I am new to Azure pipelines. I am trying to create pipeline for deploying simple python application.
But I get error
No hosted parallelism has been purchased or granted
As I understand microsoft disabled the free grant of parallel jobs for public projects and for certain private projects in new organizations. But what if I don't need parallel jobs? I need jobs just to run one after the other. Can I turn off using of parallel jobs?
I chose template "Python package" and set environment variables "python.version" only one version "3.7". But it doesn't help. I still have the same error
No hosted parallelism has been purchased or granted
Free tier supports 1 Parallel job which is 1 job only.
See the microsoft's defination of parallel job below-
What is a parallel job?
When you define a pipeline, you can define it as a collection of jobs. When a pipeline runs, you can run multiple jobs as part of that pipeline. Each running job consumes a parallel job that runs on an agent. When there aren't enough parallel jobs available for your organization, the jobs are queued up and run one after the other.
As you rightly mentioned it's temporarily disabled by msft for Private Projects. However, you can ask for granting access to the free job. This can take upto take 2-3 days for you to get access.
To request the free grant for public or private projects, submit a request here
Related
I know this is not a direct code related question but more a best practices. I have several azure HTTPfunctions running but they timeout due to long calculations. I have added Durable orchestrations but even they time out.
As certain processes are long and time consuming (aka training an AI model) I have switched to Azure VM. What I would like to add to this is the possibility to start an Python task from an HTTP request on my azure VM.
basically doing the exact same as the Azure HTTPFunctions. What would be the best way to do this, any great documentation or recommendations much appreciated. So running an API on my VM in Python.
I have a Python script that pulls some data from an Azure Data Lake cluster, performs some simple compute, then stores it into a SQL Server DB on Azure. The whole shebang runs in about 20 seconds. It needs sqlalchemy, pandas, and some Azure data libraries. I need to run this script daily. We also have a Service Fabric cluster available to use.
What are my best options? I thought of containerizing it with Docker and making it into an http triggered API, but then how do I trigger it 1x per day? I'm not good with Azure or microservices design so this is where I need the help.
You can use Web Jobs in App Service. It has two types of Azure Web Jobs for you to choose: Continuous and Trigger. As I see you need the type Trigger
You could refer to the document here for more details.In addition, here shows how to run tasks in WebJobs.
Also, you can use Azure function timer-based on python which was made generally available in recent months.
I am fairly new using AWS and I need to run a batch process (daily ) and store the data in a MySQL database. It would take approximately 30 minutes for extraction and transformation. As a side note, I need to run pandas.
I was reading that lambda functions are limited to 5 minutes. http://docs.aws.amazon.com/lambda/latest/dg/limits.html
I was thinking of using an EC2 micro instance with Ubuntu or an Elastic Beanstalk instance. And Amazon RDS for a MySQL DB.
Am I on the right path? Where is the best place to run my python code in AWS?
If you need to run these operations once or twice a day, you may want to look into the new AWS Batch service, which will let you run batch jobs without having to worry about DevOps.
If you have enough jobs to keep up the computer busy for most of the day, I believe the best solution is to run a Docker based solution, which will allow you to more easily manage your image and be able to test on your local host ( and more easily move to another cloud if you ever have to). AWS ECS makes this as easy as Elastic beanstalk.
I have my front end running on Elastic beanstalk and my back end workers running on ECS. In my case, my python workers are running on an infinite loop checking for SQS messages so the server can communicate with them via SQS messages. But I also have CloudWatch rules ( as cron jobs ) that wake up and call Lambda functions which then post SQS messages for the workers to handle. I can then have three worker containers running on the same t2.small ECS instance. If one of the workers ever fails, ECS will recreate one.
To summarize, use python on Docker on AWS ECS.
I'm using about 2-3 Ubuntu EC2 instances just to run Python scripts (via cronjob) for different purposes and using RDS for PostgresDB, all of them work well so far. So I think you should give EC2 and RDS a try. Good luck!
I would create an EC2 instance, install Python and MySQL, and host everything on that instance. If you need higher availability you could use an ASG to maintain at least 1 instance running. If one AZ goes down, or the system fails, ASG will launch another instance in a different AZ. Use CloudWatch for EC2 instance monitoring.
If you do not need 24 hour availability for the database, you could even schedule your instance to start and stop when it is not needed reducing costs.
Coming from AWS, I am completely new to Azure in general and to Cloud Services spesifically.
I want to write a python application that leverages GPU on Azure in a PaaS architecture (Platform as a Service). The application will hopefully be deployed somewhere central, and then a number of GPU enabled nodes will spin up and run the application until it is done before closing down again.
I want to know, what is the shortest way to accomplish this in Azure?
Is my assumption correct that I will need to use what is called Cloud Services with a worker role, or will I have to create my own infrastructure based on single VMs running in IaaS?
It sounds like you created an application which need to do some general-purpose computing on GPU via Cuda or OpenCL. If so, you need to install GPGPU driver on Azure to support your Python application, so the Azure NC & NV Series VMs are suitable for this scenario like on AWS, as the figure below from here.
Hope it helps. Any concern, please feel free to let me know.
I have a Spark batch processing code (basically, the model training) that I execute with spark-submit from AWS EMR cluster. Now I want to be able to launch this job each day at specific time.
What is the standard way to do it?
Should I change the code and add the scheduling inside the code? Or is there any way to schedule spark-submit job?
Or maybe should I make it as a Spark Streaming job executed every 24 hours? (though I am interested in a specific time slot, i.e. between 11:00pm and 12pm)
Cron is more traditional... although it is good, Another way/option is RunDeck.
Use Rundeck as an easier to manage and more secure replacement for Cron or as a replacement for legacy tools like Control-M or HP Operations Orchestration. Rundeck gives your users a simple web interface (GUI or API) to go to for both on-demand and scheduled operations tasks.
What is Rundeck?
Rundeck is open source software that helps you automate routine operational procedures in data center or cloud environments. Rundeck provides a number of features that will alleviate time-consuming grunt work and make it easy for you to scale up your automation efforts and create self service for others. Teams can collaborate to share how processes are automated while others are given trust to view operational activity or execute tasks.
Rundeck allows you to run tasks on any number of nodes from a web-based or command-line interface. Rundeck also includes other features that make it easy to scale up your automation efforts including: access control, workflow building, scheduling, logging, and integration with external sources for node and option data.
If you are using Linux you can setup a Cron job to call the spark-submit script
http://kvz.io/blog/2007/07/29/schedule-tasks-on-linux-using-crontab/