CM/CMX v3.4.3 release (#1359)

README.md

@@ -11,26 +11,26 @@
 
 ### About
 
-[Collective Knowledge (CK)](https://cKnowledge.org) in an educational project
-to help researchers and engineers automate their repetitive, tedious and time-consuming tasks
-to build, run, benchmark and optimize AI, ML and other applications and systems 
-across diverse and continuously changing models, data, software and hardware.
+[Collective Knowledge (CK, CM, CM4MLOps, CM4MLPerf and CMX)](https://cKnowledge.org) 
+is an educational community project to learn how to run AI, ML and other emerging workloads 
+in the most efficient and cost-effective way across diverse models, data sets, software and hardware.
 
 CK consists of several sub-projects:
 
 * [Collective Mind framework (CM)](cm) - a very lightweight Python-based framework with minimal dependencies
-  to help users implement, share and reuse cross-platform automation recipes to 
-  build, benchmark and optimize applications on any platform
-  with any software and hardware. 
-
-  * [CM interface to run MLPerf inference benchmarks](https://docs.mlcommons.org/inference)
+  intended to help researchers and engineers automate their repetitive, tedious and time-consuming tasks
+  to build, run, benchmark and optimize AI, ML and other applications and systems 
+  across diverse and continuously changing models, data, software and hardware.
 
   * [CM4MLOPS](https://github.com/mlcommons/cm4mlops) - 
     a collection of portable, extensible and technology-agnostic automation recipes
     with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications 
     on diverse platforms with any software and hardware: see [online catalog at CK playground](https://access.cknowledge.org/playground/?action=scripts),
     [online MLCommons catalog](https://docs.mlcommons.org/cm4mlops/scripts) 
 
+  * [CM interface to run MLPerf inference benchmarks](https://docs.mlcommons.org/inference)
+
+
   * [CM4ABTF](https://github.com/mlcommons/cm4abtf) - a unified CM interface and automation recipes
     to run automotive benchmark across different models, data sets, software and hardware from different vendors.
 

cm/CHANGES.md

@@ -1,4 +1,4 @@
-## V3.4.2.1
+## V3.4.3
    - fixed pyproject.toml
    - fixed docs generation
    - improved  cm pull repo with branch and SHA (#1355)

cm/cmind/__init__.py

@@ -2,7 +2,7 @@
 #
 # Written by Grigori Fursin
 
-__version__ = "3.4.2.1"
+__version__ = "3.4.3"
 
 from cmind.core import access
 from cmind.core import x

cm/cmind/repos.py

@@ -99,6 +99,17 @@ def load(self, init = False):
             r = utils.save_json(full_path_to_repo_paths, meta = self.paths)
             if r['return']>0: return r
 
+        # Skip internal repos
+        skip_internal_repos = os.environ.get('CM_CORE_SKIP_INTERNAL_REPOS','').strip().lower()
+        if skip_internal_repos not in ['1', 'true', 'yes']:
+            import pkgutil
+            for mi, name, ispkg in pkgutil.iter_modules():
+                if name.startswith('cm') and name != 'cmind':
+                    path = os.path.join(mi.path, name, 'repo')
+                    path_cmr = os.path.join(path, 'cmr.yaml')
+                    if os.path.isfile(path_cmr) and path not in self.paths:
+                        self.paths.insert(0, path)
+
         # Check internal repo (will be after local)
         if self.path_to_internal_repo != '' and os.path.isdir(self.path_to_internal_repo):
             self.paths.insert(0, self.path_to_internal_repo)