I wonder if it is possible to design ML experiments without using the drag&drop functionality (which is very nice btw)? I want to use Python code in the notebook (within Azure ML studio) to access the algorithms (e.g., matchbox recommender, regression models, etc) in the studio and design experiments? Is this possible?
I appreciate any information and suggestion!
The algorithms used as modules in Azure ML Studio are not currently able to be used directly as code for Python programming.
That being said: you can attempt to publish the outputs of the out-of-the-box algorithms as web services, which can be consumed by Python code in the Azure ML Studio notebooks. You can also create your own algorithms and use them as custom Python or R modules.
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I completed the Google Cloud tutorial called Intro to Vertex Pipelines and I now want to adapt the completed Jupyter notebook for two things:
Build and train a regression ML model instead of classification (used in the tutorial).
I need to use my own dataset that is in a Google Cloud bucket.
I also went through other Google Cloud tutorials looking for code for a regression model using their pre-built components and Python packages but I have run into a variety of errors when attempting to just swap in my dataset for the tutorial's pre-made dataset.
My main question is where I need to put the location of my dataset and how I should format that information (Both tutorials I have gone through call their dataset in different ways).
For some context, I have about a year or two of coding experience with Python and have done a few, smaller projects similar to this one but am struggling with using Google's tools.
How to Create a virtual machine in python using CloudSim simulation tool in python.
To the best of my knowledge, there is no python implementation for CloudSim. But if you want to try CloudSim Plus, a full-featured, actively maintained, fully documented and easier to use framework for cloud computing, check the official home page at https://cloudsimplus.org
There is also the CloudSim Plus Gateway project that enables you to use python to build and run CloudSim Plus experiments.
Can Google Cloud Functions handle python with packages like sklearn, pandas, etc? If so, can someone point me in the direction of resources on how to do so.
I've been searching a while and it seems like this is impossible, all I've found are resources to deploy the base python language to google cloud.
Python 3.7 is supported now.
Steps to create one via the google cloud console:
go to google cloud functions in the google cloud console and click on create function
2.specify the function's properties
select trigger
4.change runtime to python 3.7
enter your cloud function logic and entry point
enter python dependencies in requirements.txt
EDIT: As of July 2018 there is now a Python runtime (3.7) available for Google Cloud Functions!
OLD ANSWER: Google Cloud Functions (GCF) are written in JavaScript (executed in a Node.js runtime), so there is no way for them to actually handle Python at this moment. There is a Python module at GitHub that you might have come across and it can be used to write and deploy GCF with one of three trigger types: http, Pub/Sub and bucket. The module takes care of translating your Python logic to a JavaScript code that is later run inside Google Cloud Platform.
When it comes to other packages like pandas, the ‘translation’ into JavaScript was not prepared for them by anyone AFAIK. If you really don’t like the idea of jumping into JavaScript and writing the Cloud Function code on your own (with the logic you intended to use in a Python script), you have a possible workaround. You can evoke your Python script from inside of the Cloud Function written in JS - the idea was discussed in this topic. Another way is using Object Change Notifications or Pub/Sub Notifications as explained here.
As of 19th July 2018, Google Cloud Functions supports Python 3.7.
Kindly check the Runtime environment to find the Python 3.7 runtime and sample script (based on Flask) .
--UPDATED--
Official Documentation for the Google Cloud Functions - Python 3.7 support Beta Release.
This is a beta release of the Python runtime for Google Cloud
Functions. This feature might be changed in backward-incompatible ways
and is not subject to any SLA or deprecation policy.
SkLearn, Numpy is supported in Google Cloud function. Also I've run a sample test to confirm the availability of Pandas as well and its working fine.
https://github.com/mkanchwala/google-functions-python-example
Hope this helps to all the "Py" lovers.
You can use AWS lambda as well if you want to work around and still use Python as your main language. Some modules/packages will need to be imported via zip file with AWS Lambda but it has a broader range of usable languages than GCF
Do you know how back up machine learning models in Azure Machine Learning Studio in case of idle time when subscription is not bought. I would preferably back those models in Azure DB/DWH on other accounts/instances of Azure. Is it actually possible to copy models' flow to another locations or share it with other users?
I would appreciate the answer.
Based on my understanding, I think you want to export your experiments in Azure Machine Learning Studio to local as a file or other type resources. There seems not to be any way for doing this on Azure offical sites, but I searched a third party tool named azuremlps which is a PowerShell module for Azure ML Studio from a MSFT. You can try to use the cmdlet Export-AmlExperimentGraph to export a specific Experiment graph to a file in JSON format.
Hope it helps.
There's no way to backup the experiments you've created directly. You can share the models with other users in two way.
Share publicly through the gallery. All can see the experiment you've created.
Share privately allows you to share an experiment with the people only have the link for your published experiment.
Use 'Publish to gallery' operation as shown in below for the above task.
Is there a book or resource for learning cloud in Python or Scala? I know Django and app-engine but I am not that interested in learning more about a client framework. I'm interested in learning the core thing.
Steve Marx published a blog post describing a python sample running in Windows Azure, with the Rocket web server. The code is on github.
This will show you some interesting elements of setting up a python app in Windows Azure, including startup tasks. You'll still want to take a look at the Windows Azure Platform Training Kit to get a deeper understanding of Windows Azure.