Experiment tracker and lab journal made for humans — and sexy human-agent interaction for AI research
Project description
buro
An experiment tracker and lab journal made for humans — and for sexy human ⇄ agent interaction in AI research. Log your runs, metrics, and media to a Buro server.
Install
pip install buro
Quickstart
import buro
run = buro.init(project="my-project") # or "team-slug/my-project"
for step in range(100):
buro.log({"loss": 1.0 / (step + 1), "acc": step / 100}, step=step)
buro.finish()
init(project=...) resolves the project against the server and auto-creates it
if it doesn't exist. project is a slug ref: "slug" (personal) or
"team-slug/slug" (team).
Authenticate
Log in once on your machine:
buro login --api-url https://<your-buro-server>
buro whoami
The SDK resolves credentials in this order:
buro.setup(api_key=..., api_url=...)in codeBURO_API_KEY/BURO_API_URLenvironment variables~/.buro/credentials(written byburo login)
On a cluster or in CI, the env-var path is usually easiest:
export BURO_API_KEY=buro_key_...
export BURO_API_URL=https://<your-buro-server>
Log media
buro.log({"sample": buro.Image("path/to/image.png")}) # numpy array or PIL image also work
# also available: buro.Audio, buro.Video
Code tracking
Every run automatically snapshots the source code that actually ran, so the compare view can show exactly what changed between two runs — not just which hyperparameters differed.
It works by tracing, not scanning: buro watches the Python modules your run
imports and keeps the ones that are your code — everything outside the
standard library, your installed packages, and buro itself. Each file is
recorded under its import path (models/encoder.py, not an absolute path on
your machine), hashed, and uploaded once — identical files are shared across
runs, so a hyperparameter sweep that doesn't touch the code uploads nothing new.
Because it follows the imports rather than walking a directory, the snapshot is:
- exactly your run's code — the entry script plus the modules it imported, across packages; never a stray sweep of your whole repo, a sibling project, or config/secret files that happen to sit nearby;
- the same on every machine — a file is identified by how it's imported, so the same code on your laptop and on a cluster diffs as unchanged;
- best-effort — snapshotting never slows down or crashes your run.
Config files (config.yaml, lockfiles, …) are not captured — their values
already live in your run config (buro.init(config=...)). For an unusual layout,
or to pin exactly what's captured, set BURO_CODE_ROOT=/path/to/project.
Docs
wandbAPI compatibility:docs/wandb-compatibility.md- Release/publishing process:
docs/publishing.md
Project details
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The following attestation bundles were made for buro-0.0.3-py3-none-any.whl:
Publisher:
sdk-publish.yml on dunnolab/buro
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Branch / Tag:
refs/tags/sdk-v0.0.3 - Owner: https://github.com/dunnolab
-
Access:
private
-
Token Issuer:
https://token.actions.githubusercontent.com -
Runner Environment:
github-hosted -
Publication workflow:
sdk-publish.yml@5b5f0a310b01f51d994e1675a64a3baa0cd8124a -
Trigger Event:
release
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Statement type: