HolisticTraceAnalysis
A python library for analyzing PyTorch Profiler traces
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific and important problem for anyone optimizing distributed PyTorch training. The MIT license imposes no restrictions. Install it if you profile distributed training workloads and need structured analysis of PyTorch Profiler traces.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch Profiler traces collected from a distributed training job; traces must reside in a single folder.
- Low friction installation as a pure Python wheel.
- Active maintenance with recent commits; last release 442 days ago.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it suitable for both open and proprietary projects.
last release 2025-05-29 (442 days) · last repo commit 2026-05-29 · 544 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 155,871 downloads/mo, #10,798 on PyPI
Alternatives
Verify before relying
pip install HolisticTraceAnalysis
from holistictraceanalysis.trace_analysis import TraceAnalysis
analyzer = TraceAnalysis(trace_dir="/path/to/traces")
temporal_breakdown = analyzer.get_temporal_breakdown()
kernel_breakdown = analyzer.get_gpu_kernel_breakdown()- Whether the package works with PyTorch versions beyond those explicitly tested in CI
- Performance characteristics when analyzing very large trace files or high-rank distributed jobs
- Compatibility with GPU types beyond NVIDIA CUDA (e.g., AMD ROCm, Intel Arc)
- Exact import path structure for the TraceAnalysis API
What it is and what it does
Holistic Trace Analysis is a performance profiling tool built to analyze PyTorch Profiler traces from distributed training workloads. It ingests trace files collected via PyTorch's Kineto profiler and provides structured breakdowns of GPU activity—computation time, communication time, memory events, and idle periods—across all training ranks. The package runs as a Python library, typically within Jupyter notebooks, and exposes an API for querying temporal breakdowns, kernel statistics, communication-computation overlap, memory bandwidth utilization, and queue lengths.
The tool helps identify where training time is spent and why GPUs are idle, which is essential for optimizing large-scale distributed training. It includes trace comparison capabilities to visualize differences between runs, CUDA kernel pattern analysis to find frequently launched operations, and experimental GPU performance counter analysis. Dependencies include numpy, pandas, networkx for data processing and plotly for visualization.
Use it for
- Identify GPU idle time causes and communication bottlenecks in multi-GPU or multi-node training runs.
- Compare performance profiles across different training configurations or hardware setups to validate optimization changes.
- Analyze kernel launch overhead and memory bandwidth utilization to guide kernel-level optimization efforts.
- Investigate temporal patterns of computation and communication overlap to improve distributed training efficiency.
- Diagnose performance regressions by comparing trace files from different training runs side-by-side.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific and important problem for anyone optimizing distributed PyTorch training. The MIT license imposes no restrictions. Install it if you profile distributed training workloads and need structured analysis of PyTorch Profiler traces.
Install
holistictraceanalysis on PyPI
Before you install
Low friction installation as a pure Python wheel. Active maintenance with recent commits; last release 442 days ago. Requires Python 3.8 or later and depends on common data science libraries (numpy, pandas, networkx, plotly) plus jupyterlab and pytest.
Requires PyTorch Profiler traces collected from a distributed training job; traces must reside in a single folder.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it suitable for both open and proprietary projects.
Quickstart
pip install HolisticTraceAnalysis
from holistictraceanalysis.trace_analysis import TraceAnalysis
analyzer = TraceAnalysis(trace_dir="/path/to/traces")
temporal_breakdown = analyzer.get_temporal_breakdown()
kernel_breakdown = analyzer.get_gpu_kernel_breakdown()Verify before relying
- Whether the package works with PyTorch versions beyond those explicitly tested in CI
- Performance characteristics when analyzing very large trace files or high-rank distributed jobs
- Compatibility with GPU types beyond NVIDIA CUDA (e.g., AMD ROCm, Intel Arc)
- Exact import path structure for the TraceAnalysis API
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesjupyterlabnumpynetworkxpandasplotlypydotpytest |
| Maintenance | Actively maintained 442 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 155,871 / month, #10,798 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: holistictraceanalysis-0.5.0-py3-none-any.whl
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See also torch-tb-profiler · torchprofile · nvidia-resiliency-ext · perfetto · torch · nvidia-cuda-nvrtc · nvidia-cuda-cupti · nvidia-nvtx · nvidia-nvtx-cu12 · nvidia-cuda-cupti-cu11