What's the proper development workflow for code that runs in a Docker container?
Solomon Hykes said that the "official" workflow involves building and running a new Docker image for each Git commit. That makes sense, but what if I want to test a change before committing it to the Git repo?
I can think of two ways to do it:
Run the code on a local development server (e.g., the Django development server). Edit a file; test on the dev server; make a Git commit; rebuild the Docker image with the new code; test again on the local Docker container.
Don't run a local dev server. Instead, build and run a new Docker image each time I edit a file, and then test the change on local Docker container.
Both approaches are pretty inefficient. Is there a better way?
A more efficient way is to run a new container from the latest image that was built (which then has the latest code).
You could start that container starting a bash shell so that you will be able to edit files from inside the container:
docker run -it <some image> bash -l
You would then run the application in that container to test the new code.
Another way to alter files in that container is to start it with a volume. The idea is to alter files in a directory on the docker host instead of messing with files from the command line from the container itself:
docker run -it -v /home/joe/tmp:/data <some image>
Any file that you will put in /home/joe/tmp on your docker host will be available under /data/ in the container. Change /data to whatever path is suitable for your case and hack away.
Related
I have cloned a repository of an API built with python on my local machine and my goal is to be able to send requests and receive responses locally.
I'm not familiar python but the code is very readable and easy to understand, however the repository contains some dependencies and configuration files to Dockerise (and I'm not familiar with Docker and containers too) .
so what are the steps to follow in order to be able to interact with the API locally?.
Here are some files in the repository for config and requirements :
requirements.txt file :
fastapi==0.70.0
pytest==7.0.1
requests==2.27.1
uvicorn==0.15.0
Dockerfile file :
FROM tiangolo/uvicorn-gunicorn:python3.9
COPY ./requirements.txt /requirements.txt
RUN pip install -r /requirements.txt
COPY ./app /app
i already installed Python3 and docker so what's next ?
Adjust Dockerfile
Assuming all code is in the /app directory you have already copied over all your code and installed all the dependencies required for the application.
But you are missing - at least (see disclaimer) - one essential line in the Dockerfile which is actually the most important line as it is the CMD command to tell Docker which command/ process should be executed when the container starts.
I am not familiar with the particular base image you are using (which is defined using the FROM command) but after googling I found this repo which suggests the following line, which does make a lot of sense to me as it starts a web server:
# open port 80 on the container to make it accesable from the outside
EXPOSE 80
# line as described in repo to start the web server
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
This should start the web server on port 80 using the application stored in a variable app in your main.py when the container starts.
Build and run container
When you have added that you need to build your image using docker build command.
docker build -t asmoun/my-container .
This builds an container image asmoun/my-container using the Dockerfile in the current directory, hence the .. So make sure you execute that when in the directory with the Dockerfile. This will take some time as the base image has to download and dependencies need to be installed.
You now have an image that you can run using docker run command:
docker run --name my-fastapi-container -d -p 80:80 asmoun/my-container
This will start a container called my-fastapi-container using the image asmoun/my-container in detached mode (-d option that makes sure your TTY is not attached to the container) and define a port mapping, which maps the port 80 on the host to port 80 on the container, which we have previously exposed in the Dockerfile (EXPOSE 80).
You should now see some ID getting printed to your console. This means the container has started. You can check its state using docker ps -a and you should see it marked as running. If it is, you should be able to connect to localhost:80 now. If it is not use docker logs my-fastapi-container to view the logs of the container and you'll hopefully learn more.
Disclaimer
Please be aware that this is only a minimal guide on how you should be able to get a simple FastAPI container up and running, but some parameters could well be different depending on the application (e.g. name of main.py could be server.py or similar stuff) in which case you will need to adjust some of the parameters but the overall process (1. adjust Dockerfile, 2. build container, 3. run container) should work. It's also possible that your application expects some other stuff to be present in the container which would need to be defined in the Dockerfile but neither me, nor you (presumably) know this, as the Dockerfile provided seems to be incomplete. This is just a best effort answer.
I have tried to link all relevant resources and commands so you can have a look at what some of them do and which options/ parameters might be of interest for you.
I want to modify files inside docker container with PyCharm. Is there possibility of doing such thing?
What you want to obtain is called Bind Mounting and it can be obtained adding -v parameter to your run command, here's an example with an nginx image:
docker run --name=nginx -d -v ~/nginxlogs:/var/log/nginx -p 5000:80 nginx
The specific parameter obtaining this result is -v.
-v ~/nginxlogs:/var/log/nginx sets up a bindmount volume that links the /var/log/nginx directory from inside the Nginx container to the ~/nginxlogs directory on the host machine.
Docker uses a : to split the host’s path from the container path, and the host path always comes first.
In other words the files that you edit on your local filesystem will be synced to the Docker folder immediately.
Source
Yes. There are multiple ways to do this, and you will need to have PyCharm installed inside the container.
Following set of instructions should work -
docker ps - This will show you details of running containers
docker exec -it *<name of container>* /bin/bash
At this point you will oh shell inside the container. If PyCharm is not installed, you will need to install. Following should work -
sudo apt-get install pycharm-community
Good to go!
Note: The installation is not persistence across Docker image builds. You should add the installation step of PyCharm on DockerFile if you need to access it regularly.
I am building a webapp (a simple flask site) that uses docker. I want my development code to not reside within docker, but be executed by the development environment (using python3) I have defined in my dockerfile. I know that I can use the COPY . . syntax in a dockerfile to copy my source code into the image for execution, but that violates my aim of separating the container from my source. Is there a way to have a docker container read and execute the code that it is in the directory I run the docker container run command from?
Right now my container uses the copy company to copy all the source code into the container. It then uses the CMD command to automatically run the flask app:
CMD [ "python", "flask_app/server.py" ]
(I'm storing all my flask code in a directory called flask_app). I'm assuming this works because all this has been copied into the container (according to the specifications given in the dockerfile) and is being executed when I run the container. I would like for the container to instead access and execute flask_app/server.py without copying this information into itself -- is this possible? If so, how?
Instead of using COPY to move the code into the container, you'll use a "bind mount" (https://docs.docker.com/storage/bind-mounts/).
When you run the container, you'll do it with a command like this:
docker run --mount type=bind,source=<path_outside_container>,target=<path_inside_container> <image_tag>
For portability, I recommending putting this line in a script intended to be run from the repository root, and having the <path_outside_container> be "$(pwd)", so that it will work on other people's computers. You'll need to adjust <path_inside_container> and your CMD depending on where you want the code to live inside the container.
(Obviously you can also put whatever other options you'd like on the command, like --it --rm or -p <whatever>.)
I am new to Docker and I am confused about containers and images somehow. I want to sue Docker for Tensorflow development. All I need is to have an easy way to write Jupyter Notebooks and use GPU powered Tensorflow.
I have the latest Tensorflow Jupyter Python 3 Image already. I run the Image with
docker run --rm --runtime=nvidia -v -it -p 8888:8888 tensorflow/tensorflow:latest-gpu-py3-jupyter
How can I make it so that my data when I work in that Image and add and edit my Jupyter Notebooks won't get lost after I exit the process. I know that Docker Images aren't meant to persist state but I am so new to this I just want something to work in with persistent data. Can someone help me guide me through this or point to a resource which will answer all my prayers?
I would also like to move some stuff into the Container that is going to be run so that I can access some custom Python libs because they contain some things that my Notebooks need to import!
Side questions:
--rm removes the container or whatever by default I run it without this flag still my data was lost
-v is for volumes? I tried with -v Bachelor:/app to mount a volume like so. It apparently doesn't make any difference. I don't know how to use the volume Bachelor that I created. Instead there are a multitude of unnamed volumes being created that are not usable whenever I run this
-it does also something no idea what
-p is the port number right?
Use Docker volumes:
Volumes are the preferred mechanism for persisting data generated by and used by Docker containers
Example:
docker run --runtime=nvidia -v ${SOURCE_FOLDER}:${DEST_FOLDER} -p 8888:8888 tensorflow/tensorflow:latest-gpu-py3-jupyter
Change SOURCE_FOLDER and DEST_FOLDER accordingly (use absolute paths!).
Now if you navigate to localhost:8888 and create a notebook on DEST_FOLDER, it also should be available on SOURCE_FOLDER.
As for your side questions:
--it runs a container in interactive mode. You generally add /bin/bash after the run command, so you can start an interactive bash session inside the container.
--rm cleans the container after it exists.
Those options aren't really necessary for your use case. Just remember to use docker ps and docker rm <ID> to clean up your container after you're done.
I'm trying to create a container to run a program. I'm using a pre-configured image and now I need to run the program. However, it's a machine learning program and I need a dataset from my computer to run.
The file is too large to be copied to the container. It would be best if the program running in the container searched the dataset in a local directory of my computer, but I don't know how I can do this.
Well, I have made the shared folder from my machine appeared using docker run -it -v ~/Volumes/Data/Studies/PhD\Work/gitlab/J2/ydk-py:/ydk-py ydkdev/ydk-py in the container, but all files in folder ydk-py are not shown. This is the safe, usually-desired behavior. But for development and instance setup, it would be immensely useful to have access to an existing file structure.
docker run with -v will automatically mount sub-directories. In your case you are using relative path, which you need to use absolute path as per this documentation.
So change your command from
docker run -it -v ~/Volumes/Data/Studies/PhD\Work/gitlab/J2/ydk-py:/ydk-py ydkdev/ydk-py
to
docker run -it -v /home/<what ever user>/Volumes/Data/Studies/PhD\Work/gitlab/J2/ydk-py:/ydk-py ydkdev/ydk-py
it will work.
Make sure you have enough permissions on directory that you are trying to mount.