संदर्भ मॉडल और प्रशिक्षित किए जा सकने वाला विद्यार्थी चुनें

import os
from fusionpointai_training import TrainingClient

client = TrainingClient(api_key=os.environ["FUSIONPOINTAI_API_KEY"])
# Set this to a reference ID included in your paid provider plan.
reference_id = int(os.environ["FUSIONPOINTAI_REFERENCE_MODEL_ID"])
session = client.open_session(
    os.environ["FUSIONPOINTAI_JOB_UUID"],
    runtime="customer_datacenter",
    reference_model_id=reference_id,
)
# Change the reference only BEFORE preparation or generation attempts.
print(session["reference_model_id"])

# Continue with the client and session from the quickstart.
state = client.get_session(session["id"])
configuration = state["configuration"]
print(configuration["student_checkpoint"]["repository"])
print(configuration["student_checkpoint"]["revision"])
print(configuration["method"])
print(configuration["sdk_training"])
# These values come from the approved job, not local overrides.

import os

supported_runtimes = {
    "fusionpointai_hosting",
    "customer_datacenter",
    "colab",
}
runtime = os.environ["FUSIONPOINTAI_TRAINING_RUNTIME"]
if runtime not in supported_runtimes:
    raise ValueError("Choose a supported training runtime")
print("Selected runtime:", runtime)
# Selection alone does not allocate compute or open a training session.