Dockerfile is utilized to construct Docker Images. It is a basic text document that comprises a bunch of directions or orders that is executed by a mechanized form process in strides from start to finish. It is utilized to make our own Docker picture and for the most part, we use a parent Docker picture to construct our own Docker picture notwithstanding, we can make a base picture also. The name of the Dockerfile should be 'Dockerfile' else Docker daemon will toss a mistake and it is case touchy. We utilize the 'docker assemble' order to make a Docker picture from a Dockerfile.

Given below shows how Dockerfile works in Docker with an example.

RUN apt-get update && apt-get install -y APP

Let’s see each instruction of Dockerfile mentioned in the above sample Dockerfile:

More often than not we utilize an authority picture on which we will construct our Docker picture with the goal that it is the foundation of our Docker picture. To make a base picture, we use 'without any preparation' in the Dockerfile. In the above Dockerfile, 'ubuntu' is utilized as a base picture, which is called parent picture. Other guidance referenced in the Dockerfile will change this Docker picture.

It is utilized to set the climate variable while making a Docker picture. It has key-esteem sets and we can set various factors utilizing "<key>=<value>". If we don't utilize the equivalent (=) sign then it permits a solitary variable as displayed in the above Dockerfile.

ENV key1=”value1” key2=”value2” \

RUN guidance is utilized to execute shell order in the new layer and submit the outcome in another Docker picture. We can run separate long or complex RUN guidance in various lines utilizing an oblique punctuation line as beneath:

RUN apt-get update && apt-get install –y \

It changes the current working catalog in the Docker picture and that can be used by additional guidance. In the above model, the working registry has been changed to "/var/www/HTML" and afterward in the following guide we use '.' to indicate that add the record 'index.html' to the functioning catalog that is "/var/www/HTML".

It is utilized to add records from the neighborhood host to the Docker picture. Duplicate guidance has comparative usefulness nonetheless, ADD has a few different highlights also like tar extraction and far off URL support. In the above Dockerfile, the index.html document from localhost has been added to the current working registry of the Docker picture.

Uncover guidance tells about the port on which a compartment tunes in for associations. It doesn't uncover the port while building the picture. It gives overall data about the port assuming we run a compartment utilizing this picture. In the above model, the holder will tune in on port 80 assuming it is made utilizing the picture that is worked by the above Dockerfile and that should be uncovered while making a compartment to get to it remotely.

It characterizes what might get executed when a compartment gets made utilizing the Docker picture or it gives the default to an executing holder. We ought to characterize just a single CMD in a Dockerfile, but on the off chance that we characterize different CMD guidelines in a Dockerfile, just the last CMD will get executed. Likewise assuming we determine any order while running the compartment, the predetermined order will come first and CMD guidance won't get executed.

We can utilize ENTRYPOINT guidance that permits making an executable compartment. It has two structures, one is an 'executive' and another is a 'shell' structure. Assuming that we pass order line contention while running the holder utilizing the 'docker run <image>' order, the determined order gets added after all components in an executive structure ENTRYPOINT. We utilize the banner '- entry point to supersede the ENTRYPOINT guidance while running the compartment utilizing the 'docker run <image>' order.

How about we fabricate the Docker picture utilizing above example Dockerfile utilizing underneath order:

$docker build -t <image_name>:<image_tag><path_of_Dockerfile>

In the above snapshot, it is clear that the build has a total of 7 steps and it is equal to the number of instructions specified in Dockerfile which means each instruction is getting executed one by one.

Step 1: Docker daemon searches for the image mentioned in the FROM instruction i.e. ubuntu, if the image is not available locally it downloads from the hub, in the above case ubuntu already exists locally.

Step 2: Set environment variable APP to Nginx.

Step 3: Updates the OS and install Nginx.

Step 4: Changes the working directory to ‘/var/www/home.

Step 5: Copy local ‘index.html’ file to Docker image to ‘/var/www/home.

Step 6: It shows PORT 80 needs to be exposed to access it externally.

Step 7: This is the last step that sets the default command that runs the Nginx daemon as a background job when any container gets created using this Docker image.

It saves our time via robotizing every one of the means with sets of guidelines that get executed during the form so we don't need to physically roll out the improvements to the holder and submit it.

We can keep Dockerfiles on any source control the executive's instruments like git, svn, and so forth and exploit presented by those devices like form control, spreading, and so on

We can share Dockerfile effectively with different partners or gatherings regardless of whether we have a focal vault for pictures.

Every guidance in Dockerfile makes a layer in the Docker picture that is valuable to get how the picture is treating the scene by actually looking at the historical backdrop of the picture. It likewise helps in the quicker picture working by utilizing reserve.

We can utilize 'docker submit' after rolling out expected improvements to the compartment to make a Docker picture also yet it isn't prescribed as we miss to take the benefits referenced above with regards to Dockerfile. There are a lot more orders accessible to use in Dockerfile.

We will know in this article, what's the About Dockerfile.

About Dockerfile

Use Docker containers as build requirements in Conan:

Conan allows specifying build requirements as packages or as build_requires, which are packages used only during the build process. Build requires are specified in the conanfile.py file and are used to provide additional dependencies that are required only during the build process.

To use Docker containers as build requirements in Conan, you can define a build_requires() method in your conanfile.py file and use the ConanFile.run() method to execute a shell command that runs a Docker container. Here's an example:

        self.build_requires("docker/1.0.0")

        self.run("docker run --rm -v $PWD:/workdir my_docker_image /bin/bash -c 'cd /workdir && ./configure && make'")

In this example, the build_requirements() method specifies that the docker package is required as a build requirement. The build() method then uses the ConanFile.run() method to execute a shell command that runs a Docker container, passing the current working directory ($PWD) as a volume mount and running the configure and make commands inside the container.

Additionally, you can also define a custom Docker image that includes all the necessary build dependencies and tools required by your package's build process. To use this custom Docker image as a build requirement, you can create a Dockerfile that installs the required build dependencies and tools and builds a Docker image.

    apt-get install -y build-essential cmake git

This Dockerfile installs the build-essential, cmake, and git packages, which are commonly used build dependencies, and sets the working directory to /app.

To use this Docker image as a build requirement in Conan, you can modify the build() method in the conanfile.py file to run a container based on the custom Docker image:

        docker_image = "my_custom_docker_image:latest"

        self.run(f"docker run --rm -v $PWD:/workdir {docker_image} /bin/bash -c 'cd /workdir && ./configure && make'")

In this example, the build() method uses the docker_image variable to specify the name and tag of the custom Docker image. The ConanFile.run() method is used to execute a shell command that runs a Docker container based on the custom Docker image and performs the build process inside the container.

Using Docker containers as build requirements in Conan can help simplify the build process by providing a consistent build environment and isolating build dependencies from the host system. It can also help ensure reproducibility of builds across different systems and platforms.

Conan is a package manager that lets developers capture artifacts created during builds of applications and libraries, so let's see how to use docker containers as build-requires in Conan.

Using docker-containers as build-requires in conan

Dockerfile has a parameter for ENTRYPOINT and while writing Kubernetes deployment YAML file, there is a parameter in Container spec for COMMAND.

I am not able to figure out what's the difference and how each is used?

Difference between Docker ENTRYPOINT and Kubernetes container spec COMMAND?

I foubd out that if I use multiple docker images in single Dockerfile , the second one would always override or delete the data installed in former one , for example :

FROM nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04
FROM python:3.8.10
CMD ["/bin/bash"]


The cuda-10.2 installed from former one is gone...But what I want is to have data installed from both docker images I use . Is there any way to achieve it ?

When using multiple FROM statements in a Dockerfile, each statement starts a new build stage, and the subsequent stages do not have access to the files or data installed in the previous stages. Therefore, in your example, the data installed in the nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04 image is not available in the python:3.8.10 image.

To achieve the desired outcome of having data from both Docker images available in the final image, you have a few options:

Use a Single Base Image: If both CUDA and Python are required in your final image, you can use a single base image that already includes both. Some community-built images combine CUDA and Python installations, which can save you from having to manage multiple stages.

Multistage Build with Copy: If you still need to use separate base images, you can use a multistage build approach to copy specific files or data from one image to another. This can be achieved using the COPY command to copy files between stages. However, note that only the copied files will be available in the final image, not the entire installation.

FROM nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04 AS cuda_base

# Add instructions to install CUDA-specific dependencies and data

# Add instructions to install Python-specific dependencies and data

# Copy necessary data from the cuda_base image to the python image

COPY --from=cuda_base /path/to/cuda/data /path/in/python/image

Data Volumes: If you need to share data between containers rather than having them in the final image, consider using data volumes or bind mounts. Data volumes allow data to be shared between containers and persist outside the container's lifecycle.

# Start the containers with shared volume

docker run -d --name container1 -v my_data_volume:/path/in/container1 image1

docker run -d --name container2 -v my_data_volume:/path/in/container2 image2

Remember that combining multiple Docker images into a single image may lead to a larger image size and unnecessary dependencies. It's essential to consider the size and maintainability of the final image. If the two functionalities are not tightly coupled, it might be more efficient to use separate images and share data through volumes or APIs. Choose the approach that best fits your use case and maintain good containerization practices for efficient and secure deployments.

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