Referencia y comandos del SDK de Python

Crear un cliente de forma segura

TrainingClient acepta una clave API, un base_url HTTPS, un tiempo de espera finito positivo y max_get_retries entre 0 y 5. El tiempo de espera predeterminado es de 60 segundos y el número de reintentos GET es 2. Se rechazan las redirecciones. allow_loopback se reserva para pruebas locales explícitas y nunca justifica enviar una clave real por una conexión remota insegura.

• Mantén en inglés el código y los nombres de variables de entorno en todos los idiomas.
• TrainingClient conserva los métodos de sesión; TrainingProject ofrece los flujos de proyecto.
• Usa fpai-training para los comandos CLI documentados y los métodos de TrainingProject para Python.

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
# Install the GPU-compatible PyTorch build for your runtime first.
python -m pip install 'fusionpointai-training[train] @ https://fusionpointai.com/downloads/fusionpointai-training.zip'
# API-only use without local training dependencies:
# python -m pip install 'fusionpointai-training @ https://fusionpointai.com/downloads/fusionpointai-training.zip'

import os
from fusionpointai_training import TrainingClient

client = TrainingClient(
    api_key=os.environ["FUSIONPOINTAI_API_KEY"],
    base_url="https://fusionpointai.com/api/v1/training",
    timeout=60.0,
    max_get_retries=2,
)
# transport optionally accepts an httpx transport for controlled integrations.
# Never enable insecure remote HTTP to work around a TLS error.

from importlib.metadata import PackageNotFoundError, version
from importlib.util import find_spec

for package in ("fusionpointai-training", "httpx", "torch", "transformers", "peft"):
    try:
        print(f"{package}=={version(package)}")
    except PackageNotFoundError:
        print(f"{package}: not installed")

for module in ("torch", "transformers", "peft", "safetensors"):
    print(f"{module} importable:", find_spec(module) is not None)
# The train extra includes optional model-training dependencies; inspect before use.

Usar los métodos públicos

open_session y create_session realizan la misma operación. prepare_session ejecuta una preparación limitada. get_session consulta el estado. download_dataset valida un DatasetManifest. post_event informa del progreso real. train ejecuta el motor local de entrenamiento. complete informa de un artefacto local y cancel cancela el estado del servicio. TrainingProject añade los métodos de proyecto documentados a continuación.

• reference_model_id solo se admite como argumento con nombre y debe ser un entero positivo, no un booleano.
• max_requests limita las solicitudes de preparación; no es el número de épocas de entrenamiento.
• train acepta un ID de sesión y un directorio de salida nuevo, no una ruta arbitraria de modelo; TrainingProject carga el modelo desde su configuración local.

from fusionpointai_training.client import DatasetManifest

# Continue with an authenticated client and an owned session.
ready = client.prepare_session(session["id"])
for item in ready["datasets"]:
    manifest = DatasetManifest(
        split=item["split"], sha256=item["sha256"],
        bytes=item["bytes"], rows=item["rows"],
    )
    rows = client.download_dataset(ready["id"], manifest)
    print(manifest.split, len(rows), manifest.sha256)
    # Each row has a text field. Do not log private training content.

# For a genuine external trainer integration, not simulated progress.
def report_progress(client, session_id, *, step, total_steps, loss):
    return client.post_event(session_id, {
        "state": "running",
        "step": step,
        "total_steps": total_steps,
        "metrics": {"loss": loss},
    })
# Supply values observed from your real trainer.
# The built-in train() already reports its progress; do not duplicate it.

import json
from pathlib import Path

# Continue with client/session. Inspect state before retrying reporting.
report = json.loads(Path("customer-model-run-001/training-report.json").read_text())
state = client.get_session(session["id"])
if state["status"] == "evaluating":
    state = client.complete(session["id"], report["completion_report"])
print(state["status"])
# Do not restart weight training just to reconcile reporting.

import os
from fusionpointai_training import TrainingClient

client = TrainingClient(api_key=os.environ["FUSIONPOINTAI_API_KEY"])
session = client.get_session(os.environ["FUSIONPOINTAI_SESSION_ID"])
report = session.get("report")
last_event = report.get("last_event", {}) if isinstance(report, dict) else {}
print("State:", session["status"], "Step:", session["step"])
print("Last reported error code:", last_event.get("error_code"))
# A missing error_code does not prove success. Read state and your local report.
# Do not dump full provider responses, private data or authorization headers.

Excepciones y resultados esperados

Usa ValueError para argumentos locales no válidos, APIError para errores del servicio o la configuración e IntegrityError para problemas de integridad de conjuntos de datos o artefactos. Al depurar, no imprimas el cliente completo, el entorno ni la cabecera de autorización. La llamada train de la guía de inicio rápido prepara, evalúa e informa; llama a complete manualmente solo al integrar un motor de entrenamiento externo real que genere el informe requerido.

• Registra en cada ejecución la versión del paquete SDK y de sus dependencias.
• No incluyas credenciales reales de la cuenta en los ejemplos de código exportados.
• Las clases de error de TrainingProject heredan de TrainingError; consulta la referencia de proyecto para conocerlas.

import os
from fusionpointai_training import TrainingClient
from fusionpointai_training.errors import APIError, IntegrityError, TrainingError

client = TrainingClient(api_key=os.environ["FUSIONPOINTAI_API_KEY"])
try:
    state = client.get_session(os.environ["FUSIONPOINTAI_SESSION_ID"])
    print("Current state:", state["status"])
except IntegrityError:
    print("Integrity check failed; preserve the report and investigate.")
except TrainingError:
    print("Training failed; inspect the local report before retrying.")
except APIError:
    print("Request failed; inspect access and session state before another write.")
except ValueError:
    print("Invalid local configuration; correct the input before retrying.")
# Never print the key or authorization headers.

import os
from fusionpointai_training import TrainingClient
from fusionpointai_training.errors import APIError

client = TrainingClient(
    api_key=os.environ["FUSIONPOINTAI_API_KEY"],
    timeout=30.0,
    max_get_retries=2,
)
try:
    session = client.get_session(os.environ["FUSIONPOINTAI_SESSION_ID"])
except APIError:
    # GET retries are bounded. Do not blindly retry a write after an error.
    print("Session inspection failed; check access and current server state.")
else:
    print("Status:", session["status"])
# Never print the API key, authorization headers, or response secrets.

Interfaz de Python

• TrainingClient(api_key: str, *, base_url: str = "https://fusionpointai.com/api/v1/training", allow_loopback: bool = False, timeout: float = 60.0, max_get_retries: int = 2, transport: httpx.BaseTransport | None = None)
• create_session(job: str, runtime: str, *, reference_model_id: int | None = None) -> dict
• open_session(job: str, runtime: str, *, reference_model_id: int | None = None) -> dict
• get_session(session_id: str) -> dict
• prepare_session(session_id: str, *, max_requests: int | None = None) -> dict
• DatasetManifest(split: str, sha256: str, bytes: int, rows: int)
• download_dataset(session_id: str, manifest: DatasetManifest) -> list[dict[str, str]]
• post_event(session_id: str, event: dict) -> dict
• train(session_id: str, output_dir: str | os.PathLike[str]) -> dict
• complete(session_id: str, report: dict) -> dict
• cancel(session_id: str) -> dict
• TrainingProject.from_file(path: str | os.PathLike[str], *, api_key: str) -> TrainingProject
• TrainingProject.plan(*, dry_run: bool = True) -> dict
• TrainingProject.run() -> dict
• TrainingProject.status() -> dict
• TrainingProject.pause() -> dict
• TrainingProject.resume() -> dict
• TrainingProject.cancel() -> dict
• TrainingProject.evaluate(*, split: str = "test") -> dict
• TrainingProject.export(*, format: str = "safetensors") -> dict
• register_evaluator(name: str, scorer: Callable, *, version: str) -> None