---

name: training description: "Plan and diagnose legacy Frustum PointNets v1 or v2 training runs, including flags, data contracts, checkpoints, and backend gates." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training

Use this route when a Researcher wants to train or resume Frustum PointNets. Read CLI reference, then training workflow before launching. This release assumes generated frustum pickles and a TensorFlow-1 graph runtime.

Safe route

1. Validate KITTI inputs and generated pickle ownership through ../kitti-data-preparation/SKILL.md.
2. Choose frustum_pointnets_v1 for a pure-TensorFlow graph path. Choose v2 only after ../runtime-and-custom-ops/SKILL.md proves all custom operators.
3. Run python scripts/inspect_training_args.py --help and use its preflight before allocating a GPU or creating logs.
4. Set a new log directory, explicit model, point count, channel mode, and restore path. Start with a short, separately approved smoke run; the repository's default is 201 epochs and is not a smoke test.

Training writes model-source copies, log_train.txt, TensorBoard summaries, and periodic model.ckpt files under the log directory. Keep these outputs outside the source tree when possible and never overwrite a checkpoint from a different model/point/channel configuration.

Backend boundary

The verified CPU TensorFlow-1 graph smoke supports API inspection and bounded v1 checks only. GPU placement, v2 custom ops, and practical training speed are not verified here. The route does not turn a CPU run into CUDA evidence. Route post-training validation to ../inference-and-evaluation/SKILL.md.