Better error message sagemaker, better documentation server env vars

docs/book/getting-started/deploying-zenml/deploy-with-docker.md

@@ -253,7 +253,7 @@ The environment variables starting with _ZENML\_SERVER\_SECURE\_HEADERS\__\* can
 
 The following secure headers environment variables are supported:
 
-* _ZENML\_SERVER\_SECURE\_HEADERS\_SERVER_\*: The `Server` HTTP header value used to identify the server. The default value is the ZenML server ID.
+* **ZENML\_SERVER\_SECURE\_HEADERS\_SERVER**: The `Server` HTTP header value used to identify the server. The default value is the ZenML server ID.
 * **ZENML\_SERVER\_SECURE\_HEADERS\_HSTS**: The `Strict-Transport-Security` HTTP header value. The default value is `max-age=63072000; includeSubDomains`.
 * **ZENML\_SERVER\_SECURE\_HEADERS\_XFO**: The `X-Frame-Options` HTTP header value. The default value is `SAMEORIGIN`.
 * **ZENML\_SERVER\_SECURE\_HEADERS\_XXP**: The `X-XSS-Protection` HTTP header value. The default value is `0`. NOTE: this header is deprecated and should not be customized anymore. The `Content-Security-Policy` header should be used instead.

examples/quickstart/.copier-answers.yml

@@ -1,5 +1,5 @@
 # Changes here will be overwritten by Copier
-_commit: 2024.06.06
+_commit: 2024.07.26
 _src_path: gh:zenml-io/template-starter
 email: [email protected]
 full_name: ZenML GmbH

examples/quickstart/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:
 
 <img src=".assets/cloud_mcp_screenshot.png" width="70%" alt="Model Control Plane">
 
@@ -165,7 +165,7 @@ While we've demonstrated a manual promotion process for clarity, a more in-depth
 
 <img src=".assets/cloud_mcp.png" width="60%" alt="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.
 
 </details>
 
@@ -184,7 +184,7 @@ that were returned in the pipeline. This completes the MLOps loop of training to
 
 <img src=".assets/inference_pipeline.png" width="45%" alt="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):
 
 <img src=".assets/cloud_mcp_predictions.png" width="70%" alt="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 <a href="https://zenml.io/slack" target="_blank">
     <img width="15" src="https://cdn3.iconfinder.com/data/icons/logos-and-brands-adobe/512/306_Slack-512.png" alt="Slack"/>

examples/quickstart/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",
     "<img src=\".assets/cloud_mcp_screenshot.png\" width=\"70%\" alt=\"Model Control Plane\">"
    ]
@@ -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)."
    ]
   }
  ],

examples/quickstart/requirements.txt

@@ -1,4 +1,4 @@
 zenml[server]>=0.50.0
 notebook
-scikit-learn<1.3
+scikit-learn
 pyarrow

src/zenml/config/server_config.py

@@ -65,6 +65,9 @@ def generate_jwt_secret_key() -> str:
 class ServerConfiguration(BaseModel):
     """ZenML Server configuration attributes.
 
+    All these attributes can be set through the environment with the `ZENML_SERVER_`-Prefix.
+    The value of the `ZENML_SERVER_DEPLOYMENT_TYPE` environment variable will be extracted to deployment_type.
+
     Attributes:
         deployment_type: The type of ZenML server deployment that is running.
         server_url: The URL where the ZenML server API is reachable. Must be

src/zenml/integrations/aws/orchestrators/sagemaker_orchestrator.py

@@ -20,6 +20,7 @@
 
 import boto3
 import sagemaker
+from botocore.exceptions import WaiterError
 from sagemaker.network import NetworkConfig
 from sagemaker.processing import ProcessingInput, ProcessingOutput
 from sagemaker.workflow.execution_variables import ExecutionVariables
@@ -373,10 +374,18 @@ def prepare_or_run_pipeline(
             logger.info(
                 "Executing synchronously. Waiting for pipeline to finish..."
             )
-            pipeline_execution.wait(
-                delay=POLLING_DELAY, max_attempts=MAX_POLLING_ATTEMPTS
-            )
-            logger.info("Pipeline completed successfully.")
+            try:
+                pipeline_execution.wait(
+                    delay=POLLING_DELAY, max_attempts=MAX_POLLING_ATTEMPTS
+                )
+                logger.info("Pipeline completed successfully.")
+            except WaiterError:
+                raise RuntimeError(
+                    "Timed out while waiting for pipeline execution to finish. For long-running "
+                    "pipelines we recommend configuring your orchestrator for asynchronous execution. "
+                    "The following command does this for you: \n"
+                    f"`zenml orchestrator update {self.name} --synchronous=False`"
+                )
 
     def _get_region_name(self) -> str:
         """Returns the AWS region name.