Typically configurations are stored in yaml files and passed into the scripts. Passing in kwargs is kind of ugly cause of two things
Non intuitive to the programmer writing the code to know what to expect in kwargs.
Validation. The key and value might not conform to programmers expectation and error checking has to be built into every method.
Ok, but I donât want my inner subroutine to have to parse a yaml file just know whether to set a particular flag.
So the same question still remains: how best to propagate the information âall the way inâ to subroutine from either the command line or a yaml file?
I ask this because Iâm doing lots of runs trying different ideas, and so far just editing the source code directly hasnât been a good practice as far as keeping a record of which runs have which settings.
Use Google Fire and add the params to your function signature. To have stuff propogate everywhere, you can use a singleton like we do in fastai (ours is called fastai.defaults) and dump config stuff there.
You can have a single config class loading the yaml file and the config instance can be a global instance, much like environment variables. I think however the solution depends on what you are actually trying to implement.
I heard of google fire for the first time in yesterdayâs class :-).
It sounds like a very neat way of adding command line args. The current args(parse args âŚetc ) lack the application context.
Hey @maxim.pechyonkin I remember youâre name from your blog, awesome series on Capsule Networks! Did you ever use a capsule based network or have seen one in the wild so far?
Anyone from Argentina?
I just realised this is worthy a share here too:
Iâve been spending much time getting better at Python.
I highly recommend following James Powell twitter: @dontusethiscode and watching any (yes, literally any-they are that good!) of his talks on YouTube for some great Python insights.
Do you know of any other great people to follow for getting better at Python?
Just came across this, shared by Ian Goodfellow on social media. Really cool!
âSemantic Image Synthesis with Spatially-Adaptive Normalizationâ
https://nvlabs.github.io/SPADE/?fbclid=IwAR3zbsP1ETOQSVjMI2c48GmP-uLpDvkcNKOAJQRmzzZ6GbOLs7AZrVn4vSo
Please fix your twitter URL, which currently links to Hortonhearsawho!
I realize now that I didnât answer part of rkingeryâs question: Iâm a remote participant. Iâm in the Denver area. A remote study group would be great for me.
Hi everybody, Iâm following the course from France, @tomsthom on twitter.
Is there anyone following from France?
I find the development process in Jupyter notebook to be very interesting!
I also feel very productive in Jupyter but didnât think it was possible to develop ârealâ software using it!
Very happy to learn how to do it with the techniques used for fastai development.
Hi, thanks for pointing that out.
It has a capital letter F in the beginning.
The correct is @Fmelobr on Twitter.
Thanks.
I agree James Powell is wonderful ! I even made a blog post about how decorators work in Python based on one of his talk 
Thanks, Fernando. You have (at least) one new twitter follower;-)
HI folks i would like to hear from you how do you manage your time studying for this course.
How many hours do you put in , how do you manage etc, perhaps this could be topic for another thread.
I have a demanding full time job i only find time to read or watch videos during weekdays commute. Any actual coding i need to wait for weekend and its kind of challenge. I would like some inspiration or advice form others if i can get.
Donât know how many of you have seen this yet, but theyâre $99 and I just ordered two of them:Jetson Nano by Nvidia.
It runs python and pytorch according to their post. Has 4 USB ports, Gig Ethernet, 128 CUDA cores, 4GB DDR 4 RAM
Hi Chandan,
I think you will find this thread very helpful:
Many Fastai veterans have posted their own study system, goals and experiences. This reply by Theodore might be something close to what you are looking for:
Happy learning!