Using llama.cpp with AWS instances #4225
Replies: 11 comments 10 replies
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Thank you! |
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this is gold. thank you. |
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Thanks for sharing! Here's a side quest for those of you using llama.cpp via Python bindings and CUDA. This is a minimalistic example of a Docker container you can deploy in smaller cloud providers like VastAI or similar. To build the image: Model: https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF Dockerfile_llamacppYou can change CUDA's version number as required. Literally, just change the number on line 1 to a valid version compatible with your hardware. app.pyWhen you run it you should see something like this at the very top (if verbose is True): Happy building/hacking |
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I'm quite sure that's not a good idea: I think ideally you'd want to mount the model as a volume inside the container, so that you can swap to different models and your images don't have those extra gigabytes. |
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I created a example of doing this in cloudformation - although I geared my example for CPU only in order to keep total costs down https://github.com/openmarmot/aws-cft-llama-cpp The template creates a EC2 instance, installs llama.cpp and runs the server, and attaches an IAM role with the permissions necessary to enable the AWS web console (Connect) for linux console access so you don't have to SSH to the instance to connect |
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A word of caution on Amazon Cloud: If you run something productive there you'll find moving out difficult as everything needs to adapt to their cloud. Not saying to stay away, it's a good way to get started and quite cheap when using at low scale or only for a couple hours. |
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What did you end up using instead? |
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Awesome, thanks! |
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Did someone tried llama.cpp on the m6g/t4g? (cpu-only, ARM; t4g - burstable) Though it is 5 times cheaper, CPU-inference should be many times slower, but Nvidia T4 is not a very powerfull GPU, so it still can make sense to compare. (beware, so many different t/g-4's) |
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Tried on c6g.2xlarge (they too use graviton) – for my scenario (medium length prompt, short reply) it was about 10x slower, in the range of 3-5 tokens per second. |
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I am unable to run quantize in the last two lines. I downloaded the repo on 5th December 2023. Could you please guide me in executing the code? I am facing the following error: ./quantize ./models/openhermes-7b-v2.5/ggml-model-f16.gguf ./models/openhermes-7b-v2.5/ggml-model-q8_0.gguf q8_0 |
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first - make LLAMA_CLBLAST=1 |
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Hi there. I wanted to clarify a couple of things about this tutorial.
running the executables fails with:
TL;DR: Is this tutorial omitting some steps? I'm having some trouble getting it to work with a pretty much identical configuration. |
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That means you’re running it on a non-nvidia GPU instance (probably graviton?) I personally run ML on AWS on PyTorch Ubuntu becaue it has nvidia drivers installed and such. |
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I'm using a g4dn.2xl.
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Amazon has some AMIs with the Nvidia drivers installed. They will have a DLAMI suffix (?) which stands for Deep learning AMI. Otherwise you will want to install and setup the Nvidia drivers yourself (you will also have to do some configuration). Note that each major instance type generally has a different NVIDIA GPU - which corresponds to a different driver family For ease of use I would suggest starting with Amazon Linux 2 or (the newer) Amazon Linux 2023 - as these are what the Amazon documentation is generally written for. If you are interested in a CPU only install you can follow along with the bash commands, or run the cloudformation file that I have here : https://github.com/openmarmot/aws-cft-llama-cpp |
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In this case I would advise either choosing different AMI (like pytorch or from the answer above), because installing nvidia drivers on ubuntu is either super easy, or a fun way to spend an unspecified amount of extra time. |
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Thanks, this helped a lot. |
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I've gone through the process of getting the Nvidia drivers installed silently on the P3 series if anyone is still looking for help on this note - this is a aws cloudformation template that can be run from the aws cloudformation console. You can also just grab the linux shell commands from the userdata area and do it yourself manually if you want. The Nvidia linux drivers are ultra picky. This template is designed for Amazon Linux 2 and a P3 series - it will likely need modification if you are using a different instance or instance type. https://github.com/openmarmot/aws-ec2-nvidia-drivers/blob/main/cft-al2-p3-series.yaml |
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update - also added a template for Amazon Linux 2023. This OS is Amazon's newest. Amazon Linux 2 is reaching EOL |
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How do you come up with the KV cache size? (1280 MiB) |
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Description
The
llama.cppproject offers unique ways of utilizing cloud computing resources. Here we will demonstrate how to deploy allama.cppserver on a AWS instance for serving quantum and full-precision F16 models to multiple clients efficiently.Select an instance
Go to AWS instance listings: https://aws.amazon.com/ec2/pricing/on-demand/
Sort by price and find the cheapest one with NVIDIA GPU:
Check the specs:
The
g4dn.xlargeinstance has 1x T4 Tensor Core GPU with 16GB VRAM. Here are the NVIDIA specs for convenience:Click me
Start the instance and login over SSH
Also, make sure to enable inbound connections to port 8888 - we will need it later for the HTTP server
Select a model and prepare
llama.cppWe have just 16GB VRAM to work with, so we likely want to choose a 7B model. Lately, the OpenHermes-2.5-Mistral-7B model is getting some traction so let's go with it.
We will clone the latest
llama.cpprepo, download the model and convert it to GGUF format:Do some performance benchmarks
The T4 GPUs have just 320GB/s memory bandwidth, so we cannot expect huge tok/s numbers, but let's work with what we have.
We want to be serving requests in parallel, so we have to have an idea about the types of queries that we are going to be processing in order to setup some limits. Let's do the following assumptions:
We assume that at any moment in time, there will be a maximum of 4 queries being processed in parallel. Each query can have a maximum individual prompt of 2048 tokens and each query can generate a maximum of 512 tokens. So in order to support this scenario, we need to have a KV cache of size
4*(2048 + 512) = 10240 tokens (1280 MiB, F16).Let's benchmark stock
llama.cppusing the F16 model:Legend
We immediately notice that there is not enough VRAM to load both the F16 model and the 10240 tokens KV cache. This means the maximum clients we can serve in this case is just 1. The TG speed is also not great as we expected: ~16 t/s.
Let's for a moment relax the requirements and say that the max prompt size would be
512instead of2048. This scenario now fits in the available VRAM and here are the results:llama.cppsupports efficient quantization formats. By using a quantum model, we can reduce the base VRAM required to store the model in memory and thus free some VRAM for a bigger KV cache. This will allow us to serve more clients with the original prompt size of2048tokens. Let's repeat the same benchmark usingQ8_0andQ4_Kquantum models:Using the quantum models and a KV cache of size
4*(2048 + 512) == 10240we can now successfully serve 4 clients in parallel and have plenty of VRAM left. The prompt processing speed is not as good as F16, but the text generation is better or similar.Note that
llama.cppsupports continuous batching and sharing a common prompt. A sample implementation is demonstrated in the parallel.cpp example. Here is a sample run with theQ4_Kquantum model, simulating 4 clients in parallel, asking short questions with a shared assistant prompt of 300 tokens, for a total of 64 requests:LLAMA_CUBLAS=1 make -j parallel && ./parallel -m ./models/openhermes-7b-v2.5/ggml-model-f16.gguf -n -1 -c 4096 --cont_batching --parallel 4 --sequences 64 --n-gpu-layers 99 -s 1Results from `parallel`
Running a demo HTTP server
The
llama.cppserver example can be build and started like this:An alternative way for really quick deployment of
llama.cppfor demo purposes is to use the server-llm.sh helper script:bash -c "$(curl -s https://ggml.ai/server-llm.sh)"For more info, see: #3868
Final notes
This was a short walkthrough of how to setup and bench
llama.cppin the cloud that I hope would be useful for people looking for a simple and efficient LLM solution. There are many details not covered here and one needs to understand some of the intricate details of thellama.cppandggmlimplementations in order to take full advantage of the available compute resources. Knowing when to use a quantum model vs F16 model for example requires understanding of the existing CUDA kernels and their limitations. The code base is still relatively simple and allows to easily customize the implementation according to the specific needs of a project. Such customizations can yield significant performance gains compared to the stockllama.cppimplementation that is available out-of-the-box frommaster.Beta Was this translation helpful? Give feedback.
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