diff --git a/Compendium_attachments/AlphaCentauri/example_pytorch_image_recognition.zip b/Compendium_attachments/AlphaCentauri/example_pytorch_image_recognition.zip
new file mode 100644
index 0000000000000000000000000000000000000000..46538b2de73e25e5f8519c1a05ecb3cd71f1c057
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diff --git a/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/SparkExample.ipynb b/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/SparkExample.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ffe1aa174859fe6697f65af7ce7bd09d526e4bc1
--- /dev/null
+++ b/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/SparkExample.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "echo $SPARK_HOME\n",
+ "echo $JAVA_HOME\n",
+ "hostname"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import os\n",
+ "os.environ['PYSPARK_PYTHON'] = sys.executable\n",
+ "os.environ['SPARK_CONF_DIR'] = os.environ['HOME'] + '/cluster-conf-' + os.environ['SLURM_JOBID'] + '/spark'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!SHELL=/bin/bash bash framework-configure.sh spark $SPARK_HOME/conf "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!start-all.sh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#import findspark\n",
+ "#findspark.init()\n",
+ "import platform\n",
+ "import pyspark\n",
+ "from pyspark import SparkContext"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sc = SparkContext(master=\"spark://\"+platform.node()+\":7077\", appName=\"RDD basic functions App\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#your Spark workflow code here, the following is just an example:\n",
+ "datafile = sc.textFile(\"SparkExample.ipynb\")\n",
+ "firstTenItems = datafile.take(10)\n",
+ "for item in firstTenItems:\n",
+ " print(item)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sc.stop()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!stop-all.sh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i java"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "haswell-py3.6-spark",
+ "language": "python",
+ "name": "haswell-py3.6-spark"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/example-spark.sbatch b/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/example-spark.sbatch
new file mode 100644
index 0000000000000000000000000000000000000000..5a418a9c5e98f70b027618a4da1158010619556b
--- /dev/null
+++ b/Compendium_attachments/BigDataFrameworks:ApacheSparkApacheFlinkApacheHadoop/example-spark.sbatch
@@ -0,0 +1,27 @@
+#!/bin/bash
+#SBATCH --time=00:03:00
+#SBATCH --partition=haswell
+#SBATCH --nodes=1
+#SBATCH --exclusive
+#SBATCH --mem=60G
+#SBATCH -J "example-spark"
+
+ml Spark
+
+function myExitHandler () {
+ stop-all.sh
+}
+
+#configuration
+. framework-configure.sh spark $SPARK_HOME/conf
+
+#register cleanup hook in case something goes wrong
+trap myExitHandler EXIT
+
+start-all.sh
+
+spark-submit --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.11-2.4.4.jar 1000
+
+stop-all.sh
+
+exit 0
diff --git a/Compendium_attachments/DataAnalyticsWithR/Launcher.png b/Compendium_attachments/DataAnalyticsWithR/Launcher.png
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index 0000000000000000000000000000000000000000..fd50be1824655ef7e39c2adf74287fa14a716148
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diff --git a/Compendium_attachments/DataAnalyticsWithR/RPSOCK.R b/Compendium_attachments/DataAnalyticsWithR/RPSOCK.R
new file mode 100644
index 0000000000000000000000000000000000000000..fbf7cad3593d87cfb910b38651b29a9b0d08c6d0
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/RPSOCK.R
@@ -0,0 +1,31 @@
+library(parallel)
+
+# here some function that needs to be executed in parallel
+average <- function(size){
+ norm_vector <- rnorm(n=size, mean=mu, sd=sigma)
+ return(mean(norm_vector))
+}
+
+# variable initialization
+mu <- 1.0
+sigma <- 1.0
+vector_length <- 10^7
+n_repeat <- 100
+sample_sizes <- rep(vector_length, times=n_repeat)
+
+# cluster setup
+
+# get number of available nodes (should be the = "ntask")
+mynodes = 8
+print(paste("The cluster of size", threads, "will be setup..."))
+
+# initialize cluster
+cl <- makeCluster(mynodes, type="PSOCK", outfile="")
+
+# distribute required variables for the execution over the cluster
+clusterExport(cl, list("mu","sigma"))
+
+list_of_averages <- parLapply(X=sample_sizes, fun=average, cl=cl)
+
+# shut down the cluster
+print(paste("Program finished"))
diff --git a/Compendium_attachments/DataAnalyticsWithR/Rmpi.R b/Compendium_attachments/DataAnalyticsWithR/Rmpi.R
new file mode 100644
index 0000000000000000000000000000000000000000..c2dd8410a5ea22f6fe78b61b1ed00ab369ec7a34
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/Rmpi.R
@@ -0,0 +1,34 @@
+library(Rmpi)
+library(parallel)
+
+# here some function that needs to be executed in parallel
+average <- function(size){
+ norm_vector <- rnorm(n=size, mean=mu, sd=sigma)
+ return(mean(norm_vector))
+}
+
+# variable initialization
+mu <- 1.0
+sigma <- 1.0
+vector_length <- 10^7
+n_repeat <- 100
+sample_sizes <- rep(vector_length, times=n_repeat)
+
+# cluster setup
+
+# get number of available MPI ranks
+threads = mpi.universe.size()-1
+print(paste("The cluster of size", threads, "will be setup..."))
+
+# initialize MPI cluster
+cl <- makeCluster(threads, type="MPI", outfile="")
+
+# distribute required variables for the execution over the cluster
+clusterExport(cl, list("mu","sigma"))
+
+list_of_averages <- parLapply(X=sample_sizes, fun=average, cl=cl)
+
+# shut down the cluster
+#snow::stopCluster(cl) # usually it hangs over here with OpenMPI > 2.0. In this case this command may be avoided, SLURM will clean up after the job finishes
+
+
diff --git a/Compendium_attachments/DataAnalyticsWithR/Rmpi_c.R b/Compendium_attachments/DataAnalyticsWithR/Rmpi_c.R
new file mode 100644
index 0000000000000000000000000000000000000000..3e4ba3c5ebb66b1295bd671544facd0052a67efc
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/Rmpi_c.R
@@ -0,0 +1,17 @@
+library(Rmpi)
+
+# initialize an Rmpi environment
+ns <- mpi.universe.size()-1
+mpi.spawn.Rslaves(nslaves=ns)
+
+# send these commands to the slaves
+mpi.bcast.cmd( id <- mpi.comm.rank() )
+mpi.bcast.cmd( ns <- mpi.comm.size() )
+mpi.bcast.cmd( host <- mpi.get.processor.name() )
+
+# all slaves execute this command
+mpi.remote.exec(paste("I am", id, "of", ns, "running on", host))
+
+# close down the Rmpi environment
+mpi.close.Rslaves(dellog = FALSE)
+mpi.exit()
diff --git a/Compendium_attachments/DataAnalyticsWithR/TensorflowMNIST.R b/Compendium_attachments/DataAnalyticsWithR/TensorflowMNIST.R
new file mode 100644
index 0000000000000000000000000000000000000000..fbc1ca165673ddc4ae3eb08739fd605b04f858cf
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/TensorflowMNIST.R
@@ -0,0 +1,80 @@
+library(tensorflow)
+library(keras)
+
+# Data Preparation
+
+batch_size <- 128
+num_classes <- 10
+epochs <- 12
+
+# Input image dimensions
+
+img_rows <- 28
+img_cols <- 28
+
+# The data, shuffled and split between train and test sets
+
+mnist <- dataset_mnist()
+x_train <- mnist$train$x
+y_train <- mnist$train$y
+x_test <- mnist$test$x
+y_test <- mnist$test$y
+
+# Redefine dimension of train/test inputs
+
+x_train <- array_reshape(x_train, c(nrow(x_train), img_rows, img_cols, 1))
+x_test <- array_reshape(x_test, c(nrow(x_test), img_rows, img_cols, 1))
+input_shape <- c(img_rows, img_cols, 1)
+
+# Transform RGB values into [0,1] range
+
+x_train <- x_train / 255
+x_test <- x_test / 255
+
+cat('x_train_shape:', dim(x_train), '\n')
+cat(nrow(x_train), 'train samples\n')
+cat(nrow(x_test), 'test samples\n')
+
+# Convert class vectors to binary class matrices
+
+y_train <- to_categorical(y_train, num_classes)
+y_test <- to_categorical(y_test, num_classes)
+
+# Define Model
+
+model <- keras_model_sequential() %>%
+ layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu',
+ input_shape = input_shape) %>%
+ layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>%
+ layer_max_pooling_2d(pool_size = c(2, 2)) %>%
+ layer_dropout(rate = 0.25) %>%
+ layer_flatten() %>%
+ layer_dense(units = 128, activation = 'relu') %>%
+ layer_dropout(rate = 0.5) %>%
+ layer_dense(units = num_classes, activation = 'softmax')
+
+# Compile model
+
+model %>% compile(
+ loss = loss_categorical_crossentropy,
+ optimizer = optimizer_adadelta(),
+ metrics = c('accuracy')
+)
+
+# Train model
+
+model %>% fit(
+ x_train, y_train,
+ batch_size = batch_size,
+ epochs = epochs,
+ validation_split = 0.2
+)
+
+
+scores <- model %>% evaluate(
+ x_test, y_test, verbose = 0
+)
+
+# Output metrics
+cat('Test loss:', scores[[1]], '\n')
+cat('Test accuracy:', scores[[2]], '\n')
diff --git a/Compendium_attachments/DataAnalyticsWithR/environments.png b/Compendium_attachments/DataAnalyticsWithR/environments.png
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index 0000000000000000000000000000000000000000..15076eb14f822a26b80730f1cba2c244a15e326f
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diff --git a/Compendium_attachments/DataAnalyticsWithR/lapply.R b/Compendium_attachments/DataAnalyticsWithR/lapply.R
new file mode 100644
index 0000000000000000000000000000000000000000..84849396caf89acc584daf75ce0b41909f88d310
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/lapply.R
@@ -0,0 +1,24 @@
+#create small dataset
+name <- c("1", "2", "3", "4", "5")
+age <- c(6, 7, 4, 5, 6)
+growth <- c(100, 107, 85, 93, 99)
+weight <- c(20, 23, 16, 19, 20)
+children <- data.frame(name, age, growth, weight)
+
+#The kmeans function is now wrapped in a custom parallel function
+#that takes the number of starting positions (nstart) as its sole parameter
+parallel.function <- function(i) {kmeans( children, centers=4, nstart=i )}
+
+#Each of the four invocations of lapply winds up calling kmeans,
+#but each call to kmeans only does 25 starts instead of the full 100
+results <- lapply( c(25, 25, 25, 25), FUN=parallel.function )
+
+
+#for the k-means result with the absolute lowest value for results$tot.withinss use the which.min() function alue.
+#for thw which.min() for the input vecor) use tot.withinss.sapply returns a vector comprised of each tot.withinss value from the list of k-means objects
+temp.vector <- sapply( results, function(result) { result$tot.withinss } )
+
+#to get the index of temp.vector that contains the minimum value
+result <- results[[which.min(temp.vector)]]
+
+print(result)
diff --git a/Compendium_attachments/DataAnalyticsWithR/multicore.R b/Compendium_attachments/DataAnalyticsWithR/multicore.R
new file mode 100644
index 0000000000000000000000000000000000000000..8404bc5e59b93aafd21ba5abf105728f0e2e2d1c
--- /dev/null
+++ b/Compendium_attachments/DataAnalyticsWithR/multicore.R
@@ -0,0 +1,22 @@
+library(parallel)
+
+# here some function that needs to be executed in parallel
+average <- function(size){
+ norm_vector <- rnorm(n=size, mean=mu, sd=sigma)
+ return(mean(norm_vector))
+}
+
+# variable initialization
+mu <- 1.0
+sigma <- 1.0
+vector_length <- 10^7
+n_repeat <- 100
+sample_sizes <- rep(vector_length, times=n_repeat)
+
+
+# shared-memory version
+threads <- as.integer(Sys.getenv("SLURM_CPUS_ON_NODE"))
+# here the name of the variable depends on the correct sbatch configuration
+# unfortunately the built-in function gets the total number of physical cores without looking on SLURM configuration
+
+list_of_averages <- mclapply(X=sample_sizes, FUN=average, mc.cores=threads) # apply function "average" 100 times
diff --git a/Compendium_attachments/Debuggers/ddt.png b/Compendium_attachments/Debuggers/ddt.png
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diff --git a/Compendium_attachments/Debuggers/parallel_debugging_ddt.pdf b/Compendium_attachments/Debuggers/parallel_debugging_ddt.pdf
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diff --git a/Compendium_attachments/DesktopCloudVisualization/jupyterlab-and-dcv.png b/Compendium_attachments/DesktopCloudVisualization/jupyterlab-and-dcv.png
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diff --git a/Compendium_attachments/EnergyMeasurement/demoHdeem_high_low_vampir_3.png b/Compendium_attachments/EnergyMeasurement/demoHdeem_high_low_vampir_3.png
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diff --git a/Compendium_attachments/FEMSoftware/ABAQUS-SLURM.pdf b/Compendium_attachments/FEMSoftware/ABAQUS-SLURM.pdf
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index 0000000000000000000000000000000000000000..779a2d3dfde289f8ee15dc1fddf8f3efdbeae15e
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diff --git a/Compendium_attachments/FEMSoftware/Rot-modell-BenjaminGroeger.inp b/Compendium_attachments/FEMSoftware/Rot-modell-BenjaminGroeger.inp
new file mode 100644
index 0000000000000000000000000000000000000000..25b20036a1a85b4e1687a23c723bc3279a0a61b6
--- /dev/null
+++ b/Compendium_attachments/FEMSoftware/Rot-modell-BenjaminGroeger.inp
@@ -0,0 +1,189 @@
+*Heading
+** Job name: Rot-modell Model name: Model-1
+** Generated by: Abaqus/CAE 2016
+*Preprint, echo=NO, model=NO, history=NO, contact=NO
+**
+** PARTS
+**
+*Part, name=Part-1
+*Node
+ 1, 98.0785294, -19.5090313, 0.
+ 2, 108.150925, -21.5125561, 0.
+ 3, 119.31588, -23.7334042, 0.
+ 4, 131.691895, -26.1951466, 0.
+ 5, 145.410339, -28.923914, 0.
+ 6, 160.616821, -31.9486732, 0.
+ 7, 177.472763, -35.3015251, 0.
+ 8, 196.157059, -39.0180626, 0.
+ 9, 99.1444855, -13.052619, 0.
+ 10, 109.326355, -14.3930874, 0.
+ 11, 120.612656, -15.8789568, 0.
+ 12, 133.123184, -17.5259991, 0.
+ 13, 146.990723, -19.351696, 0.
+ 14, 162.362473, -21.3754253, 0.
+ 15, 179.401611, -23.6186695, 0.
+ 16, 198.288971, -26.105238, 0.
+ 17, 99.7858887, -6.54031277, 0.
+ 18, 110.03363, -7.21198511, 0.
+ 19, 121.392944, -7.95651388, 0.
+ 20, 133.984406, -8.78180218, 0.
+ 21, 147.941666, -9.69660854, 0.
+ 22, 163.412857, -10.7106447, 0.
+ 23, 180.562225, -11.8346739, 0.
+ 24, 199.571777, -13.0806255, 0.
+ 25, 100., 0., 0.
+ 26, 110.26973, 0., 0.
+ 27, 121.653412, 0., 0.
+ 28, 134.271896, 0., 0.
+ 29, 148.259094, 0., 0.
+ 30, 163.763489, 0., 0.
+ 31, 180.949661, 0., 0.
+ 32, 200., 0., 0.
+ 33, 99.7858887, 6.54031277, 0.
+ 34, 110.03363, 7.21198511, 0.
+ 35, 121.392944, 7.95651388, 0.
+ 36, 133.984406, 8.78180218, 0.
+ 37, 147.941666, 9.69660854, 0.
+ 38, 163.412857, 10.7106447, 0.
+ 39, 180.562225, 11.8346739, 0.
+ 40, 199.571777, 13.0806255, 0.
+ 41, 99.1444855, 13.052619, 0.
+ 42, 109.326355, 14.3930874, 0.
+ 43, 120.612656, 15.8789568, 0.
+ 44, 133.123184, 17.5259991, 0.
+ 45, 146.990723, 19.351696, 0.
+ 46, 162.362473, 21.3754253, 0.
+ 47, 179.401611, 23.6186695, 0.
+ 48, 198.288971, 26.105238, 0.
+ 49, 98.0785294, 19.5090313, 0.
+ 50, 108.150925, 21.5125561, 0.
+ 51, 119.31588, 23.7334042, 0.
+ 52, 131.691895, 26.1951466, 0.
+ 53, 145.410339, 28.923914, 0.
+ 54, 160.616821, 31.9486732, 0.
+ 55, 177.472763, 35.3015251, 0.
+ 56, 196.157059, 39.0180626, 0.
+*Element, type=S4
+ 1, 1, 2, 10, 9
+ 2, 2, 3, 11, 10
+ 3, 3, 4, 12, 11
+ 4, 4, 5, 13, 12
+ 5, 5, 6, 14, 13
+ 6, 6, 7, 15, 14
+ 7, 7, 8, 16, 15
+ 8, 9, 10, 18, 17
+ 9, 10, 11, 19, 18
+10, 11, 12, 20, 19
+11, 12, 13, 21, 20
+12, 13, 14, 22, 21
+13, 14, 15, 23, 22
+14, 15, 16, 24, 23
+15, 17, 18, 26, 25
+16, 18, 19, 27, 26
+17, 19, 20, 28, 27
+18, 20, 21, 29, 28
+19, 21, 22, 30, 29
+20, 22, 23, 31, 30
+21, 23, 24, 32, 31
+22, 25, 26, 34, 33
+23, 26, 27, 35, 34
+24, 27, 28, 36, 35
+25, 28, 29, 37, 36
+26, 29, 30, 38, 37
+27, 30, 31, 39, 38
+28, 31, 32, 40, 39
+29, 33, 34, 42, 41
+30, 34, 35, 43, 42
+31, 35, 36, 44, 43
+32, 36, 37, 45, 44
+33, 37, 38, 46, 45
+34, 38, 39, 47, 46
+35, 39, 40, 48, 47
+36, 41, 42, 50, 49
+37, 42, 43, 51, 50
+38, 43, 44, 52, 51
+39, 44, 45, 53, 52
+40, 45, 46, 54, 53
+41, 46, 47, 55, 54
+42, 47, 48, 56, 55
+*Nset, nset=_PickedSet14, internal, generate
+ 1, 56, 1
+*Elset, elset=_PickedSet14, internal, generate
+ 1, 42, 1
+*Orientation, name=Ori-1, system=CYLINDRICAL
+ 0., 0., 0., 0., 0., 1.
+3, 0.
+** Section: Section-1
+*Shell Section, elset=_PickedSet14, material=Material-1, orientation=Ori-1
+5., 5
+*End Part
+**
+**
+** ASSEMBLY
+**
+*Assembly, name=Assembly
+**
+*Instance, name=Part-1-1, part=Part-1
+*End Instance
+**
+*Nset, nset=_PickedSet4, internal, instance=Part-1-1
+ 1, 2, 3, 4, 5, 6, 7, 8, 49, 50, 51, 52, 53, 54, 55, 56
+*Elset, elset=_PickedSet4, internal, instance=Part-1-1
+ 1, 2, 3, 4, 5, 6, 7, 36, 37, 38, 39, 40, 41, 42
+*Nset, nset=_PickedSet5, internal, instance=Part-1-1, generate
+ 1, 56, 1
+*Elset, elset=_PickedSet5, internal, instance=Part-1-1, generate
+ 1, 42, 1
+*Nset, nset="_T-Part-1-1-Datum csys-1", internal
+_PickedSet4,
+*Transform, nset="_T-Part-1-1-Datum csys-1", type=C
+ 0., 0., 0., 0., 0., 1.
+*End Assembly
+**
+** MATERIALS
+**
+*Material, name=Material-1
+*Density
+ 7.8e-09,
+*Elastic
+210000., 0.29
+** ----------------------------------------------------------------
+**
+** STEP: Step-1
+**
+*Step, name=Step-1, nlgeom=YES
+*Static
+1., 1., 1e-05, 1.
+**
+** BOUNDARY CONDITIONS
+**
+** Name: BC-1 Type: Displacement/Rotation
+*Boundary
+_PickedSet4, 2, 2
+_PickedSet4, 4, 4
+_PickedSet4, 6, 6
+**
+** LOADS
+**
+** Name: Load-1 Type: Rotational body force
+*Dload
+_PickedSet5, CENTRIF, 0.0144,0.,0.,0.,0.,0.,1.
+**
+** OUTPUT REQUESTS
+**
+*Restart, write, frequency=0
+**
+** FIELD OUTPUT: F-Output-1
+**
+*Output, field
+*Node Output
+CF, RF, U
+*Element Output, directions=YES
+E, LE, S
+*Contact Output
+CDISP, CSTRESS
+**
+** HISTORY OUTPUT: H-Output-1
+**
+*Output, history, variable=PRESELECT
+*End Step
diff --git a/Compendium_attachments/GPUProgramming/intro1.pdf b/Compendium_attachments/GPUProgramming/intro1.pdf
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diff --git a/Compendium_attachments/GetStartedWithHPCDA/machine_learning_example.py b/Compendium_attachments/GetStartedWithHPCDA/machine_learning_example.py
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--- /dev/null
+++ b/Compendium_attachments/GetStartedWithHPCDA/machine_learning_example.py
@@ -0,0 +1,21 @@
+import tensorflow as tf
+# Load and prepare the MNIST dataset. Convert the samples from integers to floating-point numbers:
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train),(x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Build the tf.keras model by stacking layers. Select an optimizer and loss function used for training
+model = tf.keras.models.Sequential([
+ tf.keras.layers.Flatten(input_shape=(28, 28)),
+ tf.keras.layers.Dense(512, activation=tf.nn.relu),
+ tf.keras.layers.Dropout(0.2),
+ tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+])
+model.compile(optimizer='adam',
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy'])
+
+# Train and evaluate model
+model.fit(x_train, y_train, epochs=5)
+model.evaluate(x_test, y_test)
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--- a/doc.zih.tu-dresden.de/README.md
+++ b/doc.zih.tu-dresden.de/README.md
@@ -424,3 +424,18 @@ Stick to the Unix rules on optional and required arguments, and seclection of it
* `[optional argument or value]`
* `{choice1|choice2|choice3}`
+
+# Graphics and Attachments
+
+All graphics and attachments are saved within `misc` directory of the respective sub directory in
+`docs`.
+
+The syntax to insert a graphic or attachment into a page is
+
+```Bash
+
+{: align="center"}
+```
+
+The attribute `align` is optional. By default, graphics are left aligned. **Note:** It is crucial to
+have `{: align="center"}` on a new line.