MLOps commands

These examples assume an authenticated active saved profile with the intended organization and project. Confirm it with tp context show before running commands. Add root --json before the command when machine parsing needs JSON output; use root --profile <name> only to override the active profile.

Check context and available resources

tp auth whoami
tp context show
tp mlops data list
tp mlops models list
tp mlops jobs list
tp mlops computes list
tp mlops environments list
tp mlops cloud list

Data assets

tp mlops data datastores
tp mlops data next-version <short_dataset_name>
tp mlops data upload-dir --name <short_dataset_name> --version v001 --local-dir ./dataset --datastore datalake --upload-mode sas --dry-run
tp mlops data upload-dir --name <short_dataset_name> --version v001 --local-dir ./dataset --datastore datalake --upload-mode sas
tp mlops data register --name <dataset_name> --version <version> --base-path <path_or_uri> --dry-run
tp mlops data versions <dataset_name>
tp mlops data files <dataset_name> <version>

Use a short logical name for new datasets. The API may return an org/project-prefixed assetName; use that returned name for versions, files, and training commands.

Bundles

tp mlops bundles validate \
  --dir ./bundle \
  --dataset-name <dataset_name> \
  --dataset-version <version> \
  --environment-name tp-bundle-cpu-general \
  --compute-name cpu-batch-cluster \
  --local-smoke \
  --local-dataset-dir ./dataset

tp mlops bundles upload --dir ./bundle --version v001 --dry-run
tp mlops bundles upload --dir ./bundle --version v001

tp mlops bundles submit \
  --bundle-name <bundle_name> \
  --bundle-version v001 \
  --model-name <model_name> \
  --dataset-name <dataset_name> \
  --dataset-version <version> \
  --environment-name tp-bundle-cpu-general \
  --compute-name cpu-batch-cluster \
  --parameters '{"max_train_rows":5000}' \
  --timeout-seconds 180

Bundle basics:

Include entrypoint/training code, dependency metadata, and output contract expected by ThinkPlatform.
training-bundle.yaml must include schema_version, name, and entry.command.
Declare a dataset_contract with required files when the bundle expects a dataset organized as a directory.
Write metrics and model outputs where ThinkPlatform registration can find them.
Always write scalar evaluation metrics to TP_ARTIFACTS_OUTPUT_DIR/metrics.json.
Use MLflow metric logging in training code when available so tp mlops jobs metrics <job_id> and the run UI can return scalar metrics.
Validate locally before starting a cloud job when possible.

Recommended training code pattern:

import json
import os
from pathlib import Path

metrics = {
    "rows": float(len(test_rows)),
    "mae": mae,
    "rmse": rmse,
    "baseline_mae": baseline_mae,
    "business_lift": business_lift,
}

artifacts_dir = Path(os.environ.get("TP_ARTIFACTS_OUTPUT_DIR", "outputs/artifacts"))
artifacts_dir.mkdir(parents=True, exist_ok=True)
(artifacts_dir / "metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8")

try:
    import mlflow

    mlflow.log_metrics({key: float(value) for key, value in metrics.items()})
except Exception as exc:
    print(f"warning: failed to log mlflow metrics: {exc}")

Metric completion check:

tp mlops jobs wait <job_id> --timeout-seconds 900
tp mlops jobs metrics <job_id>

If jobs metrics returns {} or the UI says no metrics are available, update the bundle to log metrics and rerun training. A Completed job without metrics is not a complete model validation.

Training

tp mlops train submit \
  --model-name <model_name> \
  --architecture-name <architecture_or_bundle> \
  --dataset-name <dataset_name> \
  --dataset-version <version> \
  --dry-run

tp mlops train submit \
  --model-name <model_name> \
  --architecture-name <architecture_or_bundle> \
  --dataset-name <dataset_name> \
  --dataset-version <version>

tp mlops jobs wait <job_id> --timeout-seconds 600
tp mlops jobs get <job_id>
tp mlops jobs metrics <job_id>
tp mlops jobs logs <job_id>

After every training job, capture at least the primary metric, baseline metric, business metric, and row count from tp mlops jobs metrics <job_id> in the user-facing report.

Models

tp mlops models list
tp mlops models versions <model_name>
tp mlops models validate-online --model-name <model_name> --model-version <version> --payload-file ./sample.json
tp mlops models upload-dir --name <model_name> --version v001 --local-dir ./model-package --model-type custom_model --upload-mode sas --dry-run
tp mlops models upload-dir --name <model_name> --local-dir ./model-package --model-type triton_model --dry-run

Model artifact upload and model registry promotion are separate operational checks. If upload-dir uploads files but registration fails, inspect the uploaded Data Lake path and retry tp mlops models register --name <model_name> --version <version> --base-path <uploaded_prefix> --model-type <model_type> to isolate the registration failure.

Cloud deployment and inference

tp mlops cloud create-online --logical-name <logical_name> --model-name <model_name> --model-version <version> --instance-type <instance_type> --dry-run

tp mlops cloud create-online --logical-name <logical_name> --model-name <model_name> --model-version <version> --instance-type <instance_type>

tp mlops cloud wait --endpoint-name <endpoint_name> --timeout-seconds 1800
tp mlops cloud get --endpoint-name <endpoint_name>
tp mlops cloud invoke-online --endpoint-name <endpoint_name> --triton-model-name <triton_model_name> --payload-file ./sample.json
tp mlops cloud update-online --endpoint-name <endpoint_name> --logical-name <logical_name> --model-name <model_name> --model-version <version> --dry-run
tp mlops cloud set-online-traffic --endpoint-name <endpoint_name> --deployment-name <deployment_name> --traffic 100 --dry-run

Verification checklist

Data asset exists and is readable.
Training job completed and tp mlops jobs metrics <job_id> returns non-empty metrics.
Metrics include a primary task metric, baseline comparison, business metric, and data volume.
Model version exists and passes deployability checks.
Endpoint/deployment state is healthy.
Inference sample returns expected schema and plausible output.

Get started

CLI

Python SDK

Agent skills

Check context and available resources

Data assets

Bundles

Training

Models

Cloud deployment and inference

Verification checklist

​Check context and available resources

​Data assets

​Bundles

​Training

​Models

​Cloud deployment and inference

​Verification checklist

Check context and available resources

Data assets

Bundles

Training

Models

Cloud deployment and inference

Verification checklist