选择运行环境并导出模型

fusionpointai_hosting  -> agreed FusionPointAI compute environment
customer_datacenter   -> your own compatible compute environment
colab                 -> compatible notebook runtime

Choose the runtime already approved for your paid job.
Selecting a runtime does not allocate GPUs or notebook credits.

# Run in a notebook terminal/shell after installing the SDK.
# Set FUSIONPOINTAI_API_KEY through the runtime's secret facility.
# The downloaded source includes the current Colab example.
curl --fail --silent --show-error \
  https://fusionpointai.com/downloads/fusionpointai-training.zip \
  --output fusionpointai-training.zip
unzip fusionpointai-training.zip -d fusionpointai-training-src
python fusionpointai-training-src/examples/colab_training.py \
  --job "$FUSIONPOINTAI_JOB_UUID" --output /content/customer-model-run-001
# Output must be new. Save your artifact before the notebook is terminated.

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.

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.

cat ./support-project/runs/support-001/training-report.json
cat ./support-project/runs/support-001/evaluation-report.json
cat ./support-project/runs/support-001/artifact-manifest.json
sha256sum ./support-project/runs/support-001/exports/safetensors/artifact-manifest.json
# Compare the digest with the value returned by project.export().
# Adapter exports also require the matching base model and tokenizer.