Srg broken link 21 (#24)

Closes Issues #21 and #22.

Authors:
  - https://github.com/shawn-davis

Approvers:
  - Bartley Richardson (https://github.com/BartleyR)

URL: #24

README.md

@@ -35,16 +35,16 @@ To run the morpheus pipeline for the prototype follow the instructions for setti
 
 # Current Cybersecurity Workflow Prototypes
 
-## [DGA Detection via AppShield](https://github.com/nv-morpheus/morpheus-experimental/tree/main/appshield-dga-detection)
+## [DGA Detection via AppShield](/appshield-dga-detection)
 This model is a convolution neural network model trained to classify URL domains generated by Domain-Generation-Algorithms. Domain generation algorithms (DGA) are algorithms seen in various families of malware that are used to periodically generate a large number of domain names that can be used as rendezvous points with their command and control servers. Input data comes from AppShield.
 
-## [Phishing URL Detection via AppShield](https://github.com/nv-morpheus/morpheus-experimental/tree/main/phishing-url-detection)
+## [Phishing URL Detection via AppShield](/phishing-url-detection)
 This model is a binary classifier to label phishing URLs and non-phishing URLs obtained from host process data. Input data comes from AppShield.
 
-## [String Resemblance Grouping](https://github.com/nv-morpheus/morpheus-experimental/tree/main/string-resemblance-grouping)
+## [String Resemblance Grouping](/string-resemblance-grouping)
 This technique syntactically groups system log messages and finds group representatives for data exploration and triage.
 
-## [Detection of Anomalous  authentication using Relational Graph Neural Network (RGCN)](https://github.com/nv-morpheus/morpheus-experimental/tree/main/rgcn-authentication-detection)
+## [Detection of Anomalous  authentication using Relational Graph Neural Network (RGCN)](/anomalous-auth-detection)
 This model shows an application of a graph neural network for anomalous authentication detection in Azure-AD signon heterogeneous graph. An Azure-AD signon dataset  includes four types of nodes, authentication, user, device and service application nodes are used for modeling. A relational graph neural network (RGCN)is used to identify anomalous authentications from azure-ad signon input.
 
 # Repo Structure
@@ -67,7 +67,7 @@ A script and python notebook showing how to train or fine-tune the model. The tu
 A non-morpheus pipeline script that contains data loading, preprocessing, model loading, inference, postprocessing, and serialized output file. It uses desired morpheus pipeline variables as input variables to the script (ie. threshold=0.6). It produces a reliable and repeatable output file from the inference dataset. It includes `requirements.txt` file with dependencies and versions used for non-morpheus inference.
 
 ## morpheus-pipeline (optional)
-All the necessary files for a full Morpheus pipeline of the prototype similar to pipelines found in [Morpheus Examples](https://github.com/nv-morpheus/Morpheus/tree/branch-22.09/examples) with it's own `requirements.txt` files. 
+All the necessary files for a full Morpheus pipeline of the prototype similar to pipelines found in [Morpheus Examples](https://github.com/nv-morpheus/Morpheus/blob/-/examples) with it's own `requirements.txt` files. 
 
 ## model documentation
 

anomalous-auth-detection/training-tuning-inference/inference.py

@@ -105,7 +105,7 @@ def main():
 
     args = parser.parse_args()
 
-    device = device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     target_node = args.target_node
 
     main()

string-resemblance-grouping/README.md

@@ -53,7 +53,7 @@ A single log instance or collection of logs or text files containing logs.
 The representative and a corresponding numeric label for the log instance or a dataframe containing the original logs and the assigned representative and numeric group.
 
 ### API Example
-See this [notebook](https://github.com/nv-morpheus/morpheus-experimental/string-resemblance-grouping/training-tuning/string-resemblance-grouping.ipynb) for an example on building and inferencing a SRG model.
+See this [notebook](/string-resemblance-grouping/training-tuning/string-resemblance-grouping.ipynb) for an example on building and inferencing a SRG model.
 
 ### Future work
 Currently SRG representatives and groups are output as the final result. Future work will instead leverage these representatives as initial "centroids" in a $k$-means variant using the weighted Jaccard distance and iteratively honing centroids that are the mean weights of the present shingles in the group. Once the $k$-means centroids converge or a fixed number of iterations is completed, the closest group member to each centroid is chosen as the final group representative.

string-resemblance-grouping/srg/_srg.py

@@ -37,7 +37,7 @@ class SRG:
     See `SRG README`_ for more details on how the technique is designed.
 
     .. _SRG README:
-        https://github.com/nv-morpheus/morpheus-experimental/tree/main/string-resemblance-grouping/README.md
+        /string-resemblance-grouping/README.md
     """
     def __init__(self):
         self._model_built = False