I have an apps coded in python/pandas/scipy which can be launched by anyone authorized. I want to use Google Cloud Platform to host it but I can't find a good way to set up this.
Since I want my app to be a web app, part of this is hosted on google app engine, but since google app engine does not seem compatible with big calculus and scientific libraries, I think I shall send the task to a VM with the scientific libraries.
My questions are:
1- could I create a VM each time the app is launched, in order to save money? But if I do this, I have to setup pandas scipy etc... each time, which should take some time?
2- Am I condemned to have a VM running every time and activated by the app? But if two people launch the calculus together, this can be really bad perf?
3 - Shall I package my app as a .exe and launch it as a standalone?
I am totally lost on how to handle such an architecture, can anyone give me some advices?
Thank you!
You can host your application on Managed VMs. Applications that run on managed VMs are not subject to the restrictions imposed on the sandboxed runtimes (Java, Python, PHP, and Go).
You can also choose the hosting environment (sandboxed or managed VM) separately for each module in your application. This means an application can contain a mix of modules that use different hosting environments. For instance, you might use the sandbox environment for your client-facing frontend, and use a module running in a managed VM for backend processing.
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I have a python script which run 24 hours on my local system and my script uses different third party libraries that are installed using pip in python
Libraries
BeautifulSoup
requests
m3u8
My python script is recording some live stream videos from a website and is storing on system. How google cloud will help me to run this script 24/hours daily and 7days a week.I am very new to clouds. Please help me i want to host my script on google cloud so i want to make sure that my script will work there same as it is working on local system so my money will not lost .
Yes it can. I would recommend familiarizing yourself with this Quickstart: Deploy a Python Service to Cloud Run and What is Cloud Run. When you use Cloud Run, you can provide your own Docker image that uses Python, or select from preexisting images.
Once you have a Cloud Run instance running, you can tie it into other Cloud Run instances or Cloud Functions which are scalable functions that use Cloud Run under-the-hood and allow you to easily scale your app. Additionally, these instances spin down to 0 if nobody is using the app which saves costs greatly. This can be modified of course so that the app is always spun-up.
In general I highly recommend looking at Cloud Run but also other services can handle the task such as a Compute Engine.
If you want to run 24/7 application on the cloud, whatever the cloud, you must not use solution with timeout (like Cloud Run or Cloud Functions).
You can imagine using App Engine flex, but it won't be my best advice.
The most efficient for me (low maintenance, cost efficient), is to use GKE autopilot. A Kubernetes cluster managed for you, you pay only the CPU/Memory that your workloads use.
You have to containerize your app to do that.
I have a fx vol model which I have written in python / questdb on my local machine. The standalone model works as an application in my machine and I also have lots of feeds written which are constantly updating the local machine questdb instance.
Now I want to move to web and have my application and database on the web server away from my machine.
I am not too familiar of web servers and how to install a questdb there.
From my knowledge I will need :
a VPS paid subscription where I have centOS and I have python support.
I need to install questdb here ( using docker ?)
Install all my python model here
Start the questdb in the VPS
Configure my scripts to use the hosted questdb
The model saves output in a table in questdb ; while the scripts also keep on updating new feeds to questdb
Start a webserver which provides a web access to the model results saved in the questdb
7.a I need to provide username and login for the website
7.b I need to use some vitualization
7.c I need the users once they are logged in to run some simulations
What I need some quidance is :
What sort of VPS service to look for
Has any one already installed a questdb in this way
Which webserver is the best for python
Lots of good questions there. Will try to help with all of them (disclaimer, I work for QuestDB as a developer advocate)
What sort of VPS service to look for:
Anything will work, but depending on the volume you are going to ingest/query, you will greatly benefit from a fast drive. In many cases the default disk drive for a VPS/Cloud provider will be slower than the SSD drive on your local development machine. See if the default works for you and otherwise select a better option when starting your virtual machine.
Has any one already installed a questdb in this way
Sure. If you want to install on AWS, Google Cloud, or Digital Ocean, you have detailed instructions at the QuestDB site. If you prefer to use Docker, it is fully supported, but a popular option is installing the binaries and just start QuestDB as a service.
Which webserver is the best for python
This one is tricky, as it really depends on what you will do with it. From your question I am getting the idea you want to run a small user-facing web application that will also run some background jobs. For that scenario a suitable framework can be Flask, which also offers some plugins for user/login management and for background tasks (using redis as a dependency)
I'm considering a suggestion to buy a Synology NAS server DiskStation DS720+:
https://www.synology.com/de-de/products/DS720+#specs
to run my python scripts or deploy web apps.
I am currently using heroku free account to deploy a web app.
I can't explain, why my web app delivers wrong output on heroku but works completely fine on my local computer.
No error message when deploying on heroku, eventhough the output is wrong.
Since I know nothing about NAS server, I want to ask if following tasks are possible with the diskstation:
Deploying Python Script and Web App (with Dash and Plotly as backbone)
Hosting dynamic website, not static website (like github pages)
Please pardon me, if these are silly questions, since this area is new for me.
The idea that these are possible and that I have my own cloud server is quite tempting.
Is there a better and more economical option for these purpose?
Thank you in advance for any inputs. Best regards, Gunardi Ali
The model you linked supports Docker, so that would probably be the easiest way to host dynamic websites such as Plotly dashboards.
However, if your apps are not generating the desired output when you run them on a machine other than your local computer then running them on a NAS might not work, either.
EDIT: Rereading your question, I would not suggest buying a Synology NAS if your primary use case is a web server. They are trimmed down for ease of use, but they come with their own set of limitations.
Depending on how resource intensive your applications are you might want to look at an Intel NUC or even a Raspberry Pi. If you plan to use the Synology's other features (file sharing, multi media, …) however, they are a great product.
I'm a complete novice in this area, so please excuse my ignorance.
I have three questions:
What's the best (fastest, easiest, headache-free) way of hosting a python program online?
I'm currently looking at Google App Engine and Web Frameworks for Python, but all the options are a bit overwhelming.
Which gui/viz libraries will transfer to a web app environment without problems?
I'm willing to sacrifice some performance for the sake of simplicity.
(Google App Engine can't do C libraries, so this is causing a dilemma.)
Where can I learn more about running a program locally vs. having a program continuously run on a server and taking requests from multiple users?
Currently I have a working Python program that only uses standard Python libraries. It currently uses around 2.7gb of ram, but as I increase my dataset, I'm predicting it will use closer to 6gb. I can run it on my personal machine, and everything is just peachy. I'd like to continue developing on the front end on my home machine and implement the web app later.
Here is a relevant, previous post of mine.
Depending on your knowledge with server administration, you should consider a dedicated server. I was doing running some custom Python modules with Numpy, Scipy, Pandas, etc. on some data on a shared server with Godaddy. One program I wrote took 120 seconds to complete. Recently we switched to a dedicated server and it now takes 2 seconds. The shared environment used CGI to run Python and I installed mod_python on the dedicated server.
Using a dedicated server allows COMPLETE control (including root access) to the server which allows the compilation and/or installation of anything. It is a bit pricy but if you're making money with your stuff it might be worth it.
Another option would be to use something like http://www.dyndns.com/ where you can host a domain on your own machine.
So with that said, perhaps some answers:
It depends on your requirements. ~4gb of RAM might require a dedicated server. What you are asking is not necessarily an easy task so don't be afraid to get your hands dirty.
Not sure what you mean here.
A server is just a computer that responds to requests. On the dedicated server (I keep mentioning) you are operating in a Unix (or Windows) environment just like you would locally. You use SOFTWARE (e.g. Apache web server) to serve client requests. My vote is mod_python.
It's a greater headache than a dedicated server, but it should be much closer to your needs to go with an Amazon EC2 instance.
http://aws.amazon.com/ec2/#instance
Their extra large instance should be more than large enough for what you need to do, and you only turn the instance on when you need it so you don't have the massive bill that you get with a dedicated server that's the same size.
There are some nice javascript based visualization toolkits out there, so you can model your application to return raw (json) data and render that on the client.
I can mention d3.js http://mbostock.github.com/d3/ and the JavaScript InfoVis Toolkit http://thejit.org/
I have a Django application that I would like to deploy to the desktop. I have read a little on this and see that one way is to use freeze. I have used this with varying success in the past for Python applications, but am not convinced it is the best approach for a Django application.
My questions are: what are some successful methods you have used for deploying Django applications? Is there a de facto standard method? Have you hit any dead ends? I need a cross platform solution.
I did this a couple years ago for a Django app running as a local daemon. It was launched by Twisted and wrapped by py2app for Mac and py2exe for Windows. There was both a browser as well as an Air front-end hitting it. It worked pretty well for the most part but I didn't get to deploy it out in the wild because the larger project got postponed. It's been a while and I'm a bit rusty on the details, but here are a few tips:
IIRC, the most problematic thing was Python loading C extensions. I had an Intel assembler module written with C "asm" commands that I needed to load to get low-level system data. That took a while to get working across both platforms. If you can, try to avoid C extensions.
You'll definitely need an installer. Most likely the app will end up running in the background, so you'll need to mark it as a Windows service, Unix daemon, or Mac launchd application.
In your installer you'll want to provide a way to locate a free local TCP port. You may have to write a little stub routine that the installer runs or use the installer's built-in scripting facility to find a port that hasn't been taken and save it to a config file. You then load the config file inside your settings.py and whatever front-end you're going to deploy. That's the shared port. Or you could just pick a random number and hope no other service on the desktop steps on your toes :-)
If your front-end and back-end are separate apps then you'll need to design an API for them to talk to each other. Make sure you provide a flag to return the data in both raw and human-readable form. It really helps in debugging.
If you want Django to be able to send notifications to the user, you'll want to integrate with something like Growl or get Python for Windows extensions so you can bring up toaster pop-up notifications.
You'll probably want to stick with SQLite for database in which case you'll want to make sure you use semaphores to tackle multiple requests vying for the database (or any other shared resource). If your app is accessed via a browser users can have multiple windows open and hit the app at the same time. If using a custom front-end (native, Air, etc...) then you can control how many instances are running at a given time so it won't be as much of an issue.
You'll also want some sort of access to local system logging facilities since the app will be running in the background and make sure you trap all your exceptions and route it into the syslog. A big hassle was debugging Windows service startup issues. It would have been impossible without system logging.
Be careful about hardcoded paths if you want to stay cross-platform. You may have to rely on the installer to write a config file entry with the actual installation path which you'll have to load up at startup.
Test actual deployment especially across a variety of firewalls. Some of the desktop firewalls get pretty aggressive about blocking access to network services that accept incoming requests.
That's all I can think of. Hope it helps.
If you want a good solution, you should give up on making it cross platform. Your code should all be portable, but your deployment - almost by definition - needs to be platform-specific.
I would recommend using py2exe on Windows, py2app on MacOS X, and building deb packages for Ubuntu with a .desktop file in the right place in the package for an entry to show up in the user's menu. Unfortunately for the last option there's no convenient 'py2deb' or 'py2xdg', but it's pretty easy to make the relevant text file by hand.
And of course, I'd recommend bundling in Twisted as your web server for making the application easily self-contained :).