mdlint

README.md

@@ -29,8 +29,8 @@ Mellanox
 
 ### Formulas are made with:
 
-* https://facelessuser.github.io/pymdown-extensions/extensions/arithmatex/
-* https://www.mathjax.org/
+* [https://facelessuser.github.io/pymdown-extensions/extensions/arithmatex/](https://facelessuser.github.io/pymdown-extensions/extensions/arithmatex/)
+* [https://www.mathjax.org/](https://www.mathjax.org/)
 
 You can add formula to page like this:
 

docs.it4i/anselm/software/debuggers/valgrind.md

@@ -31,19 +31,19 @@ Compile the application which you want to debug as usual. It is advisable to add
 For example, lets look at this C code, which has two problems :
 
 ```cpp
-    #include <stdlib.h>
-
-    void f(void)
-    {
-       int* x = malloc(10 * sizeof(int));
-       x[10] = 0; // problem 1: heap block overrun
-    }             // problem 2: memory leak -- x not freed
-
-    int main(void)
-    {
-       f();
-       return 0;
-    }
+#include <stdlib.h>
+
+void f(void)
+{
+   int* x = malloc(10 * sizeof(int));
+   x[10] = 0; // problem 1: heap block overrun
+}             // problem 2: memory leak -- x not freed
+
+int main(void)
+{
+   f();
+   return 0;
+}
 ```
 
 Now, compile it with Intel compiler :
@@ -161,19 +161,19 @@ so it is better to use the MPI-enabled valgrind from module. The MPI version req
 Lets look at this MPI example :
 
 ```cpp
-    #include <stdlib.h>
-    #include <mpi.h>
+#include <stdlib.h>
+#include <mpi.h>
 
-    int main(int argc, char *argv[])
-    {
-            int *data = malloc(sizeof(int)*99);
+int main(int argc, char *argv[])
+{
+        int *data = malloc(sizeof(int)*99);
 
-            MPI_Init(&argc, &argv);
-            MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
-            MPI_Finalize();
+        MPI_Init(&argc, &argv);
+        MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
+        MPI_Finalize();
 
-            return 0;
-    }
+        return 0;
+}
 ```
 
 There are two errors - use of uninitialized memory and invalid length of the buffer. Lets debug it with valgrind :

docs.it4i/anselm/software/machine-learning/tensorflow.md

@@ -6,9 +6,9 @@ TensorFlow is an open-source software library for machine intelligence.
 
 Anselm provides three different TensorFlow modules:
 
- * Tensorflow/1.1.0
- * Tensorflow/1.1.0-CUDA-7.5.18-Python-3.6.1
- * Tensorflow/1.1.0-CUDA-8.0.44-Python-3.6.1
+* Tensorflow/1.1.0
+* Tensorflow/1.1.0-CUDA-7.5.18-Python-3.6.1
+* Tensorflow/1.1.0-CUDA-8.0.44-Python-3.6.1
 
 ### Tensorflow/1.1.0 (CPU only)
 
@@ -20,8 +20,8 @@ $ ml Tensorflow/1.1.0
 
 This module was built with:
 
- * GCC/4.9.3
- * Python/3.6.1
+* GCC/4.9.3
+* Python/3.6.1
 
 ### Tensorflow/1.1.0-CUDA-7.5.18-Python-3.6.1 (GPU enabled)
 
@@ -33,10 +33,10 @@ $ ml Tensorflow/1.1.0-CUDA-7.5.18-Python-3.6.1
 
 This module was built with:
 
- * GCC/4.9.3
- * Python/3.6.1
- * CUDA/7.5.18
- * cuDNN/5.1-CUDA-7.5.18
+* GCC/4.9.3
+* Python/3.6.1
+* CUDA/7.5.18
+* cuDNN/5.1-CUDA-7.5.18
 
 ### Tensorflow/1.1.0-CUDA-8.0.44-Python-3.6.1  (GPU enabled)
 
@@ -48,10 +48,10 @@ $ ml Tensorflow/1.1.0-CUDA-8.0.44-Python-3.6.1
 
 This module was built with:
 
- * GCC/4.9.3
- * Python/3.6.1
- * CUDA/8.0.44
- * cuDNN/5.1-CUDA-8.0.44
+* GCC/4.9.3
+* Python/3.6.1
+* CUDA/8.0.44
+* cuDNN/5.1-CUDA-8.0.44
 
 ## TensorFlow application example
 

docs.it4i/salomon/software/debuggers/valgrind.md

@@ -30,19 +30,19 @@ Compile the application which you want to debug as usual. It is advisable to add
 For example, lets look at this C code, which has two problems:
 
 ```cpp
-    #include <stdlib.h>
-
-    void f(void)
-    {
-       int* x = malloc(10 * sizeof(int));
-       x[10] = 0; // problem 1: heap block overrun
-    }             // problem 2: memory leak -- x not freed
-
-    int main(void)
-    {
-       f();
-       return 0;
-    }
+#include <stdlib.h>
+
+void f(void)
+{
+   int* x = malloc(10 * sizeof(int));
+   x[10] = 0; // problem 1: heap block overrun
+}             // problem 2: memory leak -- x not freed
+
+int main(void)
+{
+   f();
+   return 0;
+}
 ```
 
 Now, compile it with Intel compiler:
@@ -164,19 +164,19 @@ which must be included in the LD_PRELOAD environment variable.
 Lets look at this MPI example:
 
 ```cpp
-    #include <stdlib.h>
-    #include <mpi.h>
+#include <stdlib.h>
+#include <mpi.h>
 
-    int main(int argc, char *argv[])
-    {
-            int *data = malloc(sizeof(int)*99);
+int main(int argc, char *argv[])
+{
+        int *data = malloc(sizeof(int)*99);
 
-            MPI_Init(&argc, &argv);
-            MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
-            MPI_Finalize();
+        MPI_Init(&argc, &argv);
+        MPI_Bcast(data, 100, MPI_INT, 0, MPI_COMM_WORLD);
+        MPI_Finalize();
 
-            return 0;
-    }
+        return 0;
+}
 ```
 
 There are two errors - use of uninitialized memory and invalid length of the buffer. Lets debug it with valgrind :

docs.it4i/salomon/software/machine-learning/tensorflow.md

@@ -6,9 +6,9 @@ TensorFlow is an open-source software library for machine intelligence.
 
 Salomon provides three different TensorFlow modules:
 
- * Tensorflow/1.1.0
- * Tensorflow/1.2.0-GCC-7.1.0-2.28
- * Tensorflow/1.2.0-intel-2017.05-mkl
+* Tensorflow/1.1.0
+* Tensorflow/1.2.0-GCC-7.1.0-2.28
+* Tensorflow/1.2.0-intel-2017.05-mkl
 
 ### Tensorflow/1.1.0 (not recommended)
 
@@ -21,8 +21,8 @@ $ ml Tensorflow/1.1.0
 
 This module was built with:
 
- * GCC/4.9.3
- * Python/3.6.1
+* GCC/4.9.3
+* Python/3.6.1
 
 ### Tensorflow/1.2.0-GCC-7.1.0-2.28 (default, recommended)
 
@@ -35,9 +35,9 @@ $ ml Tensorflow/1.2.0-GCC-7.1.0-2.28
 
 This module was built with:
 
- * GCC/7.1.0-2.28
- * Python/3.6.1
- * protobuf/3.2.0-GCC-7.1.0-2.28-Python-3.6.1
+* GCC/7.1.0-2.28
+* Python/3.6.1
+* protobuf/3.2.0-GCC-7.1.0-2.28-Python-3.6.1
 
 ### Tensorflow/1.2.0-intel-2017.05-mkl
 
@@ -50,9 +50,9 @@ $ ml Tensorflow/1.2.0-intel-2017.05-mkl
 
 This module was built with:
 
- * icc/2017.4.196-GCC-7.1.0-2.28
- * Python/3.6.1
- * protobuf/3.2.0-GCC-7.1.0-2.28-Python-3.6.1
+* icc/2017.4.196-GCC-7.1.0-2.28
+* Python/3.6.1
+* protobuf/3.2.0-GCC-7.1.0-2.28-Python-3.6.1
 
 ## TensorFlow application example
 

docs.it4i/snippets/modules_matrix_search.md

@@ -36,6 +36,6 @@ $("#searchInput").keyup(function () {
         "color": "black"
     });
 }).css({
-	    "color": "#C0C0C0"
+    "color": "#C0C0C0"
 });
 </script>

docs.it4i/software/easybuild.md

@@ -12,8 +12,8 @@ All builds and installations are performed at user level, so you don't need the
 
 EasyBuild relies on two main concepts
 
- * Toolchains
- * EasyConfig file (our easyconfigs is [here](https://code.it4i.cz/sccs/easyconfigs-it4i))
+* Toolchains
+* EasyConfig file (our easyconfigs is [here](https://code.it4i.cz/sccs/easyconfigs-it4i))
 
 Detailed documentations is available [here](http://easybuild.readthedocs.io).
 
@@ -21,8 +21,8 @@ Detailed documentations is available [here](http://easybuild.readthedocs.io).
 
 A toolchain corresponds to a compiler and a set of libraries which are commonly used to build a software. The two main toolchains frequently used on the IT4Innovations clusters are the **foss** and **intel**.
 
- * **foss** is based on the GCC compiler and on open-source libraries (OpenMPI, OpenBLAS, etc.).
- * **intel** is based on the Intel compiler and on Intel libraries (Intel MPI, Intel Math Kernel Library, etc.).
+* **foss** is based on the GCC compiler and on open-source libraries (OpenMPI, OpenBLAS, etc.).
+* **intel** is based on the Intel compiler and on Intel libraries (Intel MPI, Intel Math Kernel Library, etc.).
 
 Additional details are available on [here](https://github.com/hpcugent/easybuild/wiki/Compiler-toolchains).
 

docs.it4i/software/singularity.md

+# Singularity Container
+
 [Singularity](http://singularity.lbl.gov/) enables users to have full control of their environment. A non-privileged user can "swap out" the operating system on the host for one they control. So if the host system is running RHEL6 but your application runs in Ubuntu/RHEL7, you can create an Ubuntu/RHEL7 image, install your applications into that image, copy the image to another host, and run your application on that host in it’s native Ubuntu/RHEL7 environment.
 
 Singularity also allows you to leverage the resources of whatever host you are on. This includes HPC interconnects, resource managers, file systems, GPUs and/or accelerators, etc. Singularity does this by enabling several key facets:

docs.it4i/software/spack.md

@@ -241,7 +241,7 @@ $ spack edit git
 !!! note
     To change source link (ftp:// to http://) use `spack create URL -f` to regenerates rules.
 
-**Example**
+#### **Example**
 
 ```console
 $ spack install git
@@ -290,7 +290,7 @@ autoconf@2.69  cmake@3.7.1  expat@2.2.0       git@2.11.0     libsigsegv@2.10  m4
 bzip2@1.0.6    curl@7.50.3  gettext@0.19.8.1  libiconv@1.14  libxml2@2.9.4    ncurses@6.0  pcre@8.41       pkg-config@0.29.1  xz@5.2.2
 ```
 
-**Spack colorizes output**
+Spack colorizes output.
 
 ```console
 $ spack find | less -R
@@ -317,7 +317,7 @@ $ spack load git
       source ~/.local/easybuild/software/Spack/0.10.0/share/spack/setup-env.csh
 ```
 
-**First usage**
+### First usage
 
 ```console
 $ . ~/.local/easybuild/software/Spack/0.10.0/share/spack/setup-env.sh