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.
Related
I have an mlflow ec2 instance running on aws. I want to develop an mlflow plugin to save registered models to a specific aws s3 bucket.
I have read all the documentation on plugins for mlflow and, if my understanding is correct, to develop and use a plugin i need two things:
Write the code for a pacakage following the mlflow plugins standards as in : https://www.mlflow.org/docs/latest/plugins.html
Change the tracking uri by adding file-plugin: at the beginning:
MLFLOW_TRACKING_URI=file-plugin:$(PWD)/mlruns python examples/quickstart/mlflow_tracking.py
Now, this is simple if I want the plugin to work on a python script. I just need to install my custom plugin package in my python environment and set the tracking uri as stated above.
However, if I want the same plugin to work when using the ui when connecting to my aws instance, I am not sure how to do it. I found no way to set the MLFLOW_TRACKING_URI to include file-plugin:.
Does anyone know how to solve this issue? How can I make sure my plugin works when interacting with mlflow through the ui?
Thanks in advance!
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.
According to the documentation here
Dependency specification using the Pipfile/Pipfile.lock standard is currently not supported. Your project should not include these files.
I use Pipfile for managing my dependencies and create a requirements.txt file through
pipenv lock --requirements
Till now everything works and my gcloud function is up and running. So why should a python google cloud function not contain a Pipfile?
If it shouldn't contain, what is the preferred way suggested to manage an isolated environment ?
When you deploy your function, you deploy it on its own environment. You won't manage several environment because the cloud function deployment is dedicated to one and only one piece of code.
That's why, it's useless to have a virtual environment in a single usage environment. You could use Cloud Run to do that because you can customize your build and runtime environment. But, here again, it's useless: You won't have concurrent environment in the same container, it does not make sense.
I am working on an Azure Machine Learning Studio pipeline via the Designer. I need to install a Python library wheel (a third-party tool) in the same compute, so that I can import it into the designer. I usually install packages to compute instances via the terminal, but the Azure Machine Learning Studio designer uses a compute cluster, not a compute instance.
Is there any way to access the terminal so that I can install the wheel in the compute cluster and have access to the library via the designer? Thanks!
There isn't an easy path for this. Your options are either, switch to a code-first pipeline definition approach, or try your darndest to extend the Designer UI to meet your needs.
Define pipelines with v2 CLI or Python SDK
It looks like you're already outside of I get the impression that you know Python quite well, you should really check out the v2 CLI or the Python SDK for Pipelines. I'd recommend maybe starting with the v2 CLI as it will be the way to define AML jobs in the future.
Both require some initial learning, but will give you all the flexibility that isn't currently available in the UI.
custom Docker image
The "Execute Python Script" module allows use a custom python Docker image. I think this works? I just tried it but not with a custom .whl file, and it looked like it worked
I am making a pipeline using Python and I found that Azure's default container does not support libsndfile library. So I am trying to use docker so that I can make a container which supports libsndfile library. However, I have not used docker so I need a help.
The function app that I made is blob storage triggered function app.
upload to blob storage (blob triggered) -> Processing (function app) -> copy to another blob storage (output)
The questions are
Is it possible to make a blob storage function app using docker?
If it is possible, can you give me some hints how to use docker?
In a case where when your functions require a specific language version or have a specific dependency or configuration that isn't provided by the built-in image, you typically use a custom image. Here, you can create and deploy your code to Azure Functions as a custom Docker container using a Linux base image.
In summary, you can create Azure Function App using Docker image using Azure LCI like below:
az functionapp create --name <app_name> --storage-account <storage_name> --resource-group AzureFunctionsContainers-rg --plan myPremiumPlan --runtime <functions runtime stack> --deployment-container-image-name <docker_id>/azurefunctionsimage:v1.0.0
Do check out the above link for detailed step by step tutorial and you are good to go! It also shows you how to create output bindings.