I'm using custom Dockerfile to create environment for Azure machine learning. However everytime I run my code, I always get back "already exists" on the UI for my environment. I didn't find much documentation on this status which is why I'm asking here.
I assume that this means that an image with the same dockerfile exists in my container registry. What I don't get is: if the image already exits why my environment is unusable and set to this.
To create my environment I use this snippet:
ws = Workspace.from_config()
env = Environment.from_dockerfile(environment_name, f"./environment/{environment_name}/Dockerfile")
env.python.user_managed_dependencies = True
env.register(ws)
env.build(ws)
Am I doing something wrong there?
Thanks for your help
By default, all the environments will be working on Linux machine as it is from the docker image. With respect to the issue, we need to clear the cache of the images and then restart the run. Check out the below
syntaxes which need to be used.
docker-compose build --no-cache -> to clear the cache
and don't forget to restart the most updated image
docker-compose up -d <service> --force-recreate
The issue will be resolved. It might be because of the cache.
Checkout the documentation to check and recreate the entire operation. Link
With respect to UI. Create the DevOps image with Inference clusters.
Related
I'm trying to create an environment from a custom Dockerfile in the UI of Azure Machine Learning Studio. It previously used to work when I used the option: Create a new Docker context.
I decided to do it through code and build the image on compute, meaning I used this line to set it:
ws.update(image_build_compute = "my_compute_cluster")
But now I cannot create any environment through the UI and the docker build context anymore. I tried setting back the property image_build_compute to None or False but it doesn't work either.
Also tried deleting the property through the cli but also doesn't work. I checked on another machine learning workspace and this property doesn't exists.
Is there a way for me to completely remove this property or enable again the docker build context?
Created compute cluster with some specifications and there is a possibility to update the version of the cluster and checkout the code block.
workspace.update(image_build_compute = "Standard_DS12_v2")
We can create the compute instance using the UI of the portal using the following steps using the docker.
With the above procedure we can get to confirm that the environment was created using the docker image and file.
I'm new to using docker, and I was wondering how I could create a docker image for others to use to replicate the exact same setup I have for python when testing code. Currently what I've done is I created a docker image with the dockerfile below:
FROM python:3.7
COPY requirements.txt ./
RUN pip install -r requirements.txt
where the requirements text file contains the libraries and the versions I am using. I then ran this image on my local machine, and things looked good, so I pushed this image to a public repository on my docker hub account, so other people could pull it. This makes sense so far, but I'm still a little confused what to do next. If someone wants to test a python script on their local machine using my configuration what exactly would they do? I think they would pull my image from docker hub and then create a container from this image on their local machine. However, I tried this on my machine, testing a python file that runs and saves a file, and it's not really working how I anticipated. I can make a python file and put it in WORKDIR and have it run, but I think that's all it does. I'd really like to be able to navigate the container to find the file the python program created and then save it back on my local machine. Is there any way to do this, and am I going about this the wrong way?
you can execute bash in the container and check the files created inside of the container. Or you can share a volume between host and container. Such that you can check the files created in your host machine. Check this link, it might be helpful for you :-)
Volume
I'm new to using Docker, so I'm either looking for direct help or a link to a relevant guide. I need to train some deep learning models on my school's linux server, but I can't manually install pytorch and other python packages since I don't have root access (sudo). Another student said that he uses docker and has everything ready to go in his container.
I'm wondering how to wrap up my code and relevant packages into a container that I can push to the linux server and then run.
To address your specific problem the easiest way I found to get code into a container is to use git.
start the container in interactive mode or ssh to it if it's attached to a network.
git clone <your awesome deep learning code>. In your git repo have a requirements.txt file. Change directories into your local clone of your repo and run pip install -r requirements.txt
Run whatever script you need to run your code. Note you can easily put your pip install command in one of your run scripts.
It's important to remember that docker containers are stateless/ephemeral. You should not expect the container nor its contents to exist in some durable fashion. This specific issue is addressed by mapping a directory on the host system to a directory in the container.
Side note: I first recommend starting with the docker tutorial. You can easily skip over the installation parts if you are working on system that already has docker installed and where you have permissions to build, start, and stop containers.
I don't have root access (sudo). Another student said that he uses docker
I would like to point out that docker requires sudo permissions.
Instead I think you should look at using something like Google Colab or JupyterLab. This gives you the added benefit of code that is backed-up on a remote server
I'm on Docker for past weeks and I can say I love it and I get the idea. But what I can't figure out is how can I "transfer" my current set-up on Docker solution. I guess I'm not the only one and here is what I mean.
I'm Python guys, more specifically Django. So I usually have this:
Debian installation
My app on the server (from git repo).
Virtualenv with all the app dependencies
Supervisor that handles Gunicorn that runs my Django app.
The thing is when I want to upgrade and/or restart the app (I use fabric for these tasks) I connect to the server, navigate to the app folder, run git pull, restart the supervisor task that handles Gunicorn which reloads my app. Boom, done.
But what is the right (better, more Docker-ish) approach to modify this setup when I use Docker? Should I connect to docker image bash somehow everytime I want upgrade the app and run the upgrade or (from what I saw) should I like expose the app into folder out-of docker image and run the standard upgrade process?
Hope you get the confusion of old school dude. I bet Docker guys were thinking about that.
Cheers!
For development, docker users will typically mount a folder from their build directory into the container at the same location the Dockerfile would otherwise COPY it. This allows for rapid development where at most you need to bounce the container rather than rebuild the image.
For production, you want to include everything in the image and not change it, only persistent data goes in the volumes, your code is in the image. When you make a change to the code, you build a new image and replace the running container in production.
Logging into the container and manually updating things is something I only do to test while developing the Dockerfile, not to manage a developing application.
I am currently new to Docker, I have been through a good amount of online tutorials and still haven't grasped the entire process. I understand that most of the tutorials have you pull from online public repositories.
But for my application I feel I need to create these images and implement them into containers from my local or SSH'd computer. So I guess my overall question is, How can I create an image and implement it into a container from nothing? I want to try it on something such as Python before I move onto my big project I will be doing in the future. My future project will be containerizing a weather research model.
I do not want someone to do it for me, I just have not had any luck searching for documentation, that I understand, that gives me a basis of how to do it without pulling from repositories online. Any links or documentation would be greatly received and appreciated.
What I found confusing about Docker, and important to understand, was the difference between images and containers. Docker uses collections of files with your existing kernal to create systems within your existing system. Containers are collections of files that are updated as you run them. Images are saved copies of files that cannot be manipulated. There's more to it than that, based on what commands you can use on them, but you can learn that as you go.
First you should download an existing image that has the base files for an operating system. You can use docker search to look for one. I wanted a small operating system that was 32 bit. I decided to try Debian, so I used docker search debian32. Once you find an image, use docker pull to get it. I used docker pull hugodby/debian32 to get my base image.
Next you'll want to build a container using the image. You'll want to create a 'Dockerfile' that has all of your commands for creating the image. However, if you're not certain about what you want in the system, you can use the image that you downloaded to create a container, make the changes (while writing down what you've done), and then create your 'Dockerfile' with the commands that perform those tasks afterward.
If you create a 'Dockerfile', you would then move into the directory with the 'Dockerfile' and, to build the image, run the command: docker build -t TAG.
You can now create a container from the image and run it using:
docker run -it --name=CONTAINER_NAME TAG
CONTAINER_NAME is what you want to reference the container as and TAG was the tag from the image that you downloaded or the one that you previously assigned to the image created from the 'Dockerfile'.
Once you're inside the container you can install software and much of what you'd do with a regular Linux system.
Some additional commands that may be useful are:
CTRL-p CTRL-q # Exits the terminal of a running container without stopping it
docker --help # For a list of docker commands and options
docker COMMAND --help # For help with a specific docker command
docker ps -a # For a list of all containers (running or not)
docker ps # For a list of running containers
docker start CONTAINER_NAME # To start a container that isn't running
docker images # For a list of images
docker rmi TAG # To remove an image