Easy Model Deployer - Simple, Efficient, and Easy-to-Integrate
Latest News 🔥
- [2025/03] We officially released EMD!
About
EMD (Easy Model Deployer) is a lightweight tool designed to simplify model deployment. Built for developers who need reliable and scalable model serving without complex setup.
Key Features
- One-click deployment of models to the cloud (Amazon SageMaker, Amazon ECS, Amazon EC2)
- Diverse model types (LLMs, VLMs, Embeddings, Vision, etc.)
- Rich inference engine (vLLM, TGI, Lmdeploy, etc.)
- Different instance types (CPU/GPU/AWS Inferentia)
- Convenient integration (OpenAI Compatible API, LangChain client, etc.)
Notes
- OpenAI Compatible API is supported only for Amazon ECS and Amazon EC2 deployment.
Table of Contents
Architecture
Deploy models to the cloud with EMD will use the following components in Amazon Web Services:
Getting Started
Installation
Install EMD with pip, currently only support for Python 3.9 and above:
pip install https://github.com/aws-samples/easy-model-deployer/releases/download/main/emd-0.6.0-py3-none-any.whl
Visit our documentation to learn more.
Usage
Choose your default aws profile.
emd config set-default-profile-name
Notes: If you don't set aws profile, it will use the default profile in your env (suitable for Temporary Credentials). Whenever you want to switch deployment accounts, run emd config set-default-profile-name
Bootstrap emd stack
emd bootstrap
Notes: This is going to set up the necessary resources for model deployment. Whenever you change EMD version, run this command again.
Quickly see what models are supported by emd list-supported-models. This command will output all information related to deployment. The following command is recommended to just check the model type. (Plese check Supported Models for complete information.)
emd list-supported-models | jq -r '.[] | "\(.model_id)\t\(.model_type)"' | column -t -s $'\t' | sort
Choose deployment parameters interactively by emd deploy or deploy with one command
emd deploy --model-id DeepSeek-R1-Distill-Qwen-1.5B --instance-type g5.8xlarge --engine-type vllm --framework-type fastapi --service-type sagemaker --extra-params {} --skip-confirm
Notes: Get complete parameters by emd deploy --help and find the values of the required parameters here
When you see "Waiting for model: ...", it means the deployment task has started, you can quit the current task by ctrl+c.
Check deployment status.
emd status
Notes: EMD allows to launch multiple deployment tasks at the same time.
Quick functional verfication or check our documentation for integration examples.
emd invoke DeepSeek-R1-Distill-Qwen-1.5B
Notes: Find ModelId in the output of emd status.
Delete the deployed model
emd destroy DeepSeek-R1-Distill-Qwen-1.5B
Notes: Find ModelId in the output of emd status.
Documentation
For advanced configurations and detailed guides, visit our documentation site.
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
Metadata
Release files for aws-emd 0.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| aws_emd-0.7.0.tar.gz | 8.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| aws_emd-0.7.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.9 MB
Release files / aws_emd-0.7.0.tar.gz
| Download URL | aws_emd-0.7.0.tar.gz |
|---|---|
| Size | 8.9 MB |
| Tags | Source |
|
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No |
| Uploaded via |
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Release files / aws_emd-0.7.0-py3-none-any.whl
| Download URL | aws_emd-0.7.0-py3-none-any.whl |
|---|---|
| Size | 9.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.1 CPython/3.9.11 Darwin/23.6.0
|