tbparse
Load tensorboard event logs as pandas DataFrames; Read, parse, and plot tensorboard event logs with ease!
Decision gist · record as of 2026-08-14
Yes, if you need to programmatically read and analyze TensorBoard logs. The package is stable, has low install friction, permissive licensing, and no known vulnerabilities. However, note that maintenance is dormant (last release 728 days ago), so compatibility with very recent TensorBoard versions is not guaranteed. It is well-suited for one-off analysis or established workflows but may require monitoring if you adopt it for new projects with cutting-edge tools.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.8.
- Parsing images and audio requires TensorFlow as an optional dependency.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — Distributed under Apache License 2.0 (permissive). Runtime dependencies pandas (BSD-3-Clause) and tensorboard (Apache 2.0) are also permissive, so there are no licensing restrictions on use or redistribution.
last release 2024-08-16 (728 days) · last repo commit 2024-08-16 · 209 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 308,972 downloads/mo, #7,761 on PyPI
Alternatives
Verify before relying
pip install tbparse
from tbparse import SummaryReader
reader = SummaryReader("<path_to_event_file_or_directory>")
df = reader.scalars
print(df)- Whether dormant status (728 days since last release) will affect long-term compatibility with newer TensorBoard versions.
- Performance characteristics when parsing very large event files or deeply nested directory structures.
What it is and what it does
tbparse is a parser that reads TensorBoard event log files and converts them into pandas DataFrames, making it easy to programmatically access and analyze training metrics and other logged data. It supports all major event types (scalars, tensors, histograms, images, audio, hparams, text) and works with logs generated by PyTorch, Keras/TensorFlow, and TensorboardX. The package stores parsed data in DataFrames to enable filtering, aggregation, and custom analysis workflows outside the TensorBoard dashboard.
The package is designed for researchers and engineers who need to extract and manipulate TensorBoard logs programmatically—for example, to compare experiments across runs, perform statistical analysis, or generate custom plots. It requires only pandas and tensorboard as runtime dependencies, with TensorFlow optionally needed for image and audio parsing. The codebase includes full type hints and high test coverage.
Use it for
- Extract training scalars (loss, accuracy) from multiple experiment runs and compare them in a single DataFrame.
- Programmatically filter and aggregate hyperparameter search results logged via TensorBoard.
- Parse tensorboard event logs into DataFrames for statistical analysis or custom visualization outside the dashboard.
- Batch process tensorboard logs from a hierarchical directory structure of experiments.
- Build automated pipelines to extract metrics from PyTorch or TensorFlow training runs for downstream reporting.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to programmatically read and analyze TensorBoard logs.
The package is stable, has low install friction, permissive licensing, and no known vulnerabilities. However, note that maintenance is dormant (last release 728 days ago), so compatibility with very recent TensorBoard versions is not guaranteed. It is well-suited for one-off analysis or established workflows but may require monitoring if you adopt it for new projects with cutting-edge tools.
Install
tbparse on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance is dormant—last release was 728 days ago—but the repository remains active and the package supports current Python versions (3.8–3.12).
Requires Python >= 3.8. Parsing images and audio requires TensorFlow as an optional dependency.
License in practice
Distributed under Apache License 2.0 (permissive). Runtime dependencies pandas (BSD-3-Clause) and tensorboard (Apache 2.0) are also permissive, so there are no licensing restrictions on use or redistribution.
Quickstart
pip install tbparse
from tbparse import SummaryReader
reader = SummaryReader("<path_to_event_file_or_directory>")
df = reader.scalars
print(df)Verify before relying
- Whether dormant status (728 days since last release) will affect long-term compatibility with newer TensorBoard versions.
- Performance characteristics when parsing very large event files or deeply nested directory structures.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagespandastensorboard |
| Maintenance | Dormant 728 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 308,972 / month, #7,761 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: tbparse-0.0.9-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “tensorboard event log parser”
- tbparseParses TensorBoard event log files and loads them as pandas…
- tensorboardTensorBoard is a web-based visualization suite for inspecting…
- tb-nightlyTensorBoard is a web-based visualization suite for inspecting…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also tensorboard · tensorboardX · tb-nightly · tensorboard-data-server · tensorboard-plugin-profile · tfp-nightly · torch-tb-profiler · petastorm · tflite · datasets