LLM-on-Ray is a comprehensive solution designed to empower users in building, customizing, and deploying Large Language Models (LLMs). Whether you're starting from scratch with pretraining, looking to finetuning an existing model, or aiming to deploy a production-ready LLM endpoint service, this project simplifies these complex processes into manageable steps.
LLM-on-Ray harnesses the power of Ray, an industry-leading framework for distributed computing, to scale your AI workloads efficiently. This integration ensures robust fault tolerance and cluster resource management, making your LLM projects more resilient and scalable.
LLM-on-Ray is built to operate across various hardware setups, including Intel CPU, Intel GPU and Intel Gaudi2. It incorporates several industry and Intel optimizations to maximize performance, including vLLM, llama.cpp, Intel Extension for PyTorch/DeepSpeed, IPEX-LLM, RecDP-LLM, NeuralChat and more.
LLM-on-Ray's modular workflow structure is designed to comprehensively cater to the various stages of LLM development, from pretraining and finetuning to serving. These workflows are intuitive, highly configurable, and tailored to meet the specific needs of each phase in the LLM lifecycle:
-
Pretraining Workflow: Provides the infrastructure to build LLMs from scratch.
- Data Preparation: Includes a suite of tools for preparing your training data which facilitate tasks like the removal of Personally Identifiable Information (PII), data deduplication (Dedup), and other preprocessing needs, making the data safe and efficient for training.
- Megatron-DeepSpeed Integration: Leverages the power of Megatron-DeepSpeed to enable advanced capabilities such as pipeline parallelism, tensor parallelism, data parallelism, and Zero Redundancy Optimizer (ZeRO). This integration facilitates efficient and scalable model training from the ground up.
- Robust Fault Tolerance: Offers automatic fault tolerance powered by Ray. This ensures high availability, reliability, and optimal performance for large scale pretraining.
-
Finetuning Workflow: Supports refinement of pre-trained models with proprietary or specialized data, improving models' accuracy and applicability to various use cases.
- Ease of Customization: Users can easily configure the base model and resource allocation for the training job, customize training parameters to fit their specific needs. This can be accomplished through a simple command line or via the Web UI.
- Parameter Efficient Finetuning: Supports various parameter efficient finetuning methods such as LoRA to accelerate the finetuning process.
- Reinforcement Learning with Human Feedback (RLHF): Users can further refine the model using RLHF, which leverages the proximal policy optimization (PPO).
-
Serving Workflow: Deploys a scalable and production-ready LLM serving endpoint.
- Easy Deployment of Models: Supports the deployment of both widely-used open-source models and custom finetuned models through flexible configurations.
- Autoscaling and Scale-to-Zero Capabilities: Ensures high efficiency and cost-effectiveness in model deployment. The workflow can dynamically scale resources to match demand and scale down to zero when the model is not in use, optimizing resource usage and reducing operational costs.
- Optimized for Performance and Efficiency: LLM-on-Ray incorporates several optimizations to maximize performance. This includes support for various precision levels and the utilization of advanced optimization techniques from Intel, ensuring efficient processing and reduced resource consumption.
- OpenAI-Like REST API: Provides APIs similar to OpenAI's, making it easier for users to transition to or integrate open-source models into their systems.
-
Interactive Web UI for Enhanced Usability: Except for command line, LLM-on-Ray introduces a Web UI, allowing users to easily finetune and deploy LLMs through a user-friendly interface. Additionally, the UI includes a chatbot application, enabling users to immediately test and refine the models.
This guide will assist you in setting up LLM-on-Ray on Intel CPU locally, covering the initial setup, finetuning models, and deploying them for serving.
Software requirement: Git and Conda
git clone https://github.com/intel/llm-on-ray.git
cd llm-on-ray
conda create -n llm-on-ray python=3.9
conda activate llm-on-ray
pip install .[cpu] --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/[Optional] If DeepSpeed is enabled or doing distributed finetuing, oneCCL and Intel MPI libraries should be dynamically linked in every node before Ray starts:
source $(python -c "import oneccl_bindings_for_pytorch as torch_ccl; print(torch_ccl.cwd)")/env/setvars.shStart Ray locally using the following command. To launch a Ray cluster, please follow the setup document.
ray start --headUse the following command to finetune a model using an example dataset and default configurations. The finetuned model will be stored in /tmp/llm-ray/output by default. To customize the base model, dataset and configurations, please see the finetuning document:
llm_on_ray-finetune --config_file llm_on_ray/finetune/finetune.yamlDeploy a model on Ray and expose an endpoint for serving. This command uses GPT2 as an example, but more model configuration examples can be found in the inference/models directory:
llm_on_ray-serve --config_file llm_on_ray/inference/models/gpt2.yamlYou can also use model_ids to serve directly through:
llm_on_ray-serve --models gpt2List all support model_ids with config file path:
llm_on_ray-serve --list_model_idsThe default served method is to provide an OpenAI-compatible API server (OpenAI API Reference), you can access and test it in many ways:
# using curl
export ENDPOINT_URL=http://localhost:8000/v1
curl $ENDPOINT_URL/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt2",
"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello!"}],
"temperature": 0.7
}'
# using requests library
python examples/inference/api_server_openai/query_http_requests.py
# using OpenAI SDK
pip install openai>=1.0
export OPENAI_BASE_URL=http://localhost:8000/v1
export OPENAI_API_KEY="not_a_real_key"
python examples/inference/api_server_openai/query_openai_sdk.pyOr you can serve specific model to a simple endpoint according to the port and route_prefix parameters in configuration file,
llm_on_ray-serve --config_file llm_on_ray/inference/models/gpt2.yaml --simpleAfter deploying the model endpoint, you can access and test it by using the script below:
python examples/inference/api_server_simple/query_single.py --model_endpoint http://127.0.0.1:8000/gpt2This guide will assist you in setting up LLM-on-Ray on With Docker.
git clone https://github.com/intel/llm-on-ray.git
cd llm-on-rayThe dockerfile for user is in dev/docker/Dockerfile.user.
Detailed parameter can be set up for docker in dev/scripts/docker-functions.sh.
source dev/scripts/docker-functions.shDefault cpu and deepspeed for llm serving.
build_docker Change build_docker fuction's args for different environment.
Use vllm for llm serving.
build_docker vllm Use ipex-vllm for llm serving.
build_docker ipex-llm Change any settings in dev/scripts/docker-functions.sh.
Run docker with cpu and gpt2 serving.
start_dockerRun docker with cpu and other support models serving.
start_docker llama-2-7b-chat-hfRun docker with different environment and other models start_docker {environment} {models} like:
start_docker vllm llama-2-7b-chat-hfThe model serving port in docker container has been mapped to local.
Using requests library.
python examples/inference/api_server_openai/query_http_requests.pyUsing OpenAI SDK
python examples/inference/api_server_openai/query_openai_sdk.pyThe following are detailed guidelines for pretraining, finetuning and serving LLMs in various computing environment.
- Deploy and Serve LLMs on Intel CPU/GPU/Gaudi
- Deploy and Serve LLMs with DeepSpeed
- Deploy and Serve LLMs with IPEX-LLM
To the extent that any public datasets are referenced by Intel or accessed using tools or code on this site those datasets are provided by the third party indicated as the data source. Intel does not create the data, or datasets, and does not warrant their accuracy or quality. By accessing the public dataset(s), or using a model trained on those datasets, you agree to the terms associated with those datasets and that your use complies with the applicable license.
Intel expressly disclaims the accuracy, adequacy, or completeness of any public datasets, and is not liable for any errors, omissions, or defects in the data, or for any reliance on the data. Intel is not liable for any liability or damages relating to your use of public datasets.
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