fixed results repo

cm-mlops/automation/experiment/README-extra.md

@@ -37,7 +37,7 @@ The goal is to provide a common interface to run, record, share, visualize and r
 on any platform with any software, hardware and data.
 
 The community helped us test a prototype of our "experiment" automation to record results in a unified CM format 
-from [several MLPerf benchmarks](https://github.com/mlcommons/ck_mlperf_results) 
+from [several MLPerf benchmarks](https://github.com/mlcommons/cm4mlperf-results) 
 including [MLPerf inference](https://github.com/mlcommons/inference) and [MLPerf Tiny](https://github.com/mlcommons/tiny),
 visualize them at the [MLCommons CM platform](https://access.cknowledge.org/playground/?action=experiments&tags=all),
 and improve them by the community via [public benchmarking, optimization and reproducibility challenges](https://access.cknowledge.org/playground/?action=challenges).

cm-mlops/challenge/add-derived-metrics-to-mlperf-inference-v3.1/README.md

@@ -1,6 +1,6 @@
 ### Challenge
 
-Check past MLPerf inference results in [this MLCommons repository](https://github.com/mlcommons/ck_mlperf_results)
+Check past MLPerf inference results in [this MLCommons repository](https://github.com/mlcommons/cm4mlperf-results)
 and add derived metrics such as result/No of cores, power efficiency, device cost, operational costs, etc.
 
 Add clock speed as a third dimension to graphs and improve Bar graph visualization.
@@ -30,6 +30,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

cm-mlops/challenge/optimize-mlperf-inference-v3.0-2023/README.md

@@ -29,7 +29,7 @@ Official results:
 * https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning
 
 Results in the MLCommons CK/CM format:
-* https://github.com/ctuning/ck_mlperf_results
+* https://github.com/mlcommons/cm4mlperf-results
 
 Visualization and comparison with derived metrics:
 * [MLCommons Collective Knowledge Playground](https://access.cknowledge.org/playground/?action=experiments&tags=mlperf-inference,v3.0).

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

@@ -82,6 +82,6 @@ with PRs from participants [here](https://github.com/ctuning/mlperf_inference_su
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -31,6 +31,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -31,6 +31,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -32,6 +32,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -29,6 +29,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -34,6 +34,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -33,6 +33,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -41,6 +41,6 @@ This challenge is under preparation.
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -31,6 +31,6 @@ Check [this ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) to learn
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

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

@@ -36,6 +36,6 @@ Open ticket: [GitHub](https://github.com/mlcommons/ck/issues/696)
 ### Results
 
 All accepted results will be publicly available in the CM format with derived metrics 
-in this [MLCommons repository](https://github.com/mlcommons/ck_mlperf_results),
+in this [MLCommons repository](https://github.com/mlcommons/cm4mlperf-results),
 in [MLCommons Collective Knowledge explorer](https://access.cknowledge.org/playground/?action=experiments) 
 and at official [MLCommons website](https://mlcommons.org).

cm-mlops/challenge/reproduce-and-automate-tinymlperf-v1.1-2023/README.md

@@ -36,5 +36,5 @@ in mid June 2023.
 
 ### Results
 
-All results will be available in [this GitHub repo](https://github.com/ctuning/ck_mlperf_results)
+All results will be available in [this GitHub repo](https://github.com/ctuning/cm4mlperf-results)
 and can be visualized and compared using the [MLCommons Collective Knowledge Playground](https://access.cknowledge.org/playground/?action=experiments&tags=mlperf-tiny).

cm-mlops/script/generate-mlperf-tiny-submission/README.md

@@ -145,7 +145,7 @@ ___
        - CM script: [run-how-to-run-server](https://github.com/how-to-run/server/tree/master/script/run-how-to-run-server)
        - CM script: [app-mlperf-inference-nvidia](https://github.com/cknowledge/cm-tests/tree/master/script/app-mlperf-inference-nvidia)
        - CM script: [get-axs](https://github.com/cknowledge/cm-tests/tree/master/script/get-axs)
-       - CM script: [process-mlperf-inference-results](https://github.com/mlcommons/ck_mlperf_results/tree/master/script/process-mlperf-inference-results)
+       - CM script: [process-mlperf-inference-results](https://github.com/mlcommons/cm4mlperf-results/tree/master/script/process-mlperf-inference-results)
        - CM script: [activate-python-venv](https://github.com/mlcommons/ck/tree/master/cm-mlops/script/activate-python-venv)
        - CM script: [add-custom-nvidia-system](https://github.com/mlcommons/ck/tree/master/cm-mlops/script/add-custom-nvidia-system)
        - CM script: [app-image-classification-onnx-py](https://github.com/mlcommons/ck/tree/master/cm-mlops/script/app-image-classification-onnx-py)

cm-mlops/script/import-mlperf-inference-to-experiment/README-extra.md

@@ -9,7 +9,7 @@ and link reproducibility reports as shown in these examples:
 * [Power efficiency to compare Qualcomm, Nvidia and Sima.ai devices](https://cKnowledge.org/mlcommons-mlperf-inference-gui-derived-metrics-and-conditions)
 * [Reproducibility report for Nvidia Orin](https://access.cknowledge.org/playground/?action=experiments&name=mlperf-inference--v3.0--edge--closed--image-classification--offline&result_uid=3751b230c800434a)
 
-Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/ck_mlperf_results).
+Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/cm4mlperf-results).
 
 You can see these results at [MLCommons CK playground](https://access.cknowledge.org/playground/?action=experiments&tags=mlperf-inference,all).
 
@@ -24,10 +24,10 @@ Pull the MLCommons CK repository with automation recipes for interoperable MLOps
 cm pull repo mlcommons@ck
 ```
 
-Pull already imported results (v2.0, v2.1, v3.0, v3.1) from this [mlcommons@ck_mlperf_results repo](https://github.com/mlcommons/ck_mlperf_results):
+Pull already imported results (v2.0, v2.1, v3.0, v3.1) from this [mlcommons@cm4mlperf-results repo](https://github.com/mlcommons/cm4mlperf-results):
 
 ```bash
-cm pull repo mlcommons@ck_mlperf_results
+cm pull repo mlcommons@cm4mlperf-results
 ```
 
 Install repository with raw MLPerf inference benchmark results with {NEW VERSION}:
@@ -61,7 +61,7 @@ cm run script "import mlperf inference to-experiment _skip_checker"
 Import to a specific repo:
 
 ```bash
-cm run script "import mlperf inference to-experiment" --target_repo=mlcommons@ck_mlperf_results
+cm run script "import mlperf inference to-experiment" --target_repo=mlcommons@cm4mlperf-results
 ```
 
 Visualize results on your local machine via CK playground GUI:
@@ -73,7 +73,7 @@ These results are also available in the [public CK playground](https://access.ck
 
 ## Further analysis of results
 
-Please check this [README](https://github.com/mlcommons/ck_mlperf_results#how-to-update-this-repository-with-new-results).
+Please check this [README](https://github.com/mlcommons/cm4mlperf-results#how-to-update-this-repository-with-new-results).
 
 # Contact us
 

cm-mlops/script/import-mlperf-tiny-to-experiment/README-extra.md

@@ -9,7 +9,7 @@ and link reproducibility reports as shown in these examples:
 * [Power efficiency to compare Qualcomm, Nvidia and Sima.ai devices](https://cKnowledge.org/mlcommons-mlperf-inference-gui-derived-metrics-and-conditions)
 * [Reproducibility report for Nvidia Orin](https://access.cknowledge.org/playground/?action=experiments&name=mlperf-inference--v3.0--edge--closed--image-classification--offline&result_uid=3751b230c800434a)
 
-Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/ck_mlperf_results).
+Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/cm4mlperf-results).
 
 You can see these results at [MLCommons CK playground](You can see aggregated results [here](https://access.cknowledge.org/playground/?action=experiments&tags=mlperf-tiny,all).
 

cm-mlops/script/import-mlperf-training-to-experiment/README-extra.md

@@ -7,7 +7,7 @@ The goal is to make it easier for the community to analyze MLPerf results,
 add derived metrics such as performance/Watt and constraints,
 and link reproducibility reports.
 
-Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/ck_mlperf_results).
+Aggreaged results are available in [this MLCommons repository](https://github.com/mlcommons/cm4mlperf-results).
 
 You can see these results at [MLCommons CK playground](https://access.cknowledge.org/playground/?action=experiments&tags=mlperf-training,all).
 

docs/mlperf/inference/README.md

@@ -236,7 +236,7 @@ You can pull all past MLPerf results in the CM format, import your current exper
 with derived metrics on your system using the Collective Knowledge Playground as follows:
 
 ```bash
-cm pull repo mlcommons@ck_mlperf_results
+cm pull repo mlcommons@cm4mlperf-results
 cmr "get git repo _repo.https://github.com/ctuning/mlperf_inference_submissions_v3.1" \
     --env.CM_GIT_CHECKOUT=main \
     --extra_cache_tags=mlperf-inference-results,community,version-3.1