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slalom
2026-06-12 11:57:27 -04:00
parent 2d4d377051
commit 53e886a535
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@@ -163,15 +163,15 @@ Uploads use `s3.bucket` and `s3.data_prefix` from `config.yaml`. File uploads de
```
qc-cli train start Submit a SageMaker training job
qc-cli train start --wait Submit, wait, and finalize MLflow tracking
qc-cli train status [job-name] Show job status; defaults to the last submitted job
qc-cli train wait [job-name] Wait for completion and finalize MLflow tracking
qc-cli train list List recent training jobs
qc-cli train list --limit 3 Show a custom number of recent jobs
```
`train start` uses `s3://<bucket>/<data_prefix>/` as the training channel and writes outputs under `s3://<bucket>/<model_prefix>/`. If `sagemaker.training.source_dir` is set, the CLI packages that directory, uploads it beside the job output prefix, and passes `sagemaker_program`/`sagemaker_submit_directory` to the SageMaker container.
`train wait` checks SageMaker every 30 seconds by default. Use `--poll-interval <seconds>` to choose another positive interval. Stopping the local command does not stop the SageMaker job.
`train start --wait` checks SageMaker every 30 seconds by default. Use `--poll-interval <seconds>` to choose another positive interval. Stopping the local command does not stop the SageMaker job.
The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
@@ -219,7 +219,7 @@ The CLI uses neutral experiment naming for trained artifacts and reserves releas
Current behavior:
1. `qc-cli train start` submits a SageMaker training job.
2. `qc-cli train status` or `qc-cli train wait` finalizes the MLflow run after the job reaches a terminal state. `train wait` blocks and polls every 30 seconds by default.
2. `qc-cli train status` or `qc-cli train start --wait` finalizes the MLflow run after the job reaches a terminal state. `--wait` polls every 30 seconds by default.
3. If the job completed and `mlflow.register_trained_models` is enabled, the SageMaker `model.tar.gz` is registered as a new MLflow model version with:
- `qc_cli.stage=experiment`
- `qc_cli.artifact_kind=trained_source`