Estimated Training Time Calculation | Generated by AI

Home 2025.11 English 中文

---

Good — with the iter 100 timing you showed, I can give a concrete estimate.

Raw calculation (based on your observed pace)

• reported time per iteration at iter 100: 3995.11 ms = 3.99511 s / iter
• remaining iterations: 6000 − 100 = 5900
• remaining time = 5900 × 3.99511 s = 23,571.149 s = 6 hours 32 minutes 51 seconds

So at the current pace you’ll need ~6 hours 32 minutes 51 seconds to finish the remaining 5900 iterations.

Important assumptions & caveats

• This assumes the 3.995 s/iter remains constant for the rest of the run.

• In practice timings usually change:

  • they often improve after warmup/compilation finishes, or when caching/warmup I/O completes;
  • they can worsen if periodic evaluations, checkpoints, or data pipeline stalls happen.
• Your iter 0 was much slower (compile + warmup). iter 100 is a more realistic measure — but still early.

Practical ranges (what you might actually see)

• If it stays at 3.995 s/iter → ~6.5 hours (as above).
• If it improves to 0.2 s/iter → ~20 minutes.
• If it improves to 0.15 s/iter (optimistic for the 40M model on a 4070) → ~15 minutes.

Quick tips to reduce time if you want it faster

• Check data-loading: increase num_workers, use pin_memory, ensure the dataset is on a fast local SSD and preprocessed (parquet → memmap). I/O stalls frequently cause multi-second iterations.
• If you have headroom, increase batch_size (or reduce gradient_accumulation_steps) to reduce number of optimizer steps — but only if it fits memory.
• Try a run with compile=False as a quick experiment — sometimes torch.compile can be slower on some setups/Inductor configs.
• Disable or reduce heavy logging/eval/checkpoint frequency while profiling throughput.

If you want, paste iter 101..110 logs (or the next few) and I’ll compute the running average and a tighter ETA, and point out whether the time is trending down.

---

Back

x-ai/grok-4-fast

Donate