I am tring on microsoft azure ml to update pandas package.the problem is that i am trying to execute a python script and the supported python version are still at3.5 . unfortunately i did not notice that so my code is not execute cause is written on python 3.7 . so there is any way to update pandas in azure ml studio or the whole python ?? I have already try some solutions other stuckflow questions but are very old and not working
You can try the new "Visual Interface" in Azure ML service, which as an updated Python runtime. It is a new architecture over the UI of Azure ML Studio:
https://www.youtube.com/watch?v=QBPCaZo9xx0
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our group is starting to work on Azure Synapse for our data analytics and we've seen the integrated notebooks and sql. however, these are very minimal and might be good place to start, but soon enough we'll want to move the work to some IDE like VSCode or PyCharm...
any best practices on how or which IDEs are integrating with my Azure Synapse code? any of them where i can run sql or notebooks from my local IDE to the synapse account?
and if on the subject any best practices on developing in Synapse at all regarding how to write and test my work before firing it in the environment?
First of all install spark and hive tools in visual studio code. Then we can walk through how to submit jobs to Spark & Hive tools.
Spark and hive tools can be installed on platforms which are supported by visual studio.
Following items are required to complete the steps involved in moving notebooks from local IDE to the synapse account.
Serverless apache spark pool
Visual Studio Code
A Pyspark interactive environment for Visual Studio Code
Steps involved in order to process or move sql / notebooks from local IDE to the synapse account.
So here after meeting the above prerequisites you can install Spark & Hive Tools for Visual Studio Code by following these steps:
Open Visual Studio Code.
From the menu bar, navigate to View > Extensions.
In the search box, enter Spark & Hive.
Select Spark & Hive Tools from the search results and then select Install.
5.Select Reload when necessary.
please find the below link for more details.
https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/vscode-tool-synapse
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'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 created a Machine Learning classifier with Python, using word2vec and I want to create an API to use it in production.
What's the easiest way to do that please ?
I heard of AWS Lambda and Microsoft Azure Machine Learning Studio but I am not sure it would work with word2vec.
For example with AWS Lambda, would I need to reload the libraries each time (it takes a while to do that).
And can I install any Python package with Microsoft Azure Machine Learning Studio and choose any kind of machine (need a lot of RAM for word2vec) ?
Thanks
By now, according to the offical document Execute Python machine learning scripts in Azure Machine Learning Studio about limitations for customizing Python installation (No.4 item), as below.
Inability to customize Python installation. Currently, the only way to add custom Python modules is via the zip file mechanism described earlier. While this is feasible for small modules, it is cumbersome for large modules (especially those with native DLLs) or a large number of modules.
Unfortunately, the python package like word2vec which includes some C modules could not be installed customize on Azure ML studio.
The only workaround way is to create a VM to install word2vec for Python and create a webservice in Python for calling in the Execute Python Script module of Azure ML studio via network IO, that as the answer of Azure ML Execute Python Module: Network I/O Disabled? said Azure ML studio support Network IO for Execute Python Script module now.
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.