torch-tb-profiler
PyTorch Profiler TensorBoard Plugin
Decision gist · record as of 2026-08-14
Yes, if you actively profile PyTorch models and use TensorBoard. The low install friction, permissive license, and active maintenance make it a straightforward addition to a profiling workflow. However, the last release was in October 2023; verify compatibility with your current PyTorch version before relying on it for new projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and TensorBoard installed; GPU profiling requires CUDA-capable hardware and appropriate drivers.
- Low friction installation with only two runtime dependencies (pandas and tensorboard).
- The project maintains active status with recent commits and follows PyTorch's release schedule on a 3-month cadence.
License · maintenance · safety
BSD-3 (permissive) — BSD-3 permissive license allows commercial and private use with minimal restrictions; you must retain the license notice in distributions.
last release 2023-10-06 (1043 days) · last repo commit 2026-08-14 · 987 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 376,115 downloads/mo, #7,131 on PyPI
Alternatives
Verify before relying
pip install torch-tb-profiler
import torch_tb_profiler
from torch.profiler import profile, record_function
with profile(activities=[...], on_trace_ready=torch_tb_profiler.trace_handler):
# profile your model- Whether the package works with current PyTorch versions (last release was 2023-10-06, over 2 years old).
- Specific GPU architectures and CUDA versions supported by Libkineto.
- Whether low-overhead GPU tracing is maintained across different hardware configurations.
What it is and what it does
torch-tb-profiler is a TensorBoard plugin that integrates Libkineto, PyTorch's profiling library, to visualize and analyze ML workload performance. It focuses on low-overhead GPU timeline tracing and provides actionable recommendations for common performance bottlenecks in deep learning models. The plugin runs as an in-process profiler within PyTorch and surfaces results directly in TensorBoard's interface.
The package depends on pandas for data handling and tensorboard for visualization. It's designed for developers and researchers who need to diagnose performance issues in PyTorch models, particularly GPU-related bottlenecks. Installation is straightforward with no compiled dependencies beyond what PyTorch itself requires.
Use it for
- Visualize GPU kernel execution timelines and identify which operations consume the most compute time.
- Profile data loading, model forward/backward passes, and synchronization overhead in training loops.
- Analyze memory usage patterns and detect GPU memory bottlenecks during model execution.
- Compare profiling results across different hardware or model configurations using TensorBoard's interface.
- Generate performance recommendations for common ML training issues like GPU underutilization.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you actively profile PyTorch models and use TensorBoard.
The low install friction, permissive license, and active maintenance make it a straightforward addition to a profiling workflow. However, the last release was in October 2023; verify compatibility with your current PyTorch version before relying on it for new projects.
Install
torch-tb-profiler on PyPI
Before you install
Low friction installation with only two runtime dependencies (pandas and tensorboard). The project maintains active status with recent commits and follows PyTorch's release schedule on a 3-month cadence.
Requires PyTorch and TensorBoard installed; GPU profiling requires CUDA-capable hardware and appropriate drivers.
License in practice
BSD-3 permissive license allows commercial and private use with minimal restrictions; you must retain the license notice in distributions.
Quickstart
pip install torch-tb-profiler
import torch_tb_profiler
from torch.profiler import profile, record_function
with profile(activities=[...], on_trace_ready=torch_tb_profiler.trace_handler):
# profile your modelVerify before relying
- Whether the package works with current PyTorch versions (last release was 2023-10-06, over 2 years old).
- Specific GPU architectures and CUDA versions supported by Libkineto.
- Whether low-overhead GPU tracing is maintained across different hardware configurations.
Package facts
| License | BSD-3 permissive |
| Python support | Supports the current Python release >=3.6.2 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagespandastensorboard |
| Maintenance | Actively maintained 1,043 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 376,115 / month, #7,131 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: torch_tb_profiler-0.4.3-py3-none-any.whl
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See also HolisticTraceAnalysis · scalene · tensorboard-plugin-profile · tensorboard-plugin-wit · cloud-accelerator-diagnostics · torchprofile · xprof · nvidia-nvtx · google-cloud-mldiagnostics · nvtx