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.
Related
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.
Summary: I'm trying to create a python program to run Powershell scripts in Azure Windows VMs. But I can't find good documentation about which libraries of the SDK I can use.
Detailed: In order to automate certain administrative tasks, a python program should run, authenticate into Azure and then run Powershell scripts in certain Windows VMs. I think I have the auth part thanks to azure.identity, but I can't find any library to interact with a running VM. Sure, lots for creating or modifying a VM in Azure, but nothing to interact with it. Neither in the SDK or in the API browser. I know it can be done with Azure CLI, but id like to use the SDK, if possible at all.
My understanding is that you would like to run PowerShell remotely using the Python SDK.
AFAIK, I don't think there is a provision within the Azure Python SDK to run remote PS scripts.
Workaround :
(But please note that this outside the Azure Python SDK.)
You could create Azure Windows WinRM VM template and you could execute ps commands from the Python code using the winrm library like discussed in this thread.
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
I'll have a linux machine with a virtual machine installed for Microsoft azure soon. I need to run some data mining/graph analysis algorithms on the azure because I work with big data. I don't want to use azure machine learning stuff. just want to run my own python code. What are the steps? If needed, hoe can I install python libraries on azure?
There is no additional steps to do in comparison to Your own. local server. Linux on Azure is a standard Linux machine. If You are looking for step-by-step hopw to on running Linux VM on Azure, just search on azure.com and You will find it. I think You will not have any problems even without documentation. Azure portal is very simple to use, also CLI tool for Linux, Mac and Windows. You just need to run Linux VM and SSH-in to it. Nothing more. If You need some help, just write here.
I am using the Windows Azure SDK for Python to provision Linux VMs from a Python program using the Service Management API.
Apparently Azure makes it possible to connect VMs directly in a single virtual network:
http://www.windowsazure.com/en-us/manage/services/networking/add-a-vm-to-a-virtual-network/
This feature is exposed in the REST API, for instance to create a new VM:
http://msdn.microsoft.com/en-us/library/windowsazure/jj157181
and to create a new VM deployment in a given virtual network:
http://msdn.microsoft.com/en-us/library/windowsazure/jj157194
However by reading the source code of the Python SDK, it seems that this feature is not exposed in the Python API:
https://github.com/WindowsAzure/azure-sdk-for-python/blob/master/src/azure/servicemanagement/servicemanagementservice.py#L850
Is this a known issue? How to provision Azure virtual networks and VM from a Python program? Do I have to generate the XML of the network and the VM deployment by my-self instead?
Apparently this is a known issue: https://github.com/WindowsAzure/azure-sdk-for-python/issues/79
When creating a virtual machine with
create_virtual_machine_deployment, you cannot specify a
VirtualNetworkName for the delpoyed VM.
Possible fix : . add virtual_network_name=None parameter in
create_virtual_machine_deployment and
virtual_machine_deployment_to_xml and set VirtualNetworkName in xml
(right after ) if virtual_network_name: xml+=
_XmlSerializer.data_to_xml([('VirtualNetworkName', virtual_network_name)])