From 54126a5d3c11107e8d7045a0e7019fbf1d4c6a65 Mon Sep 17 00:00:00 2001 From: jhudsl-robot Date: Fri, 6 Oct 2023 13:05:55 +0000 Subject: [PATCH 1/2] Render toc-less --- docs/no_toc/404.html | 2 +- docs/no_toc/About.md | 101 +++++++++++---------- docs/no_toc/about-the-authors.html | 103 ++++++++++++---------- docs/no_toc/account-setup.html | 2 +- docs/no_toc/faq-and-troubleshooting.html | 2 +- docs/no_toc/feedback.html | 2 +- docs/no_toc/file-upload-and-download.html | 2 +- docs/no_toc/get-your-certificate.html | 2 +- docs/no_toc/help.html | 2 +- docs/no_toc/index.html | 4 +- docs/no_toc/index.md | 2 +- docs/no_toc/interactive-session.html | 2 +- docs/no_toc/logging-in.html | 2 +- docs/no_toc/search_index.json | 2 +- docs/no_toc/skip-to-certification.html | 2 +- docs/no_toc/submit-your-first-job.html | 2 +- docs/no_toc/summary.html | 2 +- docs/no_toc/terminal.html | 2 +- docs/no_toc/what-is-a-cluster.html | 2 +- 19 files changed, 125 insertions(+), 115 deletions(-) diff --git a/docs/no_toc/404.html b/docs/no_toc/404.html index 7aea7265..ffa2b535 100644 --- a/docs/no_toc/404.html +++ b/docs/no_toc/404.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/About.md b/docs/no_toc/About.md index 4fd3a83b..5464ce04 100644 --- a/docs/no_toc/About.md +++ b/docs/no_toc/About.md @@ -30,62 +30,67 @@ _Please note that this course is under developmnent and these credits are subjec ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.2 (2020-06-22) -## os Ubuntu 20.04.3 LTS +## os Ubuntu 20.04.5 LTS ## system x86_64, linux-gnu ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Etc/UTC -## date 2022-11-04 +## date 2023-10-06 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── -## package * version date lib source -## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.3) -## bookdown 0.24 2022-02-15 [1] Github (rstudio/bookdown@88bc4ea) -## callr 3.4.4 2020-09-07 [1] RSPM (R 4.0.2) -## cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.0) -## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0) -## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3) -## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3) -## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0) -## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3) -## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.3) -## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0) -## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3) -## glue 1.6.1 2022-01-22 [1] CRAN (R 4.0.2) -## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0) -## htmltools 0.5.0 2020-06-16 [1] RSPM (R 4.0.1) -## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2) -## knitr 1.33 2022-02-15 [1] Github (yihui/knitr@a1052d1) -## lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.0.2) -## magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.0.2) -## memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.0) -## ottrpal 0.1.2 2022-02-15 [1] Github (jhudsl/ottrpal@1018848) -## pillar 1.4.6 2020-07-10 [1] RSPM (R 4.0.2) -## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 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2.3.0 2020-09-22 [1] RSPM (R 4.0.2) -## xfun 0.26 2022-02-15 [1] Github (yihui/xfun@74c2a66) -## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3) +## package * version date lib source +## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.5) +## bookdown 0.24 2023-03-28 [1] Github (rstudio/bookdown@88bc4ea) +## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.0.2) +## cachem 1.0.7 2023-02-24 [1] CRAN (R 4.0.2) +## callr 3.5.0 2020-10-08 [1] RSPM (R 4.0.2) +## cli 3.6.1 2023-03-23 [1] CRAN (R 4.0.2) +## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0) +## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3) +## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3) +## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0) +## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3) +## evaluate 0.20 2023-01-17 [1] CRAN (R 4.0.2) +## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0) +## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.0.2) +## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3) +## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.5) +## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0) +## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.0.2) +## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2) +## jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2) +## knitr 1.33 2023-03-28 [1] Github (yihui/knitr@a1052d1) +## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.0.2) +## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.0.2) +## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2) +## ottrpal 1.0.1 2023-03-28 [1] Github (jhudsl/ottrpal@151e412) +## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.0.2) +## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2) +## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3) +## pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.3) +## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3) +## processx 3.4.4 2020-09-03 [1] RSPM (R 4.0.2) +## ps 1.4.0 2020-10-07 [1] RSPM (R 4.0.2) +## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0) +## readr 1.4.0 2020-10-05 [1] RSPM (R 4.0.2) +## remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.3) +## rlang 1.1.0 2023-03-14 [1] CRAN (R 4.0.2) +## rmarkdown 2.10 2023-03-28 [1] Github (rstudio/rmarkdown@02d3c25) +## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.0.2) +## sass 0.4.5 2023-01-24 [1] CRAN (R 4.0.2) +## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3) +## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3) +## stringr 1.4.0 2019-02-10 [1] RSPM (R 4.0.3) +## testthat 3.0.1 2023-03-28 [1] Github (R-lib/testthat@e99155a) +## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.0.2) +## usethis 1.6.3 2020-09-17 [1] RSPM (R 4.0.2) +## utf8 1.1.4 2018-05-24 [1] RSPM (R 4.0.3) +## vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.0.2) +## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2) +## xfun 0.26 2023-03-28 [1] Github (yihui/xfun@74c2a66) +## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3) ## ## [1] /usr/local/lib/R/site-library ## [2] /usr/local/lib/R/library diff --git a/docs/no_toc/about-the-authors.html b/docs/no_toc/about-the-authors.html index 89ea1a21..65430a58 100644 --- a/docs/no_toc/about-the-authors.html +++ b/docs/no_toc/about-the-authors.html @@ -23,7 +23,7 @@ - + @@ -257,62 +257,67 @@

About the Authors

## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value                       
 ##  version  R version 4.0.2 (2020-06-22)
-##  os       Ubuntu 20.04.3 LTS          
+##  os       Ubuntu 20.04.5 LTS          
 ##  system   x86_64, linux-gnu           
 ##  ui       X11                         
 ##  language (EN)                        
 ##  collate  en_US.UTF-8                 
 ##  ctype    en_US.UTF-8                 
 ##  tz       Etc/UTC                     
-##  date     2022-11-04                  
+##  date     2023-10-06                  
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
-##  package     * version    date       lib source                            
-##  assertthat    0.2.1      2019-03-21 [1] RSPM (R 4.0.3)                    
-##  bookdown      0.24       2022-02-15 [1] Github (rstudio/bookdown@88bc4ea) 
-##  callr         3.4.4      2020-09-07 [1] RSPM (R 4.0.2)                    
-##  cli           2.0.2      2020-02-28 [1] RSPM (R 4.0.0)                    
-##  crayon        1.3.4      2017-09-16 [1] RSPM (R 4.0.0)                    
-##  desc          1.2.0      2018-05-01 [1] RSPM (R 4.0.3)                    
-##  devtools      2.3.2      2020-09-18 [1] RSPM (R 4.0.3)                    
-##  digest        0.6.25     2020-02-23 [1] RSPM (R 4.0.0)                    
-##  ellipsis      0.3.1      2020-05-15 [1] RSPM (R 4.0.3)                    
-##  evaluate      0.14       2019-05-28 [1] RSPM (R 4.0.3)                    
-##  fansi         0.4.1      2020-01-08 [1] RSPM (R 4.0.0)                    
-##  fs            1.5.0      2020-07-31 [1] RSPM (R 4.0.3)                    
-##  glue          1.6.1      2022-01-22 [1] CRAN (R 4.0.2)                    
-##  hms           0.5.3      2020-01-08 [1] RSPM (R 4.0.0)                    
-##  htmltools     0.5.0      2020-06-16 [1] RSPM (R 4.0.1)                    
-##  jquerylib     0.1.4      2021-04-26 [1] CRAN (R 4.0.2)                    
-##  knitr         1.33       2022-02-15 [1] Github (yihui/knitr@a1052d1)      
-##  lifecycle     1.0.0      2021-02-15 [1] CRAN (R 4.0.2)                    
-##  magrittr      2.0.2      2022-01-26 [1] CRAN (R 4.0.2)                    
-##  memoise       1.1.0      2017-04-21 [1] RSPM (R 4.0.0)                    
-##  ottrpal       0.1.2      2022-02-15 [1] Github (jhudsl/ottrpal@1018848)   
-##  pillar        1.4.6      2020-07-10 [1] RSPM (R 4.0.2)                    
-##  pkgbuild      1.1.0      2020-07-13 [1] RSPM (R 4.0.2)                    
-##  pkgconfig     2.0.3      2019-09-22 [1] RSPM (R 4.0.3)                    
-##  pkgload       1.1.0      2020-05-29 [1] RSPM (R 4.0.3)                    
-##  prettyunits   1.1.1      2020-01-24 [1] RSPM (R 4.0.3)                    
-##  processx      3.4.4      2020-09-03 [1] RSPM (R 4.0.2)                    
-##  ps            1.3.4      2020-08-11 [1] RSPM (R 4.0.2)                    
-##  purrr         0.3.4      2020-04-17 [1] RSPM (R 4.0.3)                    
-##  R6            2.4.1      2019-11-12 [1] RSPM (R 4.0.0)                    
-##  readr         1.4.0      2020-10-05 [1] RSPM (R 4.0.2)                    
-##  remotes       2.2.0      2020-07-21 [1] RSPM (R 4.0.3)                    
-##  rlang         0.4.10     2022-02-15 [1] Github (r-lib/rlang@f0c9be5)      
-##  rmarkdown     2.10       2022-02-15 [1] Github (rstudio/rmarkdown@02d3c25)
-##  rprojroot     2.0.2      2020-11-15 [1] CRAN (R 4.0.2)                    
-##  sessioninfo   1.1.1      2018-11-05 [1] RSPM (R 4.0.3)                    
-##  stringi       1.5.3      2020-09-09 [1] RSPM (R 4.0.3)                    
-##  stringr       1.4.0      2019-02-10 [1] RSPM (R 4.0.3)                    
-##  testthat      3.0.1      2022-02-15 [1] Github (R-lib/testthat@e99155a)   
-##  tibble        3.0.3      2020-07-10 [1] RSPM (R 4.0.2)                    
-##  usethis       2.1.5.9000 2022-02-15 [1] Github (r-lib/usethis@57b109a)    
-##  vctrs         0.3.4      2020-08-29 [1] RSPM (R 4.0.2)                    
-##  withr         2.3.0      2020-09-22 [1] RSPM (R 4.0.2)                    
-##  xfun          0.26       2022-02-15 [1] Github (yihui/xfun@74c2a66)       
-##  yaml          2.2.1      2020-02-01 [1] RSPM (R 4.0.3)                    
+##  package     * version date       lib source                            
+##  assertthat    0.2.1   2019-03-21 [1] RSPM (R 4.0.5)                    
+##  bookdown      0.24    2023-03-28 [1] Github (rstudio/bookdown@88bc4ea) 
+##  bslib         0.4.2   2022-12-16 [1] CRAN (R 4.0.2)                    
+##  cachem        1.0.7   2023-02-24 [1] CRAN (R 4.0.2)                    
+##  callr         3.5.0   2020-10-08 [1] RSPM (R 4.0.2)                    
+##  cli           3.6.1   2023-03-23 [1] CRAN (R 4.0.2)                    
+##  crayon        1.3.4   2017-09-16 [1] RSPM (R 4.0.0)                    
+##  desc          1.2.0   2018-05-01 [1] RSPM (R 4.0.3)                    
+##  devtools      2.3.2   2020-09-18 [1] RSPM (R 4.0.3)                    
+##  digest        0.6.25  2020-02-23 [1] RSPM (R 4.0.0)                    
+##  ellipsis      0.3.1   2020-05-15 [1] RSPM (R 4.0.3)                    
+##  evaluate      0.20    2023-01-17 [1] CRAN (R 4.0.2)                    
+##  fansi         0.4.1   2020-01-08 [1] RSPM (R 4.0.0)                    
+##  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.0.2)                    
+##  fs            1.5.0   2020-07-31 [1] RSPM (R 4.0.3)                    
+##  glue          1.4.2   2020-08-27 [1] RSPM (R 4.0.5)                    
+##  hms           0.5.3   2020-01-08 [1] RSPM (R 4.0.0)                    
+##  htmltools     0.5.5   2023-03-23 [1] CRAN (R 4.0.2)                    
+##  jquerylib     0.1.4   2021-04-26 [1] CRAN (R 4.0.2)                    
+##  jsonlite      1.7.1   2020-09-07 [1] RSPM (R 4.0.2)                    
+##  knitr         1.33    2023-03-28 [1] Github (yihui/knitr@a1052d1)      
+##  lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.0.2)                    
+##  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.0.2)                    
+##  memoise       2.0.1   2021-11-26 [1] CRAN (R 4.0.2)                    
+##  ottrpal       1.0.1   2023-03-28 [1] Github (jhudsl/ottrpal@151e412)   
+##  pillar        1.9.0   2023-03-22 [1] CRAN (R 4.0.2)                    
+##  pkgbuild      1.1.0   2020-07-13 [1] RSPM (R 4.0.2)                    
+##  pkgconfig     2.0.3   2019-09-22 [1] RSPM (R 4.0.3)                    
+##  pkgload       1.1.0   2020-05-29 [1] RSPM (R 4.0.3)                    
+##  prettyunits   1.1.1   2020-01-24 [1] RSPM (R 4.0.3)                    
+##  processx      3.4.4   2020-09-03 [1] RSPM (R 4.0.2)                    
+##  ps            1.4.0   2020-10-07 [1] RSPM (R 4.0.2)                    
+##  R6            2.4.1   2019-11-12 [1] RSPM (R 4.0.0)                    
+##  readr         1.4.0   2020-10-05 [1] RSPM (R 4.0.2)                    
+##  remotes       2.2.0   2020-07-21 [1] RSPM (R 4.0.3)                    
+##  rlang         1.1.0   2023-03-14 [1] CRAN (R 4.0.2)                    
+##  rmarkdown     2.10    2023-03-28 [1] Github (rstudio/rmarkdown@02d3c25)
+##  rprojroot     2.0.3   2022-04-02 [1] CRAN (R 4.0.2)                    
+##  sass          0.4.5   2023-01-24 [1] CRAN (R 4.0.2)                    
+##  sessioninfo   1.1.1   2018-11-05 [1] RSPM (R 4.0.3)                    
+##  stringi       1.5.3   2020-09-09 [1] RSPM (R 4.0.3)                    
+##  stringr       1.4.0   2019-02-10 [1] RSPM (R 4.0.3)                    
+##  testthat      3.0.1   2023-03-28 [1] Github (R-lib/testthat@e99155a)   
+##  tibble        3.2.1   2023-03-20 [1] CRAN (R 4.0.2)                    
+##  usethis       1.6.3   2020-09-17 [1] RSPM (R 4.0.2)                    
+##  utf8          1.1.4   2018-05-24 [1] RSPM (R 4.0.3)                    
+##  vctrs         0.6.1   2023-03-22 [1] CRAN (R 4.0.2)                    
+##  withr         2.3.0   2020-09-22 [1] RSPM (R 4.0.2)                    
+##  xfun          0.26    2023-03-28 [1] Github (yihui/xfun@74c2a66)       
+##  yaml          2.2.1   2020-02-01 [1] RSPM (R 4.0.3)                    
 ## 
 ## [1] /usr/local/lib/R/site-library
 ## [2] /usr/local/lib/R/library
diff --git a/docs/no_toc/account-setup.html b/docs/no_toc/account-setup.html index 66508b97..3d3ac297 100644 --- a/docs/no_toc/account-setup.html +++ b/docs/no_toc/account-setup.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/faq-and-troubleshooting.html b/docs/no_toc/faq-and-troubleshooting.html index bc299199..7e6d8e55 100644 --- a/docs/no_toc/faq-and-troubleshooting.html +++ b/docs/no_toc/faq-and-troubleshooting.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/feedback.html b/docs/no_toc/feedback.html index b72c5819..bfe3421f 100644 --- a/docs/no_toc/feedback.html +++ b/docs/no_toc/feedback.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/file-upload-and-download.html b/docs/no_toc/file-upload-and-download.html index d2a74d21..297b48d7 100644 --- a/docs/no_toc/file-upload-and-download.html +++ b/docs/no_toc/file-upload-and-download.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/get-your-certificate.html b/docs/no_toc/get-your-certificate.html index 91b9b0f2..01923b00 100644 --- a/docs/no_toc/get-your-certificate.html +++ b/docs/no_toc/get-your-certificate.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/help.html b/docs/no_toc/help.html index ad7f65a0..13b3ddf5 100644 --- a/docs/no_toc/help.html +++ b/docs/no_toc/help.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/index.html b/docs/no_toc/index.html index 38d61be4..177347b5 100644 --- a/docs/no_toc/index.html +++ b/docs/no_toc/index.html @@ -23,7 +23,7 @@ - + @@ -187,7 +187,7 @@

About this Course

diff --git a/docs/no_toc/index.md b/docs/no_toc/index.md index b8379afe..78b4d4d0 100644 --- a/docs/no_toc/index.md +++ b/docs/no_toc/index.md @@ -1,6 +1,6 @@ --- title: "Fred Hutch Cluster 101" -date: "November 04, 2022" +date: "October 06, 2023" site: bookdown::bookdown_site documentclass: book bibliography: book.bib diff --git a/docs/no_toc/interactive-session.html b/docs/no_toc/interactive-session.html index 38d65324..634c7463 100644 --- a/docs/no_toc/interactive-session.html +++ b/docs/no_toc/interactive-session.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/logging-in.html b/docs/no_toc/logging-in.html index aa1e6c11..2dc367ee 100644 --- a/docs/no_toc/logging-in.html +++ b/docs/no_toc/logging-in.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/search_index.json b/docs/no_toc/search_index.json index 6e6990bc..51a0a329 100644 --- a/docs/no_toc/search_index.json +++ b/docs/no_toc/search_index.json @@ -1 +1 @@ -[["index.html", "Fred Hutch Cluster 101 About this Course", " Fred Hutch Cluster 101 November 04, 2022 About this Course Fred Hutch maintains a high performance computing cluster specifically to support work that requires intensive computing. The Fred Hutch cluster (sometimes called “Rhino” and/or “Gizmo”) allow you to do more than your average desktop computer can handle. Our goal for this course is to get you running on the Fred Hutch cluster quickly and efficiently. It is intended for everyone from brand new cluster users to researchers who have used a cluster at another institution but are new to Fred Hutch. We hope that the following modules will help you take advantage of the powerful resources the Fred Hutch has to offer! The Fred Hutch cluster is supported by a group in IT called Scientific Computing, and this course was developed by the Fred Hutch Data Science Lab in collaboration with them. Please see the author credits for more information. This course is available in Bookdown and Leanpub formats. If you want a certificate, you need to take the Leanpub version of the course. The Leanpub course can be taken for free, but you still have to put the course in your cart and check out. "],["what-is-a-cluster.html", "Chapter 1 What is a Cluster", " Chapter 1 What is a Cluster A computing cluster is a set of many computers networked together. Because there are many computers working together, the network is able to handle computationally expensive tasks, like genome assemblies or advanced algorithms. Imagine you’re building a house. It would take a long time by yourself! It’s much better to have many builders working together. Now that we have a team of workers, the next challenge is task management. A home construction team will need a manager to help delegate tasks. Similarly, the computing cluster uses management software to prioritize tasks, delegate workers (resources), and check on progress. The Fred Hutch cluster uses a common management and scheduling tool called Slurm. How is the cluster different from a laptop or desktop? First, on your laptop you most likely interact with it using an operating system like Windows or MacOS. The Fred Hutch cluster uses a Linux operating system. Second, because many people use the cluster for many tasks, there isn’t a central screen and keyboard. You access the cluster remotely from your computer! We will talk more about how to connect to the cluster in a following chapter. Computing cluster A set of computers networked together to perform large tasks. "],["account-setup.html", "Chapter 2 Account Setup 2.1 Check your HutchNet ID 2.2 Connect to a PI Account", " Chapter 2 Account Setup You will need an account to log in to the cluster. This ensures that data stays protected and that computing resources are shared fairly. 2.1 Check your HutchNet ID Your HutchNet ID is the standard login you receive when you start working at the Hutch or are an official affiliate. You can use it to login to most resources at Fred Hutch (Desktop Computer, Employee Self Service, VPN, Webmail) and our Scientific Computing systems. For example: my email is jsmith3@fredhutch.org. my HutchNet ID is jsmith3. If one of your collaborators requires access to the Fred Hutch network you can submit a non-employee action form. “Non-employee” is a generic administrative term for affiliates, students, contractors, etc. 2.2 Connect to a PI Account Your HutchNet ID must be associated with a PI cluster “account”. The Scientific Computing Team (SciComp) tries to set users up with a connection to a PI account before they need it, but this is not automatic! To ensure that you have been set up to use the cluster, please follow the following steps. You must be connected to the campus wifi network, plugged into a networked ethernet jack, or connected to the Fred Hutch VPN. Go to the SciComp Self-Service Portal Click on “Check Access” Log in using your HutchNet ID (no @fredhutch.org) and password If everything is green as shown below, you are ready to proceed. You can proceed with the course or skip to certification. If you see anything in red as shown below, click the link to e-mail SciComp to finish setting up your account. Be sure to include your HutchNetID and PI name in the email. Note that it may take just a bit of time for SciComp to see your email request, but it is usually fairly quick! Once approved, you will receive an email back from the SciComp team that you now have cluster access. The Self-Service Portal will also show that you have cluster access if you refresh the page. Now, let’s connect to the cluster! "],["skip-to-certification.html", "Skip to Certification", " Skip to Certification Are you an experienced user? Do you need certification? You can jump straight to the 10-question quiz by clicking the link below. This Leanpub quiz can be taken for free, but you still have to put the course in your cart and check out. Self-Test: Cluster 101 An experienced user: Has used SSH to connect to a computing cluster Is familiar with the methods used to connect to the Fred Hutch cluster Has used Slurm to submit a computing job Is familiar with the Cyberduck application for transferring files "],["terminal.html", "Chapter 3 Terminal Setup 3.1 What is a terminal? 3.2 Windows Setup 3.3 Mac Setup 3.4 Linux Setup", " Chapter 3 Terminal Setup The next step is getting familiar with your Terminal. This is your portal to the cluster. 3.1 What is a terminal? The Terminal is a command line interface. In other words, the Terminal is a software application that allows you to issue commands directly to your laptop or desktop computer. The Terminal is very useful because it allows you to run commands that don’t have a point-and-click equivalent. It can also connect you to computer networks, such as the Fred Hutch cluster! The Terminal setup is different depending on your operating system. Jump to the Windows, MacOS, or Linux sections below. “Terminal” used to be synonymous with “computer”. With the creation of operating systems like Windows and MacOS, computers became much easier to use and exploded in popularity! Your colleagues are almost always referring to the command line application when they say “Terminal”. 3.2 Windows Setup Click to view steps You will need to install a Terminal application called PuTTY to connect to the Fred Hutch Cluster. You should then see PuTTY available in the Software Center. Click “Install” and go through the Setup Wizard. You can also install PuTTY manually if you don’t see it in the Software Center. PuTTY should now be available in your applications. Click on PuTTY to open. You should now see the PuTTY Configuration menu. 3.3 Mac Setup Click to view steps Mac machines come with a Terminal installed. Go to Finder > Applications > Utilities > Terminal and double-click. Your Terminal should look like this: 3.4 Linux Setup Click to view steps The commonly used Linux distribution, Ubuntu, already comes with a Terminal installed. Press ctrl + alt + T. This should open a Terminal window. Update the Terminal and prepare it for connecting to the cluster by running: sudo apt install openssh-client Enter your password and enter Y when prompted. "],["logging-in.html", "Chapter 4 Logging In 4.1 What is SSH? 4.2 Connect Securely 4.3 Windows Login 4.4 Mac Login 4.5 Linux Login", " Chapter 4 Logging In Now that you have your Terminal application ready, you want to connect to the cluster. You will do this using a method called SSH, which stands for “Secure SHell”. 4.1 What is SSH? SSH is a secure way to remotely connect to another computer or network of computers. In other words, SSH helps us protect your data and the data on the Fred Hutch cluster through authentication. Hostname The hostname is the name, or label, assigned to a computer in a network. We are connecting to hostname rhino.fhcrc.org or rhino for short. 4.2 Connect Securely Before moving on, you will need to connect to the Fred Hutch wifi network, a networked ethernet jack, or the Fred Hutch VPN. This is the first layer of security. The next set of steps are specific to your operating system. 4.3 Windows Login Click to view steps Go to the PuTTY Configuration menu. Under “Host Name” type rhino and click “Open”. You will be prompted to login. Type in your HutchNetID (e.g., jsmith3). Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! 4.4 Mac Login Click to view steps Type the following commands, substituting in your HutchNet ID (no brackets): ssh [HutchID]@rhino You will see a message that looks like The authenticity of host 'rhino (XXX.XXX.XX.XX)' can't be established. Type in yes and hit enter. Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! 4.5 Linux Login Click to view steps Type the following commands, substituting in your HutchNet ID (no brackets): ssh [HutchID]@rhino Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! "],["submit-your-first-job.html", "Chapter 5 Submit Your First Job 5.1 Download the Script 5.2 Confirm the Download 5.3 Inspect the Script 5.4 Submit the Script 5.5 Check the Output", " Chapter 5 Submit Your First Job The strength of a computing cluster is the ability to do many jobs in parallel or on computers with more computing power than you have on your local computer. The best way to use the cluster is to create short snippets of instructions (a script) that a computer can perform without human input. Your script tells the computers to execute the instructions as individual jobs. Now that you’ve logged into rhino you will be able to send scripts to the networked computers in the cluster. The Fred Hutch cluster uses Slurm to organize and prioritize jobs. Based on the instructions in your script, Slurm will find computing resources within the cluster to run your job along with all the other requests from other users. In the next steps, we will go through a simple example where we download a single file. More complicated examples will use multiple files. We will discuss how to transfer files from your computer to the cluster in the following chapter. The part of the cluster where you log in is called rhino. The part of the cluster where jobs are run is called gizmo. 5.1 Download the Script We can use the wget command to download a script from GitHub. This means we don’t have to write the script from scratch. Copy and paste the following into the terminal, and hit return: wget https://raw.githubusercontent.com/FredHutch/slurm-examples/master/introduction/1-hello-world/01.sh 5.2 Confirm the Download Let’s confirm that we can see the file we just downloaded. We can use the ls (list files) command for this. Type ls and hit return. You should see the file 01.sh in your home directory. The .sh ending means this is a script meant to run from the command line. ls 5.3 Inspect the Script Let’s next inspect the script. The cat command, followed by a file name, lists the entire contents of a specific file. cat 01.sh The first line of the script, #!/bin/bash, indicates that this is a command line or “bash” script. The second line is empty, and the third line, echo \"Hello, World\" means that the computer will “echo”, or print out, “Hello, World”. 5.4 Submit the Script We use the sbatch command to submit a script and start running a job on the cluster. Copy the following and hit return. You should see a message like “Submitted batch job 12345678”. Your number will vary because this is a unique job identifier. sbatch 01.sh 5.5 Check the Output Type ls again. You should now see a log file like slurm-12345678.out listed alongside your script 01.sh. Let’s use cat to inspect the output in the log file (the new file starting with slurm and ending with .out). Make sure you replace [your-number-here] with the number in your actual file. We should see our message has been printed! cat slurm-[your-number-here].out ls This command lists the files in the current directory. cat filename This command prints the contents of a specific file (filename). sbatch filename.sh This command submits a job to the cluster with instructions specified in a .sh file. "],["file-upload-and-download.html", "Chapter 6 File Upload and Download 6.1 Download Cyberduck 6.2 Create Connection 6.3 Download and Edit the Script 6.4 Upload the New Script 6.5 Run the New Script", " Chapter 6 File Upload and Download Exchanging files with the cluster is very important. You can imagine scenarios where: You want to download log files or output files You want to upload a custom .sh script file that you wrote on your laptop You want to upload other files In this course, upload and download of files is performed using Cyberduck. Cyberduck is a tool that lets us connect to the cluster securely, browse files, and transfer files securely. If you are working with sensitive data (such as data with PHI that requires HIPAA compliance, you need to be extra cautious about transferring your data to the cluster. Your home directory is not an appropriate storage option for such data. Make sure you consider any stipulations in your data use agreements. 6.1 Download Cyberduck Download the latest version of Cyberduck here. Note that the version of Cyberduck in the Software Center or Self Service might not be current, causing compatibility issues with some operating systems. 6.2 Create Connection Launch Cyberduck and click on “Open Connection”. From the dropdown menu, select “SFTP (SSH File Transfer Protocol)” For Server, type “rhino.fhcrc.org” Fill in your HutchNetID for Username and fill in your password Click “Connect” Click “Allow”. You can also check the box to indicate “Always”. You should see your script file “01.sh” and the log file. 6.3 Download and Edit the Script Right click on “01.sh” and select “Download” You will see a “Transfers” prompt open, and the 01.sh file should now appear in your Downloads folder Open the 01.sh in your Downloads folder Edit the message to include your name and save the file. Rename the file 01-name.sh. 6.4 Upload the New Script From your Downloads folder, simply drag the file to Cyberduck. You should now see the new script among your cluster files. 6.5 Run the New Script Return to your Terminal. Submit a job with your new script by running the following. When you type ls you should see a new log file! sbatch 01-name.sh The job numbers included in log file names generally increase in number. The greater the number, the more recently the job was run. Use the cat command to inspect the log. Make sure you replace [your-number-here] to match your file. The message should show the new text that you added! cat slurm-[your-number-here].out "],["interactive-session.html", "Chapter 7 Interactive Session 7.1 Starting the session 7.2 Running Interactive Commands 7.3 Using Pre-installed Software Modules", " Chapter 7 Interactive Session While using the cluster, you might need to build and test scripts interactively before running them. Luckily, you can work directly on the cluster by creating an interactive session. When you launch an interactive session, the cluster assigns you a portion of the networked computers called a “node”. This node (or part of one) is dedicated to you for a period of time rather than using the Slurm job submission system. Because an interactive session takes up resources directly on the cluster whether you’re actively using it or not, it’s best to use interactive sessions only when a task cannot be done by submitting a script. 7.1 Starting the session When starting an interactive session, you’re going to need to think about what you are testing and what resources you might need on the node you are requesting to use. You can always start an interactive session using the default values if you aren’t sure what you need yet. Start an interactive session on a node by running the command: grabnode You will be prompted with several questions about the type of resources on the node you want. We don’t need anything fancy, so we will set up the session to use minimal resources. You can enter the following: How many CPUs/cores would you like to grab on the node? 1 How much memory (GB) would you like to grab? 20 Please enter the max number of days you would like to grab this node: 1 Do you need a GPU? N When prompted, enter your password The CPU, or Central Processing Unit, is the brain of the computer that performs and orchestrates computational tasks. Modern computers often perform multiple tasks at once, ranging from 4 tasks on a typical laptop to 48 tasks or more on higher end servers. RAM, or Random Access Memory, is often simply referred to as memory. This short term memory holds the information that the CPU needs to perform calculations. One distinctive feature of memory is that it is short term. In other words, when the electricity is shut off, the data stored in memory disappears. To save the CPU’s work, you usually save files to your computer. Running highly complicated analyses or algorithms can often require additional memory resources. The GPU, or Graphics Processing Unit, is similar to the CPU. The GPU was originally designed to quickly render graphics (such as for video games), but today can be used to run complex artificial intelligence applications or computationally intensive jobs. You will see that you are now logged on to “gizmo” instead of “rhino”. Remember that the part of the cluster where you log in is called rhino. The part of the cluster where jobs are run is called gizmo. 7.2 Running Interactive Commands You can start working on the node by running a similar command as we used in the job we submitted via script. Echo a message by running: echo "Hello, again!" 7.3 Using Pre-installed Software Modules Let’s get a bit more advanced. We can load a preconfigured software bundle called a module. This is very convenient because it means we don’t need to install anything manually! In this example, we will load a module containing R version 4.2.0. You can learn more about what modules are available and how to request new ones for the Fred Hutch cluster here. ml fhR/4.2.0-foss-2021b Next, launch R: R You can play around with R here. For example, you might run: head(mtcars) Close the R session by typing: q() R will ask if you want to save your workspace. Type n for no and hit return. Close the interactive node by typing: exit grabnode This command starts an interactive session on the cluster. Node One of the networked computers in the cluster. CPU A computer component that performs and orchestrates computational tasks. Memory A computer component that stores calculations and information in the short term. "],["help.html", "Chapter 8 Getting Help Check out the FAQ page Find Community Support on Slack Visit the SciWiki Send an Email", " Chapter 8 Getting Help The Scientific Computing group in IT manages the cluster, provides support with software, advises on data storage, and holds office hours specifically to help users. Here are some ways you can get help for your work on the cluster. Check out the FAQ page See our FAQ and Troubleshooting Page to see common errors and what they mean. If you encounter a problem that isn’t listed, let us know! Find Community Support on Slack Peer-to-peer support can be very valuable in learning and troubleshooting your work. The Fred Hutch Data Slack workspace is open to all with a fredhutch.org, uw.edu, seattlechildrens.org, or related institution email addresses (whi.org, scharp.org, etc). You can ask questions, find out about office hours, and discover other live support and training events that can help you learn more about how to leverage resources at Fred Hutch to advance your science. Visit the SciWiki The SciWiki Scientific Computing page is full of useful tips and guides. Remember when using the search that the login “nodes” to the Fred Hutch cluster are called rhino and the cluster “nodes” are called gizmo. Send an Email The primary way you can request help for a problem is to send SciComp an email, so a ticket will be created in their tracking system. This allows the details of the problem you’re having to be sent to them so they can better help you. Submitting a good email ticket helps the SciComp Team address your needs quickly and efficiently. We suggest you submit the following information: A brief overview of what the problem is. Some specifics about the problem, such as the full text (it’s ok if it’s long) of any error message or terminal command, or a screen shot of the interface you were using when you had the problem. A description of what you wanted to have happen or what your overall goal is (in case perhaps there is another strategy that might work better). "],["summary.html", "Chapter 9 Summary 9.1 Overview 9.2 Glossary 9.3 Commands (command line interface)", " Chapter 9 Summary Let’s review the key information from the previous sections. 9.1 Overview A computing cluster is a set of computers networked together to perform large tasks. We usually connect to the cluster using a command-line interface called a Terminal. The Terminal application varies based on your operating system. We connect to the cluster using a secure method called SSH. Exact SSH commands vary depending on your operating system. Before using the Fred Hutch cluster, it’s a good idea to contact the SciComp team to ensure your account is set up correctly. You must be connected to the campus wifi network, plugged into a networked ethernet jack, or connected to the Fred Hutch VPN to connect to the Fred Hutch cluster. Computer tasks are typically performed via job submission, where you tell the cluster what to do inside a script file. The Fred Hutch cluster uses Slurm to organize jobs submitted by different users. When building, testing, and debugging jobs, you might want to launch an interactive session on the cluster. You will be asked several questions about resources before your session starts. You can use the application Cyberduck to transfer smaller files between your local computer and the cluster via SFTP (SSH File Transfer Protocol). This course does not cover transferring sensitive data, which requires extra precautions. Don’t be afraid to ask for help! Check out the SciWiki, join the Slack workspace, or contact the SciComp team if you get stuck. 9.2 Glossary computing cluster - A set of computers networked together to perform large tasks. CPU - A computer component that performs and orchestrates computational tasks. Cyberduck - Third-party software which transfers files between your local machine and the cluster. hostname - The hostname is the name, or label, assigned to a computer in a network. We connect to hostname rhino.fhcrc.org or rhino for short. memory - A computer component that stores calculations and information in the short term. node - A part of the cluster; a computer in the network. SSH - A secure method for remotely connecting to another computer or network of computers; stands for “Secure SHell”. terminal - A command line interface; a software application that allows you to issue commands directly to a computer. 9.3 Commands (command line interface) Command Description Usage cat displays all contents of a file cat [filename] exit terminates an interactive session exit grabnode launches an interactive session grabnode ls lists files in the current folder ls sbatch submits a script containing job instructions sbatch [scriptname.sh] wget downloads a file from the internet wget [url] "],["feedback.html", "Chapter 10 Provide Feedback", " Chapter 10 Provide Feedback We’d love to hear from you! Please fill out the anonymous Google Form with your feedback. You can submit an issue about this course at our GitHub repository. You can also click the edit button on the top of the page in question. "],["faq-and-troubleshooting.html", "Chapter 11 FAQ and Troubleshooting 11.1 FAQ 11.2 Troubleshooting", " Chapter 11 FAQ and Troubleshooting 11.1 FAQ Here are some questions you might have. 11.1.1 How can I manually install PuTTY? Click to view steps Click here to install the latest version of PuTTY. You will choose the 64-bit x86 installation with few exceptions. Click through to install via the Setup Wizard. 11.2 Troubleshooting Here are some issues you might encounter. ssh: Could not resolve hostname rhino: nodename nor servname provided, or not known Click to view steps This error means that your computer is having trouble connecting to rhino. Ensure one of the following is true: You are connected to the Fred Hutch wifi network on campus. You are connected to the Fred Hutch VPN You are plugged into an ethernet cable on campus that taps into the Fred Hutch network. Note that not all ethernet wall jacks have this capability, so try another jack if you are having trouble. Please email the IT helpdesk and include your office number and the number on the jack if you find a jack that isn’t working. ssh: connect to host rhino port 22: Undefined error: 0 Click to view steps This likely indicates a disruption to your internet connection and/or VPN. Ensure you are connected to the internet and connected to the Fred Hutch network on campus or the VPN. Connection failed message in Cyberduck Click to view steps This likely indicates a disruption to your internet connection and/or VPN. Ensure you are connected to the internet and connected to the Fred Hutch network on campus or the VPN. Invalid account or account/partition when logging in Errors similar to this typically indicate that the account hasn’t been set up by SciComp. This is a quick fix if you use the form mentioned in the course. "],["get-your-certificate.html", "Get Your Certificate", " Get Your Certificate You must complete the quiz - Self-Test: Cluster 101 - with all questions correctly answered to earn your certificate for this course. Once complete: Go to the course homepage Click on “Complete Course” Click on “Generate Certificate” Contact the DaSL Team to submit your Cluster 101 certificate for an awesome Hex Sticker! "],["about-the-authors.html", "About the Authors", " About the Authors These credits are based on our course contributors table guidelines.     Please note that this course is under developmnent and these credits are subject to change! Credits Names Pedagogy Lead Content Instructor Ava Hoffman Content Authors The Fred Hutch SciComp Team Content Editors Amy Paguirigan Content Reviewers Elizabeth Humphries, Candace Savonen, Carrie Wright Content Consultants The Fred Hutch SciComp Team Technical Course Publishing Engineer Ava Hoffman Template Publishing Engineers Candace Savonen, Carrie Wright Publishing Maintenance Engineer Candace Savonen Technical Publishing Stylists Carrie Wright, Candace Savonen Package Developers (ottrpal) Candace Savonen, John Muschelli, Carrie Wright   ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.2 (2020-06-22) ## os Ubuntu 20.04.3 LTS ## system x86_64, linux-gnu ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Etc/UTC ## date 2022-11-04 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source ## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.3) ## bookdown 0.24 2022-02-15 [1] Github (rstudio/bookdown@88bc4ea) ## callr 3.4.4 2020-09-07 [1] RSPM (R 4.0.2) ## cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.0) ## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0) ## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3) ## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3) ## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0) ## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3) ## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.3) ## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0) ## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3) ## glue 1.6.1 2022-01-22 [1] CRAN (R 4.0.2) ## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0) ## htmltools 0.5.0 2020-06-16 [1] RSPM (R 4.0.1) ## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2) ## knitr 1.33 2022-02-15 [1] Github (yihui/knitr@a1052d1) ## lifecycle 1.0.0 2021-02-15 [1] CRAN (R 4.0.2) ## magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.0.2) ## memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.0) ## ottrpal 0.1.2 2022-02-15 [1] Github (jhudsl/ottrpal@1018848) ## pillar 1.4.6 2020-07-10 [1] RSPM (R 4.0.2) ## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2) ## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3) ## pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.3) ## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3) ## processx 3.4.4 2020-09-03 [1] RSPM (R 4.0.2) ## ps 1.3.4 2020-08-11 [1] RSPM (R 4.0.2) ## purrr 0.3.4 2020-04-17 [1] RSPM (R 4.0.3) ## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0) ## readr 1.4.0 2020-10-05 [1] RSPM (R 4.0.2) ## remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.3) ## rlang 0.4.10 2022-02-15 [1] Github (r-lib/rlang@f0c9be5) ## rmarkdown 2.10 2022-02-15 [1] Github (rstudio/rmarkdown@02d3c25) ## rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.2) ## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3) ## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3) ## stringr 1.4.0 2019-02-10 [1] RSPM (R 4.0.3) ## testthat 3.0.1 2022-02-15 [1] Github (R-lib/testthat@e99155a) ## tibble 3.0.3 2020-07-10 [1] RSPM (R 4.0.2) ## usethis 2.1.5.9000 2022-02-15 [1] Github (r-lib/usethis@57b109a) ## vctrs 0.3.4 2020-08-29 [1] RSPM (R 4.0.2) ## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2) ## xfun 0.26 2022-02-15 [1] Github (yihui/xfun@74c2a66) ## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3) ## ## [1] /usr/local/lib/R/site-library ## [2] /usr/local/lib/R/library "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]] +[["index.html", "Fred Hutch Cluster 101 About this Course", " Fred Hutch Cluster 101 October 06, 2023 About this Course Fred Hutch maintains a high performance computing cluster specifically to support work that requires intensive computing. The Fred Hutch cluster (sometimes called “Rhino” and/or “Gizmo”) allow you to do more than your average desktop computer can handle. Our goal for this course is to get you running on the Fred Hutch cluster quickly and efficiently. It is intended for everyone from brand new cluster users to researchers who have used a cluster at another institution but are new to Fred Hutch. We hope that the following modules will help you take advantage of the powerful resources the Fred Hutch has to offer! The Fred Hutch cluster is supported by a group in IT called Scientific Computing, and this course was developed by the Fred Hutch Data Science Lab in collaboration with them. Please see the author credits for more information. This course is available in Bookdown and Leanpub formats. If you want a certificate, you need to take the Leanpub version of the course. The Leanpub course can be taken for free, but you still have to put the course in your cart and check out. "],["what-is-a-cluster.html", "Chapter 1 What is a Cluster", " Chapter 1 What is a Cluster A computing cluster is a set of many computers networked together. Because there are many computers working together, the network is able to handle computationally expensive tasks, like genome assemblies or advanced algorithms. Imagine you’re building a house. It would take a long time by yourself! It’s much better to have many builders working together. Now that we have a team of workers, the next challenge is task management. A home construction team will need a manager to help delegate tasks. Similarly, the computing cluster uses management software to prioritize tasks, delegate workers (resources), and check on progress. The Fred Hutch cluster uses a common management and scheduling tool called Slurm. How is the cluster different from a laptop or desktop? First, on your laptop you most likely interact with it using an operating system like Windows or MacOS. The Fred Hutch cluster uses a Linux operating system. Second, because many people use the cluster for many tasks, there isn’t a central screen and keyboard. You access the cluster remotely from your computer! We will talk more about how to connect to the cluster in a following chapter. Computing cluster A set of computers networked together to perform large tasks. "],["account-setup.html", "Chapter 2 Account Setup 2.1 Check your HutchNet ID 2.2 Connect to a PI Account", " Chapter 2 Account Setup You will need an account to log in to the cluster. This ensures that data stays protected and that computing resources are shared fairly. 2.1 Check your HutchNet ID Your HutchNet ID is the standard login you receive when you start working at the Hutch or are an official affiliate. You can use it to login to most resources at Fred Hutch (Desktop Computer, Employee Self Service, VPN, Webmail) and our Scientific Computing systems. For example: my email is jsmith3@fredhutch.org. my HutchNet ID is jsmith3. If one of your collaborators requires access to the Fred Hutch network you can submit a non-employee action form. “Non-employee” is a generic administrative term for affiliates, students, contractors, etc. 2.2 Connect to a PI Account Your HutchNet ID must be associated with a PI cluster “account”. The Scientific Computing Team (SciComp) tries to set users up with a connection to a PI account before they need it, but this is not automatic! To ensure that you have been set up to use the cluster, please follow the following steps. You must be connected to the campus wifi network, plugged into a networked ethernet jack, or connected to the Fred Hutch VPN. Go to the SciComp Self-Service Portal Click on “Check Access” Log in using your HutchNet ID (no @fredhutch.org) and password If everything is green as shown below, you are ready to proceed. You can proceed with the course or skip to certification. If you see anything in red as shown below, click the link to e-mail SciComp to finish setting up your account. Be sure to include your HutchNetID and PI name in the email. Note that it may take just a bit of time for SciComp to see your email request, but it is usually fairly quick! Once approved, you will receive an email back from the SciComp team that you now have cluster access. The Self-Service Portal will also show that you have cluster access if you refresh the page. Now, let’s connect to the cluster! "],["skip-to-certification.html", "Skip to Certification", " Skip to Certification Are you an experienced user? Do you need certification? You can jump straight to the 10-question quiz by clicking the link below. This Leanpub quiz can be taken for free, but you still have to put the course in your cart and check out. Self-Test: Cluster 101 An experienced user: Has used SSH to connect to a computing cluster Is familiar with the methods used to connect to the Fred Hutch cluster Has used Slurm to submit a computing job Is familiar with the Cyberduck application for transferring files "],["terminal.html", "Chapter 3 Terminal Setup 3.1 What is a terminal? 3.2 Windows Setup 3.3 Mac Setup 3.4 Linux Setup", " Chapter 3 Terminal Setup The next step is getting familiar with your Terminal. This is your portal to the cluster. 3.1 What is a terminal? The Terminal is a command line interface. In other words, the Terminal is a software application that allows you to issue commands directly to your laptop or desktop computer. The Terminal is very useful because it allows you to run commands that don’t have a point-and-click equivalent. It can also connect you to computer networks, such as the Fred Hutch cluster! The Terminal setup is different depending on your operating system. Jump to the Windows, MacOS, or Linux sections below. “Terminal” used to be synonymous with “computer”. With the creation of operating systems like Windows and MacOS, computers became much easier to use and exploded in popularity! Your colleagues are almost always referring to the command line application when they say “Terminal”. 3.2 Windows Setup Click to view steps You will need to install a Terminal application called PuTTY to connect to the Fred Hutch Cluster. You should then see PuTTY available in the Software Center. Click “Install” and go through the Setup Wizard. You can also install PuTTY manually if you don’t see it in the Software Center. PuTTY should now be available in your applications. Click on PuTTY to open. You should now see the PuTTY Configuration menu. 3.3 Mac Setup Click to view steps Mac machines come with a Terminal installed. Go to Finder > Applications > Utilities > Terminal and double-click. Your Terminal should look like this: 3.4 Linux Setup Click to view steps The commonly used Linux distribution, Ubuntu, already comes with a Terminal installed. Press ctrl + alt + T. This should open a Terminal window. Update the Terminal and prepare it for connecting to the cluster by running: sudo apt install openssh-client Enter your password and enter Y when prompted. "],["logging-in.html", "Chapter 4 Logging In 4.1 What is SSH? 4.2 Connect Securely 4.3 Windows Login 4.4 Mac Login 4.5 Linux Login", " Chapter 4 Logging In Now that you have your Terminal application ready, you want to connect to the cluster. You will do this using a method called SSH, which stands for “Secure SHell”. 4.1 What is SSH? SSH is a secure way to remotely connect to another computer or network of computers. In other words, SSH helps us protect your data and the data on the Fred Hutch cluster through authentication. Hostname The hostname is the name, or label, assigned to a computer in a network. We are connecting to hostname rhino.fhcrc.org or rhino for short. 4.2 Connect Securely Before moving on, you will need to connect to the Fred Hutch wifi network, a networked ethernet jack, or the Fred Hutch VPN. This is the first layer of security. The next set of steps are specific to your operating system. 4.3 Windows Login Click to view steps Go to the PuTTY Configuration menu. Under “Host Name” type rhino and click “Open”. You will be prompted to login. Type in your HutchNetID (e.g., jsmith3). Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! 4.4 Mac Login Click to view steps Type the following commands, substituting in your HutchNet ID (no brackets): ssh [HutchID]@rhino You will see a message that looks like The authenticity of host 'rhino (XXX.XXX.XX.XX)' can't be established. Type in yes and hit enter. Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! 4.5 Linux Login Click to view steps Type the following commands, substituting in your HutchNet ID (no brackets): ssh [HutchID]@rhino Enter your password. No* or symbols will show up, so type it in carefully! You are now logged in! There should be a login message, with your name at the bottom. Congratulations! You are now logged in to the Fred Hutch cluster! "],["submit-your-first-job.html", "Chapter 5 Submit Your First Job 5.1 Download the Script 5.2 Confirm the Download 5.3 Inspect the Script 5.4 Submit the Script 5.5 Check the Output", " Chapter 5 Submit Your First Job The strength of a computing cluster is the ability to do many jobs in parallel or on computers with more computing power than you have on your local computer. The best way to use the cluster is to create short snippets of instructions (a script) that a computer can perform without human input. Your script tells the computers to execute the instructions as individual jobs. Now that you’ve logged into rhino you will be able to send scripts to the networked computers in the cluster. The Fred Hutch cluster uses Slurm to organize and prioritize jobs. Based on the instructions in your script, Slurm will find computing resources within the cluster to run your job along with all the other requests from other users. In the next steps, we will go through a simple example where we download a single file. More complicated examples will use multiple files. We will discuss how to transfer files from your computer to the cluster in the following chapter. The part of the cluster where you log in is called rhino. The part of the cluster where jobs are run is called gizmo. 5.1 Download the Script We can use the wget command to download a script from GitHub. This means we don’t have to write the script from scratch. Copy and paste the following into the terminal, and hit return: wget https://raw.githubusercontent.com/FredHutch/slurm-examples/master/introduction/1-hello-world/01.sh 5.2 Confirm the Download Let’s confirm that we can see the file we just downloaded. We can use the ls (list files) command for this. Type ls and hit return. You should see the file 01.sh in your home directory. The .sh ending means this is a script meant to run from the command line. ls 5.3 Inspect the Script Let’s next inspect the script. The cat command, followed by a file name, lists the entire contents of a specific file. cat 01.sh The first line of the script, #!/bin/bash, indicates that this is a command line or “bash” script. The second line is empty, and the third line, echo \"Hello, World\" means that the computer will “echo”, or print out, “Hello, World”. 5.4 Submit the Script We use the sbatch command to submit a script and start running a job on the cluster. Copy the following and hit return. You should see a message like “Submitted batch job 12345678”. Your number will vary because this is a unique job identifier. sbatch 01.sh 5.5 Check the Output Type ls again. You should now see a log file like slurm-12345678.out listed alongside your script 01.sh. Let’s use cat to inspect the output in the log file (the new file starting with slurm and ending with .out). Make sure you replace [your-number-here] with the number in your actual file. We should see our message has been printed! cat slurm-[your-number-here].out ls This command lists the files in the current directory. cat filename This command prints the contents of a specific file (filename). sbatch filename.sh This command submits a job to the cluster with instructions specified in a .sh file. "],["file-upload-and-download.html", "Chapter 6 File Upload and Download 6.1 Download Cyberduck 6.2 Create Connection 6.3 Download and Edit the Script 6.4 Upload the New Script 6.5 Run the New Script", " Chapter 6 File Upload and Download Exchanging files with the cluster is very important. You can imagine scenarios where: You want to download log files or output files You want to upload a custom .sh script file that you wrote on your laptop You want to upload other files In this course, upload and download of files is performed using Cyberduck. Cyberduck is a tool that lets us connect to the cluster securely, browse files, and transfer files securely. If you are working with sensitive data (such as data with PHI that requires HIPAA compliance, you need to be extra cautious about transferring your data to the cluster. Your home directory is not an appropriate storage option for such data. Make sure you consider any stipulations in your data use agreements. 6.1 Download Cyberduck Download the latest version of Cyberduck here. Note that the version of Cyberduck in the Software Center or Self Service might not be current, causing compatibility issues with some operating systems. 6.2 Create Connection Launch Cyberduck and click on “Open Connection”. From the dropdown menu, select “SFTP (SSH File Transfer Protocol)” For Server, type “rhino.fhcrc.org” Fill in your HutchNetID for Username and fill in your password Click “Connect” Click “Allow”. You can also check the box to indicate “Always”. You should see your script file “01.sh” and the log file. 6.3 Download and Edit the Script Right click on “01.sh” and select “Download” You will see a “Transfers” prompt open, and the 01.sh file should now appear in your Downloads folder Open the 01.sh in your Downloads folder Edit the message to include your name and save the file. Rename the file 01-name.sh. 6.4 Upload the New Script From your Downloads folder, simply drag the file to Cyberduck. You should now see the new script among your cluster files. 6.5 Run the New Script Return to your Terminal. Submit a job with your new script by running the following. When you type ls you should see a new log file! sbatch 01-name.sh The job numbers included in log file names generally increase in number. The greater the number, the more recently the job was run. Use the cat command to inspect the log. Make sure you replace [your-number-here] to match your file. The message should show the new text that you added! cat slurm-[your-number-here].out "],["interactive-session.html", "Chapter 7 Interactive Session 7.1 Starting the session 7.2 Running Interactive Commands 7.3 Using Pre-installed Software Modules", " Chapter 7 Interactive Session While using the cluster, you might need to build and test scripts interactively before running them. Luckily, you can work directly on the cluster by creating an interactive session. When you launch an interactive session, the cluster assigns you a portion of the networked computers called a “node”. This node (or part of one) is dedicated to you for a period of time rather than using the Slurm job submission system. Because an interactive session takes up resources directly on the cluster whether you’re actively using it or not, it’s best to use interactive sessions only when a task cannot be done by submitting a script. 7.1 Starting the session When starting an interactive session, you’re going to need to think about what you are testing and what resources you might need on the node you are requesting to use. You can always start an interactive session using the default values if you aren’t sure what you need yet. Start an interactive session on a node by running the command: grabnode You will be prompted with several questions about the type of resources on the node you want. We don’t need anything fancy, so we will set up the session to use minimal resources. You can enter the following: How many CPUs/cores would you like to grab on the node? 1 How much memory (GB) would you like to grab? 20 Please enter the max number of days you would like to grab this node: 1 Do you need a GPU? N When prompted, enter your password The CPU, or Central Processing Unit, is the brain of the computer that performs and orchestrates computational tasks. Modern computers often perform multiple tasks at once, ranging from 4 tasks on a typical laptop to 48 tasks or more on higher end servers. RAM, or Random Access Memory, is often simply referred to as memory. This short term memory holds the information that the CPU needs to perform calculations. One distinctive feature of memory is that it is short term. In other words, when the electricity is shut off, the data stored in memory disappears. To save the CPU’s work, you usually save files to your computer. Running highly complicated analyses or algorithms can often require additional memory resources. The GPU, or Graphics Processing Unit, is similar to the CPU. The GPU was originally designed to quickly render graphics (such as for video games), but today can be used to run complex artificial intelligence applications or computationally intensive jobs. You will see that you are now logged on to “gizmo” instead of “rhino”. Remember that the part of the cluster where you log in is called rhino. The part of the cluster where jobs are run is called gizmo. 7.2 Running Interactive Commands You can start working on the node by running a similar command as we used in the job we submitted via script. Echo a message by running: echo "Hello, again!" 7.3 Using Pre-installed Software Modules Let’s get a bit more advanced. We can load a preconfigured software bundle called a module. This is very convenient because it means we don’t need to install anything manually! In this example, we will load a module containing R version 4.2.0. You can learn more about what modules are available and how to request new ones for the Fred Hutch cluster here. ml fhR/4.2.0-foss-2021b Next, launch R: R You can play around with R here. For example, you might run: head(mtcars) Close the R session by typing: q() R will ask if you want to save your workspace. Type n for no and hit return. Close the interactive node by typing: exit grabnode This command starts an interactive session on the cluster. Node One of the networked computers in the cluster. CPU A computer component that performs and orchestrates computational tasks. Memory A computer component that stores calculations and information in the short term. "],["help.html", "Chapter 8 Getting Help Check out the FAQ page Find Community Support on Slack Visit the SciWiki Send an Email", " Chapter 8 Getting Help The Scientific Computing group in IT manages the cluster, provides support with software, advises on data storage, and holds office hours specifically to help users. Here are some ways you can get help for your work on the cluster. Check out the FAQ page See our FAQ and Troubleshooting Page to see common errors and what they mean. If you encounter a problem that isn’t listed, let us know! Find Community Support on Slack Peer-to-peer support can be very valuable in learning and troubleshooting your work. The Fred Hutch Data Slack workspace is open to all with a fredhutch.org, uw.edu, seattlechildrens.org, or related institution email addresses (whi.org, scharp.org, etc). You can ask questions, find out about office hours, and discover other live support and training events that can help you learn more about how to leverage resources at Fred Hutch to advance your science. Visit the SciWiki The SciWiki Scientific Computing page is full of useful tips and guides. Remember when using the search that the login “nodes” to the Fred Hutch cluster are called rhino and the cluster “nodes” are called gizmo. Send an Email The primary way you can request help for a problem is to send SciComp an email, so a ticket will be created in their tracking system. This allows the details of the problem you’re having to be sent to them so they can better help you. Submitting a good email ticket helps the SciComp Team address your needs quickly and efficiently. We suggest you submit the following information: A brief overview of what the problem is. Some specifics about the problem, such as the full text (it’s ok if it’s long) of any error message or terminal command, or a screen shot of the interface you were using when you had the problem. A description of what you wanted to have happen or what your overall goal is (in case perhaps there is another strategy that might work better). "],["summary.html", "Chapter 9 Summary 9.1 Overview 9.2 Glossary 9.3 Commands (command line interface)", " Chapter 9 Summary Let’s review the key information from the previous sections. 9.1 Overview A computing cluster is a set of computers networked together to perform large tasks. We usually connect to the cluster using a command-line interface called a Terminal. The Terminal application varies based on your operating system. We connect to the cluster using a secure method called SSH. Exact SSH commands vary depending on your operating system. Before using the Fred Hutch cluster, it’s a good idea to contact the SciComp team to ensure your account is set up correctly. You must be connected to the campus wifi network, plugged into a networked ethernet jack, or connected to the Fred Hutch VPN to connect to the Fred Hutch cluster. Computer tasks are typically performed via job submission, where you tell the cluster what to do inside a script file. The Fred Hutch cluster uses Slurm to organize jobs submitted by different users. When building, testing, and debugging jobs, you might want to launch an interactive session on the cluster. You will be asked several questions about resources before your session starts. You can use the application Cyberduck to transfer smaller files between your local computer and the cluster via SFTP (SSH File Transfer Protocol). This course does not cover transferring sensitive data, which requires extra precautions. Don’t be afraid to ask for help! Check out the SciWiki, join the Slack workspace, or contact the SciComp team if you get stuck. 9.2 Glossary computing cluster - A set of computers networked together to perform large tasks. CPU - A computer component that performs and orchestrates computational tasks. Cyberduck - Third-party software which transfers files between your local machine and the cluster. hostname - The hostname is the name, or label, assigned to a computer in a network. We connect to hostname rhino.fhcrc.org or rhino for short. memory - A computer component that stores calculations and information in the short term. node - A part of the cluster; a computer in the network. SSH - A secure method for remotely connecting to another computer or network of computers; stands for “Secure SHell”. terminal - A command line interface; a software application that allows you to issue commands directly to a computer. 9.3 Commands (command line interface) Command Description Usage cat displays all contents of a file cat [filename] exit terminates an interactive session exit grabnode launches an interactive session grabnode ls lists files in the current folder ls sbatch submits a script containing job instructions sbatch [scriptname.sh] wget downloads a file from the internet wget [url] "],["feedback.html", "Chapter 10 Provide Feedback", " Chapter 10 Provide Feedback We’d love to hear from you! Please fill out the anonymous Google Form with your feedback. You can submit an issue about this course at our GitHub repository. You can also click the edit button on the top of the page in question. "],["faq-and-troubleshooting.html", "Chapter 11 FAQ and Troubleshooting 11.1 FAQ 11.2 Troubleshooting", " Chapter 11 FAQ and Troubleshooting 11.1 FAQ Here are some questions you might have. 11.1.1 How can I manually install PuTTY? Click to view steps Click here to install the latest version of PuTTY. You will choose the 64-bit x86 installation with few exceptions. Click through to install via the Setup Wizard. 11.2 Troubleshooting Here are some issues you might encounter. ssh: Could not resolve hostname rhino: nodename nor servname provided, or not known Click to view steps This error means that your computer is having trouble connecting to rhino. Ensure one of the following is true: You are connected to the Fred Hutch wifi network on campus. You are connected to the Fred Hutch VPN You are plugged into an ethernet cable on campus that taps into the Fred Hutch network. Note that not all ethernet wall jacks have this capability, so try another jack if you are having trouble. Please email the IT helpdesk and include your office number and the number on the jack if you find a jack that isn’t working. ssh: connect to host rhino port 22: Undefined error: 0 Click to view steps This likely indicates a disruption to your internet connection and/or VPN. Ensure you are connected to the internet and connected to the Fred Hutch network on campus or the VPN. Connection failed message in Cyberduck Click to view steps This likely indicates a disruption to your internet connection and/or VPN. Ensure you are connected to the internet and connected to the Fred Hutch network on campus or the VPN. Invalid account or account/partition when logging in Errors similar to this typically indicate that the account hasn’t been set up by SciComp. This is a quick fix if you use the form mentioned in the course. "],["get-your-certificate.html", "Get Your Certificate", " Get Your Certificate You must complete the quiz - Self-Test: Cluster 101 - with all questions correctly answered to earn your certificate for this course. Once complete: Go to the course homepage Click on “Complete Course” Click on “Generate Certificate” Contact the DaSL Team to submit your Cluster 101 certificate for an awesome Hex Sticker! "],["about-the-authors.html", "About the Authors", " About the Authors These credits are based on our course contributors table guidelines.     Please note that this course is under developmnent and these credits are subject to change! Credits Names Pedagogy Lead Content Instructor Ava Hoffman Content Authors The Fred Hutch SciComp Team Content Editors Amy Paguirigan Content Reviewers Elizabeth Humphries, Candace Savonen, Carrie Wright Content Consultants The Fred Hutch SciComp Team Technical Course Publishing Engineer Ava Hoffman Template Publishing Engineers Candace Savonen, Carrie Wright Publishing Maintenance Engineer Candace Savonen Technical Publishing Stylists Carrie Wright, Candace Savonen Package Developers (ottrpal) Candace Savonen, John Muschelli, Carrie Wright   ## ─ Session info ─────────────────────────────────────────────────────────────── ## setting value ## version R version 4.0.2 (2020-06-22) ## os Ubuntu 20.04.5 LTS ## system x86_64, linux-gnu ## ui X11 ## language (EN) ## collate en_US.UTF-8 ## ctype en_US.UTF-8 ## tz Etc/UTC ## date 2023-10-06 ## ## ─ Packages ─────────────────────────────────────────────────────────────────── ## package * version date lib source ## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.5) ## bookdown 0.24 2023-03-28 [1] Github (rstudio/bookdown@88bc4ea) ## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.0.2) ## cachem 1.0.7 2023-02-24 [1] CRAN (R 4.0.2) ## callr 3.5.0 2020-10-08 [1] RSPM (R 4.0.2) ## cli 3.6.1 2023-03-23 [1] CRAN (R 4.0.2) ## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0) ## desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.3) ## devtools 2.3.2 2020-09-18 [1] RSPM (R 4.0.3) ## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0) ## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3) ## evaluate 0.20 2023-01-17 [1] CRAN (R 4.0.2) ## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0) ## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.0.2) ## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3) ## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.5) ## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0) ## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.0.2) ## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.0.2) ## jsonlite 1.7.1 2020-09-07 [1] RSPM (R 4.0.2) ## knitr 1.33 2023-03-28 [1] Github (yihui/knitr@a1052d1) ## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.0.2) ## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.0.2) ## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.0.2) ## ottrpal 1.0.1 2023-03-28 [1] Github (jhudsl/ottrpal@151e412) ## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.0.2) ## pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2) ## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3) ## pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.3) ## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3) ## processx 3.4.4 2020-09-03 [1] RSPM (R 4.0.2) ## ps 1.4.0 2020-10-07 [1] RSPM (R 4.0.2) ## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0) ## readr 1.4.0 2020-10-05 [1] RSPM (R 4.0.2) ## remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.3) ## rlang 1.1.0 2023-03-14 [1] CRAN (R 4.0.2) ## rmarkdown 2.10 2023-03-28 [1] Github (rstudio/rmarkdown@02d3c25) ## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.0.2) ## sass 0.4.5 2023-01-24 [1] CRAN (R 4.0.2) ## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3) ## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3) ## stringr 1.4.0 2019-02-10 [1] RSPM (R 4.0.3) ## testthat 3.0.1 2023-03-28 [1] Github (R-lib/testthat@e99155a) ## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.0.2) ## usethis 1.6.3 2020-09-17 [1] RSPM (R 4.0.2) ## utf8 1.1.4 2018-05-24 [1] RSPM (R 4.0.3) ## vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.0.2) ## withr 2.3.0 2020-09-22 [1] RSPM (R 4.0.2) ## xfun 0.26 2023-03-28 [1] Github (yihui/xfun@74c2a66) ## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3) ## ## [1] /usr/local/lib/R/site-library ## [2] /usr/local/lib/R/library "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]] diff --git a/docs/no_toc/skip-to-certification.html b/docs/no_toc/skip-to-certification.html index 2beee961..06ad4d35 100644 --- a/docs/no_toc/skip-to-certification.html +++ b/docs/no_toc/skip-to-certification.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/submit-your-first-job.html b/docs/no_toc/submit-your-first-job.html index c1ae94db..9defcf41 100644 --- a/docs/no_toc/submit-your-first-job.html +++ b/docs/no_toc/submit-your-first-job.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/summary.html b/docs/no_toc/summary.html index a43d5bf5..47f6ec54 100644 --- a/docs/no_toc/summary.html +++ b/docs/no_toc/summary.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/terminal.html b/docs/no_toc/terminal.html index 8b98ebbc..def4aaef 100644 --- a/docs/no_toc/terminal.html +++ b/docs/no_toc/terminal.html @@ -23,7 +23,7 @@ - + diff --git a/docs/no_toc/what-is-a-cluster.html b/docs/no_toc/what-is-a-cluster.html index 13d70e80..29420438 100644 --- a/docs/no_toc/what-is-a-cluster.html +++ b/docs/no_toc/what-is-a-cluster.html @@ -23,7 +23,7 @@ - + From a7c05f5aa25f2a45ce2485dba4f25fc1dfb5e64e Mon Sep 17 00:00:00 2001 From: jhudsl-robot Date: Fri, 6 Oct 2023 13:07:23 +0000 Subject: [PATCH 2/2] Render Leanpub --- manuscript/Book.txt | 6 +++--- .../chapt_screen_images/about-the-authors.png | Bin 75579 -> 75581 bytes .../chapt_screen_images/account-setup.png | Bin 26152 -> 88864 bytes .../chapt_screen_images/about-the-authors.png | Bin 75579 -> 75581 bytes .../chapt_screen_images/account-setup.png | Bin 26152 -> 88864 bytes 5 files changed, 3 insertions(+), 3 deletions(-) diff --git a/manuscript/Book.txt b/manuscript/Book.txt index bc09f7f0..27f95615 100644 --- a/manuscript/Book.txt +++ b/manuscript/Book.txt @@ -1,7 +1,5 @@ -About-this-Course.md 1-What-is-a-Cluster.md 2-Account-Setup.md -Skip-to-Certification.md 3-Terminal-Setup.md 4-Logging-In.md 5-Submit-Your-First-Job.md @@ -9,8 +7,10 @@ Skip-to-Certification.md 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