---

name: training description: "Configures, runs, and evaluates Luminoth training jobs, including the optional Google Cloud ML workflow." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Luminoth Training

Use this sub-skill when the task is to train or evaluate a Luminoth model, tune a configuration file, or submit the same run to Google Cloud ML Engine.

Route here for

• lumi train and lumi eval command planning.
• Merging one or more config files with --config and --override.
• Choosing between Faster R-CNN and SSD configs.
• Understanding train.job_dir, train.run_name, TensorBoard output, and evaluation watch behavior.
• The optional lumi cloud gc workflow, including train, evaluate, jobs, and logs.

Do not use this sub-skill alone for

• Raw dataset conversion or TFRecord creation; route to dataset-preparation first.
• Packaging a trained run into a checkpoint or managing the checkpoint index; route to checkpoints.
• Running predictions or the Flask demo after a checkpoint exists; route to prediction.
• Cross-cutting install/import problems; start from the root Luminoth router.

Fast operating procedure

1. Read references/configuration.md for the key config fields and override syntax.

2. Read references/workflows.md for local train/eval commands and the cloud workflow summary.

3. Run the bundled preflight before you suggest a train or eval command:

   python scripts/check_config_keys.py --config ./config.yml --mode train
   

4. If the request involves Google Cloud, read references/cloud.md and confirm the required service account, APIs, and bucket setup.

5. If the task requires a run directory or checkpoint package, route that part to checkpoints instead of keeping it here.

Common commands at a glance

lumi train -c ./config.yml
lumi train -c ./base.yml -c ./task.yml -o model.network.num_classes=3
lumi eval -c ./config.yml --split val --no-watch
lumi cloud gc train -c ./config.yml --bucket my-bucket --dataset gs://my-bucket/dataset
lumi cloud gc evaluate --train-folder gs://my-bucket/lumi_job --split val

What this sub-skill owns

• Config file structure, defaults, and override syntax.
• Training and evaluation run directories.
• TensorBoard and other training outputs written under the job directory.
• Optional Google Cloud ML submission, listing, and log retrieval.
• Safe config preflight helpers that do not start training.

What to delegate away

• If the request is about which images or annotations to ingest, move to dataset preparation.
• If the request is about a saved checkpoint alias or tarball, move to checkpoints.
• If the request is about inference, the demo server, or the public Python API, move to prediction.

Working style

Keep the answer anchored to the config keys the code actually reads:

• model.type
• dataset.type
• dataset.dir
• train.job_dir
• train.run_name

Then explain any model-specific or cloud-specific fields only as far as the current task needs them.