ExpMan Python Wrapper

The expman Python package provides a seamless, idiomatic interface for logging experiments in Python projects while leveraging the high-performance Rust core.

Key Features

• Idiomatic Python: Uses context managers and singletons for clean integration into scripts and notebooks.
• Fast Logging: Native calls to the Rust engine ensure sub-microsecond logging latency.
• Automatic Cleanup: Ensures all file handles are closed and data is flushed upon script completion.
• TensorBoard Drop-in: Replace from torch.utils.tensorboard import SummaryWriter with from expman import SummaryWriter.

Installation

pip install expman-rs

Basic Usage

Global Singleton

import expman as exp

exp.init("my_model")
exp.log_vector({"loss": 0.42})

Context Manager

from expman import Experiment

with Experiment("my_model") as exp:
    exp.log_vector({"loss": 0.42})

TensorBoard SummaryWriter (Drop-in Replacement)

Replace your existing TensorBoard import with expman's drop-in replacement. All metrics are stored in expman's high-performance Parquet format instead of TensorBoard event files.

# Before:
# from torch.utils.tensorboard import SummaryWriter

# After:
from expman import SummaryWriter

writer = SummaryWriter(log_dir="runs/my_experiment")

for epoch in range(100):
    loss = 1.0 / (epoch + 1)
    writer.add_scalar("train/loss", loss, epoch)

writer.add_scalars("metrics", {"accuracy": 0.95, "f1": 0.92}, 100)
writer.add_hparams({"lr": 0.001, "batch_size": 32}, {"hparam/accuracy": 0.95})

writer.close()

Supported methods:

Internal Architecture

The wrapper uses PyO3 to create native Python extension modules that link directly to the Rust expman library.