Merge branch 'issue_1' into 'master'

Add table of contents

Closes #1

See merge request khalid1/ml_dl_on_supercomputers!1

README.md

@@ -14,7 +14,30 @@ more of the following frameworks:
 above mentioned frameworks. If you are interested in educational resources for beginners, please
 visit [this](https://gitlab.version.fz-juelich.de/MLDL_FZJ/MLDL_FZJ_Wiki/wikis/Education) page.
 
-### A word regarding the code samples
+# Table of contents
+<!-- TOC -->
+
+1. [A word regarding the code samples](#1-a-word-regarding-the-code-samples)
+2. [Changes made to support loading of pre-downloaded datasets](#2-changes-made-to-support-loading-of-pre-downloaded-datasets)
+3. [Applying for user accounts on supercomputers](#3-applying-for-user-accounts-on-supercomputers)
+    * [3.1. JURECA and JUWELS](#31-jureca-and-juwels)
+    * [3.2. JURON](#32-juron)
+4. [Logging on to the supercomputers](#4-logging-on-to-the-supercomputers)
+    * [4.1. JURECA](#41-jureca)
+    * [4.2. JURON](#42-juron)
+5. [Cloning the repository](#5-cloning-the-repository)
+    * [5.1. JURECA](#51-jureca)
+    * [5.2. JURON](#52-juron)
+6. [Running a sample](#6-running-a-sample)
+    * [6.1. JURECA](#61-jureca)
+    * [6.2. JURON](#62-juron)
+7. [Python 2 support](#7-python-2-support)
+8. [Distributed training](#8-distributed-training)
+9. [Credits](#9-credits)
+
+<!-- /TOC -->
+
+## 1. A word regarding the code samples
 
 Samples for each framework are available in the correspondingly named directory. Each such 
 directory typically contains at least one code sample, which trains a simple artificial neural 
@@ -26,7 +49,7 @@ a `README.md` file contains further information about the contents of the direct
 **Disclaimer:** Neither are the samples intended to serve as examples of optimized code, nor do these 
 represent programming best practices.
 
-### Changes made to support loading of pre-downloaded datasets
+## 2. Changes made to support loading of pre-downloaded datasets
 
 It is worth mentioning that all the code samples were taken from the corresponding framework's 
 official samples/tutorials repository, as practitioners are likely familiar with these (links 
@@ -38,17 +61,17 @@ the `datasets` directory included in this repository; specific code changes, at
 have been marked by comments prefixed with the `[HPCNS]` tag. For more information see the `README.md` 
 available in the `datasets` directory.
 
-## 1. Applying for user accounts on supercomputers
+## 3. Applying for user accounts on supercomputers
 
 In case you do not already have an account on your supercomputer of interest, please take a look at the 
 instructions provided in the following sub-sections.
 
-### 1.1 JURECA and JUWELS
+### 3.1 JURECA and JUWELS
 
 For more information on getting accounts on JURECA and JUWELS, click 
 [here](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/ComputingTime/computingTime_node.html).
 
-### 1.2 JURON
+### 3.2 JURON
 
 To get a user account on JURON, please follow the steps below:
 
@@ -59,9 +82,9 @@ in which please introduce yourself and mention why you need the account.
 If your work is related to the Human Brain Project (HBP), please join the `PCP0` and `CPCP0` projects. 
 Otherwise please join the `PADC` and `CPADC` projects.
 
-## 2. Logging on to the supercomputers
+## 4. Logging on to the supercomputers
 
-### 2.1 JURECA
+### 4.1 JURECA
 
 Following are the steps required to login (more information 
 [here](http://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JURECA/UserInfo/QuickIntroduction.html?nn=1803700)).
@@ -79,7 +102,7 @@ Following are the steps required to login (more information
 You should be in your project directory at this point. If you'd like to clone this repository 
 elsewhere, please change to that directory.
 
-### 2.2 JURON
+### 4.2 JURON
 
 Following are the steps required to login.
 
@@ -101,7 +124,7 @@ are shared amongst many users, it is a good idea to create a personal directory
 username) withing the project directory. You can then use your personal directory for all your work, 
 including cloning this tutorial. 
 
-## 3. Cloning the repository
+## 5. Cloning the repository
 
 In order to store the datasets within the repository, we use Git LFS. This makes cloning the 
 repository a little bit different. Please find below the instructions on how to clone on different 
@@ -110,7 +133,7 @@ systems. To learn more about Git LFS, click [here](http://gitlab.pages.jsc.fz-ju
 **Note:** During the cloning process you will most likely be prompted for your username and 
 password twice; this is as expected.
 
-### 3.1 JURECA
+### 5.1 JURECA
 
 1.  Load the Git LFS module:
 
@@ -122,20 +145,20 @@ password twice; this is as expected.
 
     `git lfs clone https://gitlab.version.fz-juelich.de/khalid1/ml_dl_on_supercomputers.git`
 
-### 3.2 JURON
+### 5.2 JURON
 
 No additional setup is required on JURON. You can simply clone the repository along with the
 datasets using the following command:
 
     git lfs clone https://gitlab.version.fz-juelich.de/khalid1/ml_dl_on_supercomputers.git
 
-## 4. Running a sample
+## 6. Running a sample
 
 Let us consider a scenario where you would like to run the `mnist.py` sample available in the `keras` 
 directory. This sample trains a CNN on MNIST using Keras on a single GPU. The following sub-sections list 
 the steps required for different supercomputers.
 
-### 4.1 JURECA
+### 6.1 JURECA
 
 1.  Assuming you are in the repository root, change to the keras directory:
 
@@ -154,7 +177,7 @@ email address).
 Output from the job is available in the `error` and `output` files, as specified in the job 
 configuration.
 
-### 4.2 JURON
+### 6.2 JURON
 
 1.  Assuming you are in the repository root, change to the keras directory:
 
@@ -172,7 +195,7 @@ supported on JURON. For more information on how to use LSF on JURON, use the fol
 Output from the job is available in the `error` and `output` files, as specified in the job 
 configuration.
 
-## 5. Python 2 support
+## 7. Python 2 support
 
 All the code samples are compatible with both Python 2 and Python 3. However, not all frameworks on all 
 machines are available for Python 2 (yet); in certain cases these are only available for Python 3. We have 
@@ -181,7 +204,7 @@ supported, only the job submission script for Python 3 is available. We will try
 all frameworks available with Python 2 as well, but this will not be a priority as the official support 
 for Python 2 will be discontinued in the year 2020.
 
-## 6. Distributed training
+## 8. Distributed training
 
 [Horovod](https://github.com/horovod/horovod) provides a simple and efficient solution for 
 training artificial neural networks on multiple GPUs across multiple nodes in a cluster. It can 
@@ -202,7 +225,7 @@ via the C/C++ interface). The `mnist_cmd` sample in the `caffe` directory contai
 script that can be used to train the model on multiple GPUs. Please see the 
 directory-local `README.md` for further information.
 
-## Credits
+## 9. Credits
 
 *  **Created by:** Fahad Khalid (SLNS/HPCNS, JSC)
 *  **Installation of modules on JURON:** Andreas Herten (HPCNS, JSC)

utils/data_utils.py

@@ -49,7 +49,7 @@ class DataValidator:
             if not os.path.exists(data_dir):
                 data_dir = os.path.join(os.path.abspath('../../datasets'))
 
-            print('Using %s as the data directory.' % data_dir)
+            print('Using {} as the data directory.'.format(data_dir))
 
         # Check if the directory exists
         assert os.path.exists(data_dir), \