[arch-commits] Commit in root/repos (11 files)

Konstantin Gizdov kgizdov at archlinux.org
Thu Jul 23 20:24:25 UTC 2020


    Date: Thursday, July 23, 2020 @ 20:24:24
  Author: kgizdov
Revision: 665091

archrelease: copy trunk to community-testing-x86_64

Added:
  root/repos/community-testing-x86_64/
  root/repos/community-testing-x86_64/PKGBUILD
    (from rev 665090, root/trunk/PKGBUILD)
  root/repos/community-testing-x86_64/ROOFIT_LICENSE
    (from rev 665090, root/trunk/ROOFIT_LICENSE)
  root/repos/community-testing-x86_64/adapt_tmva_to_support_cudnn8.patch
    (from rev 665090, root/trunk/adapt_tmva_to_support_cudnn8.patch)
  root/repos/community-testing-x86_64/jupyter_notebook_config.py
    (from rev 665090, root/trunk/jupyter_notebook_config.py)
  root/repos/community-testing-x86_64/nbman-for-arch.patch
    (from rev 665090, root/trunk/nbman-for-arch.patch)
  root/repos/community-testing-x86_64/root.pc.tpl
    (from rev 665090, root/trunk/root.pc.tpl)
  root/repos/community-testing-x86_64/root.xml
    (from rev 665090, root/trunk/root.xml)
  root/repos/community-testing-x86_64/settings-cuda.cmake
    (from rev 665090, root/trunk/settings-cuda.cmake)
  root/repos/community-testing-x86_64/settings.cmake
    (from rev 665090, root/trunk/settings.cmake)
  root/repos/community-testing-x86_64/thisroot.fail
    (from rev 665090, root/trunk/thisroot.fail)

------------------------------------+
 PKGBUILD                           |  281 ++++++++
 ROOFIT_LICENSE                     |   22 
 adapt_tmva_to_support_cudnn8.patch | 1130 +++++++++++++++++++++++++++++++++++
 jupyter_notebook_config.py         |    1 
 nbman-for-arch.patch               |  177 +++++
 root.pc.tpl                        |   12 
 root.xml                           |   14 
 settings-cuda.cmake                |  110 +++
 settings.cmake                     |  110 +++
 thisroot.fail                      |   12 
 10 files changed, 1869 insertions(+)

Copied: root/repos/community-testing-x86_64/PKGBUILD (from rev 665090, root/trunk/PKGBUILD)
===================================================================
--- community-testing-x86_64/PKGBUILD	                        (rev 0)
+++ community-testing-x86_64/PKGBUILD	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,281 @@
+# Maintainer: Konstantin Gizdov < arch at kge dot pw >
+# Contributor: Frank Siegert < frank.siegert at googlemail dot com >
+# Contributor: Scott Lawrence < bytbox at gmail dot com >
+# Contributor: Thomas Dziedzic < gostrc at gmail dot com >
+# Contributor: Sebastian Voecking < voeck at web dot de >
+
+pkgbase=root
+pkgname=('root' 'root-cuda')
+pkgver=6.22.00
+pkgrel=1
+pkgdesc='C++ data analysis framework and interpreter from CERN'
+arch=('x86_64')
+url='https://root.cern'
+license=('LGPL2.1' 'GPL' 'custom:University of California and Stanford University License')
+makedepends=(
+    'ccache'
+    'cern-vdt'
+    'chromium'
+    'cfitsio'
+    'cmake'
+    'cuda'
+    'cudnn'
+    'gcc-fortran'
+    'gcc9-fortran'
+    'git'
+    'go'
+    'libxml2'
+    'libmariadbclient'
+    'ocaml'
+    'ocaml-ctypes'
+    'openmp'
+    'openmpi'
+    'openssl'
+    'postgresql-libs'
+    'pythia8>=8.2.40-1'
+    'qt5-webengine'
+    'sqlite'
+    'unuran'
+    'vc'
+    'xrootd>=4.6.0-2'
+    'z3'
+)
+depends=(
+    'blas'
+    'desktop-file-utils'
+    'fcgi'
+    'fftw'
+    'ftgl'
+    'giflib'
+    'gl2ps'
+    'glew'
+    'graphviz'
+    'gsl'
+    'hicolor-icon-theme'
+    'intel-tbb'
+    'libafterimage'
+    'librsvg'
+    'libxpm'
+    'python'
+    'python-numpy'
+    'tex-gyre-fonts'
+    'unixodbc'
+    'xxhash>=0.6.5-1'
+    'zstd'
+)
+optdepends=(
+    'cern-vdt: Add a set of fast and vectorisable mathematical functions'
+    'chromium: Support for WebGUI'
+    'cfitsio: Read images and data from FITS files'
+    'libmariadbclient: MySQL support'
+    'libxml2: XML parser interface'
+    'openmp: Support OpenMP extensions in Minuit2'
+    'openmpi: Support OpenMPI extensions in Minuit2'
+    'openssl: OpenSSL support'
+    'postgresql-libs: PostgreSQL support'
+    'pythia8>=8.2.40-1: Pythia8 EG support'
+    'qt5-webengine: Support for WebGUI'
+    'sqlite: SQLite support'
+    'tcsh: Legacy CSH support'
+    'unuran: Support non-uniform random numbers'
+    'vc: Add types for portable and intuitive SIMD programming'
+    'xrootd: Support remote file server and client'
+    'z3: Suuport the Z3 theorem prover'
+)
+source=(
+    "https://root.cern.ch/download/root_v${pkgver}.source.tar.gz"
+    'ROOFIT_LICENSE'
+    'root.xml'
+    'root.pc.tpl'
+    'settings.cmake'
+    'settings-cuda.cmake'
+    'jupyter_notebook_config.py'
+    'nbman-for-arch.patch'
+    'thisroot.fail'
+    'adapt_tmva_to_support_cudnn8.patch'
+)
+sha512sums=('9e3c54bbc146b0abb0a2d960af380255ec59d0b3a11a4a97a2a25cb7ac567b07280c4eb48dddf99c1fa2e692881f6396a842ce125d3a253037e52f719739f01e'
+            'af8f178fc9df66997d5495b271e38adcd1636aab4c8fc994c6600c2496127829d831250d73d3fc229b02dfe49b9867d0be979beacb959f2f3a05351b8118a4a6'
+            '1fe6f4aa09d583d33f27cc766f4935510bb7ab6bbb8d4700baa1aaab92ea6c876500b67da1e4f6e0b510aa5616e4e193b860264b86925de85f2d9f558d75d5dc'
+            '3c81d255a17b902ffac0187af1752847036137e16641a88b17eef0d9c944e6f0d3c954bc93307d6270603f43f6c23f2e04f98dc7a68f9d076dbaa8006a2527d6'
+            '9ee5b6606dbd352608a2a4998344ca4026d677c86823e62fff615f6e84efcecdffc07a1e9182a356aa35035e7f35df5a107127722a6bad4b97d1f49cffebf5b9'
+            '7665bc8cbe79162e0b969b08802e1b7b2ed22ed8b1402d50cf194172a644f647dcaf0f5abb76f8b6007dfab8dbc811604479be826b345d8fd77edfb51032110b'
+            '1c905ee7a3f8f5f3f567d957f9be6b503a8631565d4d9b9bfea5e496ef86865c5a8be1a1f8c7842754029879cf0afd2465249f532a116cc43660aa2e460ae682'
+            '12814f50b7016bd86d3f91e0e31c052783a0c0fa72b7d6a072d3ae6f86c2437323d585e531235377ebbfdd9cb76abd7da84d9631de821151547f1d4b13417e69'
+            'ff555ac4db568affe139701907f86d919a2206f3e304f69dd317b756ea0904b5934d9364a524060778aa507809ce78448621619bb34039ba34c5a71af71a4a8c'
+            '2ae126795df4127c27a6287a1499bdb8b2bacb74cfbec17dabe378a5fb9fc7c755644e4090a4da1d0045bf5d4f542f06da827a0f48a5927ee8509874045f18b6')
+
+get_pyver () {
+    python -c 'import sys; print(str(sys.version_info[0]) + "." + str(sys.version_info[1]))'
+}
+
+prepare() {
+    local src
+    for src in "${source[@]}"; do
+        src="${src%%::*}"
+        src="${src##*/}"
+        [[ $src = *.patch ]] || continue
+        echo "  -> Applying patch $src..."
+        patch -Np1 -i "../$src" -d "${srcdir}/${pkgbase}-${pkgver}"
+    done
+
+    # specify some custom flags
+    # needed by vc to link properly
+    CUSTOM_CMAKE_FLAGS="-DTARGET_ARCHITECTURE:STRING=generic"
+    # make sure it finds python
+    CUSTOM_CMAKE_FLAGS+=" -DPYTHON_EXECUTABLE:PATH=/usr/bin/python"
+    # need to set install prefix like so
+    CUSTOM_CMAKE_FLAGS+=" -DINSTALL_PREFIX=/usr"
+    export CUSTOM_CMAKE_FLAGS
+
+    # update system flags
+    # don't let ROOT play around with lib paths
+    # the following is no longer necessary
+    # sed -i -e 's at SetLibraryPath();@@g' \
+    #     "${srcdir}/${pkgbase}-${pkgver}/rootx/src/rootx.cxx"
+    # now only depends on IS_RPATH_BUILD being set
+    # so pass it to GCC
+    export CPPFLAGS="${CPPFLAGS} -DIS_RPATH_BUILD=1"
+    # make sure pthread gets detected
+    CUSTOM_COMPILER_FLAGS="${CPPFLAGS} -pthread"
+    export CFLAGS="${CFLAGS} ${CUSTOM_COMPILER_FLAGS}"
+    export CXXFLAGS="${CXXFLAGS} ${CUSTOM_COMPILER_FLAGS}"
+    # do not link undefined
+    CUSTOM_COMPILER_FLAGS+=" -Wl,--no-undefined"
+    export LDFLAGS="${LDFLAGS} ${CUSTOM_COMPILER_FLAGS}"
+
+    # go flags for built-in clang
+    export CGO_LDFLAGS="${LDFLAGS}"
+    export GOFLAGS="-buildmode=pie -trimpath -modcacherw"
+
+    cp -r "${pkgbase}-${pkgver}" "${pkgbase}-${pkgver}-cuda"
+}
+
+build() {
+    ## ROOT
+    mkdir -p "${srcdir}/build"
+    cd "${srcdir}/build"
+
+    cmake -C "${srcdir}/settings.cmake" \
+        ${CUSTOM_CMAKE_FLAGS} \
+        "${srcdir}/${pkgbase}-${pkgver}"
+    make
+
+    ## ROOT with CUDA
+    mkdir -p "${srcdir}/build-cuda"
+    cd "${srcdir}/build-cuda"
+
+    CC=/usr/bin/gcc-9 \
+    CXX=/usr/bin/g++-9 \
+    cmake -C "${srcdir}/settings-cuda.cmake" \
+        ${CUSTOM_CMAKE_FLAGS} \
+        "${srcdir}/${pkgbase}-${pkgver}-cuda"
+    make
+}
+
+_package() {
+    local bld_dir="${srcdir}/${1}"
+    cd "${bld_dir}"
+
+    make DESTDIR="${pkgdir}" install
+
+    # fix missing hardlinks for genreflex and rootcint
+    cd "${pkgdir}"/usr/bin
+    ln -f rootcling rootcint
+    ln -f rootcling genreflex
+    cd "${bld_dir}" # go back
+
+    # fix python env call
+    sed -e 's/@python@/python/' -i "${pkgdir}/usr/lib/root/cmdLineUtils.py"
+
+    # try to deal with weird PyROOT, PyMVA and JupyROOT stuff
+    rm -rf "${pkgdir}/usr/lib/root/__pycache__"
+    local _pyver=$(get_pyver)
+    local _pydir="${pkgdir}/usr/lib/python${_pyver}/site-packages"
+    install -d "${_pydir}"
+    find "${pkgdir}/usr/lib/root" -maxdepth 1 -mindepth 1 \( -iname "*py*" -or -name "*Js*" \) \
+            ! \( -name "*EGPythia8*" -or -iname "*.rootmap" -or -iname "*.pcm" \) -print0 | while read -rd $'\0' _lib; do
+        _base=$(basename "${_lib}")
+        ln -sf "/usr/lib/root/${_base}" "${pkgdir}/usr/lib/python${_pyver}/site-packages/${_base}"
+    done
+
+    # recompile pycache to strip $pkgdir from embedded paths
+    python -m compileall -d "/usr/lib/python${_pyver}" \
+        "${pkgdir}/usr/lib/python${_pyver}"
+    python -O -m compileall -d "/usr/lib/python${_pyver}" \
+        "${pkgdir}/usr/lib/python${_pyver}"
+
+    # icon, shortcut and mime
+    install -Dm644 "${srcdir}/${pkgbase}-${pkgver}/icons/Root6Icon.png" \
+        "${pkgdir}/usr/share/icons/hicolor/48x48/apps/root.png"
+    install -Dm644 "${srcdir}/${pkgbase}-${pkgver}/etc/root.desktop" \
+        "${pkgdir}/usr/share/applications/root.desktop"
+    echo 'Icon=root.png' >> "${pkgdir}/usr/share/applications/root.desktop"
+    install -Dm644 "${srcdir}/root.xml" \
+        "${pkgdir}/usr/share/mime/packages/root.xml"
+
+    # use a file that pacman can track instead of adding directly to ld.so.conf
+    install -d "${pkgdir}/etc/ld.so.conf.d"
+    echo '/usr/lib/root' > "${pkgdir}/etc/ld.so.conf.d/root.conf"
+
+    # create pkg-config file
+    local _prefix _exec_prefix _bindir _libdir _incdir _pkg_ver _libs _cflags _requires
+    _prefix="$("${pkgdir}"/usr/bin/root-config --prefix)"
+    _exec_prefix="$("${pkgdir}"/usr/bin/root-config --exec-prefix)"
+    _bindir="$("${pkgdir}"/usr/bin/root-config --bindir)"
+    _libdir="$("${pkgdir}"/usr/bin/root-config --libdir)"
+    _incdir="$("${pkgdir}"/usr/bin/root-config --incdir)"
+    _pkg_ver="$(sed -n 's,.*ROOT_RELEASE *\"\(.*\)\".*,\1,p' < "${pkgdir}"/usr/include/RVersion.h)"
+    _libs="$("${pkgdir}"/usr/bin/root-config --libs)"
+    _cflags="$("${pkgdir}"/usr/bin/root-config --cflags)"
+    printf -v _requires '%s,' "${depends[@]}"
+    cp "${srcdir}/root.pc.tpl" "${bld_dir}"/
+    sed -e "s at _PREFIX@${_prefix}@" -e "s at _EXECPREFIX@${_exec_prefix}@" \
+        -e "s at _LIBDIR@${_libdir}@" -e "s at _INCDIR@${_incdir}@" \
+        -e "s at _PKGVERSION@${_pkg_ver}@" -e "s at _LIBRARIES@${_libs}@" \
+        -e "s at _CFLAGS@${_cflags}@" -e "s at _UPSTREAM_URL@${url}@" \
+        -e "s at _REQUIRES@${_requires}@" \
+        -i "${bld_dir}/root.pc.tpl"
+    install -Dm644 "${bld_dir}/root.pc.tpl" "${pkgdir}/usr/lib/pkgconfig/root.pc"
+
+    # install all licenses & docs
+    install -d "${pkgdir}/usr/share/licenses/roofit"
+    install "${srcdir}/ROOFIT_LICENSE" "${pkgdir}/usr/share/licenses/roofit/LICENSE"
+    install -d "${pkgdir}/usr/share/licenses/${pkgname}"
+    ln -s '/usr/share/doc/root/LICENSE' "${pkgdir}/usr/share/licenses/${pkgname}/LICENSE"
+    for fold in fonts js; do
+      install -d "${pkgdir}/usr/share/licenses/${pkgname}/${fold}"
+      ln -s "/usr/share/root/${fold}/LICENSE" "${pkgdir}/usr/share/licenses/${pkgname}/${fold}"/
+    done
+    ln -s '/usr/share/licenses/roofit' "${pkgdir}/usr/share/licenses/${pkgname}/roofit"
+    if [ "${pkgname}" != "root" ]; then
+        ln -s "/usr/share/licenses/${pkgname}" "${pkgdir}/usr/share/licenses/root"
+        ln -s "/usr/share/doc/root" "${pkgdir}/usr/share/doc/${pkgname}"
+    fi
+
+    # install jupyter kernels and `root --notebook` config
+    install -d "${pkgdir}/usr/share/jupyter/kernels"
+    ln -s '/etc/root/notebook/kernels/root' "${pkgdir}/usr/share/jupyter/kernels/root"
+    install "${srcdir}/jupyter_notebook_config.py" "${pkgdir}/etc/root/notebook"/
+
+    # drop thisroot.* shell files
+    rm -rf "${pkgdir}"/usr/bin/thisroot.*
+    install -Dm755 "${srcdir}/thisroot.fail" "${pkgdir}/usr/bin/thisroot.sh"
+    for suffix in csh fish; do
+        ln -s '/usr/bin/thisroot.sh' "${pkgdir}/usr/bin/thisroot.${suffix}"
+    done
+}
+
+package_root() {
+    optdepends+=('gcc-fortran: Enable the Fortran components of ROOT')
+    _package build
+}
+
+package_root-cuda() {
+    pkgdesc='C++ data analysis framework and interpreter from CERN with GPU (CUDA) features enabled'
+    provides=('root')
+    conflicts=('root')
+    depends+=('cuda' 'cudnn')
+    optdepends+=('gcc8-fortran: Enable the Fortran components of ROOT')
+    _package build-cuda
+}

Copied: root/repos/community-testing-x86_64/ROOFIT_LICENSE (from rev 665090, root/trunk/ROOFIT_LICENSE)
===================================================================
--- community-testing-x86_64/ROOFIT_LICENSE	                        (rev 0)
+++ community-testing-x86_64/ROOFIT_LICENSE	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,22 @@
+RooFit --- Copyright (c) 2000-2005, Regents of the University of California and Stanford University
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  - Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  - Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS
+OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Copied: root/repos/community-testing-x86_64/adapt_tmva_to_support_cudnn8.patch (from rev 665090, root/trunk/adapt_tmva_to_support_cudnn8.patch)
===================================================================
--- community-testing-x86_64/adapt_tmva_to_support_cudnn8.patch	                        (rev 0)
+++ community-testing-x86_64/adapt_tmva_to_support_cudnn8.patch	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,1130 @@
+From 05739e6b01fb34b5ef40e1a584107876e68e4b77 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Tue, 21 Jul 2020 15:13:57 +0300
+Subject: [PATCH 01/10] update deprecated function call name to backward
+ compatible one
+
+---
+ tmva/tmva/src/DNN/Architectures/Cudnn/RecurrentPropagation.cu | 4 ++++
+ 1 file changed, 4 insertions(+)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/RecurrentPropagation.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/RecurrentPropagation.cu
+index 058cee28424..60289ec2fdd 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/RecurrentPropagation.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/RecurrentPropagation.cu
+@@ -132,7 +132,11 @@ void TCudnn<AFloat>::InitializeRecurrentDescriptors(TDescriptors *&descriptors,
+    cudnnDataType_t mathPrec = CUDNN_DATA_FLOAT;
+    if      (std::is_same<AFloat, double>::value) { mathPrec = CUDNN_DATA_DOUBLE;}
+ 
++#if (CUDNN_VERSION >= 8000)
++   CUDNNCHECK(cudnnSetRNNDescriptor_v6(handle, rnnDescriptors->LayerDescriptor, hiddenSize, numLayers, rnnDescriptors->HelperDescriptor,
++#else
+    CUDNNCHECK(cudnnSetRNNDescriptor(handle, rnnDescriptors->LayerDescriptor, hiddenSize, numLayers, rnnDescriptors->HelperDescriptor,
++#endif
+       inputMode, direction, mode, algo, mathPrec) );
+ 
+ 
+
+From 90baa4f6ad10076fa148f5aa06ef432bd0f34208 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Tue, 21 Jul 2020 19:06:09 +0300
+Subject: [PATCH 02/10] adapt convolution forward to cuDNN 8
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 77 ++++++++++++++++++-
+ 1 file changed, 76 insertions(+), 1 deletion(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 7a57b6bf104..cc953ee45f9 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -27,6 +27,9 @@
+ // #include "Kernels.cuh"*/
+ // #include <math.h>
+ 
++// for std::numeric_limits<T>::max()
++#include <limits>
++
+ namespace TMVA {
+ namespace DNN  {
+ 
+@@ -378,7 +381,78 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    cudnnHandle_t cudnnHandle = outputTensor.GetCudnnHandle();
+ 
+    // cuDNN decides which algorithm to use
+-   // More detailed alternative: cudnnFindConvolutionForwardAlgorithm
++#if (CUDNN_VERSION >= 8000)
++   /**
++    * I'm sure there may be a faster way, but this works
++    */
++   int convRequestedAlgoCount{8}; // requestedAlgoCount is setting how many algorithms to try, can be tuned, fixed for now as all available
++   cudnnConvolutionDescriptor_t tempConvDescriptor;
++   CUDDNCHECK(cudnnCreateConvolutionDescriptor(&tempConvDescriptor));
++   cudnnTensorDescriptor_t  outputTensorDescriptor;
++   CUDNNCHECK(cudnnCreateTensorDescriptor(&outputTensorDescriptor));
++   CUDNNCHECK(cudnnSetTensor4dDescriptor(outputTensorDescriptor,
++                                             CUDNN_TENSOR_NCHW,  // Layout of the tensor in memory
++                                             Tensor_t::GetDataType(),
++                                             (int)L->GetBatchSize(),
++                                             (int)L->GetDepth(),
++                                             (int)L->GetHeight(),
++                                             (int)L->GetWidth()));
++   int algoCount;
++   cudnnConvolutionFwdAlgoPerf_t convPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   CUDNNCHECK(cudnnFindConvolutionForwardAlgorithm(
++      cudnnHandle,
++      inputTensorDescriptor,
++      convDescriptors->WeightsDescriptor,
++      tempConvDescriptor,
++      outputTensorDescriptor,
++      convRequestedAlgoCount,
++      &algoCount,
++      &convPerfResults));
++   // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
++   // but we arrive at an chicken or egg problem:
++   // workspace size is calculated from chosen forward algorithm,
++   // but finding a forward algorithm depends on workspace size...
++   // i.e.
++   // Tensor_t & inputTensor = L->GetInput();
++   // inputTensor = Tensor_t(inputTensor.GetDeviceBuffer(),{ L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth() },GetTensorLayout(),0,0);
++   // CUDNNCHECK(cudnnFindConvolutionForwardAlgorithmEx(
++   //    cudnnHandle,
++   //    inputTensorDescriptor,
++   //    &inputTensor,
++   //    convDescriptors->WeightsDescriptor,
++   //    &filters,
++   //    tempConvDescriptor,
++   //    outputTensorDescriptor,
++   //    &outputTensor,
++   //    convRequestedAlgoCount,
++   //    &algoCount,
++   //    &convPerfResults,
++   //    &convWorkspace,
++   //    convWorkspace->ForwardWorkspaceSize));
++   // instead choose either fastest or lowest memory algo as per preference
++   int algoIdx{0};
++   if (CNNOptions::ConvMaxWorkspaceSize != 0) {  // prefer fastest
++      float temp_runtime{std::numeric_limits<float>::max()};
++      for (int i = 0; i < algoCount; ++i) {
++         if (convPerfResults[i].status != 0) continue;
++         if (convPerfResults[i].time < temp_runtime) {
++            temp_runtime = convPerfResults[i].time;
++            algoIdx = i;
++         }
++      }
++   } else {  // prefer smallest workspace size
++      size_t temp_memsize{std::numeric_limits<size_t>::max()};
++      for (int i = 0; i < algoCount; ++i) {
++         if (convPerfResults[i].status != 0) continue;
++         if (convPerfResults[i].memory < temp_memsize) {
++            temp_memsize = convPerfResults[i].memory;
++            algoIdx = i;
++         }
++      }
++   }
++   convWorkspace->AlgorithmForward = convPerfResults[algoIdx].algo;
++#else
++   // More detailed alternative: cudnnFindConvolutionForwardAlgorithm (only option in newer cuDNN versions)
+    cudnnConvolutionFwdPreference_t preferenceFwd = (CNNOptions::ConvMaxWorkspaceSize !=0) ? CUDNN_CONVOLUTION_FWD_PREFER_FASTEST :
+                                                    CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
+ 
+@@ -389,6 +463,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       outputTensor.GetTensorDescriptor(), preferenceFwd,
+       memLimit, // Memory limit in bytes for mode CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
+       &convWorkspace->AlgorithmForward));
++#endif
+ 
+    // Allocate memory for the convolution
+    //size_t workSpaceSizeInBytes = 0;
+
+From d9b5e2f82917e7183b9f45a49135641981741477 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Tue, 21 Jul 2020 19:34:00 +0300
+Subject: [PATCH 03/10] adapt convolution backward to cuDNN 8
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 72 +++++++++++++++++++
+ 1 file changed, 72 insertions(+)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index cc953ee45f9..85a5c3aa175 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -515,6 +515,77 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    // dx : Activation gradient to be computed                               -> activationGradients [in place op]
+    // dy : Gradient of activation from the following layer (backpropagation)-> activationGradients
+ 
++#if (CUDNN_VERSION >= 8000)
++   /**
++    * I'm sure there may be a faster way, but this works
++    */
++   convRequestedAlgoCount = 6; // reset to max number of available backward algorithms
++   cudnnConvolutionDescriptor_t tempConvBwdDescriptor;
++   CUDDNCHECK(cudnnCreateConvolutionDescriptor(&tempConvBwdDescriptor));
++   cudnnTensorDescriptor_t  outputBwdTensorDescriptor;
++   CUDNNCHECK(cudnnCreateTensorDescriptor(&outputBwdTensorDescriptor));
++   CUDNNCHECK(cudnnSetTensor4dDescriptor(outputBwdTensorDescriptor,
++                                             CUDNN_TENSOR_NCHW,  // Layout of the tensor in memory
++                                             Tensor_t::GetDataType(),
++                                             (int)L->GetBatchSize(),
++                                             (int)L->GetInputDepth(),
++                                             (int)L->GetInputHeight(),
++                                             (int)L->GetInputWidth()));
++   int algoCount;
++   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithm(
++      cudnnHandle,
++      convDescriptors->WeightsDescriptor,
++      activationGradientsBackwardDescriptor,
++      tempConvBwdDescriptor,
++      outputBwdTensorDescriptor,
++      convRequestedAlgoCount,
++      &algoCount,
++      &convPerfBwdResults));
++   // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
++   // but we arrive at an chicken or egg problem:
++   // workspace size is calculated from chosen forward algorithm,
++   // but finding a forward algorithm depends on workspace size...
++   // i.e.
++   // Tensor_t & outputBwdTensor = L->GetInput();
++   // outputBwdTensor = Tensor_t(outputBwdTensor.GetDeviceBuffer(),{ L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth() },GetTensorLayout(),0,0);
++   // CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithmEx(
++   //    cudnnHandle,
++   //    convDescriptors->WeightsDescriptor,
++   //    &filters,
++   //    activationGradientsBackwardDescriptor,
++   //    &activationGradientsBackwardTensor,
++   //    tempConvBwdDescriptor,
++   //    outputBwdTensorDescriptor,
++   //    &outputBwdTensor,
++   //    convRequestedAlgoCount,
++   //    &algoCount,
++   //    &convPerfBwdResults,
++   //    &convWorkspace,
++   //    convWorkspace->ForwardWorkspaceSize));
++   // instead choose either fastest or lowest memory algo as per preference
++   int algoIdx{0};
++   if (CNNOptions::ConvMaxWorkspaceSize != 0) {  // prefer fastest
++      float temp_runtime{std::numeric_limits<float>::max()};
++      for (int i = 0; i < algoCount; ++i) {
++         if (convPerfBwdResults[i].status != 0) continue;
++         if (convPerfBwdResults[i].time < temp_runtime) {
++            temp_runtime = convPerfBwdResults[i].time;
++            algoIdx = i;
++         }
++      }
++   } else {  // prefer smallest workspace size
++      size_t temp_memsize{std::numeric_limits<size_t>::max()};
++      for (int i = 0; i < algoCount; ++i) {
++         if (convPerfBwdResults[i].status != 0) continue;
++         if (convPerfBwdResults[i].memory < temp_memsize) {
++            temp_memsize = convPerfBwdResults[i].memory;
++            algoIdx = i;
++         }
++      }
++   }
++   convWorkspace->AlgorithmBackward = convPerfBwdResults[algoIdx].algo;
++#else
+    cudnnConvolutionBwdDataPreference_t preferenceBwdData =
+       (CNNOptions::ConvMaxWorkspaceSize != 0) ? CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST : CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
+ 
+@@ -525,6 +596,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+                                                       activationGradientsBackwardDescriptor,
+                                                       preferenceBwdData, memLimit,
+                                                       &convWorkspace->AlgorithmBackward));
++#endif
+ 
+    std::cout << "CONV BWD Data Algo used  is "  << convWorkspace->AlgorithmBackward << std::endl;
+    //CUDNNCHECK(cudnnSetConvolutionMathType(convDescriptors->LayerDescriptor, CUDNN_TENSOR_OP_MATH));
+
+From 526b7177c0201be1d0c6b36de0772b7d2ecb90d5 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Wed, 22 Jul 2020 11:50:29 +0300
+Subject: [PATCH 04/10] fix typo and re-declarations
+
+---
+ tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu | 11 +++++------
+ 1 file changed, 5 insertions(+), 6 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 85a5c3aa175..1b7e3e845d8 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -387,7 +387,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+     */
+    int convRequestedAlgoCount{8}; // requestedAlgoCount is setting how many algorithms to try, can be tuned, fixed for now as all available
+    cudnnConvolutionDescriptor_t tempConvDescriptor;
+-   CUDDNCHECK(cudnnCreateConvolutionDescriptor(&tempConvDescriptor));
++   CUDNNCHECK(cudnnCreateConvolutionDescriptor(&tempConvDescriptor));
+    cudnnTensorDescriptor_t  outputTensorDescriptor;
+    CUDNNCHECK(cudnnCreateTensorDescriptor(&outputTensorDescriptor));
+    CUDNNCHECK(cudnnSetTensor4dDescriptor(outputTensorDescriptor,
+@@ -407,7 +407,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       outputTensorDescriptor,
+       convRequestedAlgoCount,
+       &algoCount,
+-      &convPerfResults));
++      convPerfResults));
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+    // but we arrive at an chicken or egg problem:
+    // workspace size is calculated from chosen forward algorithm,
+@@ -521,7 +521,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+     */
+    convRequestedAlgoCount = 6; // reset to max number of available backward algorithms
+    cudnnConvolutionDescriptor_t tempConvBwdDescriptor;
+-   CUDDNCHECK(cudnnCreateConvolutionDescriptor(&tempConvBwdDescriptor));
++   CUDNNCHECK(cudnnCreateConvolutionDescriptor(&tempConvBwdDescriptor));
+    cudnnTensorDescriptor_t  outputBwdTensorDescriptor;
+    CUDNNCHECK(cudnnCreateTensorDescriptor(&outputBwdTensorDescriptor));
+    CUDNNCHECK(cudnnSetTensor4dDescriptor(outputBwdTensorDescriptor,
+@@ -531,7 +531,6 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+                                              (int)L->GetInputDepth(),
+                                              (int)L->GetInputHeight(),
+                                              (int)L->GetInputWidth()));
+-   int algoCount;
+    cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithm(
+       cudnnHandle,
+@@ -541,7 +540,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       outputBwdTensorDescriptor,
+       convRequestedAlgoCount,
+       &algoCount,
+-      &convPerfBwdResults));
++      convPerfBwdResults));
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+    // but we arrive at an chicken or egg problem:
+    // workspace size is calculated from chosen forward algorithm,
+@@ -564,7 +563,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    convWorkspace->ForwardWorkspaceSize));
+    // instead choose either fastest or lowest memory algo as per preference
+-   int algoIdx{0};
++   algoIdx = 0;
+    if (CNNOptions::ConvMaxWorkspaceSize != 0) {  // prefer fastest
+       float temp_runtime{std::numeric_limits<float>::max()};
+       for (int i = 0; i < algoCount; ++i) {
+
+From 6d84e765322a72c48de00b4a9b7471da8a15fece Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Wed, 22 Jul 2020 17:00:01 +0300
+Subject: [PATCH 05/10] implement workspace limits, fix an algoruthm preference
+ bug and rewrite relevant sections
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 273 ++++++++++--------
+ 1 file changed, 151 insertions(+), 122 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 1b7e3e845d8..2049e2b9195 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -333,35 +333,108 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+                                              TDescriptors * & descriptors,
+                                              const DNN::CNN::TConvParams & /*params*/,
+                                              ConvLayer_t *L) {
+-   auto convWorkspace = new ConvWorkspace_t ();
++   auto convWorkspace = new ConvWorkspace_t();
++   size_t memLimit = (CNNOptions::ConvMaxWorkspaceSize > 0) ? static_cast<size_t>(CNNOptions::ConvMaxWorkspaceSize) : 0;
+    auto convDescriptors = static_cast<ConvDescriptors_t *>(descriptors);
++   // can we do the following and substitute below???
++   // auto weightsDescriptor{convDescriptors->WeightsDescriptor};
++   // auto convDescriptor{convDescriptors->LayerDescriptor};
+ 
++#if (CUDNN_VERSION >= 8000)
++   enum algoPreference { no_workspace, fastest, workspace_limit };
++   algoPreference algoChoice;
++   auto choose_algo = [](algoPreference const& algoPref, auto&& perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
++      int algoIdx{0};
++      if (algoPref == algoPreference::fastest) {  // prefer fastest
++         float temp_runtime{std::numeric_limits<float>::max()};
++         for (int i = 0; i < algoCount; ++i) {
++            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].time < temp_runtime) {
++               temp_runtime = PerfResults[i].time;
++               algoIdx = i;
++            }
++         }
++      } else if (algoPref == algoPreference::workspace_limit) {  // constrain to workspace size
++         float temp_runtime{std::numeric_limits<float>::max()};
++         for (int i = 0; i < algoCount; ++i) {
++            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].time < temp_runtime && PerfResults[i].memory <= memLim) {
++               temp_runtime = PerfResults[i].time;
++               algoIdx = i;
++            }
++         }
++      } else {  // prefer smallest workspace size
++         size_t temp_memsize{std::numeric_limits<size_t>::max()};
++         for (int i = 0; i < algoCount; ++i) {
++            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].memory < temp_memsize) {
++               temp_memsize = PerfResults[i].memory;
++               algoIdx = i;
++            }
++         }
++      }
++      return algoIdx;
++   };
++#else
++   // More detailed alternative: cudnnFindConvolutionForwardAlgorithm (only option in newer cuDNN versions)
++   cudnnConvolutionFwdPreference_t preferenceFwd;
++   cudnnConvolutionBwdDataPreference_t preferenceBwdData;
++   cudnnConvolutionBwdFilterPreference_t preferenceBwdFilter;
++#endif
++   // decide on algorithm preference early
++   if (CNNOptions::ConvMaxWorkspaceSize < 0) {
++      // no workspace case
++#if (CUDNN_VERSION >= 8000)
++      algoChoice = no_workspace;
++#else
++      preferenceFwd = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
++      preferenceBwdData = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
++      preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
++#endif
++
++   } else if (CNNOptions::ConvMaxWorkspaceSize == 0) {
++      // fastest overall
++#if (CUDNN_VERSION >= 8000)
++      algoChoice = fastest;
++#else
++      preferenceFwd = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
++      preferenceBwdData = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST;
++      preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
++#endif
++
++   } else {
++      // fastest in memory limit
++#if (CUDNN_VERSION >= 8000)
++      algoChoice = workspace_limit;
++#else
++      preferenceFwd = CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT;
++      preferenceBwdData = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT;
++      preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT;
++#endif
++   }
+    // fix the weight tensor shapes
+    // by default the weights are columnmajor, set them to be row major . At this points
+    // they are not yet initialized
+    Tensor_t & filters = L->GetWeightsAt(0);
+-   filters = Tensor_t (filters.GetDeviceBuffer(), {L->GetDepth(),L->GetInputDepth(), L->GetFilterHeight(),L->GetFilterWidth()}, MemoryLayout::RowMajor, 0, 0 );
+-   //PrintTensor(L->GetWeightsAt(0));
++   filters = Tensor_t(filters.GetDeviceBuffer(), {L->GetDepth(), L->GetInputDepth(), L->GetFilterHeight(), L->GetFilterWidth()}, MemoryLayout::RowMajor, 0, 0);
++   // PrintTensor(L->GetWeightsAt(0));
+    Tensor_t & biases = L->GetBiasesAt(0);
+-   biases = Tensor_t (biases.GetDeviceBuffer(), {1, L->GetDepth(),1,1}, GetTensorLayout(), 0, 0 );
++   biases = Tensor_t(biases.GetDeviceBuffer(), {1, L->GetDepth(), 1, 1}, GetTensorLayout(), 0, 0);
+ 
+    Tensor_t & outputTensor = L->GetOutput();
+-   outputTensor = Tensor_t(outputTensor.GetDeviceBuffer(),{ L->GetBatchSize(), L->GetDepth(), L->GetHeight(), L->GetWidth() },GetTensorLayout(),0,0 );
++   outputTensor = Tensor_t(outputTensor.GetDeviceBuffer(), {L->GetBatchSize(), L->GetDepth(), L->GetHeight(), L->GetWidth()}, GetTensorLayout(), 0, 0);
+    Tensor_t & inputActivation = L->GetInputActivation();
+-   inputActivation = Tensor_t(inputActivation.GetDeviceBuffer(),outputTensor.GetShape() ,GetTensorLayout(),0,0 );
++   inputActivation = Tensor_t(inputActivation.GetDeviceBuffer(),outputTensor.GetShape() ,GetTensorLayout(), 0, 0);
+ 
+    Tensor_t &  activationGradients = L->GetActivationGradients();
+-   activationGradients =  Tensor_t(activationGradients.GetDeviceBuffer(),outputTensor.GetShape() ,GetTensorLayout(),0,0 );
++   activationGradients = Tensor_t(activationGradients.GetDeviceBuffer(),outputTensor.GetShape(), GetTensorLayout(), 0, 0);
+ 
+    Tensor_t & weightGradients = L->GetWeightGradientsAt(0);
+-   weightGradients = Tensor_t( weightGradients.GetDeviceBuffer(), filters.GetShape(), GetTensorLayout(), 0, 0 );
++   weightGradients = Tensor_t(weightGradients.GetDeviceBuffer(), filters.GetShape(), GetTensorLayout(), 0, 0);
+ 
+    Tensor_t & biasGradients = L->GetBiasGradientsAt(0);
+-   biasGradients = Tensor_t( biasGradients.GetDeviceBuffer(), biases.GetShape(), GetTensorLayout(), 0, 0 );
++   biasGradients = Tensor_t(biasGradients.GetDeviceBuffer(), biases.GetShape(), GetTensorLayout(), 0, 0);
+ 
+ 
+    // FIXME: Use descriptors instead (Tensor device memory is otherwise allocated during initialization)
+-   //Tensor_t inputTensor  ({L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth()}, MemoryLayout::RowMajor, 0, 0);
++   // Tensor_t inputTensor  ({L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth()}, MemoryLayout::RowMajor, 0, 0);
+    cudnnTensorDescriptor_t  inputTensorDescriptor;
+    CUDNNCHECK(cudnnCreateTensorDescriptor(&inputTensorDescriptor) );
+    CUDNNCHECK(cudnnSetTensor4dDescriptor(inputTensorDescriptor,
+@@ -385,79 +458,44 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    /**
+     * I'm sure there may be a faster way, but this works
+     */
+-   int convRequestedAlgoCount{8}; // requestedAlgoCount is setting how many algorithms to try, can be tuned, fixed for now as all available
+-   cudnnConvolutionDescriptor_t tempConvDescriptor;
+-   CUDNNCHECK(cudnnCreateConvolutionDescriptor(&tempConvDescriptor));
+-   cudnnTensorDescriptor_t  outputTensorDescriptor;
+-   CUDNNCHECK(cudnnCreateTensorDescriptor(&outputTensorDescriptor));
+-   CUDNNCHECK(cudnnSetTensor4dDescriptor(outputTensorDescriptor,
+-                                             CUDNN_TENSOR_NCHW,  // Layout of the tensor in memory
+-                                             Tensor_t::GetDataType(),
+-                                             (int)L->GetBatchSize(),
+-                                             (int)L->GetDepth(),
+-                                             (int)L->GetHeight(),
+-                                             (int)L->GetWidth()));
++   int convRequestedAlgoCount{8};  // requestedAlgoCount is setting how many algorithms to try, can be tuned, fixed for now as all available
++
+    int algoCount;
+    cudnnConvolutionFwdAlgoPerf_t convPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+-   CUDNNCHECK(cudnnFindConvolutionForwardAlgorithm(
+-      cudnnHandle,
+-      inputTensorDescriptor,
+-      convDescriptors->WeightsDescriptor,
+-      tempConvDescriptor,
+-      outputTensorDescriptor,
+-      convRequestedAlgoCount,
+-      &algoCount,
+-      convPerfResults));
++   CUDNNCHECK(
++      cudnnFindConvolutionForwardAlgorithm(
++         cudnnHandle,
++         inputTensorDescriptor,
++         convDescriptors->WeightsDescriptor,
++         convDescriptors->LayerDescriptor,
++         outputTensor.GetTensorDescriptor(),
++         convRequestedAlgoCount,
++         &algoCount,
++         convPerfResults
++      )
++   );
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+-   // but we arrive at an chicken or egg problem:
+-   // workspace size is calculated from chosen forward algorithm,
+-   // but finding a forward algorithm depends on workspace size...
+    // i.e.
+-   // Tensor_t & inputTensor = L->GetInput();
+-   // inputTensor = Tensor_t(inputTensor.GetDeviceBuffer(),{ L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth() },GetTensorLayout(),0,0);
++   // create an input tensor before the inputTensorDescriptor
++   // and get the descriptor from there
++   // Tensor_t inputTensor({L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth()}, MemoryLayout::RowMajor, 0, 0);
+    // CUDNNCHECK(cudnnFindConvolutionForwardAlgorithmEx(
+    //    cudnnHandle,
+-   //    inputTensorDescriptor,
++   //    inputTensor.GetTensorDescriptor(),
+    //    &inputTensor,
+    //    convDescriptors->WeightsDescriptor,
+    //    &filters,
+-   //    tempConvDescriptor,
+-   //    outputTensorDescriptor,
++   //    convDescriptors->LayerDescriptor,
++   //    outputTensor.GetTensorDescriptor(),
+    //    &outputTensor,
+    //    convRequestedAlgoCount,
+    //    &algoCount,
+    //    &convPerfResults,
+    //    &convWorkspace,
+-   //    convWorkspace->ForwardWorkspaceSize));
++   //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   int algoIdx{0};
+-   if (CNNOptions::ConvMaxWorkspaceSize != 0) {  // prefer fastest
+-      float temp_runtime{std::numeric_limits<float>::max()};
+-      for (int i = 0; i < algoCount; ++i) {
+-         if (convPerfResults[i].status != 0) continue;
+-         if (convPerfResults[i].time < temp_runtime) {
+-            temp_runtime = convPerfResults[i].time;
+-            algoIdx = i;
+-         }
+-      }
+-   } else {  // prefer smallest workspace size
+-      size_t temp_memsize{std::numeric_limits<size_t>::max()};
+-      for (int i = 0; i < algoCount; ++i) {
+-         if (convPerfResults[i].status != 0) continue;
+-         if (convPerfResults[i].memory < temp_memsize) {
+-            temp_memsize = convPerfResults[i].memory;
+-            algoIdx = i;
+-         }
+-      }
+-   }
+-   convWorkspace->AlgorithmForward = convPerfResults[algoIdx].algo;
++   convWorkspace->AlgorithmForward = convPerfResults[choose_algo(algoChoice, convPerfResults, memLimit)].algo;
+ #else
+-   // More detailed alternative: cudnnFindConvolutionForwardAlgorithm (only option in newer cuDNN versions)
+-   cudnnConvolutionFwdPreference_t preferenceFwd = (CNNOptions::ConvMaxWorkspaceSize !=0) ? CUDNN_CONVOLUTION_FWD_PREFER_FASTEST :
+-                                                   CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
+-
+-   size_t memLimit = (CNNOptions::ConvMaxWorkspaceSize > 0) ? (size_t) CNNOptions::ConvMaxWorkspaceSize : 0;
+-
+    CUDNNCHECK(cudnnGetConvolutionForwardAlgorithm(
+       cudnnHandle, inputTensorDescriptor, convDescriptors->WeightsDescriptor, convDescriptors->LayerDescriptor,
+       outputTensor.GetTensorDescriptor(), preferenceFwd,
+@@ -519,75 +557,36 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    /**
+     * I'm sure there may be a faster way, but this works
+     */
+-   convRequestedAlgoCount = 6; // reset to max number of available backward algorithms
+-   cudnnConvolutionDescriptor_t tempConvBwdDescriptor;
+-   CUDNNCHECK(cudnnCreateConvolutionDescriptor(&tempConvBwdDescriptor));
+-   cudnnTensorDescriptor_t  outputBwdTensorDescriptor;
+-   CUDNNCHECK(cudnnCreateTensorDescriptor(&outputBwdTensorDescriptor));
+-   CUDNNCHECK(cudnnSetTensor4dDescriptor(outputBwdTensorDescriptor,
+-                                             CUDNN_TENSOR_NCHW,  // Layout of the tensor in memory
+-                                             Tensor_t::GetDataType(),
+-                                             (int)L->GetBatchSize(),
+-                                             (int)L->GetInputDepth(),
+-                                             (int)L->GetInputHeight(),
+-                                             (int)L->GetInputWidth()));
+-   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   convRequestedAlgoCount = 6;  // reset to max number of available backward algorithms
++   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdDataResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithm(
+       cudnnHandle,
+       convDescriptors->WeightsDescriptor,
++      activationGradients.GetTensorDescriptor(),
++      convDescriptors->LayerDescriptor,
+       activationGradientsBackwardDescriptor,
+-      tempConvBwdDescriptor,
+-      outputBwdTensorDescriptor,
+       convRequestedAlgoCount,
+       &algoCount,
+-      convPerfBwdResults));
++      convPerfBwdDataResults));
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+-   // but we arrive at an chicken or egg problem:
+-   // workspace size is calculated from chosen forward algorithm,
+-   // but finding a forward algorithm depends on workspace size...
+    // i.e.
+-   // Tensor_t & outputBwdTensor = L->GetInput();
+-   // outputBwdTensor = Tensor_t(outputBwdTensor.GetDeviceBuffer(),{ L->GetBatchSize(), L->GetInputDepth(), L->GetInputHeight(), L->GetInputWidth() },GetTensorLayout(),0,0);
+    // CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithmEx(
+    //    cudnnHandle,
+    //    convDescriptors->WeightsDescriptor,
+    //    &filters,
++   //    activationGradients.GetTensorDescriptor(),
++   //    &activationGradients,
++   //    convDescriptors->LayerDescriptor,
+    //    activationGradientsBackwardDescriptor,
+-   //    &activationGradientsBackwardTensor,
+-   //    tempConvBwdDescriptor,
+-   //    outputBwdTensorDescriptor,
+-   //    &outputBwdTensor,
++   //    &inputTensor,
+    //    convRequestedAlgoCount,
+    //    &algoCount,
+    //    &convPerfBwdResults,
+    //    &convWorkspace,
+-   //    convWorkspace->ForwardWorkspaceSize));
++   //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   algoIdx = 0;
+-   if (CNNOptions::ConvMaxWorkspaceSize != 0) {  // prefer fastest
+-      float temp_runtime{std::numeric_limits<float>::max()};
+-      for (int i = 0; i < algoCount; ++i) {
+-         if (convPerfBwdResults[i].status != 0) continue;
+-         if (convPerfBwdResults[i].time < temp_runtime) {
+-            temp_runtime = convPerfBwdResults[i].time;
+-            algoIdx = i;
+-         }
+-      }
+-   } else {  // prefer smallest workspace size
+-      size_t temp_memsize{std::numeric_limits<size_t>::max()};
+-      for (int i = 0; i < algoCount; ++i) {
+-         if (convPerfBwdResults[i].status != 0) continue;
+-         if (convPerfBwdResults[i].memory < temp_memsize) {
+-            temp_memsize = convPerfBwdResults[i].memory;
+-            algoIdx = i;
+-         }
+-      }
+-   }
+-   convWorkspace->AlgorithmBackward = convPerfBwdResults[algoIdx].algo;
++   convWorkspace->AlgorithmBackward = convPerfBwdDataResults[choose_algo(algoChoice, convPerfBwdDataResults, memLimit)].algo;
+ #else
+-   cudnnConvolutionBwdDataPreference_t preferenceBwdData =
+-      (CNNOptions::ConvMaxWorkspaceSize != 0) ? CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST : CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
+-
+    CUDNNCHECK(cudnnGetConvolutionBackwardDataAlgorithm(cudnnHandle,
+                                                       convDescriptors->WeightsDescriptor,
+                                                       activationGradients.GetTensorDescriptor(),
+@@ -628,11 +627,40 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    // here should be able to use inputTensorDescriptor
+    cudnnTensorDescriptor_t activationBackwardDescriptor = inputTensorDescriptor;
+ 
+-   // cudnnConvolutionBwdFilterPreference_t preference =
+-   cudnnConvolutionBwdFilterPreference_t preferenceBwdFilter = (CNNOptions::ConvMaxWorkspaceSize != 0)
+-                                                                  ? CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE
+-                                                                  : CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
+-
++#if (CUDNN_VERSION >= 8000)
++   /**
++    * I'm sure there may be a faster way, but this works
++    */
++   convRequestedAlgoCount = 6;  // reset to max number of available backward algorithms
++   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdFilterResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   CUDNNCHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
++      cudnnHandle,
++      activationBackwardDescriptor,
++      activationGradients.GetTensorDescriptor(),
++      convDescriptors->LayerDescriptor,
++      convDescriptors->WeightsDescriptor,
++      convRequestedAlgoCount,
++      &algoCount,
++      convPerfBwdFilterResults));
++   // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
++   // i.e.
++   // CUDNNCHECK(cudnnFindConvolutionBackwardFilterAlgorithmEx(
++   //    cudnnHandle,
++   //    activationBackwardDescriptor,
++   //    &inputTensor,
++   //    activationGradients.GetTensorDescriptor(),
++   //    &activationGradients,
++   //    convDescriptors->LayerDescriptor,
++   //    convDescriptors->WeightsDescriptor,
++   //    &filters,
++   //    convRequestedAlgoCount,
++   //    &algoCount,
++   //    &convPerfBwdFilterResults,
++   //    &convWorkspace,
++   //    memLimit));  // use memLimit for workspace size
++   // instead choose either fastest or lowest memory algo as per preference
++   convWorkspace->AlgorithmBackward = convPerfBwdFilterResults[choose_algo(algoChoice, convPerfBwdFilterResults, memLimit)].algo;
++#else
+    CUDNNCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(cudnnHandle,
+                                                          activationBackwardDescriptor,
+                                                          activationGradients.GetTensorDescriptor(),
+@@ -641,6 +669,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+                                                          preferenceBwdFilter,
+                                                          memLimit,
+                                                          &convWorkspace->HelperAlgorithm));
++#endif
+ 
+    std::cout << "CONV BWD Filter Algo used  is " << convWorkspace->HelperAlgorithm << std::endl;
+ 
+
+From a9d39cc9ccf9ae474d90b6671d3e0d69d4cf6872 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Wed, 22 Jul 2020 17:11:30 +0300
+Subject: [PATCH 06/10] implement correct logic behind cudnn logarithm
+ preference
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 20 +++++++++----------
+ 1 file changed, 10 insertions(+), 10 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 2049e2b9195..b74c99d1a99 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -380,18 +380,8 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+ #endif
+    // decide on algorithm preference early
+    if (CNNOptions::ConvMaxWorkspaceSize < 0) {
+-      // no workspace case
+ #if (CUDNN_VERSION >= 8000)
+-      algoChoice = no_workspace;
+-#else
+-      preferenceFwd = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
+-      preferenceBwdData = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
+-      preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
+-#endif
+-
+-   } else if (CNNOptions::ConvMaxWorkspaceSize == 0) {
+       // fastest overall
+-#if (CUDNN_VERSION >= 8000)
+       algoChoice = fastest;
+ #else
+       preferenceFwd = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST;
+@@ -399,6 +389,16 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST;
+ #endif
+ 
++   } else if (CNNOptions::ConvMaxWorkspaceSize == 0) {
++      // no workspace case
++#if (CUDNN_VERSION >= 8000)
++      algoChoice = no_workspace;
++#else
++      preferenceFwd = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE;
++      preferenceBwdData = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE;
++      preferenceBwdFilter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE;
++#endif
++
+    } else {
+       // fastest in memory limit
+ #if (CUDNN_VERSION >= 8000)
+
+From 6282dfa816c7f51af5c0ecaa0065514e3f627631 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Wed, 22 Jul 2020 18:51:56 +0300
+Subject: [PATCH 07/10] use decltype instead of auto, fix typos
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 22 +++++++++----------
+ 1 file changed, 11 insertions(+), 11 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index b74c99d1a99..6cefd72c099 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -343,29 +343,29 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+ #if (CUDNN_VERSION >= 8000)
+    enum algoPreference { no_workspace, fastest, workspace_limit };
+    algoPreference algoChoice;
+-   auto choose_algo = [](algoPreference const& algoPref, auto&& perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
++   auto choose_algo = [](algoPreference const& algoPref, int const algoCount, decltype(perfResults) const& perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
+       int algoIdx{0};
+       if (algoPref == algoPreference::fastest) {  // prefer fastest
+          float temp_runtime{std::numeric_limits<float>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].time < temp_runtime) {
+-               temp_runtime = PerfResults[i].time;
++            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].time < temp_runtime) {
++               temp_runtime = perfResults[i].time;
+                algoIdx = i;
+             }
+          }
+       } else if (algoPref == algoPreference::workspace_limit) {  // constrain to workspace size
+          float temp_runtime{std::numeric_limits<float>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].time < temp_runtime && PerfResults[i].memory <= memLim) {
+-               temp_runtime = PerfResults[i].time;
++            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].time < temp_runtime && perfResults[i].memory <= memLim) {
++               temp_runtime = perfResults[i].time;
+                algoIdx = i;
+             }
+          }
+       } else {  // prefer smallest workspace size
+          size_t temp_memsize{std::numeric_limits<size_t>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (PerfResults[i].status == CUDNN_STATUS_SUCCESS && PerfResults[i].memory < temp_memsize) {
+-               temp_memsize = PerfResults[i].memory;
++            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].memory < temp_memsize) {
++               temp_memsize = perfResults[i].memory;
+                algoIdx = i;
+             }
+          }
+@@ -494,7 +494,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmForward = convPerfResults[choose_algo(algoChoice, convPerfResults, memLimit)].algo;
++   convWorkspace->AlgorithmForward = convPerfResults[choose_algo(algoChoice, algoCount, convPerfResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionForwardAlgorithm(
+       cudnnHandle, inputTensorDescriptor, convDescriptors->WeightsDescriptor, convDescriptors->LayerDescriptor,
+@@ -585,7 +585,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmBackward = convPerfBwdDataResults[choose_algo(algoChoice, convPerfBwdDataResults, memLimit)].algo;
++   convWorkspace->AlgorithmBackward = convPerfBwdDataResults[choose_algo(algoChoice, algoCount, convPerfBwdDataResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardDataAlgorithm(cudnnHandle,
+                                                       convDescriptors->WeightsDescriptor,
+@@ -632,7 +632,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+     * I'm sure there may be a faster way, but this works
+     */
+    convRequestedAlgoCount = 6;  // reset to max number of available backward algorithms
+-   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdFilterResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   cudnnConvolutionBwdFilterAlgoPerf_t convPerfBwdFilterResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
+       cudnnHandle,
+       activationBackwardDescriptor,
+@@ -659,7 +659,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmBackward = convPerfBwdFilterResults[choose_algo(algoChoice, convPerfBwdFilterResults, memLimit)].algo;
++   convWorkspace->AlgorithmBackward = convPerfBwdFilterResults[choose_algo(algoChoice, algoCount, convPerfBwdFilterResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(cudnnHandle,
+                                                          activationBackwardDescriptor,
+
+From 259c1c9c4d86391d1987f6635a2aece8cae587ac Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Wed, 22 Jul 2020 19:39:40 +0300
+Subject: [PATCH 08/10] assign backward filter algo to correct place
+
+---
+ tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu | 2 +-
+ 1 file changed, 1 insertion(+), 1 deletion(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 6cefd72c099..5a80dfbc03d 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -659,7 +659,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmBackward = convPerfBwdFilterResults[choose_algo(algoChoice, algoCount, convPerfBwdFilterResults, memLimit)].algo;
++   convWorkspace->HelperAlgorithm = convPerfBwdFilterResults[choose_algo(algoChoice, algoCount, convPerfBwdFilterResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(cudnnHandle,
+                                                          activationBackwardDescriptor,
+
+From 2c109efea0e970b380a62f6102a286542676912a Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Thu, 23 Jul 2020 17:58:58 +0300
+Subject: [PATCH 09/10] make it compile and support C++11
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 49 ++++++++++++-------
+ 1 file changed, 30 insertions(+), 19 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 5a80dfbc03d..66ce64a5efc 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -343,29 +343,37 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+ #if (CUDNN_VERSION >= 8000)
+    enum algoPreference { no_workspace, fastest, workspace_limit };
+    algoPreference algoChoice;
+-   auto choose_algo = [](algoPreference const& algoPref, int const algoCount, decltype(perfResults) const& perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
++   // C++11 lambdas cannot be templated, so we have to do this HORRIBLE stuff...
++   union LocalPerf_t {
++      // these three type are absolutely equivalent
++      // and one can access them as they wish to get info
++      cudnnConvolutionFwdAlgoPerf_t * fwd;
++      cudnnConvolutionBwdFilterAlgoPerf_t * bwdFilter;
++      cudnnConvolutionBwdDataAlgoPerf_t * bwdData;
++   };
++   auto choose_algo = [](algoPreference const & algoPref, int const algoCount, LocalPerf_t const & perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
+       int algoIdx{0};
+       if (algoPref == algoPreference::fastest) {  // prefer fastest
+          float temp_runtime{std::numeric_limits<float>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].time < temp_runtime) {
+-               temp_runtime = perfResults[i].time;
++            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].time < temp_runtime) {
++               temp_runtime = perfResults.fwd[i].time;
+                algoIdx = i;
+             }
+          }
+       } else if (algoPref == algoPreference::workspace_limit) {  // constrain to workspace size
+          float temp_runtime{std::numeric_limits<float>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].time < temp_runtime && perfResults[i].memory <= memLim) {
+-               temp_runtime = perfResults[i].time;
++            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].time < temp_runtime && perfResults.fwd[i].memory <= memLim) {
++               temp_runtime = perfResults.fwd[i].time;
+                algoIdx = i;
+             }
+          }
+       } else {  // prefer smallest workspace size
+          size_t temp_memsize{std::numeric_limits<size_t>::max()};
+          for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults[i].status == CUDNN_STATUS_SUCCESS && perfResults[i].memory < temp_memsize) {
+-               temp_memsize = perfResults[i].memory;
++            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].memory < temp_memsize) {
++               temp_memsize = perfResults.fwd[i].memory;
+                algoIdx = i;
+             }
+          }
+@@ -461,7 +469,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    int convRequestedAlgoCount{8};  // requestedAlgoCount is setting how many algorithms to try, can be tuned, fixed for now as all available
+ 
+    int algoCount;
+-   cudnnConvolutionFwdAlgoPerf_t convPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   cudnnConvolutionFwdAlgoPerf_t convFwdPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(
+       cudnnFindConvolutionForwardAlgorithm(
+          cudnnHandle,
+@@ -471,7 +479,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+          outputTensor.GetTensorDescriptor(),
+          convRequestedAlgoCount,
+          &algoCount,
+-         convPerfResults
++         convFwdPerfResults
+       )
+    );
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+@@ -490,11 +498,12 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &outputTensor,
+    //    convRequestedAlgoCount,
+    //    &algoCount,
+-   //    &convPerfResults,
++   //    &convFwdPerfResults,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmForward = convPerfResults[choose_algo(algoChoice, algoCount, convPerfResults, memLimit)].algo;
++   LocalPerf_t fwdPerfResults{convFwdPerfResults};
++   convWorkspace->AlgorithmForward = convFwdPerfResults[choose_algo(algoChoice, algoCount, fwdPerfResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionForwardAlgorithm(
+       cudnnHandle, inputTensorDescriptor, convDescriptors->WeightsDescriptor, convDescriptors->LayerDescriptor,
+@@ -558,7 +567,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+     * I'm sure there may be a faster way, but this works
+     */
+    convRequestedAlgoCount = 6;  // reset to max number of available backward algorithms
+-   cudnnConvolutionBwdDataAlgoPerf_t convPerfBwdDataResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   cudnnConvolutionBwdDataAlgoPerf_t convBwdDataPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithm(
+       cudnnHandle,
+       convDescriptors->WeightsDescriptor,
+@@ -567,7 +576,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       activationGradientsBackwardDescriptor,
+       convRequestedAlgoCount,
+       &algoCount,
+-      convPerfBwdDataResults));
++      convBwdDataPerfResults));
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+    // i.e.
+    // CUDNNCHECK(cudnnFindConvolutionBackwardDataAlgorithmEx(
+@@ -581,11 +590,12 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &inputTensor,
+    //    convRequestedAlgoCount,
+    //    &algoCount,
+-   //    &convPerfBwdResults,
++   //    &convBwdDataPerfResults,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->AlgorithmBackward = convPerfBwdDataResults[choose_algo(algoChoice, algoCount, convPerfBwdDataResults, memLimit)].algo;
++   LocalPerf_t bwdDataPerfResults{convBwdDataPerfResults};
++   convWorkspace->AlgorithmBackward = convBwdDataPerfResults[choose_algo(algoChoice, algoCount, bwdDataPerfResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardDataAlgorithm(cudnnHandle,
+                                                       convDescriptors->WeightsDescriptor,
+@@ -632,7 +642,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+     * I'm sure there may be a faster way, but this works
+     */
+    convRequestedAlgoCount = 6;  // reset to max number of available backward algorithms
+-   cudnnConvolutionBwdFilterAlgoPerf_t convPerfBwdFilterResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
++   cudnnConvolutionBwdFilterAlgoPerf_t convBwdFilterPerfResults[convRequestedAlgoCount];  // this will store metrics to choose convolution algorithm
+    CUDNNCHECK(cudnnFindConvolutionBackwardFilterAlgorithm(
+       cudnnHandle,
+       activationBackwardDescriptor,
+@@ -641,7 +651,7 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+       convDescriptors->WeightsDescriptor,
+       convRequestedAlgoCount,
+       &algoCount,
+-      convPerfBwdFilterResults));
++      convBwdFilterPerfResults));
+    // we could also do it with the expert mode (cudnnFindConvolutionForwardAlgorithmEx),
+    // i.e.
+    // CUDNNCHECK(cudnnFindConvolutionBackwardFilterAlgorithmEx(
+@@ -655,11 +665,12 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &filters,
+    //    convRequestedAlgoCount,
+    //    &algoCount,
+-   //    &convPerfBwdFilterResults,
++   //    &convBwdFilterPerfResults,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   convWorkspace->HelperAlgorithm = convPerfBwdFilterResults[choose_algo(algoChoice, algoCount, convPerfBwdFilterResults, memLimit)].algo;
++   LocalPerf_t bwdFilterPerfResults{convBwdFilterPerfResults};
++   convWorkspace->HelperAlgorithm = convBwdFilterPerfResults[choose_algo(algoChoice, algoCount, bwdFilterPerfResults, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(cudnnHandle,
+                                                          activationBackwardDescriptor,
+
+From 1f1dfbbac06c29df98bdebdd9367bf566f2e7ce8 Mon Sep 17 00:00:00 2001
+From: Konstantin Gizdov <kgizdov at gmail.com>
+Date: Thu, 23 Jul 2020 21:37:33 +0300
+Subject: [PATCH 10/10] compiles completely
+
+---
+ .../src/DNN/Architectures/Cudnn/Propagate.cu  | 83 ++++++++++---------
+ 1 file changed, 46 insertions(+), 37 deletions(-)
+
+diff --git a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+index 66ce64a5efc..0694369860a 100644
+--- a/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
++++ b/tmva/tmva/src/DNN/Architectures/Cudnn/Propagate.cu
+@@ -344,41 +344,50 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    enum algoPreference { no_workspace, fastest, workspace_limit };
+    algoPreference algoChoice;
+    // C++11 lambdas cannot be templated, so we have to do this HORRIBLE stuff...
+-   union LocalPerf_t {
+-      // these three type are absolutely equivalent
+-      // and one can access them as they wish to get info
+-      cudnnConvolutionFwdAlgoPerf_t * fwd;
+-      cudnnConvolutionBwdFilterAlgoPerf_t * bwdFilter;
+-      cudnnConvolutionBwdDataAlgoPerf_t * bwdData;
+-   };
+-   auto choose_algo = [](algoPreference const & algoPref, int const algoCount, LocalPerf_t const & perfResults, size_t memLim = std::numeric_limits<size_t>::max()) -> int {
+-      int algoIdx{0};
+-      if (algoPref == algoPreference::fastest) {  // prefer fastest
+-         float temp_runtime{std::numeric_limits<float>::max()};
+-         for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].time < temp_runtime) {
+-               temp_runtime = perfResults.fwd[i].time;
+-               algoIdx = i;
++   class LocalPerf {
++    public:
++      LocalPerf(cudnnConvolutionFwdAlgoPerf_t * fwd) {m_fwd = fwd;}
++      LocalPerf(cudnnConvolutionBwdFilterAlgoPerf_t * bwdFilter) {m_bwdFilter = bwdFilter;}
++      LocalPerf(cudnnConvolutionBwdDataAlgoPerf_t * bwdData) {m_bwdData = bwdData;}
++      size_t getMemory(int i) {return m_fwd != nullptr ? m_fwd[i].memory : m_bwdFilter != nullptr ? m_bwdFilter[i].memory : m_bwdData != nullptr ? m_bwdData[i].memory : 0;}
++      float getTime(int i) {return m_fwd != nullptr ? m_fwd[i].time : m_bwdFilter != nullptr ? m_bwdFilter[i].time : m_bwdData != nullptr ? m_bwdData[i].time : 0;}
++      cudnnStatus_t getStatus(int i) {return m_fwd != nullptr ? m_fwd[i].status : m_bwdFilter != nullptr ? m_bwdFilter[i].status : m_bwdData != nullptr ? m_bwdData[i].status : CUDNN_STATUS_BAD_PARAM;}
++      int getIdx(algoPreference const & algoPref, int const algoCount, size_t memLim = std::numeric_limits<size_t>::max()) {
++         int algoIdx{0};
++         if (algoPref == algoPreference::fastest) {  // prefer fastest
++            float temp_runtime{std::numeric_limits<float>::max()};
++            for (int i = 0; i < algoCount; ++i) {
++               if (getStatus(i) == CUDNN_STATUS_SUCCESS && getTime(i) < temp_runtime) {
++                  temp_runtime = getTime(i);
++                  algoIdx = i;
++               }
+             }
+-         }
+-      } else if (algoPref == algoPreference::workspace_limit) {  // constrain to workspace size
+-         float temp_runtime{std::numeric_limits<float>::max()};
+-         for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].time < temp_runtime && perfResults.fwd[i].memory <= memLim) {
+-               temp_runtime = perfResults.fwd[i].time;
+-               algoIdx = i;
++         } else if (algoPref == algoPreference::workspace_limit) {  // constrain to workspace size
++            float temp_runtime{std::numeric_limits<float>::max()};
++            for (int i = 0; i < algoCount; ++i) {
++               if (getStatus(i) == CUDNN_STATUS_SUCCESS && getTime(i) < temp_runtime && getMemory(i) <= memLim) {
++                  temp_runtime = getTime(i);
++                  algoIdx = i;
++               }
+             }
+-         }
+-      } else {  // prefer smallest workspace size
+-         size_t temp_memsize{std::numeric_limits<size_t>::max()};
+-         for (int i = 0; i < algoCount; ++i) {
+-            if (perfResults.fwd[i].status == CUDNN_STATUS_SUCCESS && perfResults.fwd[i].memory < temp_memsize) {
+-               temp_memsize = perfResults.fwd[i].memory;
+-               algoIdx = i;
++         } else {  // prefer smallest workspace size
++            size_t temp_memsize{std::numeric_limits<size_t>::max()};
++            for (int i = 0; i < algoCount; ++i) {
++               if (getStatus(i) == CUDNN_STATUS_SUCCESS && getMemory(i) < temp_memsize) {
++                  temp_memsize = getMemory(i);
++                  algoIdx = i;
++               }
+             }
+          }
+-      }
+-      return algoIdx;
++         return algoIdx;
++      };
++    private:
++      LocalPerf();
++      // these three type are absolutely equivalent
++      // and one can access them as they wish to get info
++      cudnnConvolutionFwdAlgoPerf_t * m_fwd;
++      cudnnConvolutionBwdFilterAlgoPerf_t * m_bwdFilter;
++      cudnnConvolutionBwdDataAlgoPerf_t * m_bwdData;
+    };
+ #else
+    // More detailed alternative: cudnnFindConvolutionForwardAlgorithm (only option in newer cuDNN versions)
+@@ -502,8 +511,8 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   LocalPerf_t fwdPerfResults{convFwdPerfResults};
+-   convWorkspace->AlgorithmForward = convFwdPerfResults[choose_algo(algoChoice, algoCount, fwdPerfResults, memLimit)].algo;
++   LocalPerf fwdPerfResults{convFwdPerfResults};
++   convWorkspace->AlgorithmForward = convFwdPerfResults[fwdPerfResults.getIdx(algoChoice, algoCount, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionForwardAlgorithm(
+       cudnnHandle, inputTensorDescriptor, convDescriptors->WeightsDescriptor, convDescriptors->LayerDescriptor,
+@@ -594,8 +603,8 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   LocalPerf_t bwdDataPerfResults{convBwdDataPerfResults};
+-   convWorkspace->AlgorithmBackward = convBwdDataPerfResults[choose_algo(algoChoice, algoCount, bwdDataPerfResults, memLimit)].algo;
++   LocalPerf bwdDataPerfResults{convBwdDataPerfResults};
++   convWorkspace->AlgorithmBackward = convBwdDataPerfResults[bwdDataPerfResults.getIdx(algoChoice, algoCount, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardDataAlgorithm(cudnnHandle,
+                                                       convDescriptors->WeightsDescriptor,
+@@ -669,8 +678,8 @@ void TCudnn<AFloat>::InitializeConvWorkspace(TWorkspace * & workspace,
+    //    &convWorkspace,
+    //    memLimit));  // use memLimit for workspace size
+    // instead choose either fastest or lowest memory algo as per preference
+-   LocalPerf_t bwdFilterPerfResults{convBwdFilterPerfResults};
+-   convWorkspace->HelperAlgorithm = convBwdFilterPerfResults[choose_algo(algoChoice, algoCount, bwdFilterPerfResults, memLimit)].algo;
++   LocalPerf bwdFilterPerfResults{convBwdFilterPerfResults};
++   convWorkspace->HelperAlgorithm = convBwdFilterPerfResults[bwdFilterPerfResults.getIdx(algoChoice, algoCount, memLimit)].algo;
+ #else
+    CUDNNCHECK(cudnnGetConvolutionBackwardFilterAlgorithm(cudnnHandle,
+                                                          activationBackwardDescriptor,

Copied: root/repos/community-testing-x86_64/jupyter_notebook_config.py (from rev 665090, root/trunk/jupyter_notebook_config.py)
===================================================================
--- community-testing-x86_64/jupyter_notebook_config.py	                        (rev 0)
+++ community-testing-x86_64/jupyter_notebook_config.py	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1 @@
+c.NotebookApp.ip = '*'

Copied: root/repos/community-testing-x86_64/nbman-for-arch.patch (from rev 665090, root/trunk/nbman-for-arch.patch)
===================================================================
--- community-testing-x86_64/nbman-for-arch.patch	                        (rev 0)
+++ community-testing-x86_64/nbman-for-arch.patch	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,177 @@
+diff --color -aur root-6.22.00-old/main/src/nbmain.cxx root-6.22.00-new/main/src/nbmain.cxx
+--- root-6.22.00-old/main/src/nbmain.cxx	2020-07-20 15:26:53.983725609 +0300
++++ root-6.22.00-new/main/src/nbmain.cxx	2020-07-20 15:29:53.940386060 +0300
+@@ -33,10 +33,6 @@
+ #define NB_OPT             "notebook"
+ #define JUPYTER_CONF_DIR_V "JUPYTER_CONFIG_DIR"
+ #define JUPYTER_PATH_V     "JUPYTER_PATH"
+-#define NB_CONF_DIR        "notebook"
+-#define ROOTNB_DIR         ".rootnb"
+-#define COMMIT_FILE        ".rootcommit"
+-#define JUPYTER_CONFIG     "jupyter_notebook_config.py"
+
+ using namespace std;
+
+@@ -46,161 +46,12 @@
+ #endif
+ 
+ ////////////////////////////////////////////////////////////////////////////////
+-/// Checks whether ROOT notebook files are installed and they are
+-/// the current version.
+-
+-static int CheckNbInstallation(string dir)
+-{
+-   string commit(gROOT->GetGitCommit());
+-   string inputfname(dir + pathsep + ROOTNB_DIR + pathsep + COMMIT_FILE);
+-   ifstream in(inputfname);
+-   if (in.is_open()) {
+-      string line;
+-      in >> line;
+-      in.close();
+-      if (line.compare(commit) == 0) return  0; // already installed
+-      else                           return -1; // install, it's outdated
+-   }
+-   else if (gSystem->AccessPathName(inputfname.c_str())) {
+-      // There is no installation
+-      return -1;
+-   }
+-   else {
+-      fprintf(stderr,
+-              "Error checking notebook installation -- cannot open %s\n",
+-              inputfname.c_str());
+-      return -2;
+-   }
+-}
+-
+-////////////////////////////////////////////////////////////////////////////////
+-/// Installs ROOT notebook files in the user's home directory.
+-
+-static bool InstallNbFiles(string source, string dest)
+-{
+-   // Create installation directory
+-   if (gSystem->AccessPathName(dest.c_str())) {
+-      if (gSystem->mkdir(dest.c_str())) {
+-         fprintf(stderr,
+-                 "Error installing notebook configuration files -- cannot create directory %s\n",
+-                 dest.c_str());
+-         return false;
+-      }
+-   }
+-
+-   // Copy files in source to dest
+-   TSystemDirectory dir(source.c_str(), source.c_str());
+-   std::unique_ptr<TList> files;
+-   files.reset(dir.GetListOfFiles());
+-   if (files) {
+-      TSystemFile *file;
+-      TListIter it(files.get());
+-      while ((file = (TSystemFile*)it())) {
+-         TString s = file->GetName();
+-         string fname(s.Data());
+-         string sourcefile = source + pathsep + fname;
+-         string destfile   = dest   + pathsep + fname;
+-         if (!file->IsDirectory()) {
+-            if (gSystem->CopyFile(sourcefile.c_str(), destfile.c_str(), true)) {
+-               fprintf(stderr,
+-                       "Error installing notebook configuration files -- cannot copy file %s to %s\n",
+-                       sourcefile.c_str(), destfile.c_str());
+-               return false;
+-            }
+-         }
+-         else if (fname.compare(".") && fname.compare("..") && fname.compare("html")) {
+-            if (!InstallNbFiles(sourcefile, destfile))
+-               return false;
+-         }
+-      }
+-   }
+-
+-   return true;
+-}
+-
+-////////////////////////////////////////////////////////////////////////////////
+-/// Creates the Jupyter notebook configuration file that sets the
+-/// necessary environment.
+-
+-static bool CreateJupyterConfig(string dest, string rootbin, string rootlib, string rootdata)
+-{
+-   string jupyconfig = dest + pathsep + JUPYTER_CONFIG;
+-   ofstream out(jupyconfig, ios::trunc);
+-   if (out.is_open()) {
+-      out << "import os" << endl;
+-      out << "rootbin = '" << rootbin << "'" << endl;
+-      out << "rootlib = '" << rootlib << "'" << endl;
+-#ifdef WIN32
+-      string jsrootsys = rootdata + "\\js\\";
+-      out << "os.environ['PYTHONPATH']      = '%s' % rootlib + ':' + os.getenv('PYTHONPATH', '')" << endl;
+-      out << "os.environ['PATH']            = '%s:%s\\bin' % (rootbin,rootbin) + ':' + '%s' % rootlib + ':' + os.getenv('PATH', '')" << endl;
+-#else
+-      string jsrootsys = rootdata + "/js/";
+-      out << "os.environ['PYTHONPATH']      = '%s' % rootlib + ':' + os.getenv('PYTHONPATH', '')" << endl;
+-      out << "os.environ['PATH']            = '%s:%s/bin' % (rootbin,rootbin) + ':' + os.getenv('PATH', '')" << endl;
+-      out << "os.environ['LD_LIBRARY_PATH'] = '%s' % rootlib + ':' + os.getenv('LD_LIBRARY_PATH', '')" << endl;
+-#endif
+-      out << "c.NotebookApp.extra_static_paths = ['" << jsrootsys << "']" << endl;
+-      out.close();
+-      return true;
+-   }
+-   else {
+-      fprintf(stderr,
+-              "Error installing notebook configuration files -- cannot create IPython config file at %s\n",
+-              jupyconfig.c_str());
+-      return false;
+-   }
+-}
+-
+-////////////////////////////////////////////////////////////////////////////////
+-/// Creates a file that stores the current commit id in it.
+-
+-static bool CreateStamp(string dest)
+-{
+-   ofstream out(dest + pathsep + COMMIT_FILE, ios::trunc);
+-   if (out.is_open()) {
+-      out << gROOT->GetGitCommit();
+-      out.close();
+-      return true;
+-   }
+-   else {
+-      fprintf(stderr,
+-              "Error installing notebook configuration files -- cannot create %s\n",
+-              COMMIT_FILE);
+-      return false;
+-   }
+-}
+-
+-////////////////////////////////////////////////////////////////////////////////
+ /// Spawn a Jupyter notebook customised by ROOT.
+ 
+ int main(int argc, char **argv)
+ {
+-   string rootbin(TROOT::GetBinDir().Data());
+-   string rootlib(TROOT::GetLibDir().Data());
+-   string rootetc(TROOT::GetEtcDir().Data());
+-   string rootdata(TROOT::GetDataDir().Data());
+-
+-   // If needed, install ROOT notebook files in the user's home directory
+-#ifdef WIN32
+-   string homedir(getenv("USERPROFILE"));
+-#else
+-   string homedir(getenv("HOME"));
+-#endif
+-   int inst = CheckNbInstallation(homedir);
+-   if (inst == -1) {
+-      // The etc directory contains the ROOT notebook files to install
+-      string source(rootetc + pathsep + NB_CONF_DIR);
+-      string dest(homedir + pathsep + ROOTNB_DIR);
+-      bool res = InstallNbFiles(source, dest) &&
+-                 CreateJupyterConfig(dest, rootbin, rootlib, rootdata) &&
+-                 CreateStamp(dest);
+-      if (!res) return 1;
+-   }
+-   else if (inst == -2) return 1;
+-
+    // Set IPython directory for the ROOT notebook flavour
+-   string rootnbpath = homedir + pathsep + ROOTNB_DIR;
++   string rootnbpath = pathsep + string("etc") + pathsep + string("root") + pathsep + string("notebook");
+    string jupyconfdir(JUPYTER_CONF_DIR_V + ("=" + rootnbpath));
+    string jupypathdir(JUPYTER_PATH_V + ("=" + rootnbpath));
+    putenv((char *)jupyconfdir.c_str());

Copied: root/repos/community-testing-x86_64/root.pc.tpl (from rev 665090, root/trunk/root.pc.tpl)
===================================================================
--- community-testing-x86_64/root.pc.tpl	                        (rev 0)
+++ community-testing-x86_64/root.pc.tpl	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,12 @@
+prefix=_PREFIX
+exec_prefix=_EXECPREFIX
+libdir=_LIBDIR
+includedir=_INCDIR
+
+Name: ROOT
+Description: C++ data analysis framework and interpreter from CERN
+Version: _PKGVERSION
+URL: _UPSTREAM_URL
+Requires: _REQUIRES
+Libs: _LIBRARIES
+Cflags: _CFLAGS

Copied: root/repos/community-testing-x86_64/root.xml (from rev 665090, root/trunk/root.xml)
===================================================================
--- community-testing-x86_64/root.xml	                        (rev 0)
+++ community-testing-x86_64/root.xml	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<mime-info xmlns="http://www.freedesktop.org/standards/shared-mime-info">
+  <mime-type type="application/x-root">
+    <comment>ROOT file</comment>
+    <comment xml:lang="de">ROOT-Datei</comment>
+    <comment xml:lang="en">ROOT-File</comment>
+    <comment xml:lang="fr">ROOT-Fichier</comment>
+    <comment xml:lang="it">ROOT-File</comment>
+    <glob pattern="*.root"/>
+    <magic priority="80">
+      <match value="root" type="string" offset="0:64"/>
+    </magic>
+  </mime-type>
+</mime-info>

Copied: root/repos/community-testing-x86_64/settings-cuda.cmake (from rev 665090, root/trunk/settings-cuda.cmake)
===================================================================
--- community-testing-x86_64/settings-cuda.cmake	                        (rev 0)
+++ community-testing-x86_64/settings-cuda.cmake	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,110 @@
+set (CMAKE_BUILD_TYPE Release CACHE STRING "" FORCE)
+set (BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE)
+set (CMAKE_INSTALL_PREFIX /usr CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_CMAKEDIR /usr/lib/cmake/ROOT CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_BINDIR /usr/bin CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_LIBDIR /usr/lib/root CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_INCLUDEDIR /usr/include CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_SYSCONFDIR /etc/root CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_DATAROOTDIR /usr/share CACHE PATH "" FORCE)
+set (CMAKE_CXX_STANDARD 17 CACHE STRING "" FORCE)
+set (CMAKE_CUDA_STANDARD 14 CACHE STRING "" FORCE)
+set (PYTHIA8_DATA /usr/share/pythia8/xmldoc CACHE PATH "" FORCE)  # sync with pythia8 package
+set (GLEW_DIR /usr/include/GL CACHE PATH "" FORCE)  # need to set manually
+set (alien OFF CACHE BOOL "" FORCE)
+set (all OFF CACHE BOOL "" FORCE)
+set (asimage ON CACHE BOOL "" FORCE)
+set (builtin_afterimage OFF CACHE BOOL "" FORCE)
+set (builtin_clang ON CACHE BOOL "" FORCE)
+set (CLANG_ENABLE_STATIC_ANALYZER ON CACHE BOOL "" FORCE)
+set (CLANG_ANALYZER_BUILD_Z3 ON CACHE BOOL "" FORCE)
+set (builtin_cfitsio OFF CACHE BOOL "" FORCE)
+set (builtin_davix OFF CACHE BOOL "" FORCE)
+set (builtin_fftw3 OFF CACHE BOOL "" FORCE)
+set (builtin_ftgl OFF CACHE BOOL "" FORCE)
+set (builtin_freetype OFF CACHE BOOL "" FORCE)
+set (builtin_gl2ps OFF CACHE BOOL "" FORCE)
+set (builtin_glew OFF CACHE BOOL "" FORCE)
+set (builtin_gsl OFF CACHE BOOL "" FORCE)
+set (builtin_lzma OFF CACHE BOOL "" FORCE)
+set (builtin_llvm ON CACHE BOOL "" FORCE)
+set (builtin_openssl OFF CACHE BOOL "" FORCE)
+set (builtin_pcre OFF CACHE BOOL "" FORCE)
+set (builtin_tbb OFF CACHE BOOL "" FORCE)
+set (builtin_unuran OFF CACHE BOOL "" FORCE)
+set (builtin_vc OFF CACHE BOOL "" FORCE)
+set (builtin_xxhash OFF CACHE BOOL "" FORCE)
+set (builtin_xrootd OFF CACHE BOOL "" FORCE)
+set (builtin_zlib OFF CACHE BOOL "" FORCE)
+set (ccache ON CACHE BOOL "" FORCE)
+set (clad ON CACHE BOOL "" FORCE)
+set (cocoa OFF CACHE BOOL "" FORCE)  # MacOS only
+set (cuda ON CACHE BOOL "" FORCE)
+set (cudnn ON CACHE BOOL "" FORCE)
+set (dataframe ON CACHE BOOL "" FORCE)
+set (davix OFF CACHE BOOL "" FORCE)
+set (dcache OFF CACHE BOOL "" FORCE)
+set (exceptions ON CACHE BOOL "" FORCE)
+set (fail-on-missing ON CACHE BOOL "" FORCE)
+set (fcgi ON CACHE BOOL "" FORCE)
+set (fftw3 ON CACHE BOOL "" FORCE)
+set (fitsio ON CACHE BOOL "" FORCE)
+set (fortran ON CACHE BOOL "" FORCE)
+set (gdml ON CACHE BOOL "" FORCE)
+set (genvector ON CACHE BOOL "" FORCE)
+set (gfal OFF CACHE BOOL "" FORCE)
+set (gl2ps ON CACHE BOOL "" FORCE)
+set (gminimal OFF CACHE BOOL "" FORCE)
+set (gnuinstall ON CACHE BOOL "" FORCE)
+set (gsl_shared ON CACHE BOOL "" FORCE)
+set (gviz ON CACHE BOOL "" FORCE)
+set (http ON CACHE BOOL "" FORCE)
+set (imt ON CACHE BOOL "" FORCE)
+set (jemalloc OFF CACHE BOOL "" FORCE)
+set (mathmore ON CACHE BOOL "" FORCE)
+set (minimal OFF CACHE BOOL "" FORCE)
+set (minuit2 ON CACHE BOOL "" FORCE)
+set (minuit2_mpi ON CACHE BOOL "" FORCE)
+set (minuit2_omp ON CACHE BOOL "" FORCE)
+set (mlp ON CACHE BOOL "" FORCE)
+set (monalisa OFF CACHE BOOL "" FORCE)
+set (mpi ON CACHE BOOL "" FORCE)
+set (mt ON CACHE BOOL "" FORCE)
+set (mysql ON CACHE BOOL "" FORCE)
+set (odbc ON CACHE BOOL "" FORCE)
+set (opengl ON CACHE BOOL "" FORCE)
+set (OpenGL_GL_PREFERENCE GLVND CACHE STRING "" FORCE)  # use new policy since 3.11
+set (oracle OFF CACHE BOOL "" FORCE)
+set (pgsql ON CACHE BOOL "" FORCE)
+set (pythia6 OFF CACHE BOOL "" FORCE)
+set (pythia6_nolink OFF CACHE BOOL "" FORCE)
+set (pythia8 ON CACHE BOOL "" FORCE)
+set (pyroot ON CACHE BOOL "" FORCE)
+set (qt5web ON CACHE BOOL "" FORCE)
+set (roofit ON CACHE BOOL "" FORCE)
+set (root7 ON CACHE BOOL "" FORCE)
+set (roottest OFF CACHE BOOL "" FORCE)
+set (rpath OFF CACHE BOOL "" FORCE)
+set (runtime_cxxmodules OFF CACHE BOOL "" FORCE)  # breaks python
+set (r OFF CACHE BOOL "" FORCE)  # requires r-rcpp
+set (shadowpw ON CACHE BOOL "" FORCE)
+set (shared ON CACHE BOOL "" FORCE)
+set (soversion OFF CACHE BOOL "" FORCE)
+set (spectrum ON CACHE BOOL "" FORCE)
+set (sqlite ON CACHE BOOL "" FORCE)
+set (ssl ON CACHE BOOL "" FORCE)
+set (tbb ON CACHE BOOL "" FORCE)
+set (tcmalloc OFF CACHE BOOL "" FORCE)
+set (testing OFF CACHE BOOL "" FORCE)
+set (tmva ON CACHE BOOL "" FORCE)
+set (tmva-cpu OFF CACHE BOOL "" FORCE)
+set (tmva-gpu ON CACHE BOOL "" FORCE)
+set (tmva-pymva ON CACHE BOOL "" FORCE)
+set (unuran ON CACHE BOOL "" FORCE)
+set (vc ON CACHE BOOL "" FORCE)
+set (vdt ON CACHE BOOL "" FORCE)
+set (winrtdebug OFF CACHE BOOL "" FORCE)  # windows only
+set (webgui ON CACHE BOOL "" FORCE)
+set (x11 ON CACHE BOOL "" FORCE)
+set (xml ON CACHE BOOL "" FORCE)
+set (xrootd ON CACHE BOOL "" FORCE)

Copied: root/repos/community-testing-x86_64/settings.cmake (from rev 665090, root/trunk/settings.cmake)
===================================================================
--- community-testing-x86_64/settings.cmake	                        (rev 0)
+++ community-testing-x86_64/settings.cmake	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,110 @@
+set (CMAKE_BUILD_TYPE Release CACHE STRING "" FORCE)
+set (BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE)
+set (CMAKE_INSTALL_PREFIX /usr CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_CMAKEDIR /usr/lib/cmake/ROOT CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_BINDIR /usr/bin CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_LIBDIR /usr/lib/root CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_INCLUDEDIR /usr/include CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_SYSCONFDIR /etc/root CACHE PATH "" FORCE)
+set (CMAKE_INSTALL_DATAROOTDIR /usr/share CACHE PATH "" FORCE)
+set (CMAKE_CXX_STANDARD 17 CACHE STRING "" FORCE)
+set (CMAKE_CUDA_STANDARD 14 CACHE STRING "" FORCE)
+set (PYTHIA8_DATA /usr/share/pythia8/xmldoc CACHE PATH "" FORCE)  # sync with pythia8 package
+set (GLEW_DIR /usr/include/GL CACHE PATH "" FORCE)  # need to set manually
+set (alien OFF CACHE BOOL "" FORCE)
+set (all OFF CACHE BOOL "" FORCE)
+set (asimage ON CACHE BOOL "" FORCE)
+set (builtin_afterimage OFF CACHE BOOL "" FORCE)
+set (builtin_clang ON CACHE BOOL "" FORCE)
+set (CLANG_ENABLE_STATIC_ANALYZER ON CACHE BOOL "" FORCE)
+set (CLANG_ANALYZER_BUILD_Z3 ON CACHE BOOL "" FORCE)
+set (builtin_cfitsio OFF CACHE BOOL "" FORCE)
+set (builtin_davix OFF CACHE BOOL "" FORCE)
+set (builtin_fftw3 OFF CACHE BOOL "" FORCE)
+set (builtin_ftgl OFF CACHE BOOL "" FORCE)
+set (builtin_freetype OFF CACHE BOOL "" FORCE)
+set (builtin_gl2ps OFF CACHE BOOL "" FORCE)
+set (builtin_glew OFF CACHE BOOL "" FORCE)
+set (builtin_gsl OFF CACHE BOOL "" FORCE)
+set (builtin_lzma OFF CACHE BOOL "" FORCE)
+set (builtin_llvm ON CACHE BOOL "" FORCE)
+set (builtin_openssl OFF CACHE BOOL "" FORCE)
+set (builtin_pcre OFF CACHE BOOL "" FORCE)
+set (builtin_tbb OFF CACHE BOOL "" FORCE)
+set (builtin_unuran OFF CACHE BOOL "" FORCE)
+set (builtin_vc OFF CACHE BOOL "" FORCE)
+set (builtin_xxhash OFF CACHE BOOL "" FORCE)
+set (builtin_xrootd OFF CACHE BOOL "" FORCE)
+set (builtin_zlib OFF CACHE BOOL "" FORCE)
+set (ccache ON CACHE BOOL "" FORCE)
+set (clad ON CACHE BOOL "" FORCE)
+set (cocoa OFF CACHE BOOL "" FORCE)  # MacOS only
+set (cuda OFF CACHE BOOL "" FORCE)
+set (cudnn OFF CACHE BOOL "" FORCE)
+set (dataframe ON CACHE BOOL "" FORCE)
+set (davix OFF CACHE BOOL "" FORCE)
+set (dcache OFF CACHE BOOL "" FORCE)
+set (exceptions ON CACHE BOOL "" FORCE)
+set (fail-on-missing ON CACHE BOOL "" FORCE)
+set (fcgi ON CACHE BOOL "" FORCE)
+set (fftw3 ON CACHE BOOL "" FORCE)
+set (fitsio ON CACHE BOOL "" FORCE)
+set (fortran ON CACHE BOOL "" FORCE)
+set (gdml ON CACHE BOOL "" FORCE)
+set (genvector ON CACHE BOOL "" FORCE)
+set (gfal OFF CACHE BOOL "" FORCE)
+set (gl2ps ON CACHE BOOL "" FORCE)
+set (gminimal OFF CACHE BOOL "" FORCE)
+set (gnuinstall ON CACHE BOOL "" FORCE)
+set (gsl_shared ON CACHE BOOL "" FORCE)
+set (gviz ON CACHE BOOL "" FORCE)
+set (http ON CACHE BOOL "" FORCE)
+set (imt ON CACHE BOOL "" FORCE)
+set (jemalloc OFF CACHE BOOL "" FORCE)
+set (mathmore ON CACHE BOOL "" FORCE)
+set (minimal OFF CACHE BOOL "" FORCE)
+set (minuit2 ON CACHE BOOL "" FORCE)
+set (minuit2_mpi ON CACHE BOOL "" FORCE)
+set (minuit2_omp ON CACHE BOOL "" FORCE)
+set (mlp ON CACHE BOOL "" FORCE)
+set (monalisa OFF CACHE BOOL "" FORCE)
+set (mpi ON CACHE BOOL "" FORCE)
+set (mt ON CACHE BOOL "" FORCE)
+set (mysql ON CACHE BOOL "" FORCE)
+set (odbc ON CACHE BOOL "" FORCE)
+set (opengl ON CACHE BOOL "" FORCE)
+set (OpenGL_GL_PREFERENCE GLVND CACHE STRING "" FORCE)  # use new policy since 3.11
+set (oracle OFF CACHE BOOL "" FORCE)
+set (pgsql ON CACHE BOOL "" FORCE)
+set (pythia6 OFF CACHE BOOL "" FORCE)
+set (pythia6_nolink OFF CACHE BOOL "" FORCE)
+set (pythia8 ON CACHE BOOL "" FORCE)
+set (pyroot ON CACHE BOOL "" FORCE)
+set (qt5web ON CACHE BOOL "" FORCE)
+set (roofit ON CACHE BOOL "" FORCE)
+set (root7 ON CACHE BOOL "" FORCE)
+set (roottest OFF CACHE BOOL "" FORCE)
+set (rpath OFF CACHE BOOL "" FORCE)
+set (runtime_cxxmodules OFF CACHE BOOL "" FORCE)  # breaks python
+set (r OFF CACHE BOOL "" FORCE)  # requires r-rcpp
+set (shadowpw ON CACHE BOOL "" FORCE)
+set (shared ON CACHE BOOL "" FORCE)
+set (soversion OFF CACHE BOOL "" FORCE)
+set (spectrum ON CACHE BOOL "" FORCE)
+set (sqlite ON CACHE BOOL "" FORCE)
+set (ssl ON CACHE BOOL "" FORCE)
+set (tbb ON CACHE BOOL "" FORCE)
+set (tcmalloc OFF CACHE BOOL "" FORCE)
+set (testing OFF CACHE BOOL "" FORCE)
+set (tmva ON CACHE BOOL "" FORCE)
+set (tmva-cpu ON CACHE BOOL "" FORCE)
+set (tmva-gpu OFF CACHE BOOL "" FORCE)
+set (tmva-pymva ON CACHE BOOL "" FORCE)
+set (unuran ON CACHE BOOL "" FORCE)
+set (vc ON CACHE BOOL "" FORCE)
+set (vdt ON CACHE BOOL "" FORCE)
+set (winrtdebug OFF CACHE BOOL "" FORCE)  # windows only
+set (webgui ON CACHE BOOL "" FORCE)
+set (x11 ON CACHE BOOL "" FORCE)
+set (xml ON CACHE BOOL "" FORCE)
+set (xrootd ON CACHE BOOL "" FORCE)

Copied: root/repos/community-testing-x86_64/thisroot.fail (from rev 665090, root/trunk/thisroot.fail)
===================================================================
--- community-testing-x86_64/thisroot.fail	                        (rev 0)
+++ community-testing-x86_64/thisroot.fail	2020-07-23 20:24:24 UTC (rev 665091)
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+# thisroot.* scripts should not be used to
+# configure ROOT on Arch. Notify user and
+# return an error
+
+function fail {
+    printf '%s\n' "$1" >&2
+    exit "${2:-$1}"
+}
+
+fail "ERROR: $(basename $0) should never be used!" 1


More information about the arch-commits mailing list