Merge branch 'develop' into docs/llms.txt

README.md

@@ -147,7 +147,7 @@ Or, through our CLI command:
 zenml stack deploy --provider aws
 ```
 
-Alternatively, if the necessary pieces of infrastructure is already deployed, you can register a cloud stack seamlessly through the stack wizard:
+Alternatively, if the necessary pieces of infrastructure are already deployed, you can register a cloud stack seamlessly through the stack wizard:
 
 ```bash
 zenml stack register <STACK_NAME> --provider aws
@@ -195,9 +195,9 @@ def trainer(training_df: pd.DataFrame) -> Annotated["model", torch.nn.Module]:
 
 ![Exploring ZenML Models](/docs/book/.gitbook/assets/readme_mcp.gif)
 
-### Purpose built for machine learning with integration to you favorite tools
+### Purpose built for machine learning with integrations to your favorite tools
 
-While ZenML brings a lot of value of the box, it also integrates into your existing tooling and infrastructure without you having to be locked in.
+While ZenML brings a lot of value out of the box, it also integrates into your existing tooling and infrastructure without you having to be locked in.
 
 ```python
 from bentoml._internal.bento import bento

docs/book/component-guide/step-operators/modal.md

@@ -86,6 +86,12 @@ def my_modal_step():
     ...
 ```
 
+{% hint style="info" %}
+Note that the `cpu` parameter in `ResourceSettings` currently only accepts a single integer value. This specifies a soft minimum limit - Modal will guarantee at least this many physical cores, but the actual usage could be higher. The CPU cores/hour will also determine the minimum price paid for the compute resources.
+
+For example, with the configuration above (2 CPUs and 32GB memory), the minimum cost would be approximately $1.03 per hour ((0.135 * 2) + (0.024 * 32) = $1.03).
+{% endhint %}
+
 This will run `my_modal_step` on a Modal instance with 1 A100 GPU, 2 CPUs, and
 32GB of CPU memory.
 

docs/book/how-to/pipeline-development/build-pipelines/hyper-parameter-tuning.md

@@ -16,7 +16,7 @@ def my_pipeline(step_count: int) -> None:
     data = load_data_step()
     after = []
     for i in range(step_count):
-        train_step(data, learning_rate=i * 0.0001, name=f"train_step_{i}")
+        train_step(data, learning_rate=i * 0.0001, id=f"train_step_{i}")
         after.append(f"train_step_{i}")
     model = select_model_step(..., after=after)
 ```

docs/book/how-to/project-setup-and-management/setting-up-a-project-repository/connect-your-git-repository.md

@@ -54,6 +54,21 @@ zenml code-repository register <NAME> --type=github \
 
 where \<REPOSITORY> is the name of the code repository you are registering, \<OWNER> is the owner of the repository, \<NAME> is the name of the repository, \<GITHUB\_TOKEN> is your GitHub Personal Access Token and \<GITHUB\_URL> is the URL of the GitHub instance which defaults to `https://github.com.` You will need to set a URL if you are using GitHub Enterprise.
 
+{% hint style="warning" %}
+Please refer to the section on using secrets for stack configuration in order to securely store your GitHub 
+Personal Access Token.
+
+```shell
+# Using central secrets management
+zenml secret create github_secret \
+    --pa_token=<GITHUB_TOKEN>
+
+# Then reference the username and password
+zenml code-repository register ... --token={{github_secret.pa_token}}
+    ...
+```
+{% endhint %}
+
 After registering the GitHub code repository, ZenML will automatically detect if your source files are being tracked by GitHub and store the commit hash for each pipeline run.
 
 <details>
@@ -96,6 +111,21 @@ zenml code-repository register <NAME> --type=gitlab \
 
 where `<NAME>` is the name of the code repository you are registering, `<GROUP>` is the group of the project, `<PROJECT>` is the name of the project, \<GITLAB\_TOKEN> is your GitLab Personal Access Token, and \<GITLAB\_URL> is the URL of the GitLab instance which defaults to `https://gitlab.com.` You will need to set a URL if you have a self-hosted GitLab instance.
 
+{% hint style="warning" %}
+Please refer to the section on using secrets for stack configuration in order to securely store your GitLab
+Personal Access Token.
+
+```shell
+# Using central secrets management
+zenml secret create gitlab_secret \
+    --pa_token=<GITLAB_TOKEN>
+
+# Then reference the username and password
+zenml code-repository register ... --token={{gitlab_secret.pa_token}}
+    ...
+```
+{% endhint %}
+
 After registering the GitLab code repository, ZenML will automatically detect if your source files are being tracked by GitLab and store the commit hash for each pipeline run.
 
 <details>

scripts/test-migrations.sh

@@ -23,7 +23,7 @@ else
 fi
 
 # List of versions to test
-VERSIONS=("0.40.3" "0.43.0" "0.44.3" "0.45.6" "0.47.0" "0.50.0" "0.51.0" "0.52.0" "0.53.1" "0.54.1" "0.55.5" "0.56.4" "0.57.1" "0.60.0" "0.61.0" "0.62.0" "0.63.0" "0.64.0" "0.65.0" "0.68.0" "0.70.0")
+VERSIONS=("0.40.3" "0.43.0" "0.44.3" "0.45.6" "0.47.0" "0.50.0" "0.51.0" "0.52.0" "0.53.1" "0.54.1" "0.55.5" "0.56.4" "0.57.1" "0.60.0" "0.61.0" "0.62.0" "0.63.0" "0.64.0" "0.65.0" "0.68.0" "0.70.0" "0.71.0")
 
 # Try to get the latest version using pip index
 version=$(pip index versions zenml 2>/dev/null | grep -v YANKED | head -n1 | awk '{print $2}' | tr -d '()')

src/zenml/artifacts/artifact_config.py

@@ -104,15 +104,18 @@ def _remove_old_attributes(cls, data: Dict[str, Any]) -> Dict[str, Any]:
             )
         elif is_model_artifact:
             logger.warning(
-                "`ArtifactConfig.is_model_artifact` is deprecated and will be "
-                "removed soon. Use `ArtifactConfig.artifact_type` instead."
+                "`ArtifactConfig(..., is_model_artifact=True)` is deprecated "
+                "and will be removed soon. Use `ArtifactConfig(..., "
+                "artifact_type=ArtifactType.MODEL)` instead. For more info: "
+                "https://docs.zenml.io/user-guide/starter-guide/manage-artifacts"
             )
             data.setdefault("artifact_type", ArtifactType.MODEL)
         elif is_deployment_artifact:
             logger.warning(
-                "`ArtifactConfig.is_deployment_artifact` is deprecated and "
-                "will be removed soon. Use `ArtifactConfig.artifact_type` "
-                "instead."
+                "`ArtifactConfig(..., is_deployment_artifact=True)` is "
+                "deprecated and will be removed soon. Use `ArtifactConfig(..., "
+                "artifact_type=ArtifactType.SERVICE)` instead. For more info: "
+                "https://docs.zenml.io/user-guide/starter-guide/manage-artifacts"
             )
             data.setdefault("artifact_type", ArtifactType.SERVICE)
 

src/zenml/artifacts/utils.py

@@ -414,7 +414,9 @@ def log_artifact_metadata(
     """
     logger.warning(
         "The `log_artifact_metadata` function is deprecated and will soon be "
-        "removed. Please use `log_metadata` instead."
+        "removed. Instead, you can consider using: "
+        "`log_metadata(metadata={...}, infer_artifact=True, ...)` instead. For more "
+        "info: https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/attach-metadata-to-an-artifact"
     )
 
     from zenml import log_metadata

src/zenml/cli/server.py

@@ -587,25 +587,6 @@ def server_list(verbose: bool = False, all: bool = False) -> None:
             accessible_pro_servers = client.tenant.list(member_only=not all)
         except AuthorizationException as e:
             cli_utils.warning(f"ZenML Pro authorization error: {e}")
-        else:
-            if not all:
-                accessible_pro_servers = [
-                    s
-                    for s in accessible_pro_servers
-                    if s.status == TenantStatus.AVAILABLE
-                ]
-
-            if not accessible_pro_servers:
-                cli_utils.declare(
-                    "No ZenML Pro servers that are accessible to the current "
-                    "user could be found."
-                )
-                if not all:
-                    cli_utils.declare(
-                        "Hint: use the `--all` flag to show all ZenML servers, "
-                        "including those that the client is not currently "
-                        "authorized to access or are not running."
-                    )
 
         # We update the list of stored ZenML Pro servers with the ones that the
         # client is a member of
@@ -633,6 +614,25 @@ def server_list(verbose: bool = False, all: bool = False) -> None:
                 stored_server.update_server_info(accessible_server)
                 pro_servers.append(stored_server)
 
+        if not all:
+            accessible_pro_servers = [
+                s
+                for s in accessible_pro_servers
+                if s.status == TenantStatus.AVAILABLE
+            ]
+
+        if not accessible_pro_servers:
+            cli_utils.declare(
+                "No ZenML Pro servers that are accessible to the current "
+                "user could be found."
+            )
+            if not all:
+                cli_utils.declare(
+                    "Hint: use the `--all` flag to show all ZenML servers, "
+                    "including those that the client is not currently "
+                    "authorized to access or are not running."
+                )
+
     elif pro_servers:
         cli_utils.warning(
             "The ZenML Pro authentication has expired. Please re-login "