Run performance tests on a Real-time system without leaving the office

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Here at Greeneye, our system is composed of many processes and jobs responsible for the entire pipeline, from taking pictures in Real-time to deciding whether we want to spray the pictured spot.

The pipeline itself consists of multiple hardware components and physical constraints - from the tractor itself, which carries the entire system, through the speed sensor for detecting and sampling speed, the cameras, nozzles, and more.

Step 1: Get rid of hardware dependencies.

Running performance tests for a hardware-dependent system will require setting up a complicated, expansive, and not-scalable environment.
The second step in our progress was to design our system to be hardware-injected. What is that mean?
Imagine your environment includes a speed sensor. Whenever the speed sensor indicates a positive speed, the system starts working.

Mocking the system’s speed will grant us two essential benefits:

  1. Starting the system without having to actually drive a vehicle.

  2. Test and benchmark your system at a dynamic speed.

Implementing the hardware-injection will be as described in figure 2.
A configurable variable will determine whether we will use the real or mock hardware. The mocked hardware return-value will be configurable as well.
Both real and mock hardware write their output to the exact same shared-object, it prevents the hardware consumers from being affected by setting real/mock hardware source.

“It does not matter how intelligent you are, if you guess and that guess cannot be backed up by experimental evidence–then it is still a guess.”

-Richard Feynman

One of the most important principles in developing a performant system is the ability to measure and monitor your performance. Metrics, benchmarking and profiling representation will be the best indicators for your development efforts.

In order to make our system testable, we marked few goals:

  1. Test the influence of a new detection/classification model on the prediction accuracy.

  2. Benchmark every new model.

  3. Test every model with a large set of parameters and thresholds, in order to find the set that maximizes our prediction accuracy. i.e: Hypterparameter tunning.

  4. Profiling our system’s bottlenecks.

  5. Be able to run the tests on a custom independent cloud agent.

Before running into implementation, we encountered two fundamentals issues:

  1. The system must be configurable, in order to be able to inject different and multiple sets of configurations without changing the code itself.

  2. Our system depends on many hardware components. we must get rid of these dependencies to be able to run performance tests at scale.

Step 2: Make your system configurable

The first step towards running performance tests was converting all configurable variables to be injected from a configuration file.

As described in figure 1, for giving us the ability to define a different and meaningful set of parameters for every test, we use a configuration file and a runtime process to handle edit requests for that configuration file.

figure 1.

figure 1.

figure 2.

figure 2.

Step 3: Simulating the system

Having the ability to set configurable variables at runtime and mock the hardware dependencies, the last piece of the puzzle will be completed by orchestrating the sets of configuration and test their outputs.
Implementing the simulation process will be described in figure 3.
The simulator process loads the desired test configurations. For every test permutation, it will simulate the system and save the relevant output and configuration for a unique location.
After simulating the system for all the different sets of configurations, the only thing left is to post-process and test the output corresponding to the attached configuration.

figure 3.

figure 3.

Step 4: Tests and Visualization

The last part of our process will be handling the system’s output and opening a window into our system’s performance. Having multiple sets of results, including predictions/benchmarks/logs data and the specific configuration that produced those performances, can be leveraged quickly to find the best configuration for our system. 

Here at Greeneye, we use the ClearML platform to analyze, research, and visualize our tests and performances. A cool feature in ClearML is the ability to compare different runs. Figure 4 shows a Comparison between multiple sets of configuration outputs, helping us choose the best detection models for our system. Using the configuration attached for each experiment, we can quickly reproduce the best parameters for our system and use them in our production environment. Another bonus in ClearML is that each run saves not only the output but also the input i.e, the configuration file that produced that results

figure 4

figure 4

In addition, we used the simulator to efficiently operate a benchmarking test. The simulator sends different speed values for our system and analyzes the performances for every single speed. That helped us discovering our system’s weaknesses, points of failure, and limits. At figure 5, you can see a graph of dropped frames as a function of the tractor’s speed. Thanks to step 2, The tractor is still parking inside the garage, the tests are running on the cloud.

Figure 5.

Figure 5.

Summary

Developing a Real-time system will always require testing its performance. To do so, and do it fast, elegant, scalable, and without requiring many resources, we must design our code to be configurable and capable of mocking hardware dependencies. Keeps those in mind will give you one of the best gifts a complex-system engineer can ask- the ability to run your system independently, in a lightweight mode, and configured exactly the way you want it.

Livne Rosenblum,
Tractor Team Lead @ Greeneye Technology