What Actually Slows Down PyTorch Training? I Surveyed ML Engineers

I surveyed ML engineers about their training bottlenecks. The results were eye-opening.

The Setup

• 69% work with NLP/Transformers
• 84% have training runs lasting 1+ hours
• Most train on local GPUs (77%) or multi-GPU setups (46%)

These aren't quick experiments; performance matters.

The Problems

Top 3 pain points:

1. GPU Out-of-Memory: 62% - The nightmare scenario
2. Slow dataloader: 39% - Classic CPU bottleneck
3. Low GPU utilization: 31% - Expensive GPU sitting idle

77% of engineers hit OOM errors at least occasionally. This isn't rare, it's a regular frustration.

The Real Issue

When I asked what is slowing down their training:

46% said "I don't know"

Let that sink in. Nearly half of ML engineers can't identify their bottlenecks.

The other 31% pointed to forward pass, dataloader, or batch size issues. Only 8% had it figured out.

Current Tools Aren't Enough

What people use today:

• PyTorch Profiler (55%)
• TensorBoard (45%)
• Custom print statements (36%)
• WandB (27%)

The problem? These tools fall into two camps:

• Heavy profilers (PyTorch Profiler): Great detail, but 10-50% overhead
• Aggregate monitoring (TensorBoard, WandB): Shows overall metrics, not layer-level bottlenecks

What Engineers Actually Want

Most requested features:

1. Slowest layers and timings: 58%
2. Per-layer memory usage: 50%
3. CPU/GPU utilization: 50%
4. Dataloader breakdown: 42%

The pattern is clear: people want layer-level visibility without killing performance.

Why I Built TraceML

The gap is obvious—we need lightweight, always-on profiling that shows:

• Which layers are slow (forward and backward)
• Real-time updates during training
• Minimal overhead (1-2% measured on NVIDIA T4)
• Layer-level memory tracking

The dashboard shows you in real-time:

• Which layer takes 40% of your training time
• Whether your dataloader is actually the bottleneck
• Where to optimize first

No guessing. Just data.

Try It

GitHub: https://github.com/traceopt-ai/traceml/

If you've ever wondered why your training is slow or hit mysterious OOM errors, give it a try. Would love your feedback.

⭐ Star on GitHub if you find it useful