The latest articles on Forem by Abhinav Srivastav (@abhinav_srivastav_5a243fd). https://forem.com/abhinav_srivastav_5a243fd https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3678495%2F63f66254-8a07-42c1-bd46-f0855b93a105.jpg https://forem.com/abhinav_srivastav_5a243fd en Abhinav Srivastav Thu, 25 Dec 2025 15:50:22 +0000 https://forem.com/abhinav_srivastav_5a243fd/what-actually-slows-down-pytorch-training-i-surveyed-ml-engineers-2m8j https://forem.com/abhinav_srivastav_5a243fd/what-actually-slows-down-pytorch-training-i-surveyed-ml-engineers-2m8j <p>I surveyed ML engineers about their training bottlenecks. The results were eye-opening.</p> <h2> The Setup </h2> <ul> <li><strong>69% work with NLP/Transformers</strong></li> <li><strong>84% have training runs lasting 1+ hours</strong></li> <li>Most train on local GPUs (77%) or multi-GPU setups (46%)</li> </ul> <p>These aren't quick experiments; performance matters.</p> <h2> The Problems </h2> <p><strong>Top 3 pain points:</strong></p> <ol> <li> <strong>GPU Out-of-Memory: 62%</strong> - The nightmare scenario</li> <li> <strong>Slow dataloader: 39%</strong> - Classic CPU bottleneck </li> <li> <strong>Low GPU utilization: 31%</strong> - Expensive GPU sitting idle</li> </ol> <p><strong>77% of engineers hit OOM errors at least occasionally.</strong> This isn't rare, it's a regular frustration.</p> <h2> The Real Issue </h2> <p>When I asked what is slowing down their training:</p> <p><strong>46% said "I don't know"</strong></p> <p>Let that sink in. Nearly half of ML engineers can't identify their bottlenecks.</p> <p>The other 31% pointed to forward pass, dataloader, or batch size issues. Only 8% had it figured out.</p> <h2> Current Tools Aren't Enough </h2> <p>What people use today:</p> <ul> <li>PyTorch Profiler (55%)</li> <li>TensorBoard (45%)</li> <li>Custom print statements (36%)</li> <li>WandB (27%)</li> </ul> <p>The problem? These tools fall into two camps:</p> <ul> <li> <strong>Heavy profilers</strong> (PyTorch Profiler): Great detail, but 10-50% overhead</li> <li> <strong>Aggregate monitoring</strong> (TensorBoard, WandB): Shows overall metrics, not layer-level bottlenecks</li> </ul> <h2> What Engineers Actually Want </h2> <p><strong>Most requested features:</strong></p> <ol> <li>Slowest layers and timings: 58%</li> <li>Per-layer memory usage: 50% </li> <li>CPU/GPU utilization: 50%</li> <li>Dataloader breakdown: 42%</li> </ol> <p>The pattern is clear: <strong>people want layer-level visibility without killing performance.</strong></p> <h2> Why I Built TraceML </h2> <p>The gap is obvious—we need lightweight, always-on profiling that shows:</p> <ul> <li>Which layers are slow (forward and backward) </li> <li>Real-time updates during training </li> <li>Minimal overhead (1-2% measured on NVIDIA T4) </li> <li>Layer-level memory tracking</li> </ul> <p>The dashboard shows you in real-time:</p> <ul> <li>Which layer takes 40% of your training time</li> <li>Whether your dataloader is actually the bottleneck</li> <li>Where to optimize first</li> </ul> <p>No guessing. Just data.</p> <h2> Try It </h2> <p><strong>GitHub:</strong> <a href="https://github.com/traceopt-ai/traceml/" rel="noopener noreferrer">https://github.com/traceopt-ai/traceml/</a></p> <p>If you've ever wondered why your training is slow or hit mysterious OOM errors, give it a try. Would love your feedback.</p> <p>⭐ <strong>Star on GitHub</strong> if you find it useful</p> ai opensource machinelearning developer