BlobRouter v0.1 · Last updated: August 2026 · Architecture version: 2.0
priority="archive" for checkpoints (prefer B2 when connected).
Examples
Python AI / ML
Store model checkpoints and embeddings with the BlobRouter Python SDK.
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Store model checkpoints and embeddings with the BlobRouter Python SDK.
priority="archive" for checkpoints (prefer B2 when connected).
import io
import os
import torch
from blobrouter import BlobRouter
storage = BlobRouter(api_key=os.environ["BLOBROUTER_API_KEY"])
buffer = io.BytesIO()
torch.save(model.state_dict(), buffer)
buffer.seek(0)
result = storage.upload(
file=buffer,
file_name="checkpoint_epoch_10.pt",
content_type="application/octet-stream",
file_size_bytes=buffer.getbuffer().nbytes,
priority="archive",
)
print(result["file_id"], result["provider"], result["saved_vs_aws"])
def persist_artifact(path: str, name: str) -> str:
storage = BlobRouter(api_key=os.environ["BLOBROUTER_API_KEY"])
size = os.path.getsize(path)
with open(path, "rb") as f:
result = storage.upload(
file=f,
file_name=name,
content_type="application/octet-stream",
file_size_bytes=size,
priority="archive",
)
return result["file_id"]