Running AutoScientist

Create a run

The SDK reads ADAPTION_API_KEY from the environment. Call autoscientist.create with the prepared dataset:

from adaption import Adaption

client = Adaption()

dataset_id = "dataset_abc123"
run = client.autoscientist.create(dataset_id=dataset_id)
print(run.id, run.status)  # pending

Parameter	Required	Notes
dataset_id	Yes	An adapted dataset ID, or a raw dataset.
max_iterations	No	From 1 through 10.
target_win_rate	No	Greater than 0 and at most 1. Stops the loop early once reached.
model	No	An ID returned by client.autoscientist.list_models(). If omitted, AutoScientist selects a model for the dataset.
training_type	No	lora by default, or full when supported by the model.
hyperparams	No	Overrides to the platform recipe; unset keys retain platform defaults.
augmentation_domain_rows	No	Domain-targeted rows added before training.
augmentation_general_rows	No	General-purpose rows added before training.
idempotency_key	No	Repeated requests with the same key return the existing run.
column_mapping	No	Maps columns in the adapted dataset schema. Omit it to let the platform infer the mapping.

Training is supervised (sft); create has no method parameter.

column_mapping is validated against the adapted dataset’s schema, not the uploaded file’s original headers. Adaptation can add columns such as enhanced_prompt. A mapping to a missing column fails with Selected column '...' for prompt is not in this dataset.

Wait for completion

Run status is pending, running, succeeded, failed, or cancelled.

from adaption import TrainingTimeout

try:
    run = client.autoscientist.wait_for_completion(run.id)
except TrainingTimeout as exc:
    print(exc.resource_id, exc.last_status)
    raise

if run.status == "failed":
    raise RuntimeError(f"failed: {run.error}")
if run.status != "succeeded":
    raise RuntimeError(f"Run ended with status: {run.status}")

print(run.iterations_completed, run.max_iterations, run.best_win_rate)

wait_for_completion backs off from 10 to 60 seconds and has a four-hour default timeout. A timeout stops waiting, not training. Call it again with the same run ID to resume tracking.

To control the polling schedule yourself:

import time

while True:
    run = client.autoscientist.get(run.id)
    print(run.status, run.iterations_completed, run.best_win_rate)

    if run.status == "succeeded":
        break
    if run.status == "failed":
        raise RuntimeError(f"failed: {run.error}")
    if run.status == "cancelled":
        raise RuntimeError("Run was cancelled")

    time.sleep(30)

A successful run either reached target_win_rate or used its final iteration. Compare best_win_rate with the target to determine which occurred. Use client.autoscientist.cancel(run.id) to stop a run in flight.

Constrain model size

In the app, Tiny AutoScientist limits selection to models under 10B parameters. With the SDK, review the supported models, list the currently available models, and pass an eligible ID explicitly:

response = client.autoscientist.list_models()
for model in response.models:
    print(model.id)

model_id = input("Eligible model ID: ").strip()
run = client.autoscientist.create(
    dataset_id=dataset_id,
    model=model_id,
)

Model availability and metadata can change. Confirm the current values with autoscientist.list_models rather than inferring model size from its ID.