AutoKeras is built on Keras framework and Google automl is built on tensorflow. So on which framework does the h2o build ? or is it made from scratch ?
H2O's AutoML (first released in 2017) is built on the Open Source H2O distributed machine learning stack. It's built on Java but it has Python and R bindings, so you can use those languages.
Related
Trying to load ssdlite v2 model with intel inference engine on raspberry Pi 3. For this, I need to build opencv-4.0 with Intel Inference API engine. I am unable to build open CV using CMAKE with -DWITH_INF_ENGINE=ON ^
-DENABLE_CXX11=ON flags. Does anyone know how to do it?
First you need install or compile this engine, see you Intel OpenVINO documentation.
Opencv has dnn module which they introduced in 3.3 version. Earlier it was in opencv-contrib only.
I am not able to find out which caffe version and repo , opencv use for dnn module implementation?
caffe has various repos and versions like
1. BVLC Caffe
2. Nvidia Caffe
3. weiliu89/caffe for SSD
I am facing issued while using Nvidia caffe trained model when importing in opencv 3.4 dnn module.
#sagar.bot05, OpenCV doesn't depend on Caffe library. It can import models trained in Caffe and run them using own implementation. Networks from the most known Caffe's forks such SSD, Faster-RCNN frameworks and Nvidia's Caffe also supported.
I builded OpenCV 3.4 with CUDA, Intel TBB, Intel MKL in VS 2015 like this
When I run traincascade for classifier training, 100% of CPU is used, but the GPU is not used 0%.
Does the OpenCV-traincascade use the functions of the library CUDA for calculations on the GPU?
No
https://devtalk.nvidia.com/default/topic/951477/jetson-tk1/are-tools-like-opencv_traincascade-gpu-accelerated-in-opencv4tegra-/
To train cascade is meant to be used as an offline tool to create a cascade detector, you should try using a powerful desktop system for training, and then use OpenCV4Tegra on Jetson to run the trained classifier on the device.
There is a CUDA accelerated version of the cascade training tool available in the Ubuntu Desktop x64 version of the OpenCV4Tegra package, which can be downloaded here:
http://developer.nvidia.com/embedded/dlc/l4t-24-1-opencv4tegra-ubuntu
Which sums it up more eloquantly than I could.
Also No - answered here
In Summary
The opencv_traincascade functionality is not developed using GPU code, for reasons I do not know. This tool however is meant to be run offline, and then the results from this training used in your actual detection run-time code which can be GPU optimised.
I cloned tensorflow (version 1.2) from github repo. I am having some problem with grpc in distributed tensorflow. Basically I want to compile tensorflow (1.2) with latest grpc library.
I downloaded and compiled grpc library so I have library files and the headers. How do I instruct bazel to take this already compiled library files and headers, while building tensorflow.
There's no easy way. If I were to do this, I'd clone tensorflow, and update tensorflow/workspace.bzl to pull newer grpc version, or replace patched_http_archive with new_local_repository or new_http_archive. I assume you'd need to apply the patch from third_party/grpc. Then you should be able to build tensorflow using default procedure: ./configure, and bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package.
Quickly scanning through tensorflow BUILD files it seems tensorflow uses third_party dependencies of grpc (e.g. nanopb), so having a compiled library and headers wouldn't be enought anyway. If you insisted on using your prebuilt grpc, you'd need to provide these dependencies, and update BUILD files and WORKSPACE file. Bazel doesn't have this feature 'replace repository with its outputs' built-in yet.
I have created a HOG based human detection code using OpenCV. While the code is runs well on my Windows 7 system, I now need to port this on a WinCE07 platform. How do I compile OpenCV for WinCE07 platform.
OpenCV site has instruction to build OpenCV from source, see "Installation in Windows - Installation by Making Your Own Libraries from the Source Files".
Basically you need:
A suitable development environment for Windows CE (some years ago the environment was eMbedded Visual C++ 4.0, I do not know what Microsoft has now).
The source code of OpenCV.
CMake, it is the program for managing the build. CMake will create the projects and solutions files. Usually CMake automatically recognize your development environment.