fixing cmx test (mlcommons#1414)

Author: gfursin

.github/workflows/test-cmx.yml

@@ -38,7 +38,6 @@ jobs:
         python3 -m pip install --ignore-installed --verbose pip setuptools
         python3 -m pip install cmind
         python3 -m cmind
-#        cmx pull repo mlcommons@ck --dir=cm4mlops/cm4mlops
         cmx pull repo mlcommons@ck --dir2=cm4mlops
     - name: Test
       run: |

cm4mlops/cm4mlops/repo/script/import-mlperf-inference-to-experiment/README-extra.md

@@ -21,7 +21,7 @@ Install [MLCommons CM framework](https://github.com/mlcommons/ck/blob/master/doc
 
 Pull the MLCommons CK repository with automation recipes for interoperable MLOps:
 ```bash
-cm pull repo mlcommons@cm4mlops --checkout=dev
+cm pull repo mlcommons@ck --dir2=cm4mlops
 ```
 
 Pull already imported results (v2.0, v2.1, v3.0, v3.1) from this [mlcommons@cm4mlperf-results repo](https://github.com/mlcommons/cm4mlperf-results):

cm4mlops/cm4mlops/repo/script/import-mlperf-inference-to-experiment/customize.py

@@ -35,14 +35,19 @@
     "gptj-99.9": "language-processing",
     "llama2-70b-99": "language-processing",
     "llama2-70b-99.9": "language-processing",
+    "llama2-70b-interactive-99": "language-processing",
+    "llama2-70b-interactive-99.9": "language-processing",
+    "llama3.1-405b": "languge-processing",
     "mixtral-8x7b": "languge-processing",
     "dlrm-99": "recommendation",
     "dlrm-v2-99": "recommendation",
     "dlrm-99.9": "recommendation",
     "dlrm-v2-99.9": "recommendation",
     "3d-unet-99": "image-segmentation",
     "3d-unet-99.9": "image-segmentation",
-    "stable-diffusion-xl": "text-to-image"
+    "stable-diffusion-xl": "text-to-image",
+    "pointpainting": "3d-object-detection",
+    "rgat": "graph"
 }
 
 
@@ -191,9 +196,12 @@ def convert_summary_csv_to_experiment(path, version, env):
                 accuracy = result.get('Accuracy', 0.0)
 #
 #                print (accuracy, type(accuracy))
-                if accuracy is not None and accuracy != 'None' and accuracy > 0:
-                    result['Accuracy_div_100'] = float(
-                        '{:.5f}'.format(result['Accuracy'] / 100))
+                try:
+                    if accuracy is not None and accuracy != 'None' and accuracy > 0:
+                        result['Accuracy_div_100'] = float(
+                            '{:.5f}'.format(result['Accuracy'] / 100))
+                except:
+                    pass
 
                 # Add ratios