Collective Knowledge solution

This portal has been archived. Explore the next generation of this technology.

Object detection • TensorFlow CUDA • KITTI 0009 • Linux • self-driving car

solution:demo-obj-detection-kitti-0009-tf-cuda-linux (v1.0.0)

Portable solution description

Install and run this solution on your platform in several simple steps. Our goal is to make it simpler to reproduce results from research papers, participate in crowd-benchmarking, and enable "live" papers.

What is the portable CK solution: docs
Automation framework: CK
CK repository: A collection of all AI and ML workflows and packages in the CK format
Benchmarking pipeline (build, run, validate): program:squeezedet
Workflow CLI and Python API (compile, run, benchmark, validate): module:program
Workflow dependencies (model, dataset, framework): View

Don't hesitate to get in touch if you encounter any issues or would like to discuss this community project!

Check the prerequisites for your system

Install manually from the command line (to be automated in the future):

You need to install CUDA 10.0! CK will automatically detect them.

Install cBench (docs)

Install cBench from the command line (a small Python library to manage CK solutions):

pip3 install cbench
 or 
python3 -m pip install cbench
 or
pip install cbench

Note that you may need to add the --user flag if you install in your user space, i.e. "python3 -m pip install cbench --user"

Init this solution with the portable workflow on your machine

Run manually from your command line (cBench will attempt to automatically adapt this workflow to your system - you may need to press Enter several times to select default answers for some questions):

cb init demo-obj-detection-kitti-0009-tf-cuda-linux

Start cBench (status: disconnected)

cb start

Run this workflow locally

or start local run manually from the command line:

cb run demo-obj-detection-kitti-0009-tf-cuda-linux

  # Note that the following CK program pipeline will be executed:
  ck compile program:squeezedet --cmd_key=use_continuous --speed
  ck run program:squeezedet --cmd_key=use_continuous

Live test of this workflow via your browser

Successfully tested configuration

Host OS: linux-64 (Ubuntu 18.04.3 LTS)
Target OS: linux-64 (Ubuntu 18.04.3 LTS)
Target machine: LENOVO ThinkPad T470p (20J6CTO1WW)
Target CPU: Intel(R) Core(TM) i5-7300HQ CPU @ 2.50GHz
Target CPUs:

Python version for virtual env: 3.6.8

Dependencies

Reused CK components

These components are automatically installed by cBench from this portal:

pip install opencv-python

ck install package --tags=dataset,object-detection,kitti-drive-0009

ck install package --tags=tensorflow,demo,squeezedet

ck install package --tags=model,squeezedet

ck install package:lib-tensorflow-1.14.0-cuda

Comments

Please log in to add your comments!

If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!