Merge branch 'upstream-master' into sync/upstream-11-20-2024

.github/workflows/scheduled-image-scan.yml

@@ -24,6 +24,7 @@ jobs:
               file: python/storage-initializer.Dockerfile,
             },
             { name: router, file: router.Dockerfile },
+            { name: kserve-localmodel-controller, file: localmodel.Dockerfile },
           ]
 
     steps:
@@ -70,6 +71,8 @@ jobs:
             { name: xgbserver, file: python/xgb.Dockerfile },
             { name: pmmlserver, file: python/pmml.Dockerfile },
             { name: paddleserver, file: python/paddle.Dockerfile },
+            { name: lgbserver, file: python/lgb.Dockerfile },
+            { name: huggingfaceserver, file: python/huggingface_server.Dockerfile },
           ]
 
     steps:

README.md

@@ -4,6 +4,7 @@
 [![Releases](https://img.shields.io/github/release-pre/opendatahub-io/kserve.svg?sort=semver)](https://github.com/opendatahub-io/kserve/releases)
 [![LICENSE](https://img.shields.io/github/license/opendatahub-io/kserve.svg)](https://github.com/opendatahub-io/kserve/blob/master/LICENSE)
 [![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://github.com/kserve/community/blob/main/README.md#questions-and-issues)
+[![Gurubase](https://img.shields.io/badge/Gurubase-Ask%20KServe%20Guru-006BFF)](https://gurubase.io/g/kserve)
 
 KServe provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving predictive and generative machine learning (ML) models. It aims to solve production model serving use cases by providing high abstraction interfaces for Tensorflow, XGBoost, ScikitLearn, PyTorch, Huggingface Transformer/LLM models using standardized data plane protocols.
 

docs/samples/explanation/alibi/alibiexplainer/README.md

@@ -17,9 +17,9 @@ After poetry has installed dependencies you should see:
 You can check for successful installation by running the following command
 
 ```
-usage: __main__.py [-h] [--http_port HTTP_PORT] [--grpc_port GRPC_PORT]
-                   [--predict_url PREDICT_URL] [--method {ExplainerMethod.anchor_tabular}]
-__main__.py: error: the following arguments are required: --predict_url
+$ python alibiexplainer/__main__.py {AnchorTabular|AnchorText|AnchorImages}  
+...
+2024-10-17 15:48:59.751 51916 kserve INFO [explainer.py:__init__():54] Predict URL set to None
 ```
 
 ## Samples
@@ -44,7 +44,7 @@ The following indicates a successful install.
 To run static type checks:
 
 ```bash
-mypy --ignore-missing-imports sklearnserver
+mypy --ignore-missing-imports alibiexplainer
 ```
 An empty result will indicate success.
 

docs/samples/explanation/alibi/alibiexplainer/alibiexplainer/__main__.py

@@ -33,8 +33,8 @@
 
 def main():
     args, extra = parse_args(sys.argv[1:])
-    # Pretrained Alibi explainer
 
+    # Pretrained Alibi explainer
     alibi_model = None
     if args.storage_uri is not None:
         alibi_model = os.path.join(

docs/samples/explanation/alibi/alibiexplainer/pyproject.toml

@@ -10,14 +10,14 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.12"
+python = ">=3.9,<=3.12"
 kserve = { path = "../../../../../python/kserve", extras = ["storage"], develop = true }
 alibi = { version = "^0.9.4", extras = ["shap", "tensorflow"] } # From 0.9.5 alibi uses BSL license
-tensorflow = ">=2.12.0,<2.14"  # the range that supports python 3.8 -- 3.11
-dill = "^0.3.6"
+tensorflow = ">=2.14"  # version that starts to support python 3.9
+dill = "^0.3.8"
 nest-asyncio = "~1.4.0"
 llvmlite = ">0.38.1" # needed since poetry chooses lower version of llvmlite which is not supported by python 3.9 above
-tensorflow-io-gcs-filesystem = "0.34.0"
+tensorflow-io-gcs-filesystem = "0.37.1"
 
 [tool.poetry.group.test]
 optional = true

pkg/agent/storage/gcs.go

@@ -131,33 +131,14 @@ func (g *GCSObjectDownloader) DownloadFile(client stiface.Client, attrs *gstorag
 			log.Error(closeErr, "failed to close reader")
 		}
 	}(reader)
-	data, err := io.ReadAll(reader)
-	if err != nil {
-		return fmt.Errorf("failed to read object(%s) in bucket(%s): %w",
+	if _, err := io.Copy(file, reader); err != nil {
+		return fmt.Errorf(
+			"failed to copy object(%s) from bucket(%s) to file: %w",
 			attrs.Name,
 			attrs.Bucket,
 			err,
 		)
 	}
-	return g.WriteToFile(data, attrs, file)
-}
-
-func (g *GCSObjectDownloader) WriteToFile(data []byte, attrs *gstorage.ObjectAttrs, file *os.File) error {
-	_, err := file.Write(data)
-	if err != nil {
-		return fmt.Errorf("failed to write data to file(%s): from object(%s) in bucket(%s): %w",
-			file.Name(),
-			attrs.Name,
-			attrs.Bucket,
-			err,
-		)
-	}
-	defer func(file *os.File) {
-		closeErr := file.Close()
-		if closeErr != nil {
-			log.Error(closeErr, "failed to close file")
-		}
-	}(file)
 	log.Info("Wrote " + attrs.Prefix + " to file " + file.Name())
 	return nil
 }