I have been studying about Kubeflow and trying to grasp how do I write my first hollo world program in it and run locally on my mac. I have kfp and kubectl installed locally on my machine. For testing purpose I want to write a simple pipeline with two functions: get_data() and add_data(). The doc is overwhelming that I am not clear how to program locally without k8s installed, connecting remote GCP machine and debug locally before creating zip and upload or there way to execute code locally and see how is it running on Google cloud?
Currently you need Kubernetes to run KFP pipelines.
The easiest way to deploy KFP is when you use the Google Cloud Marketplace
Alternatively you can locally install Docker Desktop which includes Kubernetes and install standalone version of KFP on it.
After that you can try this tutorial: Data passing in python components
Actually you can install a reduced version of kubeflow with minikf. More info https://www.kubeflow.org/docs/distributions/minikf/minikf-vagrant/
Check whether you are using kubeflow pipelines from the google cloud marketplace, or a custom kubernetes cluster. If you are using the managed one, you can see your pipeline running through the kubeflow pipelines management console.
for details about how to create components based on functions, you can check https://www.kubeflow.org/docs/components/pipelines/sdk/python-function-components/#getting-started-with-python-function-based-components
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I want to use this repo and I have created and activated a virtualenv and installed the required dependencies.
I get an error when I run pytest.
And under the file binance_cdk/app.py it describes the following tasks:
App (PSVM method) entry point of the program.
Note:
Steps tp setup CDK:
install npm
cdk -init (creates an empty project)
Add in your infrastructure code.
Run CDK synth
CDK bootstrap <aws_account>/
Run CDK deploy ---> This creates a cloudformation .yml file and the aws resources will be created as per the mentioned stack.
I'm stuck on step 3, what do I add in this infrastructure code, and if I want to use this on amazon sagemaker which I am not familiar with, do I even bother doing this on my local terminal, or do I do the whole process regardless on sagemaker?
Thank you in advance for your time and answers !
The infrastructure code is the Python code that you want to write for the resources you want to provision with SageMaker. In the example you provided for example the infra code they have is creating a Lambda function. You can do this locally on your machine, the question is what do you want to achieve with SageMaker? If you want to create an endpoint then following the CDK Python docs with SageMaker to identify the steps for creating an endpoint. Here's two guides, the first is an introduction to the AWS CDK and getting started. The second is an example of using the CDK with SageMaker to create an endpoint for inference.
CDK Python Starter: https://towardsdatascience.com/build-your-first-aws-cdk-project-18b1fee2ed2d
CDK SageMaker Example: https://github.com/philschmid/cdk-samples/tree/master/sagemaker-serverless-huggingface-endpoint
I want to create a machine learning pipeline using python with PyCharm and run everything in azure machine learning service workspace. Then I want to integrate my pycharm script in a way when I edit and save my script, it runs a new experiment in Azure ML workspace.
I have check all the tutorials on using Azure ML service using python sdk, however, every time it is via notebooks but not with pycharm.
Azure Machine Learning service can be used from any editor that supports Python 3.5 - 3.7: PyCharm, VSCode or just plain python.exe. We've used Notebooks because it makes it easy to package and present the examples, however you should be able to copy-paste the Python code and run in any editor.
I have a requirement where I have to use the Python libraries I created on my machine, in the cloud, such that whenever any new dataset is loaded, this Python library have to start acting on it.
How can I do this? Where will I put the dataset and the python codes in Azure?
Thanks,
Shyam
There are more possibility to do that.
Run your Python code on Azure Web Apps for Containers—a Linux-based, managed application platform
Azure Functions allows running Python code in a serverless environment that scales on-demand.
Use a managed Hadoop and Spark cluster with Azure HDInsights, suitable for enterprise-grade production workloads.
Use a friction-free data science environment that contains popular tools for data exploration, modeling, and development activities.
Azure Kubernetes Service (AKS) offers a fully-managed Kubernetes cluster to run your Python apps and services, as well as any other Docker container. Easily integrate with other Azure services using Open Service Broker for Azure.
Use your favorite Linux distribution, such as Ubuntu, CentOS, and Debian, or Windows Server. Run your code with scalable Azure Virtual Machines and Virtual Machine Scale Sets.
Run your own Python data science experiments using a fully-managed Jupyter notebook with Azure Notebooks.
The easiest and fastest way to run your code is 1. option. Create a web app and a web job in there.
I'm new to Spark.I installed a Spark 2.3.0 in Stand-Alone-Mode on an Ubuntu 16.04.3 server. That runs well so far. Now I would like to start developing with pyspark because I've got more experience using python than scala.
Ok. Even after using google for a while I'm not sure how I should setup my development environment. My local machine is a windows 10 laptop with eclipse neon and pydev configured. What are the neccessary steps to set ist up that I can develop in a local context and submit my modules to the spark cluster on my server?
Thank for helping.
use spark-submit to run locally or on a cluster. There are many online tutorials for this. I like the AWS documentation which explains the architecture, has sample spark code, and gives examples of local and remote commands. Even if you are not using AWS EMR the basics are the same.
give it a try and let us know how it goes
I have a small Python (Flask) application running in a Docker container.
The container orchestrator is Kubernetes, all running in Azure.
What is the best approach to set up centralized logging? (similar to Graylog)
Is it possible to get the application logs over OMS to Azure Log Analytics?
Thank you,
Tibor
I have a similar requirement. I have a continuously running Python application running in a Docker container. So far I have found that the Azure SDK for Python supports lots of integration into Azure from Python. This page might be able to help:
https://pypi.org/project/azure-storage-logging/
Here is also a package and guide how to set up Blob Storage and enable logging:
https://github.com/Azure/azure-storage-python/tree/master/azure-storage-blob