TraceML
Find out if your GPU is waiting for data during PyTorch training.
Quickstart • Try in Colab • Integrations • Documentation • GitHub Issues
TraceML is an open-source tool designed for lightweight, always-on diagnostics during PyTorch training. See how much time is spent waiting for the next batch, where step time goes, and whether a slow worker is holding up a distributed run.
At the end of a run, it gives you:
- A diagnosis: waiting for input, costly data transfers, compute-heavy steps, or a likely slow distributed worker.
- The evidence: step timings, CPU/GPU usage, memory trends, and per-worker comparisons.
- The next step: what part of your training setup to investigate first.
Example diagnosis
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| TraceML Run Summary |
| bert_finetune · 1 rank · 1 GPU observed · 256 common steps · 52.4s |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| |
| Verdict: INPUT-BOUND (CRITICAL) |
| Why: Input Wait took 64% of Step Time. |
| Next: Increase workers, prefetch, or storage throughput. |
| |
| STEP TIMING (Window Average), GPU Clock || STEP MEMORY: BALANCED |
| Step Time 200.4 ms 100% || |
| ├─ Input Wait 128.0 ms 64% ◀ cause || |
| ├─ Compute 68.0 ms 34% || avg per-step peak avg |
| │ ├─ Forward 24.0 ms 12% || Allocated 2.9 GB |
| │ ├─ Backward 38.0 ms 19% || Reserved 3.2 GB |
| │ └─ Optimizer 6.0 ms 3% || |
| ├─ H2D 0.4 ms <1% || |
| └─ Residual 3.6 ms 2% || |
| DataLoader fetch: 120.0 ms (CPU, supplemental) || |
| |
| SYSTEM METRICS: LOW GPU UTIL || PROCESS METRICS: NORMAL |
| Evidence: GPU utilization averaged 24%. || |
| || |
| avg || avg |
| CPU 18% || CPU capacity 14% |
| RAM used 6.2 GB (19%) || RSS used 3.1 GB (10%) |
| GPU util 24% || CUDA allocated 2.9 GB |
| GPU memory/device 3.3 GB (21%) || CUDA reserved 3.2 GB (20%) |
| GPU temperature 42C || |
| GPU power 58W || |
| |
| |
| Full evidence: logs/bert_finetune/final_summary.json (--html-report) |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
TraceML produces this diagnosis at the end of the instrumented training run.
Want the complete evidence? Jump to the single-run and distributed example reports.
Quickstart
1. Install
TraceML expects an existing PyTorch project. Install the TraceML package with:
pip install traceml-ai
Using uv instead? Run uv add traceml-ai.
2. Instrument the training step
Add TraceML around the core step in your existing PyTorch training script:
+ import traceml_ai as traceml
+ traceml.init(mode="auto")
for batch in dataloader:
+ with traceml.trace_step(model):
optimizer.zero_grad(set_to_none=True)
outputs = model(batch["x"])
loss = criterion(outputs, batch["y"])
loss.backward()
optimizer.step()
3. Run
traceml run train.py
Summary mode is the default. TraceML prints the final diagnosis and writes
final_summary.json and final_summary.txt under logs/<run_name>/.
No training script ready? Try the Colab example.
Example Reports
See the complete single-run report
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| TraceML Run Summary |
| bert_finetune · 1 rank · 1 GPU observed · 256 common steps · 52.4s |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| |
| Verdict: INPUT-BOUND (CRITICAL) |
| Why: Input Wait took 64% of Step Time. |
| Next: Increase workers, prefetch, or storage throughput. |
| |
| STEP TIMING (Window Average), GPU Clock || STEP MEMORY: BALANCED |
| Step Time 200.4 ms 100% || |
| ├─ Input Wait 128.0 ms 64% ◀ cause || |
| ├─ Compute 68.0 ms 34% || avg per-step peak avg |
| │ ├─ Forward 24.0 ms 12% || Allocated 2.9 GB |
| │ ├─ Backward 38.0 ms 19% || Reserved 3.2 GB |
| │ └─ Optimizer 6.0 ms 3% || |
| ├─ H2D 0.4 ms <1% || |
| └─ Residual 3.6 ms 2% || |
| DataLoader fetch: 120.0 ms (CPU, supplemental) || |
| |
| SYSTEM METRICS: LOW GPU UTIL || PROCESS METRICS: NORMAL |
| Evidence: GPU utilization averaged 24%. || |
| || |
| avg || avg |
| CPU 18% || CPU capacity 14% |
| RAM used 6.2 GB (19%) || RSS used 3.1 GB (10%) |
| GPU util 24% || CUDA allocated 2.9 GB |
| GPU memory/device 3.3 GB (21%) || CUDA reserved 3.2 GB (20%) |
| GPU temperature 42C || |
| GPU power 58W || |
| |
| |
| Full evidence: logs/bert_finetune/final_summary.json (--html-report) |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
Running distributed training? See a rank-straggler diagnosis
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| TraceML Run Summary |
| ddp_pretrain · 4/4 ranks · 4 GPUs observed · 2/2 nodes · 250 common steps · 40.1s |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
| |
| Verdict: INPUT STRAGGLER (CRITICAL) |
| Why: R0/N0 waited 254.5 ms for input; R1/N0 waited 3.8 ms for input. |
| Next: Inspect input wait on the slow rank. |
| Scope: N = node · R = global rank · G = GPU index |
| |
| STEP TIMING (Median R1/N0), GPU Clock || STEP MEMORY: BALANCED · 4/4 ranks |
| Step Time 303.7 ms 100% || |
| ├─ Input Wait 3.8 ms 1% || |
| ├─ Compute 259.5 ms 85% || avg per-step peak median rank avg worst rank avg |
| │ ├─ Forward 80.0 ms 26% || Allocated 8.5 GB 9.4 GB, R2/N1 |
| │ ├─ Backward 169.5 ms 56% || Reserved 8.9 GB 9.8 GB, R2/N1 |
| │ └─ Optimizer 10.0 ms 3% || |
| ├─ H2D 1.1 ms <1% || |
| └─ Residual 39.3 ms 13% || |
| DataLoader fetch: 3.7 ms (CPU, supplemental) || |
| |
| SYSTEM METRICS: LOW GPU UTIL · 2/2 nodes || PROCESS METRICS: NORMAL · 4/4 ranks |
| Evidence: GPU utilization averaged 14%. || |
| || |
| median node avg worst node avg || median rank avg worst rank avg |
| CPU 18% 26%, N1 || CPU capacity 12% 81%, R2/N1 |
| RAM used 16.0 GB (27%) 20.8 GB (35%), N1 || RSS used 3.1 GB (10%) 5.4 GB (17%), R1/N0 |
| GPU util 9% 9%, N1 || CUDA allocated 2.9 GB 4.6 GB, R3/N1 |
| GPU memory/device 5.0 GB (31%) 7.0 GB (44%), N1 || CUDA reserved 3.2 GB (20%) 6.8 GB (43%), R3/N1 |
| GPU temperature 58C 70C, N1 || |
| GPU power 220W 280W, N1 || |
| |
| |
| Full evidence: logs/ddp_pretrain/final_summary.json (--html-report) |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+
Read How to Read TraceML Output for the complete field definitions, diagnosis rules, evidence, and recommended actions.
What TraceML Diagnoses
| Diagnosis | Where to investigate |
|---|---|
| Input-bound | DataLoader workers, transforms, tokenization, collation, or storage |
| H2D-bound | Pinned memory, non-blocking copies, batch size, or transfer overlap |
| Compute-bound | Model compute, mixed precision, batch size, or deeper profiling |
| Residual-heavy | Work outside traced phases, CPU stalls, logging, checkpointing, validation, or unobserved transfers |
| Rank straggler | Rank-local input, data imbalance, node variance, or networking |
| Memory creep | Retained tensors, logging references, or cached activations |
Compare Runs
After fixing a bottleneck, compare two summaries to see whether training improved and what changed:
traceml compare before/final_summary.json after/final_summary.json
For example, reducing the DataLoader bottleneck shown above changes the diagnosis and cuts step time:
+--------------------------------------------------------------------------------------+
| TraceML Compare |
+--------------------------------------------------------------------------------------+
| |
| A: before_dataloader_fix |
| B: after_dataloader_fix |
| Delta: B - A |
| Primary diagnosis: INPUT-BOUND -> COMPUTE-BOUND (changed) |
| |
| Verdict: IMPROVEMENT |
| Why: GPU Step Time decreased by 59.9%. |
+--------------------------------------------------------------------------------------+
See the full comparison
+--------------------------------------------------------------------------------------+
| TraceML Compare |
+--------------------------------------------------------------------------------------+
| |
| A: before_dataloader_fix |
| B: after_dataloader_fix |
| Delta: B - A |
| Primary diagnosis: INPUT-BOUND -> COMPUTE-BOUND (changed) |
| |
| Verdict: IMPROVEMENT |
| Why: GPU Step Time decreased by 59.9%. |
| |
| Step Time (GPU comparison clock) |
| Metric A B Delta |
| Step time diagnosis INPUT-BOUND COMPUTE-BOUND changed |
| GPU Step Time 200.4 ms 80.4 ms -120.0 ms (-59.9%) |
| Input 128.0 ms 8.0 ms -120.0 ms (-93.8%) |
| H2D 0.4 ms 0.4 ms +0.0 ms (+0.0%) |
| Compute 68.0 ms 68.0 ms +0.0 ms (+0.0%) |
| Residual 3.6 ms 3.6 ms +0.0 ms (+0.0%) |
| |
| Step Memory |
| Metric A B Delta |
| Step memory diagnosis BALANCED BALANCED same |
| Peak reserved 3.1 GB 3.1 GB 0 B (+0.0%) |
| Memory skew 0.0% 0.0% +0.0 pp |
| |
| Process |
| Metric A B Delta |
| Process diagnosis NORMAL NORMAL same |
| Process CPU avg 95.0% 110.0% +15.0 pp |
| Process RSS avg 1.4 GB 1.6 GB +0.2 GB (+14.3%) |
| |
| System |
| Metric A B Delta |
| System diagnosis LOW GPU UTIL NORMAL changed |
| System CPU avg 18.4% 32.0% +13.6 pp |
| System RAM avg 12.0 GB 13.5 GB +1.5 GB (+12.5%) |
| GPU util avg 24.0% 88.0% +64.0 pp |
| GPU memory avg 18.0% 18.0% +0.0 pp |
+--------------------------------------------------------------------------------------+
See Compare Runs for the complete workflow and artifact format.
Save the Result
Send the compact diagnosis to an existing W&B run:
import traceml_ai as traceml
import wandb
...
summary = traceml.summary(print_text=True)
if summary is not None:
wandb.log(summary)
The same result can be stored in MLflow. See W&B and MLflow for complete examples.
Want live diagnostics during training?
Use the live terminal view locally or over SSH:
traceml run train.py --mode=cli
Use the browser dashboard on a single node:
traceml run train.py --mode=dashboard
For remote browser access and SSH tunneling, see the full quickstart.
Distributed Training and Integrations
- Distributed: DDP, FSDP, and multi-node or Slurm
- Frameworks: Hugging Face, PyTorch Lightning, Ray Train, and DeepSpeed
- Trackers: W&B and MLflow
Summary mode is the documented path for single-node and multi-node runs. Live terminal and dashboard modes are explicit single-node options. See the FAQ for current support and limitations.
Distributed GPU analysis currently assumes homogeneous GPU hardware across ranks. Heterogeneous GPU configurations may produce inaccurate cross-rank analysis and diagnoses.
Learn More
Community
If TraceML helps you find a bottleneck, consider starring the repository. Contributions and real-world slowdown reports are welcome:
License
Apache 2.0. See LICENSE.
1 maintainer
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| Uploaded via |
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