Merge pull request #62 from bharatk-parallel/formatting

README Update for individual labs, Presentation folder added, References to Other Lab

README.md

@@ -1,3 +1,6 @@
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/gpuhackathons-org/gpubootcamp?include_prereleases)](https://github.com/gpuhackathons-org/gpubootcamp/releases/latest) [![GitHub issues](https://img.shields.io/github/issues/gpuhackathons-org/gpubootcamp)](https://github.com/gpuhackathons-org/gpubootcamp/issues)
+
+
 #  GPUBootcamp Official Training Materials
 GPU Bootcamps are designed to help build confidence in Accelerated Computing and eventually prepare developers to enroll for [Hackathons](http://gpuhackathons.org/)
 
@@ -8,16 +11,17 @@ The bootcamp content focuses on how to follow the Analyze, Parallelize and Optim
 
 | Lab      | Description |
 | ----------- | ----------- |
-| [N-Ways](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc/nways)      | This lab will cover multiple GPU programming models and choose the one that best fits your needs. The material supports different programmin glangauges including C ( CUDA C, OpenACC C, OpenMP C, C++ stdpar ),  Fortran ( CUDA Fortran, OpenACC Fortran, OpenMP Fortran, ISO DO CONCURRENT       |
+| [N-Ways](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc/nways)      | This lab will cover multiple GPU programming models and choose the one that best fits your needs. The material supports different programming langauges including C ( CUDA C, OpenACC C, OpenMP C, C++ stdpar ),  Fortran ( CUDA Fortran, OpenACC Fortran, OpenMP Fortran, ISO DO CONCURRENT ) Python ( Numba, CuPy )       |
 | [OpenACC](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc/openacc)   | The lab will cover how to write portable parallel program that can run on multicore CPUs and accelerators like GPUs and how to apply incremental parallelization strategies using OpenACC       |
 
 - [Convergence of HPC and AI](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai) :: 
-The bootcamp content focuses on how AI can accelerate HPC simulations by introducing concepts of Deep Neural Networks, including data pre-processing, techniques on how to build, compare and improve accuracy of deep learning models.
+The bootcamp content focuses on how AI can accelerate HPC simulations by introducing concepts of Deep Neural Networks, including data pre-processing, techniques on how to build, compare and improve accuracy of deep learning models. 
 
 | Lab      | Description |
 | ----------- | ----------- |
 | [Weather Pattern Recognition](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_climate)      | This Bootcamp will introduce developers to fundamentals of AI and how data driven approach can be applied to Climate/Weather domain |
 | [CFD Flow Prediction](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_cfd)      | This Bootcamp will introduce developers to fundamentals of AI and how they can be applied to CFD (Computational Fluid Dynamics) |
+| [PINN](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_cfd)      | This Bootcamp will introduce developers to fundamentals of using Physics Informed Neural Network and how they can be applied to different scientific domains using Nvidia SimNet |
 
 - [AI](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/ai)::
 The bootcamp content focuses on using popular accelerated AI frameworks and using optimization techniques to get max performance from accelerators like GPU.
@@ -35,9 +39,14 @@ Each lab contains docker and singularity definition files. Follow the readme fil
 - The repository uses Apache 2.0 license. For more details on folder structure developers may refer to CONTRIBUTING.md file.
 - A project template for reference is located at [Template](https://github.com/bharatk-parallel/gpubootcamp-1/tree/nways_md_fortran/misc/jupyter_lab_template/appName)
 
+## Authors and Acknowledgment
+
+See [Contributors](https://github.com/gpuhackathons-org/gpubootcamp/graphs/contributors) for a list of contributors towards this Bootcamp.
+
+
 # Feature Request or filing issues
 - Bootcamp users may request for newer training material or file a bug by filing a github issues
 - Please do go through the existing list of issues to get more details of upcoming features and bugs currently being fixed [Issues](https://github.com/gpuhackathons-org/gpubootcamp/issues)
 
-## Questions?
-Please join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for questions.
+## Join OpenACC Community
+Please join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup).

ai/DeepStream/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

ai/DeepStream/English/python/jupyter_notebook/Getting_started_with_Deepstream_Pipeline.ipynb

@@ -547,7 +547,7 @@
     "\n",
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/DeepStream/English/python/jupyter_notebook/Introduction_to_Deepstream_and_Gstreamer.ipynb

@@ -222,7 +222,7 @@
    "source": [
     " ## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).).\n",
     "    \n",
     "     \n",
     "     \n",

ai/DeepStream/English/python/jupyter_notebook/Introduction_to_Multi-DNN_pipeline.ipynb

@@ -540,7 +540,7 @@
     "\n",
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/DeepStream/English/python/jupyter_notebook/Multi-stream_Multi_DNN.ipynb

@@ -676,9 +676,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might be of interest: \n",
+    "\n",
+    "- [DeepStream Pipeline Optimization using Profiling](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/ai/DeepStream_Perf_Lab)\n",
+    "\n",
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "[Previous Notebook](Multi-stream_pipeline.ipynb)\n",
     "     \n",

ai/DeepStream/English/python/jupyter_notebook/Multi-stream_pipeline.ipynb

@@ -570,7 +570,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "[Previous Notebook](Introduction_to_Multi-DNN_pipeline.ipynb)\n",
     "     \n",

ai/DeepStream/README.md

@@ -1,6 +1,20 @@
 
 # openacc-training-materials
-Training materials provided by OpenACC.org. The objective of this lab is to give an introduction to using Nvidia DeepStream Framework in a Intelligent Video Analytics Domain.  
+This repository contains mini applications for GPU Bootcamps. The objective of this Bootcamp is to give an introduction to using NVIDIA DeepStream Framework and apply to Intelligent Video Analytics Domain.  
+
+- Introduction to Deepstream and Gstreamer
+- Lab 1: Getting started with Deepstream Pipeline
+- Lab 2: Introduction to Multi-DNN pipeline
+- Lab 3: Creationg multi-stream pipeline
+- Lab 4: Mini-Challenge : Combining Multi-stream with Multi-DNN pipeline
+
+## Target Audience:
+
+The target audience for this bootcamp are AI developers working in domain of Intelligent Video Anaytics and looking at optimizing the application using NVIDIA DeepStream SDK.
+
+## Tutorial Duration
+
+The overall lab should take approximate 3.5 hours. There is an additional mini-challenge provided at the end of lab.  
 
 ## Prerequisites
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -43,8 +57,10 @@ Then, run the container:
 Then, open the jupyter notebook in browser: http://localhost:8888
 Start working on the lab by clicking on the `Start_Here.ipynb` notebook.
 
-## Troubleshooting
+## Known issues
 
 Q. "ResourceExhaustedError" error is observed while running the labs
+
 A. Currently the batch size and network model is set to consume 16GB GPU memory. In order to use the labs without any modifications it is recommended to have GPU with minimum 16GB GPU memory. Else the users can play with batch size to reduce the memory footprint
 
+- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

ai/DeepStream_Perf_Lab/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

ai/DeepStream_Perf_Lab/English/python/jupyter_notebook/Introduction_to_Performance_analysis.ipynb

@@ -795,7 +795,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "     \n",
     "     \n",

ai/DeepStream_Perf_Lab/English/python/jupyter_notebook/Performance_Analysis_using_NSight_systems.ipynb

@@ -456,7 +456,7 @@
     "\n",
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/DeepStream_Perf_Lab/English/python/jupyter_notebook/Performance_Analysis_using_NSight_systems_Continued.ipynb

@@ -721,7 +721,7 @@
     "\n",
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/DeepStream_Perf_Lab/README.md

@@ -1,6 +1,19 @@
 
 # openacc-training-materials
-Training materials provided by OpenACC.org. The objective of this lab is to provide insight into DeepStream performance optimization cycle. The lab will make use of Nvidia Nsight System for profiling Nvidia DeepStream pipeline in a Intelligent Video Analytics Domain.  
+This repository contains mini applications for GPU Bootcamps. The objective of this Bootcamp is to provide insight into DeepStream performance optimization cycle. The lab will make use of NVIDIA Nsight System for profiling Nvidia DeepStream pipeline in a Intelligent Video Analytics Domain.  
+
+- Introduction: Performance analysis
+- Lab 1: Performance Analysis using NVIDIA Nsight systems
+- Lab 2: COVID-19 Social Distancing Application plugin optimization
+
+## Target Audience:
+
+The target audience for this bootcamp are NVIDIA DeepStream users and looking at understanding performance optimization cycle using profilers. Users are recommended to go through basic of [DeepStream SDK](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/ai/DeepStream) if not already done. 
+
+## Tutorial Duration
+
+The overall lab should take approximate 3.5 hours.
+
 
 ## Prerequisites
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -48,5 +61,7 @@ Then, run the container:
 Then, open the jupyter notebook in browser: http://localhost:8888
 Start working on the lab by clicking on the `Start_Here.ipynb` notebook.
 
-## Troubleshooting
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
+
 

ai/RAPIDS/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

ai/RAPIDS/English/Python/START_HERE.ipynb

@@ -83,7 +83,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],

ai/RAPIDS/English/Python/jupyter_notebook/Challenge/Bike-Rental-Prediction/Backup.ipynb

@@ -1538,7 +1538,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Challenge/Bike-Rental-Prediction/Challenge.ipynb

@@ -1520,7 +1520,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Challenge/Gene-Expression-Classification/Backup.ipynb

@@ -1997,7 +1997,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Challenge/Gene-Expression-Classification/Challenge.ipynb

@@ -609,7 +609,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuDF/01-Intro_to_cuDF.ipynb

@@ -1366,7 +1366,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuDF/02-Intro_to_cuDF_UDFs.ipynb

@@ -730,7 +730,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuDF/03-Cudf_Exercise.ipynb

@@ -363,7 +363,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuDF/04-Cudf_Solution.ipynb

@@ -1111,7 +1111,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {
@@ -1141,13 +1141,6 @@
     "   \n",
     "[Home Page](../START_HERE.ipynb)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

ai/RAPIDS/English/Python/jupyter_notebook/CuDF/Backup.ipynb

@@ -364,7 +364,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuML/01-LinearRegression-Hyperparam.ipynb

@@ -739,7 +739,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuML/03_CuML_Exercise.ipynb

@@ -679,7 +679,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuML/04_CuML_Solution.ipynb

@@ -1191,7 +1191,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuML/Backup.ipynb

@@ -685,7 +685,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/CuML/Bonus_Lab-LogisticRegression.ipynb

@@ -329,7 +329,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {
@@ -342,13 +342,6 @@
     "[[4]]()\n",
     "[[5]]()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -367,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

ai/RAPIDS/English/Python/jupyter_notebook/Dask/01-Intro_to_Dask.ipynb

@@ -255,7 +255,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Dask/02-CuDF_and_Dask.ipynb

@@ -1416,7 +1416,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Dask/03-CuML_and_Dask.ipynb

@@ -286,7 +286,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Dask/04-Challenge.ipynb

@@ -530,7 +530,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Dask/05-Challenge_Solution.ipynb

@@ -670,7 +670,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Dask/Backup.ipynb

@@ -536,7 +536,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/Introduction_To_Rapids.ipynb

@@ -93,7 +93,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "\n"
    ]

ai/RAPIDS/English/Python/jupyter_notebook/References.ipynb

@@ -19,7 +19,12 @@
     "- https://elitedatascience.com/imbalanced-classes\n",
     "- https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/\n",
     "- https://github.com/zronaghi/nasa-ml-workshop\n",
-    "-  https://github.com/rapidsai/notebooks"
+    "-  https://github.com/rapidsai/notebooks\n",
+    "\n",
+    "# Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might be of interest: \n",
+    "\n",
+    "- [Intelligent Video Analytic using DeepStream](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/ai/DeepStream)\n"
    ]
   },
   {

ai/RAPIDS/English/Python/jupyter_notebook/START_HERE.ipynb

@@ -83,7 +83,7 @@
    "source": [
     "## Licensing\n",
     "  \n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],

ai/RAPIDS/README.MD

@@ -2,14 +2,25 @@
 
 ## GPU Bootcamp for RAPIDS AI
 
-This repository consists of gpu bootcamp material for RAPIDS AI. The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. In this series you can access RAPIDS learning resources in the form of labs. The modules covered in this Bootcamp are CuDF, CuML, Dask and Challenge.
+This repository contains mini applications for GPU Bootcamps. This repository consists of GPU Bootcamp material for RAPIDS AI. The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. In this series you can access RAPIDS learning resources in the form of labs. The modules covered in this Bootcamp are CuDF, CuML, Dask and Challenge.
+
+- Introduction to RAPIDS
+- Lab 1: Using RAPIDS CuDF
+- Lab 2: Using RAPIDS CuML
+- Lab 3: Using RAPIDS Dask ( Multi-GPU )
+- Mini-challenge: Gene Expression Classification/Bike Rental Prediction
+
+
+## Tutorial Duration
+
+The overall Bootcamp should take approximate 3.5 hours. There is an additional mini-challenge provided at the end of Bootcamp.  
 
 ## Prerequisites
 To run this tutorial you will need a machine with NVIDIA GPU.
 
 - Install the latest [Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) or [Singularity](https://sylabs.io/docs/).
 
-- The base containers required for the lab may require users to create a NGC account and generate an API key (https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account)
+- The base containers required for the Bootcamp may require users to create a NGC account and generate an API key (https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account)
 
 ## Creating containers
 To start with, you will have to build a Docker or Singularity container.
@@ -51,4 +62,7 @@ Q. Out of memory Error
 
 A. The bootcamp is designed considering a GPU with minimum 16 GB memory. The users can reduce the overall size of the array sizes to reduce the overall memory footprint if required based on GPU card RAM .
 
-# For more information about RAPIDS applications and Docker, please refer <a href="https://hub.docker.com/r/rapidsai/rapidsai/"> here</a>
+## For more information about RAPIDS applications and Docker, please refer to <a href="https://hub.docker.com/r/rapidsai/rapidsai/"> here</a>
+
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc/miniprofiler/English/C/jupyter_notebook/miniweather.ipynb

@@ -85,7 +85,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -106,7 +106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab1.ipynb

@@ -170,7 +170,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -191,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab2.ipynb

@@ -155,7 +155,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -176,7 +176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab3.ipynb

@@ -220,7 +220,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -241,7 +241,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab4.ipynb

@@ -158,7 +158,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -179,7 +179,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c-lab5.ipynb

@@ -339,7 +339,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -360,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/C/jupyter_notebook/profiling-c.ipynb

@@ -198,7 +198,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -219,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/miniweather.ipynb

@@ -85,7 +85,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -106,7 +106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab1.ipynb

@@ -163,7 +163,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -184,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab3.ipynb

@@ -208,7 +208,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -229,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab4.ipynb

@@ -159,7 +159,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -180,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran-lab5.ipynb

@@ -339,7 +339,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -360,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Fortran/jupyter_notebook/profiling-fortran.ipynb

@@ -209,7 +209,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -230,7 +230,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc/miniprofiler/English/profiling_start.ipynb

@@ -72,7 +72,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). \n"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n"
    ]
   }
  ],
@@ -93,7 +93,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/miniprofiler/README.md

@@ -1,5 +1,21 @@
 # Nsight Tool Tutorial
-This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA driver 440.82**)
+This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA driver 440.82**). In this bootcamp, we will be optimizing the serial Weather Simulation application written in both C and Fortran programming language
+
+- Introduction: Overview of profiling tools and Mini Weather application
+- Lab 1: Profile Serial application to find hotspots using NVIDIA Nsight System
+- Lab 2: Parallelise the serial application using OpenACC compute directives
+- Lab 3: OpenACC optimization techniques
+- Lab 4: Apply incremental parallelization strategies and use profiler's report for the next step
+- Lab 5: Nsight Compute Kernel Level Analysis ( Optional )
+
+## Target Audience
+
+The target audience for this bootcamp are researchers/graduate students and developers who are interested in getting hands on experience with the NVIDIA Nsight System through profiling a real life parallel application using OpenACC programming model and NVTX.
+
+## Tutorial Duration
+
+The bootcamp material would take approximately 2 hours. Link to material is available for download at the end of the lab.
+
 
 ## Prerequisites:
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -46,5 +62,5 @@ Then, run the container:
 Once inside the container, open the jupyter notebook in browser: http://localhost:8888, and start the lab by clicking on the `START_profiling.ipynb` notebook.
 
 
-## Questions?
-Please join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for questions.
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc/nways/README.md

@@ -1,27 +1,38 @@
 # Nways to GPU programming
-This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA driver 440.82**). This labs comprises Nways to GPU programming implemented with the following programning approaches:
+This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA driver 440.82**). This bootcamp comprises N-Ways to GPU programming implemented with the following programming approaches:
 
 **C programming language**
+  - std::par
   - OpenACC
-  - Kokkos
-  - PSTL
   - OpenMP
-  - CUDA C
-  
-  
+  - CUDA
   
 **Fortran programming language**
   - do-concurrent
   - OpenACC
   - OpenMP
-  - CUDA Fortran
+  - CUDA
   
   
 **Python programming language**
   - CuPy
   - Numba
 
-We showcase above ways using mini applications in MD domain and CFD.
+We showcase above ways using mini applications in domains like Molecular Dynamics, Computational Fluid Dynamics etc.
+
+## Target Audience:
+
+The target audience for this bootcamp are researchers/graduate students and developers who are interested in learning about various ways of GPU programming to accelerate their scientific applications. Basic experience with C/C++ or Python or Fortran programming is needed. No GPU programming knowledge is required.
+
+## Tutorial Duration
+
+N-Ways bootcamp is designed to be modular and the participants can choose one of the ways to go through the contents in this bootcamp: 
+
+- Depth Learning: Choose one of the GPU programming approach and dive deep with optimaztion techniques.  This approach is recommended for developers who have already decided to use a programming approach and want to learn best practises for same. e.g. Learn different features of OpenACC C and  apply best programming practise to application.
+- Breadth Learning: Cover at high level all the N-Ways to GPU programming. This approach is recommended for developers starting with GPU programming and yet to converge on the best available option to port to GPU.
+
+Individual labs in the bootcamp take 1 hour each and based on path chosen total Bootcamp can take approximate 8 hours. 
+
 
 ## Prerequisites:
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -86,5 +97,5 @@ Then, run the container:
 Once inside the container, open the jupyter notebook in browser: http://localhost:8888, and start the lab by clicking on the `nways_start.ipynb` notebook.
 
 
-## Questions?
-Please join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for questions.
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc/nways/nways_labs/nways_MD/English/C/jupyter_notebook/Final_Remarks.ipynb

@@ -88,6 +88,11 @@
     "\n",
     "--- \n",
     "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might of interest: \n",
+    "\n",
+    "- [AI for HPC](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_climate)\n",
+    "\n",
     "## Licensing \n",
     "\n",
     "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
@@ -110,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/nways/nways_labs/nways_MD/English/Fortran/jupyter_notebook/Final_Remarks.ipynb

@@ -85,6 +85,12 @@
     "\n",
     "--- \n",
     "\n",
+    "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might of interest: \n",
+    "\n",
+    "- [AI for HPC](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_climate)\n",
+    "\n",
     "## Licensing \n",
     "\n",
     "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "

hpc/nways/nways_labs/nways_MD/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc/nways/nways_labs/nways_MD/English/Python/jupyter_notebook/Final_Remarks.ipynb

@@ -80,6 +80,13 @@
     "\n",
     "--- \n",
     "\n",
+    "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might of interest : \n",
+    "\n",
+    "- [AI for HPC](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/ai_science_climate)\n",
+    "\n",
+    "\n",
     "## Licensing \n",
     "\n",
     "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
@@ -102,7 +109,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/nways/nways_labs/nways_start.ipynb

@@ -76,7 +76,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab1-bonus.ipynb

@@ -145,7 +145,7 @@
     "\n",
     "---\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -165,7 +165,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab1.ipynb

@@ -520,7 +520,7 @@
     "\n",
     "---\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -540,7 +540,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab2.ipynb

@@ -648,7 +648,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -668,7 +668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab3-bonus.ipynb

@@ -215,7 +215,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -235,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/C/jupyter_notebook/openacc_c_lab3.ipynb

@@ -379,11 +379,16 @@
    "source": [
     "**After** executing the above zip command, you should be able to download the zip file [here](../openacc_files.zip)\n",
     "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some addional Bootcamp which might of interest: \n",
+    "\n",
+    "- [N-Ways to GPU Programming](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc/nways)\n",
+    "\n",
     "--- \n",
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -403,7 +408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab1-bonus.ipynb

@@ -111,7 +111,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -131,7 +131,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab1.ipynb

@@ -523,7 +523,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -543,7 +543,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab2.ipynb

@@ -632,7 +632,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -652,7 +652,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab3-bonus.ipynb

@@ -194,7 +194,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -214,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Fortran/jupyter_notebook/openacc_fortran_lab3.ipynb

@@ -363,11 +363,16 @@
    "source": [
     "**After** executing the above zip command, you should be able to download the zip file [here](../openacc_files.zip)\n",
     "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some addional Bootcamp which might of interest: \n",
+    "\n",
+    "- [N-Ways to GPU Programming](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc/nways)\n",
+    "\n",
     "--- \n",
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -387,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/Lab3.ipynb

@@ -29,7 +29,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/English/openacc_start.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "## Licensing \n",
     "\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0). "
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],
@@ -62,7 +62,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc/openacc/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc/openacc/README.md

@@ -1,5 +1,19 @@
 # openacc-training-materials
-Training materials provided by OpenACC.org.(**Tested on NVIDIA driver 440.82**)
+
+This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA driver 440.82**). This bootcamp covers how to program GPUs with OpenACC though hands on experience.
+
+**Tutorial Outline**
+- Lab 1: Introduction to OpenACC 
+- Lab 2: OpenACC Data Management 
+- Lab 3: Loop Optimizations with OpenACC
+
+## Target Audience
+
+The target audience for this lab is researchers/graduate students and developers who are interested in learning about programming GPUs with OpenACC.
+
+## Tutorial Duration
+
+The total bootcamp material  would take approximately 3 hours (  1 hour per Lab ).
 
 ## Prerequisites:
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -46,5 +60,6 @@ Then, run the container:
 Once inside the container, open the jupyter notebook in browser: http://localhost:8888, and start the lab by clicking on the `START_profiling.ipynb` notebook.
 
 
-## Questions?
-Please join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for questions.
+
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc_ai/PINN/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc_ai/PINN/English/python/Start_Here.ipynb

@@ -37,7 +37,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

hpc_ai/PINN/README.MD

@@ -1,5 +1,18 @@
 # openacc-training-materials
-Training materials provided by OpenACC.org. The objective of this lab is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing (HPC) Simulations ). This Bootcamp will provide introduction to fundamentals of using Physics Informed Neural Network and how they can be applied to real world scientific domains using NVIDIA SimNet. 
+This repository contains mini applications for GPU Bootcamps. The objective of this lab is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing (HPC) Simulations ). This Bootcamp will provide introduction to fundamentals of using Physics Informed Neural Network and how they can be applied to real world scientific domains using NVIDIA SimNet.
+
+- Introduction: Data Driven vs PINN approach
+- Lab 1: Solving Partial Differential Equations using SimNet
+- Lab 2: Solving transient problems and inverse problems using SimNet
+- Lab 3: Mini Challenge
+
+## Target Audience:
+
+The target audience for this bootcamp are researchers/graduate students and developers who are new to field of Artifical Intelligence and interested in learning difference between Data and Physics Informed Neural Network approach applied to Simulation domains. Basic Python programming knowledge is required. 
+
+## Tutorial Duration
+
+The overall bootcamp will take approximately 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
 
 ## Prerequisites
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -36,3 +49,5 @@ Then, run the container:
 Then, open the jupyter notebook in browser: http://localhost:8888
 Start working on the lab by clicking on the `Start_Here.ipynb` notebook.
 
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc_ai/ai_science_cfd/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/CFD/Competition.ipynb

@@ -437,8 +437,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might be of interest: \n",
+    "\n",
+    "- [Physics Informed Neural Network](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/PINN)\n",
+    "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "[Previous Notebook](Part4.ipynb)\n",
     "      \n",
@@ -465,13 +471,6 @@
     "    \n",
     "[Home Page](../Start_Here.ipynb)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/CFD/Part2.ipynb

@@ -560,7 +560,7 @@
     "\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/CFD/Part3.ipynb

@@ -792,6 +792,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## License\n",
+    "\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
     "\n",
     "[Previous Notebook](Part2.ipynb)\n",
     "      \n",

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/CFD/Part4.ipynb

@@ -589,7 +589,7 @@
    "metadata": {},
    "source": [
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/CFD/Start_Here.ipynb

@@ -55,7 +55,7 @@
     "We will implement neural networks to predict the steady state flow. We will start with a simple fully connected model, then implement the convolutional model. Finally, we will implement an U-network model based on [this](https://arxiv.org/abs/1710.10352) paper.\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)\n"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n"
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/CNN's.ipynb

@@ -511,7 +511,7 @@
     "[Comprehensive introduction to Convolution](https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215)\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/Part_2.ipynb

@@ -386,7 +386,7 @@
     "\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/Resnets.ipynb

@@ -569,7 +569,11 @@
     "\n",
     "## Important\n",
     "\n",
-    "<mark>Shut down the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>"
+    "<mark>Shut down the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
+    "\n",
+    "\n",
+    "## License\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Start_Here.ipynb

@@ -91,7 +91,7 @@
     "- Benchmark between different models and how they compare against one another\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   }
  ],

hpc_ai/ai_science_cfd/README.MD

@@ -1,5 +1,17 @@
 # openacc-training-materials
-Training materials provided by OpenACC.org. The objective of this lab is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing(HPC) Simulations ). This Bootcamp will introduce you to fundamentals of AI and how they can be applied to CFD (Computational Fluid Dynamics)
+This repository contains mini applications for GPU Bootcamps. The objective of this bootcamp is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing(HPC) Simulations ). This Bootcamp will introduce  fundamentals of AI and how they can be applied to CFD (Computational Fluid Dynamics)
+
+- Introduction to AI and Convolution Neural Network with Keras
+- Using AI for Steady State Flow using Neural Networks
+
+## Target Audience:
+
+The target audience for this bootcamp are researchers/graduate students and developers who are new to field of Artifical Intelligence and interested in learning about how it can be applied to Simulation domains like Computational Fluid Dynamics. Basic Python programming knowledge is required. 
+
+## Tutorial Duration
+
+The overall bootcamp will take approximately 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
+
 
 ## Prerequisites:
 To run this tutorial you will need a machine with NVIDIA GPU.
@@ -56,3 +68,5 @@ A. Some notebooks depend on writing logs to /tmp directory. While creating conta
 Q. "ResourceExhaustedError" error is observed while running the labs
 A. Currently the batch size and network model is set to consume 16GB GPU memory. In order to use the labs without any modifications it is recommended to have GPU with minimum 16GB GPU memory. Else the users can play with batch size to reduce the memory footprint
 
+## Known issues
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).

hpc_ai/ai_science_climate/English/Presentations/README.md

@@ -0,0 +1,5 @@
+For Partners who are interested in delivering the critical hands-on skills needed to advance science in form of Bootcamp can reach out to us at [GPU Hackathon Partner](https://gpuhackathons.org/partners) website. In addition to current bootcamp material the Partners will be provided with the following:
+
+- Presentation: All the Bootcamps are accompanied with training material presentations which can be used during the Bootcamp session.
+- Mini challenge : To test the knowledge gained during this Bootcamp a mini application challenge is provided along with sample Solution.
+- Additional Support: On case to case basis the Partners can also be trained on how to effectively deliver the Bootcamp with maximal impact.

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Intro_to_DL/CNN's.ipynb

@@ -524,7 +524,7 @@
     "[Comprehensive introduction to Convolution](https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215)\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Intro_to_DL/Part_2.ipynb

@@ -389,7 +389,7 @@
     "<mark>Shutdown the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Approach_to_the_Problem_&_Inspecting_and_Cleaning_the_Required_Data.ipynb

@@ -437,7 +437,7 @@
     "Now determining the velocity at any time instance with this interpolated data is going to be deviated from the truth value, but we know that a class has a range of velocity so the probability that our interpolated class being correct is more realstic as compared to the former.\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Competition.ipynb

@@ -361,6 +361,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Other Bootcamps\n",
+    "The contents of this Bootcamp originates from [OpenACC GPU Bootcamp Github](https://github.com/gpuhackathons-org/gpubootcamp).  Here are some additional Bootcamp which might be of interest: \n",
+    "\n",
+    "- [Physics Informed Neural Network](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/hpc_ai/PINN)\n",
+    "\n",
+    "## License\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0).\n",
+    "\n",
     "[Previous Notebook](Manipulation_of_Image_Data_and_Category_Determination_using_Text_Data.ipynb)\n",
     "     \n",
     "     \n",

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Countering_Data_Imbalance.ipynb

@@ -415,7 +415,7 @@
    "metadata": {},
    "source": [
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Manipulation_of_Image_Data_and_Category_Determination_using_Text_Data.ipynb

@@ -654,7 +654,7 @@
     "<mark>Shutdown the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
     "\n",
     "## Licensing\n",
-    "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
    ]
   },
   {

hpc_ai/ai_science_climate/English/python/jupyter_notebook/Tropical_Cyclone_Intensity_Estimation/Pre-Processing_Text_Data.ipynb

@@ -349,6 +349,15 @@
    "source": [
     "atlantic_storms.to_csv(\"atlantic.csv\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## License\n",
+    "\n",
+    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
+   ]
   }
  ],
  "metadata": {
@@ -367,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.6.2"
   }
  },
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