def predict(ctx, provider, filename, model_id, threshold, locally, output):
"""Predict with deployed model."""
A2MLModel(ctx, provider).predict(filename,
model_id,
threshold=threshold,
locally=locally,
output=output)
def actual(ctx, provider, filename, model_id):
"""Predict with deployed model."""
A2MLModel(ctx, provider).actual(filename, model_id)
def deploy(ctx, provider, model_id, locally):
"""Deploy trained model."""
A2MLModel(ctx, provider).deploy(model_id, locally)
def predict_model_task(params):
return with_context(
params, lambda ctx: A2MLModel(ctx, None).predict(
*params['args'], **params['kwargs']))
def deploy_model_task(params):
return with_context(
params, lambda ctx: A2MLModel(ctx, None).deploy(
*params['args'], **params['kwargs']))
def actual_model_task(params):
return with_context(
params, lambda ctx: A2MLModel(ctx, None).actual(
*params['args'], **params['kwargs']))
def actuals(ctx, provider, filename, model_id, locally):
"""Predict with deployed model."""
A2MLModel(ctx, provider).actuals(model_id,
filename=filename,
locally=locally)
def review(ctx, provider, model_id, output):
"""Predict with deployed model."""
A2MLModel(ctx, provider).review(model_id)
def _predict(ctx):
return A2MLModel(ctx).predict(*params['args'], **params['kwargs'])