Merge branch 'main' of https://bitbucket.ngage.netapp.com/scm/sie-bb/netapp-dataops-toolkit into release-v2.7.0

Author: mboglesby

netapp_dataops_k8s/netapp_dataops/k8s/__init__.py

@@ -4,7 +4,7 @@
 by applications using the import method of utilizing the toolkit.
 """
 
-__version__ = "2.5.0"
+__version__ = "2.6.0"
 
 import base64
 from datetime import datetime

netapp_dataops_traditional/Examples/README.md

@@ -9,8 +9,3 @@ This directory contains different examples including Jupyter Notebooks, YAML fil
 
 In this example we demonstrate an AI workflow in a Jupyter Notebook using Keras and MLflow. This notebook's goal is to give you an understanding of how to use the DataOps Toolkit with MLFlow for dataset-to-model traceability.
 
-## SageMaker Notebook Instances with FSx for NetApp ONTAP
-
-[notebookInstance-FSXVolume-stack.yaml](notebookInstance-FSXVolume-stack.yaml)
-
-An AWS CloudFormation template for deploying Amazon SageMaker Notebook instances with access to an Amazon FSx for NetApp ONTAP filesystem.

netapp_dataops_traditional/Examples/notebookInstance-FSXVolume-stack.yaml

@@ -19,7 +19,7 @@ def load_credentials():
 
     # If path not found, raise an error that config.json file doesn't exist
     if not os.path.exists(credentials_path):
-        raise FileNotFoundError("Credentials file 'config.json' not found in the netapp_dataops directory.")
+        raise FileNotFoundError("Credentials file 'config.json' not found in the ~/.netapp_dataops directory.")
 
     # Opens the file specified, reads its contents, parses the contents as JSON, and stores the resulting data
     with open(credentials_path, 'r') as f:
@@ -48,4 +48,4 @@ def load_credentials():
         if key not in config:
             raise ValueError(f"Missing required key '{key}' in 'config.json' file.")
 
-    return config # Returns the validated configuration dictionary
+    return config # Returns the validated configuration dictionary

netapp_dataops_traditional/netapp_dataops/mcp_server/config.py

@@ -34,7 +34,7 @@
 import yaml
 
 
-__version__ = "2.5.0"
+__version__ = "2.6.0"
 
 
 # Using this decorator in lieu of using a dependency to manage deprecation