2,433 karma · joined December 5, 2012
Also, some of your, and other users', feedback were also very helpful to bring some changes to the infrastructure for the next release, to maximize the usage of the cluster's resources.
disclaimer: I am the author of Polyaxon.
I would like to outline a couple of differences between CometML and Polyaxon, as mentioned before, we are also trying to solve issues related to technical debt in ML, but not only, Polyaxon tries also to simplify training and scheduling parallel and distributed learning. there are also a couple of differences, I see CometML as dashboard, Polyaxon does not have an extensive dashboard as CometML, but it leverages Tensorboard for most of the visualisations. We use the CLI or the API for programatic access to the platform. Most importantly, Polyaxon aims to be an open source and to be installed on premise or in the cloud, it solves the issue related to code tracking based on an internal git and a docker registry, and as someone else mentioned that resources for running an experiment could be an issue for future reproducibility, Polyaxon restarts the experiments with the same resources and dockerfiles, it also tracks hyper params as part of the configuration.
For hyper params tuning and suggestion, Polyaxon can also do hyper params search based on a couple of algorithms, and for the next release, it will include also a service similar to vizier for suggesting more experiments/group of experiments based on a given search space.
Disclaimer: I am the author of Polyaxon
Also the repo is under tensorflow organisation.
Also, you can check the Google Python Style Guide:
http://code.google.com/p/soc/wiki/PythonStyleGuide
http://google-styleguide.googlecode.com/svn/trunk/pyguide.ht...
http://deansofer.com/posts/view/14/AngularJs-Tips-and-Tricks...
http://www.yearofmoo.com/2012/10/more-angularjs-magic-to-sup...
http://blog.petermolgaard.com/tag/angularjs/
http://onehungrymind.com/angularjs-sticky-notes-pt-1-archite...
http://www.raymondcamden.com/index.cfm/2011/11/29/My-first-A...
A google discussions about the difference between factory and service:
https://groups.google.com/forum/?fromgroups=#!searchin/angul...
For the case when the user isn't willing to give access to his/her twitter account.
The error returned is KeyError, since you are trying to access :
user = User.objects.get(username = authorized_tokens['screen_name'])
I see that you have put a try, except blocks. But you are not catching the KeyError exception. You are only making an exception when the user doesn't exist.
you should add a block : except KeyError. And figure out the logic for this particular case. (probably just redirecting the user to the home page)
[1]I don't know if you are familiar with the french educational system, and specifically the CPGE( classes préparatoires aux grandes écoles). It consists of two very intensive years of mathematics, physics, philosophy and engineering science.
Email : - mouradmourafiq [at] gmail
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Web Developer, RESTful API integration and development, also interested by data analysis.
I have experience with current technologies:
-Python
* numpy
* scipy
* pandas
* django
-Hadoop-javascript
* jquery
* angularjs (learning)
* momentjs
-Posgresql-mysql
-sphinx search
-elasticsearch
-twitter bootstrap
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Github - http://www.github.com/mouradmourafiq
Also, if you happen to notice something that you don't like or you don't agree with, try to make more constructive comments, so that people can respond or enhance.
In this way the backend is responsible for deep validations and making the data as json format for later use.
So in the end :
*Model : data comes from the server json format, becomes jaavscript objects.
*View: HTML, and directives when you need to manipulate the DOM
*Controller (also your services):
->Queries to the API
->Validation (probably duplicate some of your server's side)
->Business logic.
->Rooting of templates.
You might think that there's a duplication, in terms of controls and validation, but the thing is, you should think of it as two separate level : One for your API (could be used for many purposes), Second a robust, fast client side that deals with data. * numpy
* scipy
* pandas
* django
-Hadoop-javascript
* jquery
* angularjs (learning)
* momentjs
-Posgresql-twitter bootstrap
As for me I had a side project, geo-social planning tool, that had 5000 users, but not actively using the app. I shut it down, because it was unlikely to succeed, it was strange to the way people meet and go out. And also, because I noticed that other similar projects were also stuck in the chicken and egg problem.