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winml inspect

Inspect a model's tasks, classes, and hierarchy before committing to an export.

When to use this

Use winml inspect to understand how winml-cli will treat a HuggingFace model before running winml export or winml build. It answers questions like "which task will be auto-detected?", "which HF model class will be loaded?", and "does this model have a supported exporter?" without downloading weights or writing any files.

Synopsis

$ winml inspect -m <model_id> [options]

Flags

Flag Short Type Default Description
--model -m string required HuggingFace model ID (e.g. openai/clip-vit-base-patch32). Required unless --list-tasks or --help is used.
--format -f table | json table Output format. table renders rich panels; json emits a machine-readable object.
--task -t string null Override the auto-detected task (e.g. image-classification, feature-extraction).
--hierarchy/--no-hierarchy -H flag false Print the PyTorch module tree. Instantiates the model with random weights — no weight download required.
--verbose -v flag false Show full configuration details.
--list-tasks flag false List all known tasks and exit. Does not require --model.
--model-type string null Override model type (e.g. bert, resnet). Can be used without --model.
--model-class string null Override model class (e.g. BertForMaskedLM). Can be used without --model.
--help -h flag — Show help and exit.

winml inspect does not accept --device, --ep, --precision, or --output. It is a read-only discovery command that does not produce any artifacts.

How it works

winml inspect calls into the winml-cli registry to resolve the model ID against the known loader and exporter configurations. It fetches only the model's config.json from HuggingFace Hub (no weights), uses the architecture field to look up the matching HF model class and WinML inference class, and then renders the result. When --hierarchy is supplied, the model is instantiated locally with random weights using AutoModel.from_config(), and a forward-pass trace records the full PyTorch module tree. Because no real weights are downloaded, hierarchy inspection is fast even for large models.

Examples

# Basic inspection — check task detection and loader/exporter classes
$ winml inspect -m microsoft/resnet-50

+--------------------------- microsoft/resnet-50 ---------------------------+
| Task          image-classification                                         |
| Model Class   ResNetForImageClassification                                 |
| Exporter      OptimumExporter                                              |
| WinML Class   WinMLImageClassificationModel                                |
| Status        Supported                                                    |
+---------------------------------------------------------------------------+

# JSON output — useful for scripting or CI pre-flight checks
$ winml inspect -m bert-base-uncased --format json

# Override task when auto-detection picks the wrong one
$ winml inspect -m bert-base-uncased --task feature-extraction

# Print the full PyTorch module hierarchy (no weight download)
$ winml inspect -m openai/clip-vit-base-patch32 --hierarchy

# Combine verbose logging with hierarchy for deep diagnostics
$ winml inspect -m facebook/convnext-tiny-224 -v -H

Common pitfalls

  • --model is required for model inspection. The flag is marked required for model-specific lookups; omitting it returns an error. The only exception is --list-tasks, which lists all known tasks and exits without needing a model.
  • Hierarchy requires a locally installable model config. If the model config references a custom architecture not in the local transformers installation, --hierarchy will fail with an import error. Update transformers or omit the flag.
  • Task override affects all output. Passing --task changes which exporter and WinML class are reported, not just the task field. If the override is incompatible with the model architecture, the status will show as unsupported.
  • --format json is silent on unsupported models. When the model is not found in the winml-cli registry, the command raises a ClickException. Wrap the call in winml inspect ... && ... or check the exit code when scripting.
  • No weight download does not mean no network access. The config.json is always fetched from HuggingFace Hub. Set HF_HUB_OFFLINE=1 if you need fully offline inspection of a locally cached model.

See also