13. CUDA out of memory 28. Loss went to NaN on step 1 37. I’ll document this later 15. Someone hogged all GPUs 19. Server rebooted overnight. RIP my results. 38. Nice pajama top on the call 25. Extreme class imbalance strikes again 18. SSH connection died mid- experiment 11. Who left training running ALL weekend? 30. Forgot to shuffle the dataset 16. OOM killed at epoch 99 of 100 24. The data is too noisy to use 23. Dataset has 47 samples. Total. 29. Works perfectly on my machine 27. Ground truth isn’t actually true 21. The labels are completely wrong 31. Wait… which cell did I run last? 3. A meeting could have been an email 10. When will the model be production- ready? 26. Test data leaked into training set 39. Pet photobombed the video call 20. We need more data 33. Restart kernel, run all, pray 5. Can we make it real-time? 34. This notebook is 2000 cells long 6. Stakeholder ghosted after week 2 36. “Quick 5-min sync” = 45 minutes 12. Server crashed during the live demo 35. As per my last email… 2. Actually, let’s pivot the entire approach 4. Requirements changed… again 7. This should be simple, right? 40. Internet died at demo time Free! 32. Jupyter kernel died mysteriously 14. The server is suspiciously slow today 22. Who annotated this?! 9. The business need has… evolved 8. Just add ONE more feature before launch 17. Forgot to kill my process… sorry team! 1. Can we get 99.9% accuracy? 13. CUDA out of memory 28. Loss went to NaN on step 1 37. I’ll document this later 15. Someone hogged all GPUs 19. Server rebooted overnight. RIP my results. 38. Nice pajama top on the call 25. Extreme class imbalance strikes again 18. SSH connection died mid- experiment 11. Who left training running ALL weekend? 30. Forgot to shuffle the dataset 16. OOM killed at epoch 99 of 100 24. The data is too noisy to use 23. Dataset has 47 samples. Total. 29. Works perfectly on my machine 27. Ground truth isn’t actually true 21. The labels are completely wrong 31. Wait… which cell did I run last? 3. A meeting could have been an email 10. When will the model be production- ready? 26. Test data leaked into training set 39. Pet photobombed the video call 20. We need more data 33. Restart kernel, run all, pray 5. Can we make it real-time? 34. This notebook is 2000 cells long 6. Stakeholder ghosted after week 2 36. “Quick 5-min sync” = 45 minutes 12. Server crashed during the live demo 35. As per my last email… 2. Actually, let’s pivot the entire approach 4. Requirements changed… again 7. This should be simple, right? 40. Internet died at demo time Free! 32. Jupyter kernel died mysteriously 14. The server is suspiciously slow today 22. Who annotated this?! 9. The business need has… evolved 8. Just add ONE more feature before launch 17. Forgot to kill my process… sorry team! 1. Can we get 99.9% accuracy?
(Print)
13. CUDA out of memory
28. Loss went to NaN on step 1
37. I’ll document this later
15. Someone hogged all GPUs
19. Server rebooted overnight. RIP my results.
38. Nice pajama top on the call
25. Extreme class imbalance strikes again
18. SSH connection died mid-experiment
11. Who left training running ALL weekend?
30. Forgot to shuffle the dataset
16. OOM killed at epoch 99 of 100
24. The data is too noisy to use
23. Dataset has 47 samples. Total.
29. Works perfectly on my machine
27. Ground truth isn’t actually true
21. The labels are completely wrong
31. Wait… which cell did I run last?
3. A meeting could have been an email
10. When will the model be production-ready?
26. Test data leaked into training set
39. Pet photobombed the video call
20. We need more data
33. Restart kernel, run all, pray
5. Can we make it real-time?
34. This notebook is 2000 cells long
6. Stakeholder ghosted after week 2
36. “Quick 5-min sync” = 45 minutes
12. Server crashed during the live demo
35. As per my last email…
2. Actually, let’s pivot the entire approach
4. Requirements changed… again
7. This should be simple, right?
40. Internet died at demo time
Free!
32. Jupyter kernel died mysteriously
14. The server is suspiciously slow today
22. Who annotated this?!
9. The business need has… evolved
8. Just add ONE more feature before launch
17. Forgot to kill my process… sorry team!
1. Can we get 99.9% accuracy?
