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Production ML — Training Stack

PyTorch

From minimal training loops to gradient accumulation, EMAs, distributed primitives, and checkpoints. Train models at scale.

0 / 12 solved
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  1. 1. Not solved yet. Mini-Batch Training
  2. 2. Not solved yet. Training Loop
  3. 3. Not solved yet. Gradient Accumulation
  4. 4. Not solved yet. Gradient Clipping
  5. 5. Not solved yet. Eval Loop with Metrics
  6. 6. Not solved yet. Exponential Moving Average
  7. 7. Not solved yet. Model Checkpointing
  8. 8. Not solved yet. Data Collator with Padding
  9. 9. Not solved yet. Ring All-Reduce
  10. 10. Not solved yet. Distributed Training Step End-to-End
  11. 11. Not solved yet. JIT Compile a Function
  12. 12. Not solved yet. Vectorize with vmap

Check yourself

4 questions · one attempt each

These do not count toward finishing the track. They are here to catch the things that are easy to read past.

0 / 4

Why does mixed-precision training keep a float32 copy of the weights? 

fp16: 1.0 + 1e-4
fp32: 1.0 + 1e-4
Question 1 of 4