Log runs, compare metrics, inspect artifacts, and export your data without waiting on the dashboard.
The Python SDK buffers metrics and sends them to a Rust + ClickHouse backend. Run lists stay summary-backed. Charts fetch bounded series.
The Python SDK buffers init, log, and finish straight from the trainer, and spools offline when the network drops.
Metrics land as typed summaries with indexed search, so run lists and best-metric sorting stay sub-second on large projects.
Open charts, compare runs side by side, and pull everything back out. Artifacts and checkpoints live in your own S3 / R2.
Streamed scalar series, grouped averages, smoothing, zoom, and hover readouts.
Current hosted benchmarks keep a 50k-run project sub-second for newest pages, best-metric sorting, and 1,000-point chart reads.
init · log · artifact · checkpoint · finish.
Configs, metrics, tags, artifacts, histograms, and media stay queryable.
Import W&B, MLflow, and Neptune JSON. Shadow W&B scalar logs during migration.
The SDK is small on purpose: init, log, and finish cover the daily loop. Artifacts and checkpoints are files.
$ pip install instantml && python train.py# Start a run, log metrics, finish.
import instantml as im
run = im.init(project="llm-7b-sft", config=cfg)
for step, batch in enumerate(loader):
loss = train_step(batch)
run.log({"loss": loss}, step=step)
run.log_checkpoint_file("./ckpt/model.pt", step=step)
run.finish()We'll help you import it, compare real runs, and decide if InstantML earns a place in your training loop.
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