Installation

Installation Guide

Prerequisites

Python 3.9 or higher
pip (Python package installer)

Setting up the Environment

Create a virtual environment:
```
python -m venv emd-env
```
Activate the virtual environment:
```
source emd-env/bin/activate
```

Install the required packages:

pip install https://github.com/aws-samples/easy-model-deployer/releases/download/emd-0.7.1/emd-0.7.1-py3-none-any.whl

Deployment parameters

--force-update-env-stack

No additional emd bootstrap required for deployment. Because of other commands, status/destroy etc. require pre-bootstrapping. Therefore, it is recommended to run emd bootstrap separately after each upgrade.

--extra-params

Extra parameters passed to the model deployment. extra-params should be a Json object of dictionary format as follows:

{

  "model_params": {
  },
  "service_params":{
  },
  "instance_params":{
  },
  "engine_params":{
      "cli_args": "<command line arguments of current engine>",
      "api_key":"<api key>"
  },
  "framework_params":{
      "uvicorn_log_level":"info",
      "limit_concurrency":200
  }
}

To learn some practice examples, please refer to the Best Deployment Practices.

Local deployment on the ec2 instance

This is suitable for deploying models using local GPU resources.

Pre-requisites

Start and connect to EC2 instance

It is recommended to launch the instance using the AMI "Deep Learning OSS Nvidia Driver AMI GPU PyTorch 2.6 (Ubuntu 22.04)".

Deploy model using EMD

emd deploy --allow-local-deploy

There some EMD configuration sample settings for model deployment in the following two sections: Non-reasoning Model deployment configuration and Reasoning Model deployment configuration. Wait for the model deployment to complete.

Non-reasoning Model deployment configuration

Qwen2.5-72B-Instruct-AWQ

? Select the model series: qwen2.5
? Select the model name: Qwen2.5-72B-Instruct-AWQ
? Select the service for deployment: Local
? input the local gpu ids to deploy the model (e.g. 0,1,2): 0,1,2,3
? Select the inference engine to use: tgi
? (Optional) Additional deployment parameters (JSON string or local file path), you can skip by pressing Enter: {"engine_params":{"api_key":"<YOUR_API_KEY>", "default_cli_args": "--max-total-tokens 30000 --max-concurrent-requests 30"}}

llama-3.3-70b-instruct-awq

? Select the model series: llama
? Select the model name: llama-3.3-70b-instruct-awq
? Select the service for deployment: Local
? input the local gpu ids to deploy the model (e.g. 0,1,2): 0,1,2,3
engine type: tgi
framework type: fastapi
? (Optional) Additional deployment parameters (JSON string or local file path), you can skip by pressing Enter: {"engine_params":{"api_key":"<YOUR_API_KEY>", "default_cli_args": "--max-total-tokens 30000 --max-concurrent-requests 30"}}

Reasoning Model deployment configuration

DeepSeek-R1-Distill-Qwen-32B

? Select the model series: deepseek reasoning model
? Select the model name: DeepSeek-R1-Distill-Qwen-32B
? Select the service for deployment: Local
? input the local gpu ids to deploy the model (e.g. 0,1,2): 0,1,2,3
engine type: vllm
framework type: fastapi
? (Optional) Additional deployment parameters (JSON string or local file path), you can skip by pressing Enter: {"engine_params":{"api_key":"<YOUR_API_KEY>", "default_cli_args": "--enable-reasoning --reasoning-parser deepseek_r1 --max_model_len 16000 --disable-log-stats --chat-template emd/models/chat_templates/deepseek_r1_distill.jinja --max_num_seq 20 --gpu_memory_utilization 0.9"}}

deepseek-r1-distill-llama-70b-awq

? Select the model series: deepseek reasoning model
? Select the model name: deepseek-r1-distill-llama-70b-awq
? Select the service for deployment: Local
? input the local gpu ids to deploy the model (e.g. 0,1,2): 0,1,2,3
? Select the inference engine to use: tgi
framework type: fastapi
? (Optional) Additional deployment parameters (JSON string or local file path), you can skip by pressing Enter: {"engine_params":{"api_key":"<YOUR_API_KEY>", "default_cli_args": "--max-total-tokens 30000 --max-concurrent-requests 30"}}