[README]

Dockerfile

@@ -0,0 +1,23 @@
+# Use an official CUDA runtime as a parent image
+FROM nvidia/cuda:11.4.2-runtime-ubuntu20.04
+
+# Set the working directory in the container to /app
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN apt-get update && apt-get install -y \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+# Make port 80 available to the world outside this container
+EXPOSE 80
+
+# Define environment variable
+# ENV NAME World
+
+# Run app.py when the container launches
+CMD ["python3", "example.py"]

README.md

@@ -1210,6 +1210,32 @@ The swarms package has been meticlously crafted for extreme use-ability and unde
 └── workers
 ```
 
+## Docker Instructions
+
+This application uses Docker with CUDA support. To build and run the Docker container, follow these steps:
+
+### Prerequisites
+
+- Make sure you have [Docker installed](https://docs.docker.com/get-docker/) on your machine.
+- Ensure your machine has an NVIDIA GPU and [NVIDIA Docker support](https://github.com/NVIDIA/nvidia-docker) installed.
+
+### Building the Docker Image
+
+To build the Docker image, navigate to the root directory containing the `Dockerfile` and run the following command:
+
+```bash
+docker build --gpus all -t swarms
+``` 
+### Running the Docker Container
+To run the Docker container, use the following command:
+
+`docker run --gpus all -p 4000:80 your-image-name`
+
+Replace your-image-name with the name of your Docker image, and replace 4000:80 with your actual port mapping. The format is hostPort:containerPort.
+
+Now, your application should be running with CUDA support!
+
+
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