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tensorflow-rs
A blazing-fast pure-Rust library for reading and writing TensorBoard event files (.tfevents), with Python bindings via PyO3 no TensorFlow dependency required.
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tensorflow-rs
A blazing-fast, pure-Rust library for reading and writing TensorBoard event files — with first-class Python bindings via PyO3.
tensorflow-rs lets you write and read TensorBoard .tfevents.* files directly from Rust — no TensorFlow, no Python required (unless you want the bindings!).
TensorBoard is the standard visualization toolkit in deep learning — displaying training metrics like loss, accuracy, and gradients over time. This library generates those log files natively from Rust (or Python via PyO3), making it perfect for:
Use Case
Description
🦀 Rust ML Training
Works with Candle, tch-rs, and any Rust ML framework
🐍 Python Pipelines
Rust-speed logging with a clean Python API via PyO3/maturin
📊 Experiment Tracking
Emit TensorBoard-compatible logs from any custom tool
✨ Features
📈 Scalars — log training loss, accuracy, learning rate, and any float metric
📊 Histograms — visualize weight and gradient distributions over training
🖼️ Images — log generated or debug images at any training step
🔊 Audio — log WAV audio samples directly from raw PCM data
use tboard::EventWriter;
fn main() -> tboard::Result<()> {
let mut writer = EventWriter::create("./logs")?;
for step in 0..1000i64 {
let loss = 1.0 / (1.0 + step as f32 * 0.01);
writer.write_scalar(step, "loss/train", loss)?;
}
writer.flush()?;
Ok(())
}
Then visualize in TensorBoard:
tensorboard --logdir ./logs
Python Usage
Build and install the Python wheel with maturin:
cd tboard-pyo3
pip install maturin
maturin develop
Then use it in Python:
import tboard, math
# --- Writing ---
tb = tboard.EventWriter("/tmp/my-experiment")
for step in range(10000):
tb.add_scalar("loss", math.exp(-step * 0.001), step)
tb.add_scalar("accuracy", 1 - math.exp(-step * 0.001), step)
tb.flush()
print("Wrote to:", tb.filename)
# --- Reading ---
reader = tboard.EventReader(tb.filename)
for event in reader:
print(event)
📚 API Reference
EventWriter
The main struct for writing TensorBoard event files.
// Create a new writer in a log directory
let mut writer = EventWriter::create("./logdir")?;
// Write a scalar
writer.write_scalar(step: i64, tag: &str, value: f32)?;
// Write a histogram
writer.write_histo(step, tag, min, max, num, sum, sum_squares, bucket, bucket_limit)?;
// Write an image (encoded bytes, e.g. PNG)
writer.write_image(step, tag, width, height, colorspace, encoded_image_string)?;
// Write audio from raw PCM samples (auto-encodes to WAV)
writer.write_pcm_as_wav(step, tag, pcm_data: &[f32], sample_rate: u32)?;
// Write a typed tensor
writer.write_tensor::<f32>(step, tag, values: Vec<f32>)?;
// Flush buffered data
writer.flush()?;
writer = tboard.EventWriter(logdir: str, on_error: str = "raise")
# on_error: "raise" (default) | "log" (print errors, don't crash)
writer.add_scalar(tag: str, scalar_value: float, global_step: int = 0)
writer.add_audio(tag: str, pcm_data: List[float], sample_rate: int, global_step: int = 0)
writer.flush()
writer.logdir # the log directory
writer.filename # full path of the current event file
SummaryReader
An iterator-based reader for existing TensorBoard event files.
use tboard::SummaryReader;
use std::fs::File;
let reader = SummaryReader::new(File::open("events.out.tfevents....")?);
for event in reader {
let event = event?;
println!("step={} wall_time={:.3}", event.step, event.wall_time);
}
reader = tboard.EventReader(filename: str)
for event in reader:
# event is a dict with keys:
# - "step": int
# - "wall_time": float
# - "kind": str ("summary", "file_version", "graph_def", ...)
# - "what": list (for summaries: [{"tag": str, "value": float}])
print(event)
💡 Examples
Rust CLI Reader
Read and pretty-print any .tfevents file:
cargo run --example read -- path/to/events.out.tfevents.XXXXXXXXXX
Sample output:
2024-02-04 12:00:01 UTC step: 0 loss/train: 1.0000
2024-02-04 12:00:01 UTC step: 1 loss/train: 0.9901
2024-02-04 12:00:01 UTC step: 2 loss/train: 0.9803
Python Quick Demo
python basics.py
Writes 100,000 scalar steps of sin(step × 1e-4) to /tmp/test-event-writer, then reads them back and prints each event.
🔨 Building from Source
# Clone the repo
git clone https://github.com/PRATHAM777P/tensorflow-rs.git
cd tensorflow-rs
# Build (protoc must be installed)
cargo build --release
# Run tests
cargo test
# Run Clippy lints
cargo clippy -- -D warnings
# Check formatting
cargo fmt --all -- --check
Python bindings:
cd tboard-pyo3
maturin develop # install into current virtualenv
maturin build --release # build a distributable .whl wheel
🖥️ CI / Supported Platforms
CI runs on every push and pull request across 6 matrix targets:
