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Introduction: Docker is a powerful tool that allows for easy containerization of applications, providing a consistent and isolated environment. In this tutorial, we will walk through the process of installing Docker, setting up a local virtual machine, installing Python and required libraries, and finally, running a machine learning model inside a Docker container. Let’s dive in!
TASK 1️⃣: Install Docker on your local virtual machine:
yum install docker -ysystemctl start docker
TASK 2️⃣ : Install Python and the required libraries:
- Open a terminal or command prompt.
- Install Python and pip by downloading the Python distribution for your operating system from the official Python website and following the installation instructions.
- Once Python is installed, verify the installation by running the commands:
python --version
andpip --version
. You should see the Python and pip versions displayed. - Install the required libraries by running the following pip commands:
pip install numpy
pip install scikit-learn
pip install matplotlib
TASK 3️⃣ : Run a machine learning model in Docker
- Create a new directory for your project and navigate into it using the terminal or command prompt.
- Create a new Python script, e.g.,
ml_model.py
, and write your machine learning code in it. Ensure that you import the necessary libraries and define your model. - Create a new file called
Dockerfile
in the project directory. This file will define the Docker image for your application. - Open the
Dockerfile
in a text editor and add the following lines:
FROM python:3.9
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "ml_model.py"]
Explanation:
FROM python:3.9
: This line specifies the base image as Python 3.9.WORKDIR /app
: Sets the working directory within the container to/app
.COPY requirements.txt .
: Copies therequirements.txt
file from the project directory to the/app
directory inside the container.RUN pip install --no-cache-dir -r requirements.txt
: Installs the Python dependencies listed in therequirements.txt
file using pip.
Step 3: Create the requirements.txt file:
- Create a file called
requirements.txt
in the project directory. - Open the
requirements.txt
file in a text editor. - Add the following lines to the
requirements.txt
file:
numpy
scikit-learn
matplotlib
These lines specify the required libraries.
Step 4: Build and run the Docker image:
- Build the Docker image by running the following command in the project directory:
docker build -t ml_model .
This command will use the Dockerfile
to build an image named ml_model
.
2. Once the image is built, run a Docker container using the following command:
docker run ml_model
This will start a container based on the ml_model
image, and your machine learning model will be executed inside the container.
Observe the output of your machine learning model in the terminal or command prompt.
Conclusion: Congratulations! You have successfully installed Docker, set up a local virtual machine, installed Python and necessary libraries, and executed a machine learning model inside a Docker container. Docker’s containerization capabilities provide an isolated environment for running applications, ensuring consistency and portability across different systems. You can now leverage Docker to deploy your machine learning models in production or share them with others, knowing that they will run consistently regardless of the underlying environment.
Happy coding!