docs clean up (#870)

cm-mlops/automation/experiment/module.py

@@ -57,6 +57,10 @@ def test(self, i):
 
         return {'return':0}
 
+
+
+
+
     ############################################################
     def run(self, i):
         """

cm-mlops/challenge/optimize-mlperf-inference-v3.1-2023/README.md

@@ -1,20 +1,23 @@
 ### Introduction
 
+Our goal is to help the community benchmark and optimize various AI/ML applications 
+across diverse software and hardware provided by volunteers similar to SETI@home!
+
 Open-source [MLPerf inference benchmarks](https://arxiv.org/abs/1911.02549) 
 were developed by a [consortium of 50+ companies and universities (MLCommons)](https://mlcommons.org)
 to enable trustable and reproducible comparison of AI/ML systems 
 in terms of latency, throughput, power consumption, accuracy and other metrics
 across diverse software/hardware stacks from different vendors.
 
 However, running MLPerf inference benchmarks and submitting results [turned out to be a challenge](https://doi.org/10.5281/zenodo.8144274) 
-even for experts and could easily take many weeks. That's why MLCommons, 
+even for experts and could easily take many weeks. That's why [MLCommons](https://mlcommons.org), 
 [cTuning.org](https://www.linkedin.com/company/ctuning-foundation)
 and [cKnowledge.org](https://www.linkedin.com/company/cknowledge) 
 decided to develop an open-source, technology-agnostic 
 and non-intrusive [Collective Mind automation language (CM)](https://github.com/mlcommons/ck)
 and [Collective Knowledge Playground (CK)](https://access.cknowledge.org/playground/?action=experiments) 
-to run, reproduce, optimize and compare MLPerf inference benchmarks out-of-the-box 
-across diverse software, hardware, models and data sets from any vendor.
+to help anyone run, reproduce, optimize and compare MLPerf inference benchmarks out-of-the-box 
+across diverse software, hardware, models and data sets.
 
 You can read more about our vision, open-source technology and future plans 
 in this [presentation](https://doi.org/10.5281/zenodo.8105339).
@@ -23,19 +26,20 @@ in this [presentation](https://doi.org/10.5281/zenodo.8105339).
 
 ### Challenge
 
-We would like you to run as many MLPerf inference benchmarks on as many CPUs (Intel, AMD, Arm) and Nvidia GPUs 
-as possible across different framework (ONNX, PyTorch, TF, TFLite) 
+We would like to help volunteers run various MLPerf inference benchmarks 
+on diverse CPUs (Intel, AMD, Arm) and Nvidia GPUs similar to SETI@home 
+across different framework (ONNX, PyTorch, TF, TFLite) 
 either natively or in a cloud (AWS, Azure, GCP, Alibaba, Oracle, OVHcloud, ...) 
-and submit official results to MLPerf inference v3.1.
+and submit results to MLPerf inference v3.1.
 
-However, since some benchmarks may take 1 day to run, we suggest to start in the following order:
+However, since some benchmarks may take 1..2 days to run, we suggest to start in the following order (these links describe CM commands to run benchmarks and submit results):
 * [CPU: Reference implementation of Image Classification with ResNet50 (open and then closed division)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/resnet50/README_reference.md)
+* [CPU: TFLite C++ implementation of Image classification with variations of MobileNets and EfficientNets (open division)](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/run-mlperf-inference-mobilenet-models/README-about.md)
 * [Nvidia GPU: Nvidia optimized implementation of Image Classification with ResNet50 (open and then closed division)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/resnet50/README_nvidia.md)
 * [Nvidia GPU: Nvidia optimized implementation of Language processing with BERT large (open and then closed division)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/bert/README_nvidia.md)
 * [Nvidia GPU: Reference implementation of Image Classification with ResNet50 (open and then closed division)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/bert/README_nvidia.md)
 * [Nvidia GPU: Reference implementation of Language processing with BERT large (open and then closed division)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/resnet50/README_reference.md)
 * [Nvidia GPU (24GB of memory min): Reference implementation of Language processing with GPT-J 6B (open)](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/gpt-j/README_reference.md)
-* [CPU: TFLite C++ implementation of Image classification with variations of MobileNets and EfficientNets (open division)](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/run-mlperf-inference-mobilenet-models/README-about.md)
 * [Nvidia GPU: Nvidia optimized implementation of all other models (open and closed division)](https://github.com/ctuning/mlcommons-ck/blob/master/docs/mlperf/inference/README.md#run-benchmarks-and-submit-results)
 
 Please read [this documentation](https://github.com/mlcommons/ck/blob/master/docs/mlperf/inference/README.md)
@@ -60,7 +64,7 @@ Looking forward to your submissions and happy hacking!
 * *All submitters will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All submitters will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
 * *All submitters will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The top 3 submitters by points will receive a prize of 200$ each.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/challenge/optimize-mlperf-inference-v3.1-2023/_cm.json

@@ -22,6 +22,6 @@
     "mlperf-inference-v3.1-2023",
     "v3.1"
   ],
-  "title": "Run and optimize MLPerf inference v3.1 benchmarks (latency, throughput, power consumption, accuracy, cost)",
+  "title": "Participate in collaborative benchmarking of AI/ML systems similar to SETI@home (latency, throughput, power consumption, accuracy, cost)",
   "uid": "3e971d8089014d1f"
 }

cm-mlops/challenge/optimize-mlperf-inference-v3.1-amazon-inferentia-2023/README.md

@@ -18,7 +18,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 3 points and a prize of 400$ and the fastest implementation will receive a prize of 300$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-android/README.md

@@ -19,7 +19,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 2 points and a prize of 300$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/challenge/optimize-mlperf-inference-v3.1-create-end-to-end-app/README.md

@@ -13,10 +13,10 @@ Looking forward to your submissions and happy hacking!
 
 ### Prizes
 
-*All submitters will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
-*All submitters will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
-*All submitters will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-*The top 3 submitters by points will receive a prize of 200$ each.*
+* *All submitters will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
+* *All submitters will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
+* *All submitters will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/challenge/optimize-mlperf-inference-v3.1-deepsparse/README.md

@@ -20,7 +20,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The top 3 submitters by points will receive a prize of 150$ each.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 ### Organizers
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-google-tpu-2023/README.md

@@ -20,7 +20,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 3 points and a prize of 400$ and the fastest implementation will receive a prize of 300$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 ### Organizers
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-hugging-face-models-2023/README.md

@@ -45,7 +45,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 3 points and a prize of 300$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/challenge/optimize-mlperf-inference-v3.1-intel-2023/README.md

@@ -19,8 +19,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 2 points and a prize of 200$ and the fastest implementation will receive a prize of 200$.*
-
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/challenge/optimize-mlperf-inference-v3.1-modular-mojo-2023/README.md

@@ -23,7 +23,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 * *The first implementation will receive a cache prize from organizers.*
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
-* *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 ### Organizers
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-qualcomm-ai100-2023/README.md

@@ -19,7 +19,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 3 points and a prize of 200$ and the fastest implementation will receive a prize of 200$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-tvm-2023/README.md

@@ -20,7 +20,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive 1 point for submitting valid results for 1 complete benchmark on one system.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The top 3 submitters by points will receive a prize of 150$ each.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 ### Organizers
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-universal-cpp-implementation-2023/README.md

@@ -18,7 +18,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 2 points and a prize of 200$ and the fastest implementation will receive a prize of 200$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 

cm-mlops/challenge/optimize-mlperf-inference-v3.1-windows-2023/README.md

@@ -18,7 +18,7 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 
 * *All contributors will participate in writing a common white paper about running and comparing MLPerf inference benchmarks out-of-the-box.*
 * *All contributors will receive an official MLCommons Collective Knowledge contributor award (see [this example](https://ctuning.org/awards/ck-award-202307-zhu.pdf)).*
-* *The first implementation will receive 2 points and a prize of 200$.*
+* *The top contributors will receive cash prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge)*.
 
 
 ### Organizers

cm-mlops/script/get-cuda-devices/_cm.json

@@ -14,6 +14,9 @@
   ],
   "deps": [
     {
+      "names": [
+        "cuda"
+      ],
       "tags": "get,cuda,_toolkit"
     }
   ],

cm-mlops/script/gui/playground_challenges.py

@@ -204,7 +204,7 @@ def page(st, params):
 
                     prize = row.get('prize_short','')
                     if prize!='':
-                        x += '   Prizes from [MLCommons organizations]({}): **{}**\n'.format('https://mlcommons.org', prize)
+                        x += '   Prizes from [MLCommons organizations](https://mlcommons.org) and [cKnowledge.org](https://www.linkedin.com/company/cknowledge): **{}**\n'.format(prize)
                         if awards!='': awards+=' , '
                         awards += prize
 
@@ -225,7 +225,7 @@ def page(st, params):
                 import numpy as np
 
                 df = pd.DataFrame(data,
-                                  columns=['Challenge', 'Closing&nbsp;date', 'Extension', 'Points', 'Contributor&nbsp;award and prizes from <a href="https://mlcommons.org">MLCommons&nbsp;organizations</a>'])
+                                  columns=['Challenge', 'Closing&nbsp;date', 'Extension', 'Points', 'Contributor&nbsp;award and prizes from <a href="https://mlcommons.org">MLCommons&nbsp;organizations</a> and <a href="https://www.linkedin.com/company/cknowledge">cKnowledge.org</a>'])
 
                 df.index+=1
 
@@ -347,9 +347,9 @@ def page(st, params):
             if prize_short!='':
                 z+='* **Prizes:** {}\n'.format(prize_short)
 
-            prize = meta.get('prize','')
-            if prize!='':
-                z+='* **Student prizes:** {}\n'.format(prize)
+#            prize = meta.get('prize','')
+#            if prize!='':
+#                z+='* **Student prizes:** {}\n'.format(prize)
 
 
             urls = meta.get('urls',[])

cm-mlops/script/install-cuda-prebuilt/_cm.json

@@ -68,6 +68,11 @@
       "env": {
         "CM_CUDA_LINUX_FILENAME": "cuda_12.0.0_525.60.13_linux.run"
       }
+    },
+    "12.2.0": {
+      "env": {
+        "CM_CUDA_LINUX_FILENAME": "cuda_12.2.0_535.54.03_linux.run"
+      }
     }
   },
   "variations": {

cm-mlops/script/install-cuda-prebuilt/customize.py

@@ -10,7 +10,7 @@ def preprocess(i):
     automation = i['automation']
     version = env.get('CM_VERSION')
     if version not in env.get('CM_CUDA_LINUX_FILENAME', ''):
-        return {'return': 1, 'error': "Only CUDA versions 11.7.0 and 11.8.0 are supported now!"}
+        return {'return': 1, 'error': "Only CUDA versions 11.7.0, 11.8.0, 12.0.0 and 12.2.0 are supported now!"}
 
     recursion_spaces = i['recursion_spaces']
     nvcc_bin = "nvcc"