Hi i want to deploy python notebooks to azure databricks using rest api is there a way to achieve that if you could provide me with any documentations or links to refer it would be a great help
One of the first steps in designing a CI/CD pipeline is deciding on a code commit and branching strategy to manage the development and integration of new and updated code without adversely affecting the code currently in production. Part of this decision involves choosing a version control system to contain your code and facilitate the promotion of that code. Azure Databricks supports integrations with GitHub and Bitbucket, which allow you to commit notebooks to a git repository.
If your version control system is not among those supported through direct notebook integration, or if you want more flexibility and control than the self-service git integration, you can use the Databricks CLI to export notebooks and commit them from your local machine.
You can refer this official documentation for code samples and steps.
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I am using Google Cloud Source Repositories to store code for my CI/CD pipeline. What I'm building has two repos: core and clients. The core code will be built and deployed to monitor changes to a cloud storage bucket. When it detects a new customer config in the bucket, it will copy the clients code into a new branch of the clients repo named after the customer. The idea is to enable later potential tailoring for a given customer beyond the standard clients codebase.
The solution I've been considering is to have the core deploy programmatically create the branches in the the clients repo, but have come up empty handed in my research for how to do that in Google Cloud.
The only documentation that is close to what I want to do is here.
The Google Cloud SDKs do not provide a git API. However, there are python libraries that integrate with git like gitpython as one example.
Credit goes to #JohnHanley for his comment to my question for this answer.
I have python scripts for automated trading for currency and I want to deploy them by running on Jupter Lab on a cloud instance. I have no experience with cloud computing or linux, so I have been trying weeks to get into this cloud computing mania, but I found it very difficult to participate in it.
My goal is to set up a full-fledged Python infrastructure on a cloud instance from whichever provider so that I can run my trading bot on the cloud.
I want to set up a cloud instance on whichever provider that has the latest python
installation plus the typically needed scientific packages (such as NumPy and pandas and others) in combination with a password-protected and Secure Sockets Layer (SSL)-encrypted Jupyter
Lab server installation.
So far I have gotten no where. I am currently looking at the digital ocean website for setting jupter lab up but there are so many confusing terms.
What is Ubuntu or Debian? Is it like a sub-variant of Linux operating system? Why do I have only 2 options here? I use neither of the operating system, I use the windows operating system on my laptop and it is also where I developed my python script. Do I need a window server or something?
How can I do this? I tried a lot of tutorials but I just got more confused.
Your question raises several more about what you are trying to accomplish. Are you just trying to run your script on cloud services? Or do you want to schedule a server to spin up and execute your code? Are you running a bot that trades for you? These are just some initial questions after reading your post.
Regarding your specific question regarding Ubuntu and Debian, they are indeed Linux distributions which are popular option for servers. You can set up a Windows server on AWS or another cloud provider, but Linux distributions being much more popular are going to have lots of documentation, articles, stackoverflow posts around a Linux based server.
If you just want to run a script on a cloud on demand, you would probably have a lot of success following Wayne's comment around PythonAnywhere or Google Colab.
If you want your own cloud server, I would suggest starting small and slow with a small or free tier EC2 instance by following a tutorial such as this https://dataschool.com/data-modeling-101/running-jupyter-notebook-on-an-ec2-server/ Alternatively, you could splurge for an AWS AMI which will have much more compute power and be configured.
I have similar problem and the most suiteble solution to me is using docker container for jupyter notebooks. The instructions on how to install Docker can be found at https://docs.docker.com/engine/install/ubuntu/ There is ready to use Docker image docker pull jupyter/datascience-notebook for jupyter notebook python stack. The docker compose files und sone addional insruction you will fid at https://github.com/stefanproell/jupyter-notebook-docker-compose/blob/master/README.md.
I have python scripts that I want to host as a web app in azure.
I want to keep the function scripts in azure data storage and host them as a python API in azure.
I want some devs to be able to change the scripts in the azure data storage and reflect the changes live without having to deploy.
How can I got about doing this?
Create Function App of Runtime Stack Python supported only in Linux Version in any Hosting Plan through Azure Portal.
Make the Continuous Deployment setting to GitHub under Deployment Center of the function app in the portal like below:
After authorizing, provide your GitHub Repository details in the same section.
Create the Azure Python Functions from VS Code and deploy to the GitHub Repository using Git Clone through Command Palette.
After that, you can deploy the functions to azure function app.
Here after publishing to azure, you'll get the Azure Functions Python Rest API.
I want some devs to be able to change the scripts in the azure data storage and reflect the changes live without having to deploy.
Whenever you or dev's make changes the changes in code through GitHub and commit the changes, then it automatically reflects in the Azure Portal Function App.
For more information, please refer this article and GitHub actions of editing the code/script files.
I've just switched gears to Azure esp. started working on Azure functions. It's very straightforward to do the deployment using VSCode but I've been unable to find any comprehensive working end-to-end doc/resource about how to set azure function in pycharm, publish it from PyCharm to azure cloud and debug it locally.
I've looked in Microsoft docs but couldn't find anything of value for setting up Azure Func in PyCharm. Could you please suggest if it is possible to do it in PyCharm or do I have to switch to VSCode? (Don't wanna switch just because of Azure Functions though).
PS: If it is possible to set it up in PyCharm, link or details of how-to will be helpful.
Thanks in advance for help.
Azure Function is now supported in Rider, WebStorm and IntelliJ and it supports TypeScript, Node, C#, Python and Java. But PyCharm most likely be the only JetBrains product that doesn't have a single step setup to run and debug Azure Functions.
Currently there is no Azure Function Extension with the help of which functions can be created, debugged and published from PyCharm.
If you don't want to switch to VS Code then for now you might like using IntelliJ for running, debugging and publishing Azure Functions.
Check this discussion to get more information. Also check this approach to debug it locally, it can be considered as a workaround but not solution.
At the moment Pycharm does not integrate directly Azure Functions.
I've set up a DevOps Pipeline in my function, so everytime I need to run and test it I push my code from Pycharm on a dedicated branch and the function is deployed on Azure.
Once an Apache Beam pipeline designed and tested in Google’s cloud Dataflow using Python SDK and DataflowRunner what is a convenient way to have it in the Google cloud and manage its execution?
What is a convenient way to deploy and manage execution of a Python SDK Apache Beam pipeline for Google Cloud Dataflow?
Should it be somehow packaged? Uploaded to Google storage? Create a Dataflow template? How can one schedule its execution beyond a developer execution it from its development environment?
Update
Preferably without 3rd party tools or a need in additional management tools/infrastructure beyond Google cloud and Dataflow in particular.
Intuitively you’d expect that “deploying a pipeline” section under How-to guides of the Dataflow documentation will cover that. But you find an explanation of that only 8 sections below in the “templates overview” section.
According to that section:
Cloud Dataflow templates introduce a new development and execution workflow that differs from traditional job execution workflow. The template workflow separates the development step from the staging and execution steps.
Trivially you do not deploy and execute your Dataflow pipeline from Google Cloud. But if you need to share the execution of a pipeline with nontechnical members of your cloud or simply want to trigger it without being dependant on a development environment or 3rd party tools then Dataflow templates is what you need.
Once a pipeline developed and tested you can create a Dataflow job template from it.
Please note that:
To create templates with the Cloud Dataflow SDK 2.x for Python, you must have version 2.0.0 or higher.
You will need to execute your pipeline using DataflowRunner with pipeline options that will generate a template on the Google Cloud storage rather than running it.
For more details refer to creating templates documentation section and to run it from template refer to executing templates section.
I'd say the most convenient way is to use Airflow. This allows you to author, schedule, and monitor workflows. The Dataflow Operator can start your designed data pipeline. Airflow can be started either on a small VM, or by using Cloud Composer, which is a tool on the Google Cloud Platform.
There are more options to automate your workflow, such as Jenkins, Azkaban, Rundeck, or even running a simple cronjob (which I'd discourage you to use). You might want to take a look at these options as well, but Airflow probably fits your needs.