Single machine (from scratch)

This guide provides instructions for setting up ServerlessLLM from scratch on a single machine. This 'from scratch' approach means you will manually initialize and manage the Ray cluster components. It involves using multiple terminal sessions, each configured with a distinct Conda environment, to run the head and worker processes on the same physical machine, effectively simulating a multi-node deployment locally.

note

We strongly recommend using Docker (Compose) as detailed in the Docker Compose guide. Docker provides a smoother and generally easier setup process. Follow this guide only if Docker is not a suitable option for your environment.

Installation

Requirements

Ensure your system meets the following prerequisites:

• OS: Ubuntu 20.04
• Python: 3.10
• GPU: NVIDIA GPU with compute capability 7.0 or higher

Installing with pip

Follow these steps to install ServerlessLLM using pip:

Create the head environment:

# Create and activate a conda environment
conda create -n sllm python=3.10 -y
conda activate sllm

# Install ServerlessLLM and its store component
pip install serverless-llm serverless-llm-store

Create the worker environment:

# Create and activate a conda environment
conda create -n sllm-worker python=3.10 -y
conda activate sllm-worker

# Install ServerlessLLM (worker version) and its store component
pip install "serverless-llm[worker]" serverless-llm-store

note

If you plan to integrate vLLM with ServerlessLLM, a patch needs to be applied to the vLLM repository. For detailed instructions, please refer to the vLLM Patch section.

Installing from Source

To install ServerlessLLM from source, follow these steps:

1. Clone the repository:

   git clone https://github.com/ServerlessLLM/ServerlessLLM.git
   cd ServerlessLLM

2. Create the head environment:

   # Create and activate a conda environment
   conda create -n sllm python=3.10 -y
   conda activate sllm
   
   # Install sllm_store (pip install is recommended for speed)
   cd sllm_store && rm -rf build
   pip install .
   cd ..
   
   # Install ServerlessLLM
   pip install .

3. Create the worker environment:

   # Create and activate a conda environment
   conda create -n sllm-worker python=3.10 -y
   conda activate sllm-worker
   
   # Install sllm_store (pip install is recommended for speed)
   cd sllm_store && rm -rf build
   pip install .
   cd ..
   
   # Install ServerlessLLM (worker version)
   pip install ".[worker]"

vLLM Patch

To use vLLM with ServerlessLLM, you must apply a patch. The patch file is located at sllm_store/vllm_patch/sllm_load.patch within the ServerlessLLM repository. This patch has been tested with vLLM version 0.6.6.

Apply the patch using the following script:

conda activate sllm-worker
./sllm_store/vllm_patch/patch.sh

Running ServerlessLLM Locally

These steps describe how to run ServerlessLLM on your local machine.

1. Start a Local Ray Cluster

First, initiate a local Ray cluster. This cluster will consist of one head node and one worker node (on the same machine).

Start the head node:

Open a new terminal and run:

conda activate sllm
ray start --head --port=6379 --num-cpus=4 --num-gpus=0 \
  --resources='{"control_node": 1}' --block

Start the worker node:

Open another new terminal and run:

conda activate sllm-worker
export CUDA_VISIBLE_DEVICES=0 # Or your desired GPU ID
ray start --address=0.0.0.0:6379 --num-cpus=4 --num-gpus=1 \
  --resources='{"worker_node": 1, "worker_id_0": 1}' --block

2. Start the ServerlessLLM Store Server

Next, start the ServerlessLLM Store server. By default, it uses ./models as the storage path.

Open a new terminal and run:

conda activate sllm-worker
export CUDA_VISIBLE_DEVICES=0 # Or your desired GPU ID
sllm-store start

Expected output:

$ sllm-store start
INFO 12-31 17:13:23 cli.py:58] Starting gRPC server
INFO 12-31 17:13:23 server.py:34] StorageServicer: storage_path=./models, mem_pool_size=4294967296, num_thread=4, chunk_size=33554432, registration_required=False
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20241231 17:13:23.947276 2165054 checkpoint_store.cpp:41] Number of GPUs: 1
I20241231 17:13:23.947299 2165054 checkpoint_store.cpp:43] I/O threads: 4, chunk size: 32MB
I20241231 17:13:23.947309 2165054 checkpoint_store.cpp:45] Storage path: "./models"
I20241231 17:13:24.038651 2165054 checkpoint_store.cpp:71] GPU 0 UUID: c9938b31-33b0-e02f-24c5-88bd6fbe19ad
I20241231 17:13:24.038700 2165054 pinned_memory_pool.cpp:29] Creating PinnedMemoryPool with 128 buffers of 33554432 bytes
I20241231 17:13:25.557906 2165054 checkpoint_store.cpp:83] Memory pool created with 4GB
INFO 12-31 17:13:25 server.py:243] Starting gRPC server on 0.0.0.0:8073

3. Start ServerlessLLM Serve

Now, start the ServerlessLLM Serve process ( sllm-serve).

Open a new terminal and run:

conda activate sllm
sllm-serve start

At this point, you should have four terminals open: one for the Ray head node, one for the Ray worker node, one for the ServerlessLLM Store server, and one for ServerlessLLM Serve.

4. Deploy a Model

With all services running, you can deploy a model.

Open a new terminal and run:

conda activate sllm
sllm-cli deploy --model facebook/opt-1.3b

This command downloads the specified model from Hugging Face Hub. To load a model from a local path, you can use a config.json file. Refer to the CLI API documentation for details.

5. Query the Model

Once the model is deployed, you can query it using any OpenAI API-compatible client. For example, use the following curl command:

curl http://127.0.0.1:8343/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
        "model": "facebook/opt-1.3b",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "What is your name?"}
        ]
    }'

Expected output:

{"id":"chatcmpl-9f812a40-6b96-4ef9-8584-0b8149892cb9","object":"chat.completion","created":1720021153,"model":"facebook/opt-1.3b","choices":[{"index":0,"message":{"role":"assistant","content":"system: You are a helpful assistant.\nuser: What is your name?\nsystem: I am a helpful assistant.\n"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":16,"completion_tokens":26,"total_tokens":42}}

Clean Up

To delete a deployed model, use the following command:

sllm-cli delete facebook/opt-1.3b

This command removes the specified model from the ServerlessLLM server.