Merge branch 'main' into bugfix/fixgame

Author: wjayesh

databricks-production-qa-demo/steps/deployment/deployment_deploy.py

@@ -31,10 +31,14 @@
 
 
 @step(enable_cache=False)
-def deployment_deploy() -> Annotated[
-    Optional[DatabricksDeploymentService],
-    ArtifactConfig(name="databricks_deployment", is_deployment_artifact=True),
-]:
+def deployment_deploy() -> (
+    Annotated[
+        Optional[DatabricksDeploymentService],
+        ArtifactConfig(
+            name="databricks_deployment", is_deployment_artifact=True
+        ),
+    ]
+):
     """Predictions step.
 
     This is an example of a predictions step that takes the data in and returns

end-to-end-computer-vision/README.md

@@ -20,7 +20,11 @@ model to detect ships in the images. Note that this isn't something that our
 YOLOv8 model is particularly good at out of the box, so it serves as a good
 example of how to build a pipeline that can be extended to other use cases.
 
-This project needs some infrastructure and tool setup to work. Here is a list of
+This project and the pipelines detailed below were showcased in a webinar we hosted. To watch the video click the following link:
+
+[![CleanShot 2025-03-25 at 16 11 19](https://github.com/user-attachments/assets/eb82d606-8b81-42f4-a883-d88ebbf29fd1)](https://youtu.be/77vylw-sh50?feature=shared&t=342)
+
+You will need some infrastructure and tooling setup for this project to work. Here is a list of
 things that you'll need to do.
 
 ## ZenML

end-to-end-computer-vision/steps/export_label_studio.py

@@ -28,7 +28,6 @@
 logger = get_logger(__name__)
 
 
-
 @step(
     output_materializers={
         LABELED_DATASET_NAME: LabelStudioAnnotationMaterializer

eurorate-predictor/pipelines/training.py

@@ -24,7 +24,9 @@
 
 
 @pipeline
-def ecb_predictor_model_training_pipeline(augmented_dataset_id, mode: str = "develop"):
+def ecb_predictor_model_training_pipeline(
+    augmented_dataset_id, mode: str = "develop"
+):
     """A pipeline to train an XGBoost model and promote it.
 
     Args:

eurorate-predictor/run.py

@@ -101,7 +101,9 @@ def main(
         pipeline_args["config_path"] = os.path.join(
             config_folder, f"etl_{mode}.yaml"
         )
-        ecb_predictor_etl_pipeline.with_options(**pipeline_args)(**run_args_etl)
+        ecb_predictor_etl_pipeline.with_options(**pipeline_args)(
+            **run_args_etl
+        )
         logger.info("ETL pipeline finished successfully!\n")
 
     # Execute Feature Engineering Pipeline
@@ -126,9 +128,9 @@ def main(
         pipeline_args["config_path"] = os.path.join(
             config_folder, f"feature_engineering_{mode}.yaml"
         )
-        ecb_predictor_feature_engineering_pipeline.with_options(**pipeline_args)(
-            **run_args_feature
-        )
+        ecb_predictor_feature_engineering_pipeline.with_options(
+            **pipeline_args
+        )(**run_args_feature)
         logger.info("Feature Engineering pipeline finished successfully!\n")
 
     # Execute Model Training Pipeline
@@ -153,7 +155,9 @@ def main(
         pipeline_args["config_path"] = os.path.join(
             config_folder, f"training_{mode}.yaml"
         )
-        ecb_predictor_model_training_pipeline.with_options(**pipeline_args)(**run_args_train)
+        ecb_predictor_model_training_pipeline.with_options(**pipeline_args)(
+            **run_args_train
+        )
         logger.info("Model Training pipeline finished successfully!\n")
 
 

gamesense/steps/finetune.py

@@ -28,13 +28,12 @@
 from utils.loaders import load_base_model
 from utils.tokenizer import load_tokenizer
 from zenml import ArtifactConfig, step
+from zenml.client import Client
 from zenml.enums import ArtifactType
 from zenml.logger import get_logger
 from zenml.materializers import BuiltInMaterializer
 from zenml.utils.cuda_utils import cleanup_gpu_memory
-from zenml.client import Client
 
-# Remove the GPU-specific settings
 logger = get_logger(__name__)
 
 

huggingface-sagemaker/steps/deploying/huggingface_deployment.py

@@ -47,9 +47,7 @@ def deploy_to_huggingface(
     save_model_to_deploy.entrypoint()
 
     logger.info("Model saved locally. Pushing to HuggingFace...")
-    assert secret, (
-        "No secret found with name 'huggingface_creds'. Please create one with your `token`."
-    )
+    assert secret, "No secret found with name 'huggingface_creds'. Please create one with your `token`."
 
     token = secret.secret_values["token"]
     api = HfApi(token=token)

huggingface-sagemaker/steps/promotion/promote_get_metrics.py

@@ -27,10 +27,12 @@
 
 
 @step
-def promote_get_metrics() -> Tuple[
-    Annotated[Dict[str, Any], "latest_metrics"],
-    Annotated[Dict[str, Any], "current_metrics"],
-]:
+def promote_get_metrics() -> (
+    Tuple[
+        Annotated[Dict[str, Any], "latest_metrics"],
+        Annotated[Dict[str, Any], "current_metrics"],
+    ]
+):
     """Get metrics for comparison for promoting a model.
 
     This is an example of a metric retrieval step. It is used to retrieve

llm-complete-guide/ZENML_VERSION.txt

@@ -1 +1 @@
-0.74.0
+0.75.0

llm-complete-guide/pipelines/llm_eval.py

@@ -17,7 +17,7 @@
 from typing import Optional
 
 import click
-from steps.create_prompt import create_prompt
+from steps.create_prompt import PROMPT
 from steps.eval_e2e import e2e_evaluation, e2e_evaluation_llm_judged
 from steps.eval_retrieval import (
     retrieval_evaluation_full,
@@ -26,14 +26,14 @@
     retrieval_evaluation_small_with_reranking,
 )
 from steps.eval_visualisation import visualize_evaluation_results
-from zenml import pipeline
+from zenml import pipeline, save_artifact
 
 
 @pipeline(enable_cache=True)
 def llm_eval(after: Optional[str] = None) -> None:
     """Executes the pipeline to evaluate a RAG pipeline."""
     # define prompt
-    prompt = create_prompt()
+    prompt = save_artifact(PROMPT, "prompt")
 
     # Retrieval evals
     failure_rate_retrieval = retrieval_evaluation_small(after=after)