Feature/server containerization

README.md

@@ -139,6 +139,33 @@ python fastserve.deploy.lightning --filename main.py \
     --machine "CPU"  # T4, A10G or A10G_X_4
 ```
 
+## Containerization
+
+To containerize your FastServe application, a Docker example is provided in the `examples/docker-compose-example` directory. The example is about face recognition and includes a `Dockerfile` for creating a Docker image and a `docker-compose.yml` for easy deployment. Here's a quick overview:
+
+- **Dockerfile**: Defines the environment, installs dependencies from `requirements.txt`, and specifies the command to run your FastServe application.
+- **docker-compose.yml**: Simplifies the deployment of your FastServe application by defining services, networks, and volumes.
+
+To use the example, navigate to the `examples/docker-compose-example` directory and run:
+
+```shell
+docker-compose up --build
+```
+
+This will build the Docker image and start your FastServe application in a container, making it accessible on the specified port.
+
+Note: We provide an example using face recognition. If you need to use other models, you will likely need to change the requirements.txt or the Dockerfile. Don't worry; this example is intended to serve as a quick start. Feel free to modify it as needed.
+
+## Passing Arguments to Uvicorn in `run_server()`
+FastServe leverages Uvicorn, a lightning-fast ASGI server, to serve machine learning models, making FastServe highly efficient and scalable.
+The `run_server()` method supports passing additional arguments to uvicorn by utilizing `*args` and `**kwargs`. This feature allows you to customize the server's behavior without modifying the source code. For example:
+
+```shell
+app.run_server(host='0.0.0.0', port=8000, log_level='info')
+```
+
+In this example, host, port, and log_level are passed directly to uvicorn.run() to specify the server's IP address, port, and logging level. You can pass any argument supported by `uvicorn.run()` to `run_server()` in this manner.
+
 ## Contribute
 
 **Install in editable mode:**

examples/docker-compose-example/Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.11
+
+WORKDIR /api
+
+# Copying requirements.txt to the container's working directory
+COPY requirements.txt .
+
+# Copying all other files and directories to the container's working directory
+COPY . /api
+
+# Installing dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Running the api
+CMD ["python", "api.py"]

examples/docker-compose-example/api.py

@@ -0,0 +1,11 @@
+import os
+
+from fastserve.models import FaceDetection
+
+# example of getting environment variables setted in docker-compose.yml
+batch_size = os.environ.get("BATCH_SIZE", 2)
+timeout = os.environ.get("TIMEOUT", 1)
+
+
+app = FaceDetection(batch_size=batch_size, timeout=timeout)
+app.run_server(host="0.0.0.0")

examples/docker-compose-example/docker-compose.yml

@@ -0,0 +1,11 @@
+version: '3'
+services:
+  api:
+    build: .
+    environment:
+      - BATCH_SIZE=2
+      - TIMEOUT = 1
+    volumes:
+      - .:/api
+    ports:
+      - "8000:8000"

examples/docker-compose-example/requirements.txt

@@ -0,0 +1,3 @@
+git+https://github.com/aniketmaurya/fastserve-ai.git
+torch
+face-recognition