With GPU on fia

.github/workflows/build-win.yml

@@ -40,10 +40,7 @@ jobs:
                      git clone https://github.com/Microsoft/vcpkg.git
                      cd vcpkg
                      .\bootstrap-vcpkg.bat
-                     .\vcpkg.exe install cppzmq:x64-windows
-                     .\vcpkg.exe install qt:x64-windows
-                     .\vcpkg.exe install boost:x64-windows
-                     .\vcpkg.exe install qwt:x64-windows
+                     .\vcpkg.exe install qt:x64-windows boost:x64-windows qwt:x64-windows
             # - name: Install Qt
             #   uses: jurplel/install-qt-action@v3
             #   with:

INSTALL.md

@@ -14,7 +14,7 @@ do so.
 
 Build dependencies:
 - Tools: cmake, ninja, C++ (tested with gcc and clang), gawk, NLTK,
-- Libraries and development packages for : boost , Qt5 and Qwt.
+- Libraries and development packages for : boost , Qt6 and Qwt.
 
 Optional dependencies:
 - python3:
@@ -28,57 +28,14 @@ Named Entity Recognition and soon parsing too);
 
 Under Ubuntu, most of these dependencies are installed with the following packages:
 ```
-
-sudo apt-get clean && sudo apt-get update && sudo apt-get install -y -qq locales unzip bash \
-coreutils apt-utils lsb-release git gcc g++ build-essential make cmake cmake-data \
-curl python3-nltk gawk wget python3 python3-pip ninja-build qtbase5-dev-tools \
-libqt5xmlpatterns5-dev libqt5qml5 qtdeclarative5-dev qml-module-qtquick-extras \
-qml-module-qtquick-controls qml-module-qtquick-layouts qml-module-qtquick2 \
-libtre-dev libboost-all-dev nodejs npm qml-module-qtquick-controls2 qml-module-qtquick-dialogs \
-qml-module-qtquick-privatewidgets qml-module-qtquick-scene3d qml-module-qtquick-templates2 \
-qml-module-qtquick-virtualkeyboard qml-module-qtquick-window2 qml-module-qtquick-xmllistmodel \
-libicu-dev libeigen3-dev dos2unix python-is-python3 nvidia-cuda-toolkit nvidia-cudnn \
-software-properties-common
-
-```
-
-Install python packages necessary to use the language resources install script:
-```bash
-pip3 install --user arpy requests tqdm
+sudo apt-get update && apt-get install -y locales unzip bash coreutils apt-utils lsb-release git gcc g++ build-essential make cmake cmake-data curl python3-nltk gawk wget python3 python3-pip ninja-build qt6-base-dev qt6-base-dev-tools libqt6concurrent6 qml6-module-qtqml qt6-tools-dev libqt6concurrent6 qt6-base-dev-tools qt6-declarative-dev qt6-declarative-dev-tools qt6-multimedia-dev libtre-dev libboost-all-dev nodejs npm libicu-dev libeigen3-dev dos2unix python-is-python3 nvidia-cuda-toolkit nvidia-cudnn python3-arpy python3-requests python3-tqdm
 ```
 
 qhttpserver can be downloaded and installed from
 https://github.com/aymara/qhttpserver/releases
 
 svmtool++ can be downloaded and installed from https://github.com/aymara/svmtool-cpp/releases
 
-To compile SVMTool models, you also need svm_light:
-```bash
-mkdir svm_light && cd svm_light
-wget http://osmot.cs.cornell.edu/svm_light/current/svm_light.tar.gz
-tar xvzf svm_light.tar.gz
-dos2unix *.c
-cat<<EOF > svm_light.patch
---- svm_hideo.c.s     2021-12-16 11:34:23.606959575 +0000
-+++ svm_hideo.c       2021-12-16 11:34:33.614829980 +0000
-@@ -31,7 +31,7 @@
-
- /* Common Block Declarations */
-
--long verbosity;
-+extern long verbosity;
-
- # define PRIMAL_OPTIMAL      1
-
- # define DUAL_OPTIMAL        2
-
-EOF
-patch <svm_light.patch
-
-make
-sudo cp svm_classify svm_learn /usr/bin
-```
-
 For TensorFlow, we use a specially compiled version. It can be installed with
 our ppa in Ubuntu versions starting from 18.04:
 
@@ -97,7 +54,6 @@ treebank corpus available for fair use in the NLTK data. To install, please
 refer to http://nltk.org/data.html. Under Ubuntu this can be  done like that:
 
 
-
 ```bash
 npm install -g json
 sudo sed -ie "s|DEFAULT_URL = 'http://nltk.googlecode.com/svn/trunk/nltk_data/index.xml'|DEFAULT_URL = 'http://nltk.github.com/nltk_data/'|" /usr/lib/python3/*/nltk/downloader.py

continuous_integration/Dockerfile-ubuntu22.04-fia

@@ -27,7 +27,7 @@ ARG USE_TENSORFLOW="false"
 ARG CFLAGS=""
 ARG CXXFLAGS=""
 ARG TORCH_CUDA_ARCH_LIST="6.1"
-ARG LIBTORCH_DL="download_libtorch.sh" # Use download_libtorch_gpu.sh for GPU build
+ARG LIBTORCH_DL="download_libtorch_gpu.sh" # Use download_libtorch_gpu.sh for GPU build
 
 
 RUN /bin/bash -c "if [ \"$USE_TENSORFLOW\" = true ] ; then apt-get install -y software-properties-common; add-apt-repository -y ppa:limapublisher/ppa; apt-get update; apt-get install -y libtensorflow-for-lima-dev ; fi"