docs: add external data file handling section to PyTorch export tuto…#28764
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CandyButcher27 wants to merge 2 commits into
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docs: add external data file handling section to PyTorch export tuto…#28764CandyButcher27 wants to merge 2 commits into
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@microsoft-github-policy-service agree |
xadupre
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Jun 3, 2026
| ```python | ||
| import onnx | ||
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| model = onnx.load("model.onnx", load_external_data=True) |
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@copilot what do you recommend for files > 2Gb? (not supported by protobuf)
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Updated the PR to address this — added Option B covering models ≥ 2 GB: keep both files co-located; ONNX Runtime resolves .data relative to the .onnx path at load time. Also includes the GCS/S3 upload pattern.
Split the section into two options: - Option A: models < 2GB can be merged into a single .onnx file - Option B: models >= 2GB must be deployed as a file pair (protobuf limit) Addresses reviewer feedback that merging is not possible for large models.
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Hi, just checking in — let me know if any further changes are needed from my side. |
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Fixes #28763
Description
Added a new section "Handling External Data Files (Large Models)" to the PyTorch export tutorial.
Documents a common production gotcha: exporting large PyTorch models with opset 17+ automatically splits the output into
model.onnx+model.onnx.data.InferenceSessionfails if the.datafile is missing.Also documents how to merge the split files into a single self-contained
.onnxfile for distribution using theonnxlibrary.Motivation and Context
This gap trips up anyone exporting modern PyTorch models for CPU deployment. Neither the Python inference tutorial nor the PyTorch export tutorial mentioned this behavior. Discovered while deploying an EfficientNet-B2 model exported with PyTorch 2.12 + opset 17.