this post was submitted on 27 Nov 2023
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Machine Learning
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I mark the expected duration of my experiments both in a google calendar as well as a project journal. That way, I know when it's "time" to check in on the runs.
I also use Weights & Biases, which sends me a mail if something crashes so I can check up when I really need to.
Curve watching is just a waste of time, you should train yourself to get out of the habit even if it's difficult for you.
Oooh, the calendar idea is a great tip. I’m going to borrow this from you.
Instead of relying on Weights & Biases sending me an email I’ve implemented alerts being sent to either a Slack or a Discord channel. They are always sent on error with the error message. I also receive a message after a predetermined interval of training with metrics.
Just another idea if someone doesn’t want to rely on W&B
I use pushover.net to send my phone a push notification when something noteworthy happens. Just drop a little function in my trainer loop. Extremely useful and you can programmatically set the notification message so it'll tell me what my numbers are when I'm at lunch or whatever.
Dude that's slick af
Curve watching in the initial steps of training is important, but once you get stable behavior time to go to the kitchen, grab some coffee, and do something else with the rest of the day.
No it isn’t. Has anyone here ran more than one experiment at a time? Clearly not because you can’t curve watch hundreds of runs at the same time. Anything that can be achieved by curve watching can be easily automated.
If people wrote documentation instead of watching the training progress chart get updated: *flying cars*
lol
I am stealing this, sorry
I wondered about weights and biases. Does it go well with tensorflow? Are you liking it so far?
Yeah, I'm liking it. It has strengths and weaknesses like every tool. The biggest advantages for me are:
Disadvantages:
The pros outweigh the cons for me.