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ai-ml / tensorboard-data-server
Install & Compatibility
Where this runs
tested against v0.7.2 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.910 runs
installs and imports cleanly · install 0.0s · import 0.000s · 17.8MB
glibcpy 3.10–3.910 runs
installs and imports cleanly · install 1.7s · import 0.000s · 38MB
26MB installed
● package 26MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
tensorboard_data_server
✓ import tensorboard_data_server
While the package can be imported, its primary use is as a backend component for TensorBoard, rather than direct user-level programmatic interaction. TensorBoard leverages `tensorboard_data_server.server_binary()` internally to locate the server executable.
The `tensorboard-data-server` is typically leveraged automatically by `tensorboard`. This example shows how to generate logs and then start TensorBoard, explicitly enabling the fast data server. The `--load_fast=true` flag instructs TensorBoard to use the Rust-based data server for improved performance.
import tensorflow as tf
import datetime
# Create a log directory
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(log_dir)
# Log a scalar value
with writer.as_default():
for i in range(100):
tf.summary.scalar("my_metric", i * 0.1, step=i)
writer.flush()
print(f"TensorBoard logs saved to: {log_dir}")
print("To view, run in your terminal: ")
print(f"tensorboard --logdir {log_dir} --load_fast=true")
Debug
Known issues
gotchaThe `tensorboard-data-server` version (e.g., 0.7.2) is significantly lower and has an independent release cycle compared to the main `tensorboard` package (e.g., 2.20.0). Do not assume their versions are directly correlated or that features align strictly by version number.fixAlways refer to the `tensorboard` package version for overall compatibility and feature sets. The data server's version reflects its internal development.
affects: All versions
gotchaThe PyPI classification for `tensorboard-data-server` lists its Development Status as '2 - Pre-Alpha'. While it's a dependency of the stable `tensorboard` package, direct programmatic interaction with `tensorboard-data-server` may expose unstable APIs or behaviors.fixAvoid direct imports or usage of `tensorboard-data-server` unless specifically instructed by TensorBoard documentation or for development purposes. Rely on `tensorboard` to manage its interaction.
affects: All versions
gotchaUsers on systems with older GLIBC versions (e.g., CentOS 7 with GLIBC_2.17) might encounter 'GLIBC_2.18' not found errors when trying to use the native data server binary. This can cause TensorBoard to fall back to a slower, Python-based log ingester.fixEnsure your system's GLIBC version meets the requirements of the `tensorboard-data-server` wheel. If upgrading GLIBC is not an option, be aware that performance might be degraded without the native server, as TensorBoard will use a slower Python fallback.
affects: Versions 0.7.x on systems with GLIBC < 2.18
deprecatedThe TensorBoard.dev hosted service has been shut down. Commands like `tensorboard dev upload` will fail. This is a breaking change for the broader TensorBoard ecosystem that relies on the data server.fixUse TensorBoard locally or with self-hosted solutions. The `tensorboard dev upload` command is no longer functional.
affects: TensorBoard 2.15.1 and higher (indirectly affects data server usage)
Upgrade
Version history
0.7.2latest on PyPI · released Oct 23, 2023
Audit
Dependencies
tensorboardrequiredThis package is a core dependency for the `tensorboard` visualization tool, which relies on it for fast data loading.
Rust toolchainoptionalThe server component itself is written in Rust and requires a compatible binary built with the Rust toolchain.