Experiment Tracker

A simple experiment tracker that supports wandb, trackio and local jsonl storage. Features include:

Features

Multi-Backend Support: Compatible with wandb, trackio, and local jsonl storage.
Alert System: Send alerts via Lark, with email and other platforms to be added.
Resume Functionality: Allows resuming experiments using project and name as unique identifiers.
Simple API: Provides a straightforward API similar to wandb, making it easy to integrate into existing workflows.

Usage

Add dependency to your project:

uv add expr_tracker

Simple usage example:

import expr_tracker as et

et.init(project="my_project", name="my_experiment", backends=["wandb", "jsonl"])
et.log({"accuracy": 0.95, "loss": 0.05})
et.alert("Experiment completed!", text="Your experiment has finished successfully.", subtitle="Experiment Status")
et.finish()

JSONL buffering

The jsonl backend adapts its buffering to how often you call log(), so that high-frequency logging doesn't hammer the disk (helpful on network mounts such as BlobFuse) while low-frequency logging still lands on disk immediately:

If the gap since the previous log() call is >= buffer_interval (default 1.0s), the call is considered low-frequency and is written straight through.
Otherwise records are batched in memory and flushed once buffer_size (default 50) records accumulate.
A background timer flushes records that have been buffered for more than max_buffer_seconds (default 5.0s), so a burst that suddenly stops is never stranded in memory. finish() and the atexit hook flush any remainder.

Set buffer_interval=None to disable the frequency check, or max_buffer_seconds=None to disable the background timer. Tune them via backend_kwargs:

et.init(
    project="my_project",
    name="my_experiment",
    backends=["jsonl"],
    backend_kwargs={
        "jsonl": {"buffer_size": 200, "buffer_interval": 0.5, "max_buffer_seconds": 10},
    },
)

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