Completing Dockerfile Instructions and Running the Application

your local machine into the Docker image. This file lists all Python dependencies required by the application. Copying it into the container lets the next command (`RUN pip install`) install these dependencies inside the image environment. 4. Install the Python dependencies in the image. ```dockerfile RUN pip install --no-cache-dir -r requirements.txt ``` This line uses `pip`, Python's package installer, to install the packages listed in `requirements.txt`. The `--no-cache-dir` option disables the cache, which reduces the size of the Docker image by not storing the unnecessary cache data. 5. Run additional commands. ```dockerfile RUN python -m spacy download en_core_web_sm ``` This step is specific to NLP applications that require the spaCy library. It downloads the `en_core_web_sm` model, which is a small English language model for spaCy. While not needed for this app, it's included for compatibility with other NLP applications that might use this Dockerfile. 6. Copy the application code into the image. ```dockerfile COPY *.py /app COPY entrypoint.sh /app ``` These commands copy your Python scripts and the `entrypoint.sh` script into the image's `/app` directory. This is crucial because the container needs these scripts to run the application. The `entrypoint.sh` script is particularly important as it dictates how the application starts inside the container. 7. Set permissions for the `entrypoint.sh` script. ```dockerfile RUN chmod +x /app/entrypoint.sh ``` This command modifies the file permissions of `entrypoint.sh`, making it executable. This step is necessary to ensure that the Docker container can run this script to start the application. 8. Set the entry point. ```dockerfile ENTRYPOINT ["/app/entrypoint.sh"] ``` The `ENTRYPOINT` instruction configures the container to run `entrypoint.sh` as its default executable. This means that when the container starts, it automatically executes the script. You can explore the `entrypoint.sh` script by opening it in a code or text editor. As the sample contains several applications, the script lets you specify which application to run when the container starts. ## Run the application To run the application using Docker: 1. Build the image. In a terminal, run the following command inside the directory of where the `Dockerfile` is located. ```console $ docker build -t basic-nlp . ``` The following is a break down of the command: - `docker build`: This is the primary command used to build a Docker image from a Dockerfile and a context. The context is typically a set of files at a specified location, often the directory containing the Dockerfile. - `-t basic-nlp`: This is an option for tagging the image. The `-t` flag stands for tag. It assigns a name to the image, which in this case is `basic-nlp`. Tags are a convenient way to reference images later, especially when pushing them to a registry or running containers. - `.`: This is the last part of the command and specifies the build context. The period (`.`) denotes the current directory. Docker will look for a Dockerfile in this directory. The build context (the current directory, in this case) is sent to the Docker daemon to enable the build. It includes all the files and subdirectories in the specified directory. For more details, see the [docker build CLI reference](/reference/cli/docker/buildx/build/).

The instructions for the Dockerfile continue with copying the application code and entrypoint script into the image, setting permissions for the entrypoint script to make it executable, and defining the entry point to run the script when the container starts. The entrypoint script allows specifying which application to run upon container startup. Then, the process of running the application using Docker is described, starting with the `docker build` command to create an image named `basic-nlp` from the Dockerfile in the current directory. The breakdown of the `docker build` command is explained.