ai-marketplace-sdk 0.4.0

AI Marketplace Python SDK

AIMP SDK (Python)

This package provides the class-based SDK used by model authors.

Install

python -m pip install ai-marketplace-sdk

Install (local source)

cd packages/python/sdk
uv pip install .

Development (editable)

cd packages/python/sdk
uv pip install -e .

Fine-Tune From Scratch

The fine-tune flow has two sides:

• model authoring: define the schema and training runtime inside the model project
• consumer dataset creation: build one local bundle that matches the published schema

1. Define the model-scoped tuning contract

Model authors declare fine-tune inputs by subclassing FineTuneSchema in models/<name>/tuning/dataset.py.

Available field types:

• ImageField
• AudioField
• VideoField
• FileField
• TuningTextField
• TuningJsonField

Example:

from aimp_sdk import FineTuneSchema, ImageField


def validate_png(value: object) -> None:
    if not str(value).lower().endswith(".png"):
        raise ValueError("Only PNG files are allowed")


class ModelFineTuneSchema(FineTuneSchema):
    query_image = ImageField(required=True, validators=[validate_png])
    candidate_image = ImageField(required=True, validators=[validate_png])

Use field-level validators=[...] and preprocessors=[...] when one field needs custom cleanup or validation. These hooks run during local bundle build, before upload.

2. Implement the tuning runner

Model authors implement tuning behavior in models/<name>/tuning/runner.py and bind it from models/<name>/model.py using tuning = ....

The runner declares strategy/runtime profile metadata and implements run(session) -> TuneResult.

from aimp_sdk import Model, TuneResult


class CoreTuning:
    strategy = "lora"
    training_runtime_profile = "gpu"
    dataset_requirements = {"format": "aimp_tuning_bundle_v1"}
    dataset_spec = {"format": "aimp_tuning_bundle_v1"}
    objective = {"name": "validation_loss", "direction": "minimize"}
    split_policy = {"strategy": "auto", "validation_ratio": 0.1}
    parameters = {
        "learning_rate": {
            "kind": "float",
            "default": 5e-4,
            "min": 1e-5,
            "max": 1e-3,
            "required": True,
        },
        "epochs": {
            "kind": "integer",
            "default": 3,
            "min": 1,
            "max": 5,
            "required": True,
        },
    }

    async def run(self, session) -> TuneResult:
        learning_rate = session.suggest_float("learning_rate", 1e-5, 1e-3, default=5e-4)
        epochs = session.suggest_int("epochs", 1, 5, default=3)
        for record in session.train_dataset:
            train_step(
                query_image=session.resolve_path(record.query_image),
                candidate_image=session.resolve_path(record.candidate_image),
                learning_rate=learning_rate,
            )

        _ = epochs
        validation_loss = evaluate(session.validation_dataset)
        return TuneResult(metrics={"validation_loss": validation_loss})


class CoreModel(Model):
    engine = CoreEngine
    tuning = CoreTuning

session exposes only training concerns:

• session.train_dataset
• session.validation_dataset
• session.params
• session.resolve_path(...)
• session.suggest_float(...)
• session.suggest_int(...)
• session.suggest_categorical(...)

In the current SDK, authors implement build(self, ctx) to load runtime state once. The separate load(...) method is framework-internal cache/lifecycle plumbing and is not the author-facing hook.

session.params comes from the model's published tuning.parameters contract. Defaults and search space metadata are contract-owned by the platform. Use session.suggest_* helpers for explicit default handling; the SDK does not add random or hidden fallbacks.

Model code does not handle dataset files, S3 uploads, adapter URIs, or trial orchestration.

3. Build the consumer dataset bundle

Consumers do not write model-side tuning code. They use the published schema through the CLI:

1. ai fine-tune schema --model <model-slug>
2. ai fine-tune scaffold --model <model-slug>
3. edit prepare_bundle.py and add any local assets/
4. python prepare_bundle.py
5. ai fine-tune create --model <model-slug> --output-dir .

During bundle build the SDK:

• validates required fields and field types
• runs field preprocessors
• validates the processed values
• copies binary assets into the bundle
• writes data.parquet and manifest.json

That means field preprocessors and validators run before upload, not during remote execution.

4. Use the derived model directly

Successful LoRA fine-tunes produce a derived executable model with its own asset ID and slug. Clients should execute that derived model directly, as if it were a new model:

job = client.fine_tune_job(job_id="ft-123").wait_for_completion()

result = client.models.execute(
    model=job.tuned_model_slug,
    mode="generate",
    input={"prompt": "hello"},
)

Client code should not pass adapter_id to executions. Adapter resolution is platform-internal.