fixed review comments

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/)
 
@@ -12,7 +15,7 @@ The bootcamp content focuses on how to follow the Analyze, Parallelize and Optim
 | [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 covers 
+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 |
 | ----------- | ----------- |
@@ -36,6 +39,11 @@ 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)

ai/DeepStream/English/Presentations/README.md

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.md

@@ -57,12 +57,9 @@ 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
 
-## Questions?
 - If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
-

ai/DeepStream_Perf_Lab/English/Presentations/README.md

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.md

@@ -1,6 +1,6 @@
 
 # openacc-training-materials
-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.  
+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
@@ -61,7 +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.
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps
+## 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

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.MD

@@ -13,14 +13,14 @@ This repository contains mini applications for GPU Bootcamps. This repository co
 
 ## Tutorial Duration
 
-The overall lab should take approximate 3.5 hours. There is an additional mini-challenge provided at the end of lab.  
+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.
@@ -62,8 +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>
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps
+## 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/Presentations/README.md

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.md

@@ -6,7 +6,7 @@ This repository contains mini applications for GPU Bootcamps (**Tested on NVIDIA
 - 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: Optional Nsight Compute Kernel Level Analysis
+- Lab 5: Nsight Compute Kernel Level Analysis ( Optional )
 
 ## Target Audience
 
@@ -62,6 +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?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
+## 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

@@ -31,7 +31,7 @@ N-Ways bootcamp is designed to be modular and the participants can choose one of
 - 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 labs can take approximate 8 hours. 
+Individual labs in the bootcamp take 1 hour each and based on path chosen total Bootcamp can take approximate 8 hours. 
 
 
 ## Prerequisites:
@@ -97,6 +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?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
+## 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/Presentations/README.md

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/Presentations/README.md

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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

@@ -13,7 +13,7 @@ The target audience for this lab is researchers/graduate students and developers
 
 ## Tutorial Duration
 
-The total bootcamp material  would take approximate 3 hours (  1 hour per Lab ).
+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.
@@ -61,6 +61,5 @@ Once inside the container, open the jupyter notebook in browser: http://localhos
 
 
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
+## 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

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.MD

@@ -12,7 +12,7 @@ The target audience for this bootcamp are researchers/graduate students and deve
 
 ## Tutorial Duration
 
-The overall bootcamp will take approximate 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
+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.
@@ -49,7 +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.
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
-
+## 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

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.MD

@@ -10,7 +10,7 @@ The target audience for this bootcamp are researchers/graduate students and deve
 
 ## Tutorial Duration
 
-The overall bootcamp will take approximate 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
+The overall bootcamp will take approximately 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
 
 
 ## Prerequisites:
@@ -68,6 +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
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
+## 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

@@ -1,5 +1,5 @@
-For Partners who are interested to deliver 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:
+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 used during the Bootcamp session.
+- 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/README.MD

@@ -7,7 +7,7 @@ The target audience for this bootcamp are researchers/graduate students and deve
 
 ## Tutorial Duration
 
-The overall bootcamp will take approximate 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
+The overall bootcamp will take approximately 3 hours. There is an additional mini-challenge provided at the end of bootcamp.
 
 
 ## Prerequisites
@@ -53,7 +53,8 @@ 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. cuDNN failed to initialize or GPU out of memory error
 
@@ -66,8 +67,7 @@ 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
 
-## Questions?
-- If you observe any errors, please file an issue on [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
-- Also join [OpenACC Slack Channel](https://openacclang.slack.com/messages/openaccusergroup) for general queries related to Hackathons and Bootcamps.
+- Please go through the list of exisiting bugs/issues or file a new issue at [Github](https://github.com/gpuhackathons-org/gpubootcamp/issues).
+