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TensorFI is a fault injection framework for injecting both hardware and software faults into applications written using the TensorFlow framework. You can find more information about TensorFI in the paper below.
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***TensorFI: A fault injector for TensorFlow applications

TensorFI is a fault injector for TensorFlow applications written in Python. It instruments the Tensorflow graph to inject faults at the level of individual operators. Unlike other fault injection tools, the faults are injected at a higher level of abstraction, and hence can be easily mapped to the Tensorflow graph. Further, the fault injector can be configured though a YAML file.

This file constains installation instructions and dependencies. For details of how TensorFI works, please refer to the Manual file

0.Supported/Tested Platforms

TensorFI has been tested on the following platforms and installations:

  1. Ubuntu Linux (v 4.10) with TensorFlow (v. 1.4.1)
  2. Ubuntu Linux (v 4.4) with TensorFlow (v. 1.5)
  3. Ubuntu Linux (v 16.4) with TensorFlow (v. 1.10.0)
  4. MacOSX (v10.12 and v10.13) with TensorFlow (v 1.5 and v 1.10.0)

In general, any UNIX platform should work. We haven't tested it on Windows though.


  1. TensorFlow Framework (v 1.0 or greater)

  2. Python (v2.7 or greater, but not Python 3)

  3. PyYaml (v3 or greater)

  4. SciKit module in Python

  5. Sklearn module in Python

  6. enum module in Python

  7. numpy package (part of TensorFlow)

  8. (Optional) matplotlib package in Python

  9. (Optional) tkinter package in Python

2. Installation instructions

NOTE: The easiest way to install TensorFI is to use the provided script which will install the Anaconda package manager and also install the required dependencies, and set the appropriate paths. Below, we detail the actions you need to take for a manual install if you don't want to use (to use an existing TensorFlow installation, for example), or if you have any trouble running it.

A. To install, first install PyYaml v3 and above. For example, you would type:

pip install PyYaml

B. Install TensorFlow. You don't need to install the GPU version if you don't want to. Make sure you install TensorFlow for Python 2.7, not 3. TensorFlow installation instructions can be found at:

C. install the scipy and sklearn modules. On both Ubutnu and MacOS, type:

pip install scipy

pip install sklearn

D. Set your PYTHONPATH to the TENSORFIHOME where TENSORFIHOME is where you've installed TensorFI (This assumes you're using bash as your shell).


E. Run the test files by going to the TENSORFIHOME, directory and running in Tests. All the tests should pass if your installation was successful. The script will also check if you have all of the above packages installed correctly.


NOTE: The runAll script will create new subdirectories in the TENSORFIHOME directory (faultLogs and stats), so make sure you have the permissions to do so when you run it (or you can manually create the directories). Also, make sure the python interpreter name is correct in the script (it defaults to python) - if not, change it.

F. If you want a visual demo of TensorFI, try running from TensorFIHOME directory.

python Tests/

You will see original images (without fault injection) and the faulty images (with fault injection) for different fault probabilities ranging from 0.01 to 1.0 in the images. The images are saved under the Tests/Images sub-directory in PNG format (make sure this directory exists first).

Another visual demo is when you run This will also show you the original and faulty images.

python Tests/

Yet another visual demo is when you run GANs (Generative Adversarial Networks). The images with and without faults are saved under the Tests/Images sub-directory.

python Tests/

NOTE: Both use the matplotlib and the python-tk libraries so you'll need to install the libraries for the demo.

If you find TensorFI useful, please cite the following paper: "TensorFI: A configurable fault injector for TensorFlow Applications", Guanpeng Li, Karthik Pattabiraman and Nathan DeBardeleben, 8th IEEE International Workshop on Software Certification (WoSoCER), 2018.

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