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name: deployment-conversion description: "Export PocketFlow checkpoints to GraphDef or TensorFlow Lite, validate conversion artifacts, and troubleshoot mobile deployment or benchmark workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PocketFlow Deployment and Conversion

Use this sub-skill when the user has trained/evaluated PocketFlow checkpoints and asks about exporting GraphDef/PB/TFLite artifacts, channel-pruned graph transformation, quantized export, graph collections, mobile deployment, or inference timing.

Route first

• For training a model or selecting uniform-tf, DCP, or channel-pruning learners before export, read compression-learners.
• For path.conf, TensorFlow 1.x setup, launch modes, or GPU probes, read execution-config.
• For custom model helper output names, input shapes, data formats, or detection-vs-classification contracts, read custom-models-data.

Operating checklist

1. Confirm export prerequisites: TensorFlow 1.x with tf.contrib.lite, a checkpoint-like model directory, .meta graph file, input/output collections, and the right model helper graph.
2. Read conversion-tools to choose the export path: plain PB/TFLite, channel-pruned PB/TFLite, quantized PB/TFLite, data-format conversion, graph collection editing, or inference timing.
3. Run check_conversion_artifacts.py against the model directory before attempting TensorFlow graph conversion.
4. For uniform-tf deployment, confirm quantization flags and route back to compression-learners if checkpoints were not produced by the expected learner.
5. For Android/mobile notes, read mobile-deployment. Mobile app edits are outside this skill's runnable helpers.
6. If conversion fails, read troubleshooting before changing flags; many failures are missing collections, unsupported ops, wrong .meta layout, or TF1/TFLite incompatibilities.

Bundled helper

• check_conversion_artifacts.py - validates model directory artifacts and optionally attempts a TensorFlow .meta collection inspection when run in a compatible TF1 environment.

Verification status

The generated skill verified conversion script flag surfaces and tensorflow.contrib.lite importability in a TF1.10 inspection environment. It did not run real checkpoint conversion because no small checkpoint fixture was available and conversion depends on trained model artifacts.