Python SDK: तरीके, नतीजे और त्रुटियाँ

import os
from fusionpointai_training import TrainingProject

project = TrainingProject.from_file(
    "./german-project/training.json",
    api_key=os.environ["FUSIONPOINTAI_API_KEY"],
)
plan = project.plan(dry_run=True)
print(plan["resolved_configuration"], plan["data_sharing"], plan["budgets"])
# Confirm the reviewed plan before starting real work.
result = project.run()
if result["status"] != "completed" or result["stop_reason"] != "accepted":
    raise RuntimeError("Training was not accepted; inspect the retained report.")
evaluation = project.evaluate(split="test")
# A failed required criterion raises EvaluationFailedError after saving its report.
artifact = project.export(format="safetensors")
print(artifact["manifest_path"])

TrainingProject return contract (dictionary shapes)
from_file: validates configuration, attaches its owned run; no weight updates.
plan: {schema_version, resolved_configuration, compatibility, data_sharing,
       budgets, topics, artifacts, fingerprints}; dry_run must be true.
run/resume: {run_id, status, stop_reason, report_path, unmet_criteria}.
status/pause/cancel: {run_id, status, stop_reason, report_path, unmet_criteria}.
stop_reason is null until terminal; run/resume block until terminal or paused.
pause blocks until owned workers acknowledge an atomic checkpoint.
cancel blocks until owned workers stop; a failed stop raises CancellationError.
evaluate: {run_id, split, accepted, criteria, regressions, report_path}.
export: {run_id, format, artifact_path, manifest_path, sha256}.
Returned paths are local, resolved paths; no model upload is implied.
Status output is redacted and may be read without opening an API connection.
from_file requires path: str | os.PathLike and a nonempty api_key: str.
plan accepts dry_run=True only; False is ConfigurationError, not execution.
evaluate accepts split="validation" or "test"; defaults to test.
export accepts safetensors or adapters when supported by the selected adapter.
All method keyword options are keyword-only. Unlisted arguments are rejected.

import os
from fusionpointai_training import TrainingProject
from fusionpointai_training.errors import (
    AuthenticationError, BudgetExceededError, CompatibilityError,
    ConfigurationError, EvaluationFailedError, TrainingError,
)

try:
    project = TrainingProject.from_file(
        "./german-project/training.json",
        api_key=os.environ.get("FUSIONPOINTAI_API_KEY", ""),
    )
    project.plan(dry_run=True)
    project.run()
    result = project.evaluate(split="test")
except (ConfigurationError, CompatibilityError):
    print("Correct the configuration before starting a new run.")
except AuthenticationError:
    print("Check access without logging the key.")
except BudgetExceededError:
    print("Preserve the checkpoint. A larger approved budget requires a new plan.")
except EvaluationFailedError:
    print("Preserve failed criteria; do not publish this model as accepted.")
except TrainingError:
    print("Inspect redacted diagnostics; do not blindly repeat a write.")
# CancellationError, CheckpointError and ExportError also inherit TrainingError.

from fusionpointai_training import register_evaluator

def category_match(prediction, target, row):
    return float(prediction.strip().casefold() == target.strip().casefold())

register_evaluator("category_match", category_match, version="1")
# Register before project.plan() in the same Python process.
# In rubric.json: "metric": "category_match", "minimum_score": 0.85
# This scorer measures category equality, not open-ended semantic correctness.