New Addition To TurtleBot 3 Manual: TensorFlow

TensorFlow LogoBeing on the leading edge carries its own kind of thrill. When I started looking over the TurtleBot 3 manual I noticed the index listed a “Machine Learning” chapter. As I read through all the sections in order, I was looking forward to that chapter. Sadly I was greatly disappointed when I reached that chapter and saw it was a placeholder with “Coming Soon!”

I didn’t know how soon that “soon” was going to be, but I did not expect it to be a matter of days. But when I went back to flip through the material today I was surprised to see it’s no longer a placeholder. The chapter got some minimal content within the past few days, as confirmed by Github history of that page. Nice! This is definitely a strength of an online electronic manual versus a printed one.

So it’s no longer “Coming Soon!” but it is also by no means complete. Or at least, the user is already assumed to understand machine learning via DQN algorithms. Since I put off my own TensorFlow explorations to look at ROS, I have no idea what that means or how I might tweak the parameters to improve results. This page looks especially barren when compared to the mapping section, where the manual had far more information on how the algorithm’s parameters can be modified.

Maybe they have plans to return a flesh it out some more in the future, which would be helpful. Alternatively, it’s possible that once I put some time into learning TensorFlow I will know exactly what’s going on with this page. But right now that’s not the case.

Still, it’s encouraging to know that there are documented ways to use TensorFlow machine learning algorithms in the context of driving a robot via ROS. I look forward to the day when I know enough to compose all these pieces together to build intelligent robots.

Installing TensorFlow: Adventures in Version Matching

[UPDATE: This process has grown a little easier.]

One of the big motivations to get a NVIDIA 1060 GPU is to start playing with deep learning neural networks, using tools like Google’s TensorFlow. A quick skim of the installation instructions didn’t look too bad, but once I got hands-on it was clear version mismatches were a recurring issue for running the TensorFlow binaries.

Ubuntu 16.04.4 Operating System

Since I had only a fresh installation of Ubuntu, it was not a big loss (aside from time) to restart from scratch. Goodbye, new hotness Ubuntu 18.04 LTS, we’re leaving you to return to the known quantity Ubuntu 16.04.4 LTS.

NVIDIA Graphics Driver 390

Once Ubuntu 16.04 was up and running with all its latest operating system updates, we proceed to the only part of TensorFlow setup that doesn’t require a specific (old) version: the NVIDIA graphics drivers. Going straight to NVIDIA’s website found this suggestion:

Note that many Linux distributions provide their own packages of the NVIDIA Linux Graphics Driver in the distribution’s native package management format. This may interact better with the rest of your distribution’s framework, and you may want to use this rather than NVIDIA’s official package.

Hmm… OK. Checking Ubuntu’s user support site AskUbuntu for NVIDIA drivers found this set of instructions to install drivers from a graphics drivers project. Staffed by volunteers that repackage NVIDIA’s raw binaries into Ubuntu installer-friendly “personal package archives” (PPA) infrastructure. Following the instructions, I told my installation of Ubuntu about them via:

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

Then I had to deviate from the directions because sudo apt-get install nvidia-driver-390 returned an error E: Unable to locate package nvidia-drivers-390.  A little searching using the apt search command found the correct name in the PPA:

sudo apt-get install nvidia-390

After driver installation, the graphics driver project page recommends that I test my system by running the Phoronix graphics test suite. It gave my system a good workout and provided some pretty visuals while the tests ran. After the tests were complete, I was happy to upload my system information to their database of installation successes.

NVIDIA CUDA Toolkit 9.0

As of this writing, the TensorFlow binaries released by Google requires NVIDIA CUDA toolkit version 9.0. The latest version of CUDA toolkit is 9.2 but sadly it will not work with TensorFlow binaries, so we have to go find the older version 9.0 in NVIDIA’s past version archives. Again we have to deviate from instructions because sudo apt-get install cuda would install the latest 9.2. We have to be explicit about the older version with sudo apt-get install cuda-9.0.

After the toolkit was installed, follow instructions to compile and run the tools deviceQuery and bandwidthTest to verify installation.


TensorFlow instruction specifies cnDNN v7. The latest version is 7.1.4 as of this writing and based on other experience probably wouldn’t work, so off we go into the old version archives again. There we will find two versions corresponding to CUDA 9.0: 7.0.4 and 7.0.5. Which one to use?

The hint comes from elsewhere in TensorFlow installer documentation talking about an optional component, NVIDIA TensorRT 3.0. The instructions there made a reference to so that swayed the vote on which version to use.

After the SDK was installed, follow instructions to compile and run the tool mnistCUDNN to verify installation.


After all those pre-requisites were satisfied, it was finally time to install TensorFlow itself! I decided to go with Python 3 for my TensorFlow experimentation, and followed recommendation of virtualenv, before installing the GPU-enabled variant of TensorFlow.

pip3 install --upgrade tensorflow-gpu

Finish Line

After bumping head in the dark against version mismatch error for hours, it felt great to finally cross the finish line. Hello, TensorFlow!

Hello, TensorFlow