We are using Databricks notebook with and Github as version Control. I am trying to do static analysis as either part of Github checks or as part of the Azure pipeline.
I am aware of linters available for Python and Scala. however, the challenge here is that one notebook could have Python, Scala, or a SQL code as well.
Using an linter might now work, so is there any way I could achieve this?
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I want to create CI on Github Action for QA Automation. But there is multiple language are use to install dependecies. Can i use NodeJS and Golang at the same file?
I read the documentation of Github Action, but there is configuration for each language not both. Any reference or idea i can use?
In short, you write a manifest file (in YAML) and tell GitHub Actions build agent(s) to execute the commands you wanted in an automatic way. See, there is nothing there bind to a single programming language.
You see per language samples/tutorials, simply because that's how new users/developers to get started with a CI/CD system, and it is easy to write up the necessary steps if focusing on the ecosystem of a single programming language.
The underlying GitHub Actions build machines (if managed by GitHub), however, have almost everything pre-installed, so of course you can use Node.js and Golang tools in the same manifest and you don't need any specific reference.
Open the image pages and learn what tools are preinstalled if you like.
Try it out by combining multiple manifests into the single one, and you will see how it works out.
My company is in the process of migrating all our pipelines over to Databricks from AzureML, and I have been tasked with refactoring one of our existing pipelines made with azureml-sdk (using functions such as PipelineData, PythonScriptStep etc.), and converting it into a dbx pipeline which uses a deployment.yml file.
I have found this "Deployment file reference" on dbx documentation page, and I think it's quite adequate compared to some of AzureML's documentation. However, if I had an example project to compliment that page, it would help me greatly to put it into practice.
Is there any repos/sources which gives an example of building a dbx pipeline which uses .py-files instead of notebooks?
However, if I had an example project to compliment that page, it would help me greatly to put it into practice.
Please take a look at the Quickstart doc which generates a sample project and walks you through it step by step.
If you're looking for more profound and in-depth example with orientation towards MLOps practices, take a look at the following session - MLOps on Databricks: A How-To Guide. It also links to an example repo with dbx.
Today when we've tried running our notebooks defined in synapse, we've received constantly error: 'LIBRARY_MANAGEMENT_FAILED'. We are using approach from: https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-manage-python-packages#storage-account to manage custom libraries, and it was working fine up until this point. Additionally, we've tried separate method of providing spark pool with custom library and tried to use workspace packages, but after 10 minutes of loading custom package, it timesouts with failure.
When we are removing python folder completely from storage, sparkpools run notebooks normally.
Yesterday everything was working properly. The problem also could not be in custom library, because it does not work even with empty python folder.
There were issues on Microsoft side, which were resolved and it started working next day.
I hope I am not missing something obvious here. I am using the new azure ml studio designer. I am able to use to create datasets, train models and use them just fine.
azure ml studio allows creation of Jupyter notebooks (also) and use them to do machine learning. I am able to do that too.
So, now, I am wondering, can I build my ML pipeline/experiment in ML studio designer, and once it is in good shape, export it as a python and jupyter notebook? then, use it in the same designer provided notebook option or may be use it locally?
This is not currently supported, but I am 80% sure it is in the roadmap.
An alternative would be to use the SDK to create the same pipeline using ModuleStep where I believe you can reference a Designer Module by its name to use it like a PythonScriptStep
The export Designer graph to notebook is in our roadmap. For now, please take a look at the ModuleStep in SDK and let us know if you have any questions.
Thanks,
Lu Zhang | Senior Program Manager | Azure Machine Learning
Here are the instructions to Use the studio to deploy models trained in the designer - Azure Machine Learning | Microsoft Docs and document that explains how we can get access to score.py and conda_env.yaml files under Output + logs tab for Train module.
I'm very new to using Qlik and at the moment I've only used the cloud via my browser. I would like to integrate python and Qlik such that I can run my code on data in the QlikCloud and visualize using Qlik. I am using a Mac, therefore I can not install the desktop version of Qlik to do the integration.
Do you have any suggestions on how to integrate python in Qlik while using a Mac?
Any suggestions are highly appreciated, I have not been able to find any complete answers yet.
Thank you!
Use Data Load Script
When I first started Qlik, I had a very similar situation. My goal was to manipulate data to do calculations in Python, then basically import that into Qlik. What I ended up learning and realizing is that there's a 90% chance what you're trying to calculate outside of Qlik can be done in Qlik's data load script.
Get started with the Qlik data script: https://help.qlik.com/en-US/sense/September2019/Subsystems/Hub/Content/Sense_Hub/Scripting/introduction-data-modeling.htm
In my opinion and experience, Qlik Community Forum is more active than Stack Overflow. I highly recommend checking it out for help: https://community.qlik.com/
But If You Still Need External Calculation...
That said, if you do have crazy calculations and math to do and/or need to use an external "thing", Qlik has a repo for a server-side extension. Repo at: https://github.com/qlik-oss/server-side-extension (Docs and instructions in the link)
It has extensions for Java, C++, C#, Go, and Python.
I highly recommend this Server Side Extension developed by Nabeel which you can run in a docker on your machine. https://github.com/nabeel-oz/qlik-py-tools