agentfuzzy999

agentfuzzy999 t1_j7vu3c6 wrote

Reply to Entry to a career in deep learning by No-Celebration6994

I:

  • got a job writing production code
  • graduated with a bachelors
  • just got another job writing production code

In the span of a year.

I think being involved in multi-person DL projects/competitions/papers to put on your resume, and having good interviewing skills is genuinely more important than knowing the latest and greatest models and algorithms. It’s the same as the rest of the SWE field.

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agentfuzzy999 t1_j6uzju1 wrote

Reply to Using Jupyter via GPU by AbCi16

You do not need docker. Open a notebook, import torch or tensorflow, check if GPU is available. If true, profit. If false, you have python/framework/CUDA problems.

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agentfuzzy999 t1_j5qy82t wrote

Reply to Best cloud to train models with 100-200 GB of data? by Zealousideal-Copy463

“Should I just buy a 4090”

Ok Jeff Bezos

4090 clock speed is going to be faster than similar instances that use T4s, plus wayyyyyyy more CUDA cores. Training will be significantly faster, if you can fit the model on the 4090. If you can “business expense” a 4090 for your own machine, good lord do that.

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agentfuzzy999 t1_j3pbk38 wrote

Reply to Cloud VM GPU is much slower than my local GPU by Infamous_Age_7731

I have trained locally and in the cloud on a variety of cards and server arch’s, depending on what model you are training it could be for a huge variety of reasons, but if you can fit the model on a 3080 you really aren’t going to be taking advantage of the A100s huge memory, the higher clock speed of the 3080 might simply suit this model and parameter set better.

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