensorflow/serving. You could also use the Rust bindings to load a saved model and expose it using one of the Rust HTTP servers. It doesn't matter whether you trained your model in Python as long as you export its saved model.
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Is LaMDA Sentient? – An Interview [pdf]
Most likely it's a model server running something like https://github.com/tensorflow/serving and if there isn't a lot of load, the resource could kill some of its tasks. I wouldn't imagine it's sitting around pondering deep thoughts.
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Ask HN: How to deploy a TensorFlow model for access through an HTTP endpoint?
https://github.com/tensorflow/serving
https://thenewstack.io/tutorial-deploying-tensorflow-models-...
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Popular Machine Learning Deployment Tools
GitHub
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If data science uses a lot of computational power, then why is python the most used programming language?
You serve models via https://www.tensorflow.org/tfx/guide/serving which is written entirely in C++ (https://github.com/tensorflow/serving/tree/master/tensorflow_serving/model_servers), no Python on the serving path or in the shipped product.
llama_cpp.rb
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Llama.cpp: Full CUDA GPU Acceleration
Python sits on the C-glue segment of programming languages (where Perl, PHP, Ruby and Node are also notable members). Being a glue language means having APIs to a lot of external toolchains written in not only C/C++ but many other compiled languages, APIs and system resources. Conda, virtualenv, etc. are godsend modules for making it all work, or even better, to freeze things once they all work, without resourcing to Docker, VMs or shell scripts. It's meant for application and DevOps people who need to slap together, ie, ML, Numpy, Elasticsearch, AWS APIs and REST endpoints and Get $hit Done.
It's annoying to see them "glueys" compared to the binary compiled segment where the heavy lifting is done. Python and others exist to latch on and assimilate. Resistance is futile:
https://pypi.org/project/pyllamacpp/
https://www.npmjs.com/package/llama-node
https://packagist.org/packages/kambo/llama-cpp-php
https://github.com/yoshoku/llama_cpp.rb
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Could I get a suggestion for a simple HTTP API with no GUI for llama.cpp?
Ruby: yoshoku/llama_cpp.rb
What are some alternatives?
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
go-llama.cpp - LLama.cpp golang bindings
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
llama.cpp-dotnet - Minimal C# bindings for llama.cpp + .NET core library with API host/client.
flashlight - A C++ standalone library for machine learning
LLamaSharp - A C#/.NET library to run LLM models (🦙LLaMA/LLaVA) on your local device efficiently.
XLA.jl - Julia on TPUs
flake - A Nix flake for many AI projects
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
llama-cpp.el - A client for llama-cpp server
glow - Compiler for Neural Network hardware accelerators
llama-go - Port of Facebook's LLaMA (Large Language Model Meta AI) in Golang with embedded C/C++