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name: training description: "Train LaneNet on prepared data, manage checkpoints, and debug training runs." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

LaneNet Training

Use this sub-skill when the task is to train or resume LaneNet, choose the config-driven backbone, inspect TensorBoard/checkpoints, or debug why a run produced no snapshots or unstable loss.

Route here for

• Training on prepared TFRecords from TuSimple or custom lane data.
• Selecting the front end through MODEL.FRONT_END (bisenetv2 default, vgg supported).
• Single-GPU vs multi-GPU behavior through TRAIN.MULTI_GPU.ENABLE.
• Restore-from-snapshot runs, warm-up/poly learning rate behavior, mIoU summaries, and loss inspection.
• Understanding where snapshots, TensorBoard logs, and training logs are written.

Route elsewhere

• Raw dataset conversion or TFRecord generation: data-preparation
• Checkpoint inference, evaluation, and postprocess tuning: inference-evaluation
• Frozen PB or MNN export: model-export

Required context before acting

1. Confirm the prepared data root contains LaneNet TFRecords, especially tusimple_train.tfrecords and, for multi-GPU validation, tusimple_val.tfrecords.
2. Confirm the runtime is CUDA-capable with TensorFlow 1.15 compatibility. That is the validated training path in this repo.
3. Confirm whether the user wants from-scratch training or restore-from-snapshot behavior.
4. Confirm the backbone and device mode. The current source uses config-driven backbone selection rather than a command-line --net flag.
5. Use the bundled wrapper in scripts/train_lanenet_tusimple.py; it resolves REPO_ROOT_PATH placeholders, checks the training data size, and refuses to start the no-iteration tiny-data trap.

Key outputs

• Checkpoints under TRAIN.MODEL_SAVE_DIR/<front_end>_lanenet/.
• TensorBoard summaries under TRAIN.TBOARD_SAVE_DIR/<front_end>_lanenet/.
• Training log file under LOG.SAVE_DIR/.
• A copied model_train_config.json beside the TensorBoard event files.

References and wrapper

• Workflows
• API reference
• Troubleshooting
• Training wrapper