fix whitespace in devcatalogs

analytics/classical-ml/synthetic/inference/DEVCATALOG.md

@@ -1,7 +1,7 @@
 # Wafer Insights - Inference
 
 ## Overview
-Wafer Insights is a python application that allows users to predict FMAX/IDV tokens based on multiple data sources measured in the fab. For detailed information about the workflow, go to [Wafer Insights](htts://github.com/intel/wafer-insights) GitHub repository.
+Wafer Insights is a python application that allows users to predict FMAX/IDV tokens based on multiple data sources measured in the fab. For detailed information about the workflow, go to [Wafer Insights](https://github.com/intel/wafer-insights-with-classical-ml) GitHub repository.
 
 ## How it Works
 Wafer Insights is an interactive data-visualization web application based on Dash and Plotly. It includes 2 major components: a data loader which generates synthetic fab data for visualization, and a dash app that provides an interface for users to play around with the data and gain insights into the data. Dash is written on top of Plotly.js and React.js and is an ideal framework for building and deploying data apps with customized user interfaces. The  `src/dashboard` folder contains the code for the dash app and the `src/loaders` folder contains the code for the data loader.
@@ -10,9 +10,9 @@ Wafer Insights is an interactive data-visualization web application based on Das
 
 ### **Prerequisites**
 #### Download the repo
-Clone [Wafer Insights](htts://github.com/intel/wafer-insightss) repository into your working directory.
+Clone [Wafer Insights](https://github.com/intel/wafer-insights-with-classical-ml) repository into your working directory.
 ```
-git clone -b v1.0.0 htts://github.com/intel/wafer-insights .
+git clone -b v1.0.0 https://github.com/intel/wafer-insights-with-classical-ml .
 ```
 #### Download the dataset
 The actual measurement data from the fab cannot be shared with the public. Therefore, we provide a synthetic data loader to generate synthetic data using the `make_regression` function from the sklearn library, which has the following format:
@@ -36,11 +36,11 @@ docker pull intel/ai-workflows:wafer-insights
 
 (Optional) Export related proxy into docker environment.
 ```
-export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \ 
-  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \ 
-  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \ 
-  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \ 
-  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \ 
+export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
+  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
+  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
+  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
+  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
   -e SOCKS_PROXY=${SOCKS_PROXY}"
 ```
 To run the pipeline, follow below instructions outside of docker instance. 

analytics/tensorflow/ssd_resnet34/inference/DEVCATALOG.md

@@ -47,11 +47,11 @@ docker pull intel/ai-workflows:video-streamer
 
 (Optional) Export related proxy into docker environment.
 ```
-export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \ 
-  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \ 
-  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \ 
-  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \ 
-  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \ 
+export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
+  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
+  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
+  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
+  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
   -e SOCKS_PROXY=${SOCKS_PROXY}"
 ```
 To run the pipeline, follow below instructions outside of docker instance. 

language_modeling/pytorch/bert_large/training/DEVCATALOG.md

@@ -53,11 +53,11 @@ docker pull intel/ai-workflows:document-level-sentiment-analysis
 
 (Optional) Export related proxy into docker environment.
 ```
-export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \ 
-  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \ 
-  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \ 
-  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \ 
-  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \ 
+export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
+  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
+  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
+  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
+  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
   -e SOCKS_PROXY=${SOCKS_PROXY}"
 ```
 Run different fine-tuning pipeline by replacing "Bash Command" according to the pipeline.

template/DEVCATALOG_TEMPLATE.md

@@ -32,11 +32,11 @@ docker pull <Docker image name>
 
 (Optional) Export related proxy into docker environment.
 ```
-export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \ 
-  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \ 
-  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \ 
-  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \ 
-  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \ 
+export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
+  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
+  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
+  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
+  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
   -e SOCKS_PROXY=${SOCKS_PROXY}"
 ```
 To run the pipeline, follow below instructions outside of docker instance. 
@@ -46,7 +46,7 @@ docker run \
   --volume $(pwd):/workspace \
   --workdir /workspace \
   --privileged --init -it \
-  <Docker image name> \ 
+  <Docker image name> \
   <Bash shell commands>
 ```