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I'm not sure this question is in the right Stack Exchange website, but I thought this place is a good starting point, because of the experience of users.

This question is not about artificial intelligence, but about time-management.


I am a new machine learning engineer, and I am working with Neural Networks.

However, I have difficulties to manage my time : the training time of my neurals networks are long (an hour for the simplest model, up to more than 24 hours).

I find it difficult to find good hyperparameters in these conditions. Another problem is that often, I find myself having nothing to do while training time.

I am looking for methods/advices to optimize my working time considering the training time for my network can be very long.


What I have tried :

  • Organizing my work so I have to do all the little tasks (code cleaning, documentation, etc...) while training.
  • Use this free-time to learn more.
  • Meanwhile, implement other models, to use as a baseline.
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I find it difficult to find good hyperparameters in these conditions. Another problem is that often, I find myself having nothing to do while training time.

In your practice I highly recommend starting with a small and basic model to get it working the best it can. Plot learning curves to diagnose what your next steps should be. These will ensure that you are not wasting time on a large model that won't work. That is, your process should be iterative. The early iterations should keep you busy/engaged.

The following assumes that your neural network that is training is implemented correctly and you have performed all the correct diagnostics to make sure it is running okay.

Organizing my work so I have to do all the little tasks (code cleaning, documentation, etc...) while training.

Keep doing this

Use this free-time to learn more.

Yep

Meanwhile, implement other models, to use as a baseline.

Couldn't hurt

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