Rough notes on how to install CUDA on an Ubuntu 20.04LTS 1

To anyone coming across this post, please note that Canonical does not officially support CUDA. Not being supported formally means that you could face problems that we did not while setting up our machine.

Recently we wanted to use our GPU to execute various TensorFlow projects. We had to use version 1, specifically version 1.15 of TensorFlow, on one of our attempts. That setup was causing many problems even after version 2 was working perfectly. There must be something different with version 1, and it is not supported correctly anymore. We recommend avoiding using it unless needed.

Finding out what graphics card we have

Before getting started, we executed the following command that gave us the model of our graphics card:

lspci | grep -i nvidia;
$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation TU104 [GeForce RTX 2080 Rev. A] (rev a1)
01:00.1 Audio device: NVIDIA Corporation TU104 HD Audio Controller (rev a1)
01:00.2 USB controller: NVIDIA Corporation TU104 USB 3.1 Host Controller (rev a1)
01:00.3 Serial bus controller [0c80]: NVIDIA Corporation TU104 USB Type-C UCSI Controller (rev a1)

Installing dependencies and NVidia repositories

Then we proceeded to install the headers of our Linux kernel, the repositories of NVidia, and finally install CUDA using apt-get.

sudo apt-get install linux-headers-$(uname -r);
sudo mv /etc/apt/preferences.d/cuda-repository-pin-600;
sudo apt-key adv --fetch-keys;
sudo add-apt-repository "deb /";
sudo apt-get update;
sudo apt-get -y install cuda;
sudo apt-get install nvidia-gds;

After this step, we rebooted the computer to load the NVidia graphics driver.

Playing with shadow(s)

The passwords are stored in a digital fingerprint using salt (salted hash) in the Linux system through the crypt function. You can find relevant information about the implementation in Ubuntu at (how to call this function, what the arguments are etc.) Assume that you have managed to access a secret shadow file of an Ubuntu Linux system (see the shadow file contents below). The system has two users, bob and alice.



Based on the information you will obtain from the above link, which hash function did the system use to generate the salted hashed passwords?

From the documentation, we get the following information:

The glibc2 version of this function supports additional encryption algorithms.

If salt is a character string starting with the characters "$id$"  followed  by  a  string
optionally terminated by "$", then the result has the form:


id  identifies  the encryption method used instead of DES and this then determines how the
rest of the password string is interpreted.  The following values of id are supported:

      ID  | Method
      1   | MD5
      2a  | Blowfish (not in mainline glibc; added in some
          | Linux distributions)
      5   | SHA-256 (since glibc 2.7)
      6   | SHA-512 (since glibc 2.7)

We can see in both lines of our shadow file, that between the first and second colon (:) the hashed value is following the format described above. This means that the id of the function used is between the first and second dollar sign ($) and that value is in both cases the value 6. From this result, we know that SHA-512 was used.

Which salt was used for bob and which for alice?

The salt that was used for bob is .s6xaWmE and the salt for alice aACNZdTj. We get this data on each line, between the second dollar sign ($) and the third.

From the shadow file, can you see for sure if alice and bob have chosen different passwords? Explain your answer.

Since the system used different salt while hashing each password, we cannot infer any information regarding the similarity of the passwords of bob and alice.

Your goal is to discover the passwords of these two users. To do this, you have some information about the personal life of bob and alice:

For the bob user, you know that he is now starting to learn how to use computers. He is unaware of security risks and has probably chosen an easy password (one that many users generally select).

For alice, you know some of her personal information: her phone number is 6955345671, her license plate is ZKA4221, and she likes the Rolling Stones. Alice has only a limited knowledge of security. She knows it is suitable for a password to combine letters with numbers and have several characters. Still, she does not fully understand how to choose a secure password.

Please describe the actions you will take to guess their passwords, doing your tests on the shadow file. For bob, use lists of common passwords, while for alice, use the above personal information.

To get the password of bob, we used a simple approach using John the Ripper (lovely tool by the way). As seen in the following commands, we installed John the Ripper and using the default settings we asked John to crack any passwords in our shadow file.

sudo snap install john-the-ripper;
john shadow ;

IIn seconds, John the Ripper produced the password for the user bob, which was 1234567890. John the Ripper was so quick that it felt like magic! Unfortunately (or fortunately, depending on the context), John the Ripper cannot do magic… The reason it was so fast, in this case, is because the default settings for John the Ripper instruct the tool to try many simple passwords first using wordlist files. We already knew that bob was using one of those passwords, so it made the selection of options trivial.

At this stage, it would appear that the password for alice was harder to guess, and John the Ripper did not produce a result after we let it execute for a few minutes. Something that is worth mentioning about the case of bob is the following; We could have asked John the Ripper to try and crack the password using all alphanumeric characters only ([a-z][A-Z][0-9]). It would have taken longer, but it could work since we knew from Human Intelligence (HumInt) that bob is using fairly simple passwords.

Below is the raw output of the console for the case of cracking the password of bob. Please note that we used ctrl+c to kill the execution as we did not expect John the Ripper to get the password for alice as well.

$ john shadow 
Created directory: /home/bob/snap/john-the-ripper/459/.john
Warning: detected hash type "sha512crypt", but the string is also recognized as "HMAC-SHA256"
Use the "--format=HMAC-SHA256" option to force loading these as that type instead
Warning: detected hash type "sha512crypt", but the string is also recognized as "sha512crypt-opencl"
Use the "--format=sha512crypt-opencl" option to force loading these as that type instead
Using default input encoding: UTF-8
Loaded 2 password hashes with 2 different salts (sha512crypt, crypt(3) $6$ [SHA512 256/256 AVX2 4x])
Cost 1 (iteration count) is 5000 for all loaded hashes
Will run 12 OpenMP threads
Proceeding with single, rules:Single
Press 'q' or Ctrl-C to abort, almost any other key for status
Warning: Only 46 candidates buffered for the current salt, minimum 48 needed for performance.
Warning: Only 45 candidates buffered for the current salt, minimum 48 needed for performance.
Almost done: Processing the remaining buffered candidate passwords, if any.
Warning: Only 43 candidates buffered for the current salt, minimum 48 needed for performance.
Warning: Only 33 candidates buffered for the current salt, minimum 48 needed for performance.
Proceeding with wordlist:/snap/john-the-ripper/current/run/password.lst, rules:Wordlist
1234567890       (bob)
Proceeding with incremental:ASCII

To tackle the case of alice, we had to improvise a bit. First of all, we made a text file that on each line it contained the personal information that Human Intelligence gathered for us about alice. To ensure that John the Ripper would have more material to work with, we also did some variations to some data, like splitting the license plate into two tokens. The file (keywords.lst) looked like so:

Rolling Stones

Then, we used hashcat to create a new list that would combine the previous tokens making more complex passwords. We had this hint from the Human Intelligence analysis, so it was an easy choice to make. The new and amazing list (advanced.lst) that we created had the following data in it:

sudo apt-get install hashcat;
#Type in the contents of the list above
nano keywords.lst;
#Not sure why it requires sudo
sudo hashcat -a 1 --stdout keywords.lst keywords.lst > advanced.lst;
6955345671Rolling Stones
ZKΑ4221Rolling Stones
Rolling Stones6955345671
Rolling StonesZKΑ4221
Rolling StonesRolling Stones
Rolling Stonesalice
Rolling Stoneszka4221
Rolling Stoneszka
Rolling Stones4221
Rolling StonesStones
Rolling StonesRolling
aliceRolling Stones
zka4221Rolling Stones
zkaRolling Stones
4221Rolling Stones
StonesRolling Stones
RollingRolling Stones

To use the above custom wordlist on the shadow file, we issued the following command to John the Ripper:

john --wordlist=advanced.lst --rules shadow;

A few moments later, John the Ripper produced the following output indicating that the password for alice was rollingstones4221.

$ john --wordlist=advanced.lst --rules shadow
Warning: detected hash type "sha512crypt", but the string is also recognized as "HMAC-SHA256"
Use the "--format=HMAC-SHA256" option to force loading these as that type instead
Warning: detected hash type "sha512crypt", but the string is also recognized as "sha512crypt-opencl"
Use the "--format=sha512crypt-opencl" option to force loading these as that type instead
Using default input encoding: UTF-8
Loaded 2 password hashes with 2 different salts (sha512crypt, crypt(3) $6$ [SHA512 256/256 AVX2 4x])
Remaining 1 password hash
Cost 1 (iteration count) is 5000 for all loaded hashes
Will run 12 OpenMP threads
Press 'q' or Ctrl-C to abort, almost any other key for status
rollingstones4221 (alice)
1g 0:00:00:00 DONE (2021-11-26 17:09) 3.846g/s 3765p/s 3765c/s 3765C/s 69553456716955345671..Rollingstonesing
Use the "--show" option to display all of the cracked passwords reliably
Session completed

Bonus Material

Finding previously cracked passwords

A tip for people that are new to John the Ripper. In case you forgot to write down all passwords that were produced; you can issue the following command that will show you all the passwords that John the Ripper knows for the specific input file:

john --show shadow;
$ john --show shadow

2 password hashes cracked, 0 left

Trying to crack a shadow file using Python

In case you would like to use programming and manually crack the shadow file, there are ways.
Using the Python language and the crypt package, we can write a simple program. The program will accept the salt and the unencrypted input text and produce the hashed output. It would be the same result; as a result, a Linux machine would make while creating its shadow file.

import crypt
from hmac import compare_digest as compare_hash

crypt.crypt("1234567890", "$6$aACNZdTj$")
#It would produce the following, which is the salted hash for the password of bob

Notes on how to get HarpyTM running on an Ubuntu 20.04LTS GNU/Linux

Easy Setup – Slow Execution by using the CPUs only

Getting CUDA support in OpenCV for Python can be tricky as you will need to make several changes to your PC. On many occasions, it can fail if you are not comfortable troubleshooting the procedure. For this reason, we are presenting an alternative solution that is easy to implement and should work on most machines. The problem with this solution is that it will ignore your GPU and utilize your CPUs only. This means that executions will most probably be slower than the GPU-enabled one.

Conda / Anaconda

First of all, we installed and activated anaconda on an Ubuntu 20.04LTS desktop. To do so, we installed the following dependencies from the repositories:

sudo apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6;

Then, we downloaded the 64-Bit (x86) Installer from (

Using a terminal, we followed the instructions here ( and performed the installation.

Python environment and OpenCV for Python

Following the previous step, we used the commands below to create a virtual environment for our code. We needed python version 3.9 (as highlighted here and OpenCV for python.

source ~/anaconda3/bin/activate;
conda create --yes --name HarpyTM python=3.9;
conda activate HarpyTM;
pip install numpy scipy scikit-learn opencv-python==;

Please note that we needed to limit the package for opencv-python to because we were getting the following error on newer versions:

[INFO] setting preferable backend and target to CUDA...
Traceback (most recent call last):
  File "/home/bob/Traffic/HarpyTM/", line 51, in <module>
    detectNet = detector(weights, config, conf_thresh=conf_thresh, netsize=cfg_size, nms_thresh=nms_thresh, gpu=use_gpu, classes_file=classes_file)
  File "/home/bob/Traffic/HarpyTM/src/", line 24, in __init__
    self.layers = [ln[i[0] - 1] for i in]
  File "/home/bob/Traffic/HarpyTM/src/", line 24, in <listcomp>
    self.layers = [ln[i[0] - 1] for i in]
IndexError: invalid index to scalar variable.


git clone;
cd HarpyTM/;
# We manually create the following folders and set their priviledges as we were  getting errors when they were autogenerated by the code of HarpyTM.
mkdir -p results/Detections/videos;
chmod 777 results/Detections/videos;

After successfully cloning the project, we modified the config.ini to meet our needs, specifically, we changed all paths to be inside the folder of HarpyTM and used the full resolution for the test video:

video_filename 		= ./data/DJI_0406_cut.MP4
resize 			= True
image_width 		= 1920
image_height 		= 1080
display_video		= False
display_width 		= 1920
display_height 		= 1080

save_video_results	= True
video_export_path 	= ./results/Detections/videos/
csv_path 		= ./results/
export			= True
display_track		= True

darknet_config 		= ./data/Configs/vehicles_ty3.cfg
darknet_weights 	= ./data/Configs/vehicles_ty3.weights
classes_file 		= ./data/Configs/vehicles.names
cfg_size_width		= 608
cfg_size_height		= 608
iou_thresh 		= 0.3
conf_thresh 		= 0.3
nms_thresh 		= 0.4
use_gpu 		= True 

flight_height 		= 150
sensor_height 		= 0.455
sensor_width		= 0.617
focal_length		= 0.567
reset_boxes_frames	= 40
calc_velocity_n		= 25
draw_tracks		= True
export_data		= True

Finally, we were able to execute the code as follows:


After the execution was done, we found in the folder ./results/Detections/videos/ the video showing the bounding boxes etc. The resulting video was uploaded here:

How to Reset Password on an Ubuntu with LVM

A few days ago, a client tasked us to recover the password of an Ubuntu server 20.04LTS. The machine owner only knew the username but had no idea about the complexity of the password. We’ve asked the client if it was OK for us to reset the password instead of recovering it (meaning that we would not even try to crack the mystery of what the previous password was and just set a new one), and thankfully, the client accepted our request.

The client set up the server using Ubuntu server edition 20.04LTS, and the disk partitions were using LVM (Logical Volume Manager). To our good luck, they were not using encrypted partitions. The procedure we followed to reset the password of that server was like so:

First of all, we shut down the server and booted it with a Live USB of an Ubuntu desktop 20.04LTS. Then we started a terminal and executed the following to get root access on the live system:

sudo su;

Then, we executed pvscan to list all physical volumes and gain some intelligence on which disk we needed to work on:

[email protected]:/home/ubuntu# pvscan
  /dev/sdc: open failed: No medium found
  PV /dev/sda3   VG ubuntu-vg       lvm2 [<3.64 TiB / 3.44 TiB free]
  Total: 1 [<3.64 TiB] / in use: 1 [<3.64 TiB] / in no VG: 0 [0   ]

Following that, we used vgscan to search for all volume groups:

[email protected]:/home/ubuntu# vgscan
  /dev/sdc: open failed: No medium found
  Found volume group "ubuntu-vg" using metadata type lvm2

From these two commands, it was clear that the disk /dev/sda3 contained an LVM partition with the logical volume group name ubuntu-vg. That logical volume group held the server’s filesystem, and it was the place we needed to access to change the user’s password.

So, we used vgchange to change the attributes of the volume group and activate it like so:

vgchange -a y;
[email protected]:/home/ubuntu# vgchange -a y
  /dev/sdc: open failed: No medium found
  /dev/sdc: open failed: No medium found
  1 logical volume(s) in volume group "ubuntu-vg" now active

Using lvscan, we were able to list all logical volumes in all volume groups and verify that we activated the volume group of interest successfully.

[email protected]:/home/ubuntu# lvscan
  /dev/sdc: open failed: No medium found
  ACTIVE            '/dev/ubuntu-vg/ubuntu-lv' [200.00 GiB] inherit

After these steps, we were ready to reset the password of the user finally. We continued to mount the logical volume group like any other disk on the /mnt folder:

mount /dev/ubuntu-vg/ubuntu-lv /mnt/;

Then, we used chroot to change the apparent root directory for the currently running process (and its children). This command allowed our terminal to work inside the logical volume as if we had booted the server OS itself.

chroot /mnt/;

Finally, using the passwd command, we changed the user password as so:

passwd -S bob;

To clean up, we exited the chroot environment:


Then, we unmounted the logical volume group:

umount /mnt;

And finally, we set the active flag of the volume group to no.

vgchange -a n;

After the above steps, we had safely applied all changes, so we rebooted the machine using its hard drive.

Testing free Text to Speech engines on Ubuntu GNU/Linux

Recently, we decided to test a few free text to speech engines (TtS) on GNU/Linux. We were curious on what the current capabilities that are available as we wanted to create a few videos. To play a bit, we tested espeak, festival and pico. For this reason, we created a text file (called text.txt) and added the following content to it:

I triple E has a lot of scholarships, awards, and opportunities, but it doesn't have a centralized site where members can quickly identify the right ones.
Many problems arise as a result of the lack of this platform.
One crucial issue is that many people are unaware of specific opportunities until it is too late. Many projects are squandered each year because there is insufficient knowledge about these opportunities, resulting in low participation.
Another critical difficulty is having to start over with each application. Many people find it frustrating, and it prevents them from doing so.
The lack of real-time support to answer issues while an applicant is applying is critical, leading to discouragement and abandonment of the application process.
Providing references is a topic that many individuals are uncomfortable with. They are embarrassed to seek references that need to learn new systems and maybe answer the same questions posed in other ways.

Our solution is utilizing the Collabratec platform and storing all of these opportunities there:
Collabratec already has numerous key capabilities in place, lowering development costs.
Each application may have its own community or working group where an applicant can seek special clarifications or support. Collabratec will save money on development by repurposing existing technology. It will also give such features a new purpose.
Through those working groups, experienced members can share their knowledge and potentially coach applicants during their application process. Many members would be willing to help others attain their objectives, especially after they've gone through the process and understand the frustrations others are experiencing. We could utilize badges to reward individuals who aid others and those who apply for these possibilities, which is a frequent practice in Collabratec to make certain members stand out. This approach will assist members in getting to know one another and expanding their network outside their geographic zones, resulting in a genuinely global I triple E experience.
People who create opportunities can utilize the I triple E profile of a user to pre-populate elements of their application. As a result, the applicants' effort will be reduced because they will only fill in the questions related to that particular opportunity.
Without any additional work, the system may reuse earlier references. Assume that a reference has to be updated or validated to ensure that it is still valid. In that situation, the system may send an automatic notification to that person, asking them to approve, alter, or delete their earlier contribution.
Because users can readily share each application form and the corresponding working group information, Collabratec's capabilities as a social network will significantly enhance each opportunity's reach and all related documents, public comments, and discussions.


We started off with espeak and we used the following commands to test it:

# Command to install espeak;
sudo apt install espeak;
# Command that reads the text.txt file creates an audio file from its content.
espeak -f text.txt -w espeak.wav;

The result from espeak is below:

espeak definitely does not sound human-like. It is a fun tool if you need to create an audio file that sounds robotic! In our case, it was not a solution as we wanted to use a long text, listening to a robotic voice for a lot of time can be tiring.


After that, we tested the text2wave tool of festival as follows:

sudo apt install festival;
cat text.txt | text2wave -o festival.wav;

The results from festival/text2wave are the following:

festival does sound a bit better than espeak, it’s almost smooth but not quite. You can easily tell that this is a computer-generated voice.


Finally, we tested pico utilities. We set it up and used it as follows:

sudo apt install libttspico-utils;
pico2wave -l en-US -w test.wav "$(cat text.txt)";

The results of pico2wave were pretty good! Not perfect but still good! The voice is nearly human-like and fairly smooth. Below is the result of our test:

From the three utilities, pico was the most human-like and it fit our needs more. With this tool, we will be able to create certain videos with narration without being too annoying.

Other information

To create the videos, we used ffmpeg. As in the following commands, we combined the audio wave files with static images that were looped forever.

ffmpeg -loop 1 -i TtS-pico2wave.png -i test.wav -c:a aac -c:v libx264 -pix_fmt yuv420p -shortest TtS-pico2wave.mp4;
ffmpeg -loop 1 -i TtS-espeak.png -i espeak.wav -c:a aac -c:v libx264 -pix_fmt yuv420p -shortest TtS-espeak.mp4;
ffmpeg -loop 1 -i TtS-festival.png -i festival.wav -c:a aac -c:v libx264 -pix_fmt yuv420p -shortest TtS-festival.mp4;