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Thanks for creating this absolutely awesome learning resource!
For context, I'm passing all tests up to and including the task3_1 set.
I've been stuck on 3.2 for a while, and thought I'd double check the "ground truth" for matmul tests marked task3_2 (i.e. the values stored in c2). However, printing the expected results out and then computing them separately with numpy give different results.
So I'm writing this because I'm wondering whether:
tests prior to 3.2 failed to catch some bug which messes up the "ground truth" target for me personally, or
tests for 3.2 are buggy
Do you have any thoughts on this?
Thanks again for putting together this masterpiece.
Edit: Also, skipping to run_fast_tensor.py with cpu backend seems to work (i.e. training takes place, loss goes down, metrics go up), so I'll just ignore the two failing tests for now until I run into seemingly related issues. Next, CUDA!
The text was updated successfully, but these errors were encountered:
By the way. I find that many people get frustrated at the CUDA part. You might find it helpful to try these first. They're really fun: https://github.com/srush/GPU-Puzzles/
No worries, let me know if it'd help for me to make my code accessible somehow (I also wanted to learn how to set up self-hosted git and CI/CD, so my clone of the repo lives on my tiny homelab)
By the way. I find that many people get frustrated at the CUDA part. You might find it helpful to try these first. They're really fun: https://github.com/srush/GPU-Puzzles/
Damn, thanks for the pointer, it looks like another great resource! For the moment I'll try out 3.4 (as I just finished 3.3), and will save the puzzles as a refresher for the moment when I'll actually have to write a kernel in practice :p
Hi,
Thanks for creating this absolutely awesome learning resource!
For context, I'm passing all tests up to and including the task3_1 set.
I've been stuck on 3.2 for a while, and thought I'd double check the "ground truth" for matmul tests marked
task3_2
(i.e. the values stored inc2
). However, printing the expected results out and then computing them separately with numpy give different results.So I'm writing this because I'm wondering whether:
Do you have any thoughts on this?
Thanks again for putting together this masterpiece.
Edit: Also, skipping to
run_fast_tensor.py
with cpu backend seems to work (i.e. training takes place, loss goes down, metrics go up), so I'll just ignore the two failing tests for now until I run into seemingly related issues. Next, CUDA!The text was updated successfully, but these errors were encountered: