diff --git a/template/README.md b/template/README.md index 23c8f24..10dc01e 100644 --- a/template/README.md +++ b/template/README.md @@ -139,7 +139,7 @@ zenml model list This will show you a new `breast_cancer_classifier` model with two versions, `sgd` and `rf` created. You can find out how this was configured in the [YAML pipeline configuration files](configs/). -If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard: +If you are a [ZenML Pro](https://zenml.io/pro) user, you can see all of this visualized in the dashboard: Model Control Plane @@ -165,7 +165,7 @@ While we've demonstrated a manual promotion process for clarity, a more in-depth Model Control Plane -Again, if you are a [ZenML Cloud](https://zenml.io/cloud) user, you would be able to see all this in the cloud dashboard. +Again, if you are a [ZenML Pro](https://zenml.io/pro) user, you would be able to see all this in the cloud dashboard. @@ -184,7 +184,7 @@ that were returned in the pipeline. This completes the MLOps loop of training to Inference pipeline -You can also see all predictions ever created as a complete history in the dashboard (Again only for [ZenML Cloud](https://zenml.io/cloud) users): +You can also see all predictions ever created as a complete history in the dashboard (Again only for [ZenML Pro](https://zenml.io/pro) users): Model Control Plane @@ -203,7 +203,7 @@ If you want to learn more about ZenML as a tool, then the to get started. In particular, the [Production Guide](https://docs.zenml.io/user-guide/production-guide/) goes into more detail as to how to transition these same pipelines into production on the cloud. -The best way to get a production ZenML instance up and running with all batteries included is the [ZenML Cloud](https://zenml.io/cloud). Check it out! +The best way to get a production ZenML instance up and running with all batteries included is the [ZenML Pro](https://zenml.io/pro). Check it out! Also, make sure to join our Slack diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index a91efa2..91737c9 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -101,14 +101,14 @@ "id": "966ce581", "metadata": {}, "source": [ - "## ☁️ Step 1: Connect to ZenML Cloud\n", + "## ☁️ Step 1: Connect to ZenML Pro\n", "\n", - "If you are using [ZenML Cloud](https://zenml.io/cloud), execute the following\n", + "If you are using [ZenML Pro](https://zenml.io/pro), execute the following\n", "cell with your tenant URL. Otherwise ignore.\n", "\n", - "ZenML Cloud is a managed service that provides a hosted ZenML environment. It\n", + "ZenML Pro is a managed service that provides a hosted ZenML environment. It\n", "allows you to run your pipelines on the cloud, manage your metadata, and\n", - "collaborate with your team. Sign up at [ZenML Cloud](https://zenml.io/cloud) for\n", + "collaborate with your team. Sign up [here](https://zenml.io/pro) for\n", "a free trial and to get started!" ] }, @@ -858,7 +858,7 @@ "id": "53517a9a", "metadata": {}, "source": [ - "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", + "If you are a [ZenML Pro](https://zenml.io/pro) user, you can see all of this visualized in the dashboard:\n", "\n", "\"Model" ] @@ -1102,7 +1102,7 @@ "## What next?\n", "\n", "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", - "* If you want to quickly get started with ZenML, check out the [ZenML Cloud](https://zenml.io/cloud)." + "* If you want to quickly get started with ZenML, check out [ZenML Pro](https://zenml.io/pro)." ] } ], diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index a164db3..ce10645 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -66,7 +66,7 @@ def generate_and_run_project( "--training-pipeline", "--feature-pipeline", "--inference-pipeline", - "--no-cache" + "--no-cache", ] try: @@ -83,11 +83,15 @@ def generate_and_run_project( ) from e # check the pipeline run is successful - for pipeline_name in ["training", "inference", "feature_engineering"]: + for pipeline_name, run_count in [ + ("training", 2), + ("inference", 1), + ("feature_engineering", 1), + ]: pipeline = Client().get_pipeline(pipeline_name) assert pipeline runs = pipeline.runs - assert len(runs) == 1 + assert len(runs) == run_count assert runs[0].status == ExecutionStatus.COMPLETED # clean up