Complete Workflow
This tutorial covers a full experiment lifecycle:
- initialize a run
- log metadata, metrics, and artifacts
- finalize
- reconnect later
- retrieve metadata and files

Train Script
import json
import litlogger
from litlogger import File
config = {"model": "ResNet50", "lr": 0.001}
with open("config.json", "w") as f:
json.dump(config, f)
experiment = litlogger.init(name="image-classifier")
# Set metadata
experiment["model"] = "ResNet50"
experiment["dataset"] = "CIFAR10"
# Log a static file
experiment["config"] = File("config.json")
# Log training metrics
for epoch in range(10):
experiment["loss"].append(1.0 / (epoch + 1), step=epoch)
experiment.finalize()Fetch Script
"""Fetch data from a previously logged experiment.
This script resumes the 'image-classifier' experiment and reads back
metadata and artifacts using the dict-like API.
"""
import json
import litlogger
experiment = litlogger.init(name="image-classifier")
# Read metadata via property
metadata = experiment.metadata
print(f"Metadata: {metadata}")
# Read artifacts and download
artifacts = experiment.artifacts
if "config" in artifacts:
artifacts["config"].save("/tmp/config.json")
with open("/tmp/config.json") as f:
print(f"Config: {json.load(f)}")
experiment.finalize()What This Covers
- experiment creation by name
- metadata logging
- metric logging
- artifact upload
- reconnection to an existing experiment
- file download and metadata access
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