Frequently Asked Questions

  1. Is Python3.6 a strict requirement for installation?

    Yes, our HPVM python packages require python version = 3.6. If you don’t have a Python3.6 on your system, we encourage using the provided env.yaml conda environment.

  2. What is a “target device” or the “profiling stage”? Why does the tutorial seems to suggest building HPVM on a second device?

    HPVM is capable of predictive approximation tuning which, due to its computational cost, is often done on a powerful computer, like a server, but the selected approximations are usually used to speedup your application on a less powerful device (the target device, such as an edge device). The profiling stage (using hpvm-profiler) is necessary so that the real speedup of approximations are measured, and this is also done on the target device. See our ApproxTuner paper for more details on this.

    Currently, HPVM must be built on both the server and the target device for this purpose. We will achieve better server/edge separation of HPVM in the following releases, so that only the necessary part of code are built on each device.

  3. What is the expected speedup with approximations on my target device?

    The approximation implementations in HPVM are currently only optimized for Nvidia Tegra TX2. The routines may not provide the same speedup on other hardware devices – though systems with similar hardware specifications may exhibit similar performance. We are working on providing speedups across a wider range of devices.

  4. Why doesn’t the conda environment / Python packages installation work on Jetson boards?

    You may be seeing errors like


    or other errors indicating pytorch, torchvision or other packages cannot be installed, because these packages are not prebuilt for ARM CPU on PyPI.

    The simplest solution is not to install HPVM frontends and autotuner; see this for how to do so. The job of these packages are best left to a server machine.

  5. What to do when running into “CUDA out of memory” errors?

    When the Keras/PyTorch frontends generates code, they accept a “batch size” parameter, which decides the batch size at which the DNN inference runs. You may need to reduce batch size when encountering out of memory errors.

  6. How many autotuning iterations should I use with PredTuner package in HPVM?

    The number of tuning iterations required to achieve good results varies across benchmarks and should be figured out on a per-benchmark basis. For the included 10 CNNs, we recommmend using at least 10K iterations.

  7. Does this release support combining HPVM tensor and non-tensor operations in a single program?

    Currently we do not support tensor and non-tensor code in the same application. We will support this feature in the next release.

  8. Does this release support object detection models?

    Currrently, HPVM doesn’t support object detection models, due to the limited number of operators supported in the tensor library hpvm-tensor-rt. We will add support for more operators in the next release.