Merge pull request #370 from NIEHS/container-01

_targets.R, test and run bash scripts

_targets.R

@@ -1,123 +1,80 @@
 library(targets)
 library(tarchetypes)
-library(future)
-library(future.batchtools)
 library(dplyr)
-library(
-  beethoven,
-  lib.loc = "/ddn/gs1/home/manwareme/R/x86_64-pc-linux-gnu-library/4.3"
-)
-library(tidymodels)
-library(bonsai)
-# library(
-#   torch,
-#   lib.loc = "/ddn/gs1/biotools/R/lib64/R/library"
-# )
+library(crew)
+library(future)
+library(beethoven)
+library(amadeus)
 
-Sys.setenv("LD_LIBRARY_PATH" = paste("/ddn/gs1/biotools/R/lib64/R/customlib", Sys.getenv("LD_LIBRARY_PATH"), sep = ":"))
 
-# replacing yaml file.
+# targets store location corresponds to _targets/ in the root of the project
 tar_config_set(
-  store = "/ddn/gs1/home/manwareme/beethoven/beethoven_targets"
+  store = "/opt/_targets"
+)
+
+# crew contollers
+# For now, one is set, but we can explore the use of multiple controllers
+# Can also explore making the workers input for bash script or Rscript
+geo_controller <- crew_controller_local(
+  name = "geo_controller",
+  workers = 16L,
+  launch_max = 8L,
+  seconds_idle = 120
 )
 
-# maximum future exportable object size is set 50GB
-# TODO: the maximum size error did not appear until recently
-#       and suddenly appeared. Need to investigate the cause.
-#       Should be removed after the investigation.
-# options(future.globals.maxSize = 50 * 2^30)
-options(future.globals.maxSize = 60 * 1024^3)  # 60 GiB
 
 
-generate_list_download <- FALSE
+# Setting up the NASA Earthdata token inside the container
+# This needs to be tested
+if (!nzchar(Sys.getenv("NASA_EARTHDATA_TOKEN"))){
+  tar_source("/mnt/NASA_token_setup.R")
+  file.exists(".netrc")
+  file.exists(".urs_cookies")
+  file.exists(".dodsrc")
+}
+
 
 arglist_download <-
   set_args_download(
     char_period = c("2018-01-01", "2022-12-31"),
-    char_input_dir = "input",
-    nasa_earth_data_token = NULL,#Sys.getenv("NASA_EARTHDATA_TOKEN"),
-    mod06_filelist = "inst/targets/mod06_links_2018_2022.csv",
-    export = generate_list_download,
-    path_export = "inst/targets/download_spec.qs"
+    char_input_dir = "/input",
+    nasa_earth_data_token = Sys.getenv("NASA_EARTHDATA_TOKEN"),
+    mod06_filelist = "/pipeline/targets/mod06_links_2018_2022.csv",
+    export = TRUE,
+    path_export = "/pipeline/targets/download_spec.qs"
   )
 
-generate_list_calc <- FALSE
 
-arglist_common <-
-  set_args_calc(
-    char_siteid = "site_id",
-    char_timeid = "time",
-    char_period = c("2018-01-01", "2022-12-31"),
-    num_extent = c(-126, -62, 22, 52),
-    char_user_email = paste0(Sys.getenv("USER"), "@nih.gov"),
-    export = generate_list_calc,
-    path_export = "inst/targets/calc_spec.qs",
-    char_input_dir = "/ddn/gs1/group/set/Projects/NRT-AP-Model/input"
-  )
 
-tar_source("inst/targets/targets_initialize.R")
-tar_source("inst/targets/targets_download.R")
-tar_source("inst/targets/targets_calculate_fit.R")
-tar_source("inst/targets/targets_calculate_predict.R")
-tar_source("inst/targets/targets_baselearner.R")
-tar_source("inst/targets/targets_metalearner.R")
-tar_source("inst/targets/targets_predict.R")
 
 
-# bypass option
-Sys.setenv("BTV_DOWNLOAD_PASS" = "TRUE")
 
-#
-# bind custom built GDAL
-# Users should export the right path to the GDAL library
-# by export LD_LIBRARY_PATH=.... command.
+### NOTE: It is important to source the scipts after the global variables are defined from the set_args functions
+ #tar_source("/pipeline/targets/targets_aqs.R")
+ tar_source("/pipeline/targets/targets_download.R")
 
-# arglist_common is generated above
-plan(
-  list(
-    tweak(
-      future.batchtools::batchtools_slurm,
-      template = "inst/targets/template_slurm.tmpl",
-      resources =
-        list(
-          memory = 8,
-          log.file = "slurm_run.log",
-          ncpus = 1, partition = "geo", ntasks = 1,
-          email = arglist_common$char_user_email,
-          error.file = "slurm_error.log"
-        )
-    ),
-    multicore
-  )
-)
+# Toy test files - note we will not have functions defined like this directly in
+# the _targets.R file
+my_fun_a <- function(n) {
+  rnorm(n)
+}
+
+my_fun_b <- function(x) {
+  x^2
+}
 
-# # invalidate any nodes older than 180 days: force running the pipeline
-# tar_invalidate(any_of(tar_older(Sys.time() - as.difftime(180, units = "days"))))
 
 
-# # nullify download target if bypass option is set
-if (Sys.getenv("BTV_DOWNLOAD_PASS") == "TRUE") {
-  target_download <- NULL
-}
 
-# targets options
-# For GPU support, users should be aware of setting environment
-# variables and GPU versions of the packages.
-# TODO: check if the controller and resources setting are required
 tar_option_set(
-  packages = c(
-    "beethoven", "amadeus", "chopin", "targets", "tarchetypes",
-    "data.table", "sf", "terra", "exactextractr",
-    #"crew", "crew.cluster", 
-    "tigris", "dplyr",
-    "future.batchtools", "qs", "collapse", "bonsai",
-    "tidymodels", "tune", "rsample", "torch", "brulee",
-    "glmnet", "xgboost",
-    "future", "future.apply", "future.callr", "callr",
-    "stars", "rlang", "parallelly"
-  ),
-  library = c("/ddn/gs1/group/set/isong-archive/r-libs"),
-  repository = "local",
+  packages =
+    c( "amadeus", "targets", "tarchetypes",
+      "data.table", "sf", "terra", "exactextractr",
+       "dplyr", "qs", "callr",  "stars", "rlang"),
+  controller = crew_controller_group(geo_controller),
+  resources = tar_resources(
+    crew = tar_resources_crew(controller = "geo_controller")
+  ),  
   error = "abridge",
   memory = "transient",
   format = "qs",
@@ -127,15 +84,65 @@ tar_option_set(
   seed = 202401L
 )
 
-# should run tar_make_future()
+  list(
+    tar_target(name = A, command = my_fun_a(100)),
+    tar_target(name = B, command = my_fun_b(A), pattern = A),
+    tar_target(name = save_input, command = saveRDS(B, "/input/input.rds")),
+    tar_target( # Test download data with amadeus
+      download_test,
+      amadeus::download_narr(
+      variables = c("weasd", "omega"),
+      year = c(2023, 2023),
+      directory_to_save = "/input/narr_monolevel",
+      acknowledgement = TRUE,
+      download = TRUE, 
+      remove_command = TRUE
+    )
+  ),
+   target_download
+  )
+
+
+# Style below that uses sources scripts for targets by pipeline step
+# Note that variables created in _targets.R are in the same local
+# environment as the sourced scripts
+
+# list(
+#   target_init,
+#   target_download
+  # target_calculate_fit,
+  # target_baselearner#,
+  # target_metalearner,
+  # target_calculate_predict,
+  # target_predict,
+  # # documents and summary statistics
+  # targets::tar_target(
+  #   summary_urban_rural,
+  #   summary_prediction(
+  #     grid_filled,
+  #     level = "point",
+  #     contrast = "urbanrural"))
+  # ,
+  # targets::tar_target(
+  #   summary_state,
+  #   summary_prediction(
+  #     grid_filled,
+  #     level = "point",
+  #     contrast = "state"
+  #   )
+  # )
+# )
+
+# targets::tar_visnetwork(targets_only = TRUE)
+# END OF FILE
 
-list(
-  target_init,
-  target_download,
-  target_calculate_fit,
-  target_baselearner,
-  target_metalearner,
-  target_calculate_predict#,
+# list(
+#   target_init,
+#   target_download,
+#   target_calculate_fit,
+#   target_baselearner,
+#   target_metalearner,
+#   target_calculate_predict#,
   # target_predict,
   # # documents and summary statistics
   # targets::tar_target(

container/run_container_dl_calc.sh

@@ -1,17 +1,17 @@
 #!/bin/bash
-#SBATCH --job-name=beethoven_001
+#SBATCH --job-name=download_calc
 #SBATCH --partition=geo
 #SBATCH --mem=128G
 #SBATCH --cpus-per-task=4
 #SBATCH --ntasks=16
-#SBATCH --output=slurm_messages/slurm-%j.out
-#SBATCH --error=slurm_messages/slurm-%j.err
+#SBATCH --output=../slurm_messages/slurm-%j.out
+#SBATCH --error=../slurm_messages/slurm-%j.err
 #SBATCH --mail-user=kyle.messier@nih.gov
 #SBATCH --mail-type=ALL
 
 
-
-
+# Run the container
+# .sif file sites in "root/container", thus we need to go up one level with bind mounts 
 apptainer exec \
   --bind $PWD/inst:/pipeline \
   --bind $PWD/input:/input \

container/run_dl_calc_local_tests.sh

@@ -4,8 +4,8 @@
 #SBATCH --mem=128G
 #SBATCH --cpus-per-task=4
 #SBATCH --ntasks=16
-#SBATCH --output=slurm_messages/slurm-%j.out
-#SBATCH --error=slurm_messages/slurm-%j.err
+#SBATCH --output=../slurm_messages/slurm-%j.out
+#SBATCH --error=../slurm_messages/slurm-%j.err
 #SBATCH --mail-user=kyle.messier@nih.gov
 #SBATCH --mail-type=ALL