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.
Related
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.
I am trying to find a good example of the json body for Create Build Definition in Azure Devops. Most of the documentation I find has api definitions, but I haven't been able to see an example json body to work from.
Microsoft Documentation:
https://learn.microsoft.com/en-us/rest/api/azure/devops/build/definitions/create?view=azure-devops-rest-5.1
I have found this article that describes doing something similar to what I hope to accomplish. However, they are trying to duplicate the same build definition across different projects.
Similar Example:
https://www.nebbiatech.com/2018/11/29/automating-build-pipeline-creation-using-azure-devops-services-rest-api/
Ultimately, I would like to be able to generate (either create new or clone/modify) as many standard build definitions within a single project as are necessary by my automation. Each one of these build definitions will pull from a different repository within the project and have a different cosmetic name for the pipeline, but will be otherwise identical.
Any suggestions are greatly appreciated. Thanks!
For the usage of YAML build as comment suggested, it will meet your requirements. It letting you define your build in a YAML file that lived with your code. This meant you could use the same branching and code review practices for your build definitions as you did for your code.
The best way to get started with YAML pipelines is through the quickstart guide and Customize your pipeline . After that, to learn how to configure your YAML pipeline the way you need it to work, see conceptual topics such as Build variables and Jobs.
As for a sample of application/json body when you use Rest API to create build definition. You could also refer below links:
How to create Build Definitions through VSTS REST API
Create VSTS Build Definitions using PowerShell
I've requested to my Team Lead that we start integrating a CI/CD pipeline into most, if not all, of our projects. Our newest project relies heavily on our own, external class library that is referenced in the solution ; it is under "Dependencies" as a project reference.
The project runs fine when I build it in my machine using Visual Studio 2019, and before we needed to integrate an external library, it would build and release fine using our Azure DevOps pipelines.
However, with the addition of an external class library, when I try to run a build through Azure DevOps, I get the following error:
The project file ....csproj was not found.
I fully understand why it can't find it - because I need to pull in the external class library and build that first! There doesn't seem to be a lot of online material (not that I could find anyway!) that describes solutions to this other than "use nuget" ; unfortunately, it is a requirement from my Team Lead that this is not a route we go down - which has lead to a long couple of days!
With this in mind, I can't find another way to do this in Azure DevOps. I have looked into some sort of PowerShell command but to no avail thus far.
Has anyone run into this issue before with external class libraries in DevOps and can give me advice on the best way to approach it?
Generally speaking in 99,99% cases keeping a direct reference to the project is not a good idea. You can end up with really unmaintainable CI/CD logic and/or with dll versions mismatches during deployments. Actually I am an Architect in the project where I tried to fix that issue by migrating all dependencies to the NuGet server.
Azure Artifacts
You mentioned, that you are using Azure DevOps as main CI/CD tool, so this is a great opportunity to introduce Azure Artifacts as internal nuget server which is a part of Azure DevOps. For the first 2 GB it is free, here you have pricing details.
Alternatives
If for some reason you cant use Azure Artifacts, I recommend some alernatives:
MyGet
ProGet
Own nuget server
More information about alternatives you can find in this article.
My plan is to fetch the GA API with python3 and google2Pandas.
My problem so far is that I don't know where to start first, when I look at the google2pandas README it looks easy but I have issues to build my own script with that and implementing the Oauth2 stuff.
What is the right way to start with these boiler plates?
All those functions are a bit confusing to me.
What do I really need to use the analytics v4 API and fetch some simple stuff for my dashboard? Which Parameters do I have to set and how or where in the file should I do that? Another question is, do I have to use those functions in a new python file or can I go start with the _panalysis_ga.py?
It would be really helpful if you can guide me here or at least steer me in the right direction with some example.
The link to the repository kind of has the answer, but appreciate it's not always clear if you've never seen it before. There is no need to do anything on the OAth2 process as the library seems to take care of that.
Use pip to install the google2Pandas library on your machine.
You then need to create a GCP account if you don't already have one, and follow step 1 here to get the credentials.
you can then use the Quick Demo shown on the README file of the repository (modify the query to your needs).
EDIT
Look into the New and Improved section of the README file as it is the most up to date one.
When I wanted to do a sentiment analysis project I searched alot online, and atlast I landed on this website, which explained the code but what it did not explain is how to use spark with respect to the code, I mean where to add the code.
Website :http://stdatalabs.blogspot.in/2017/09/twitter-sentiment-analysis-using-spark.html?m=1
It will be of great help, if anyone can explain me completely, as Iam a begginer and this my first project on big data.
Thank you.
In the bottom there is a link to the github (https://github.com/stdatalabs/sparkNLP-elasticsearch) you should check that out (literally)
The main class is
com.stdatalabs.SparkES.TwitterSentimentAnalysis according to the pom.xml
So running mvn package will yield you an executable .jar (user java -jar)
Running the jar will prompt you for some twitter config (keys, etc) and saves to a local es cluster using hardcoded index (& mapping) twitter_020717/tweet
You can now alter the code anyway you want, build, run, and check the results.