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xuhuisheng / rocm-build

build scripts for ROCm
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tensorflow error on gfx803 #26

Closed Dachtire closed 2 years ago

Dachtire commented 2 years ago

Environment

Hardware description
GPU - rx590
Software version
OS - debian sid
ROCm - 5.0.2 from ubuntu apt
Python - 3.9.10
tensorflow - git 2.9.0
pytorch - git 1.12

using pip tensorflow-rocm with error "hipErrorNoBinaryForGpu", so I build and install tensorflow with: 

git clone https://github.com/ROCmSoftwarePlatform/tensorflow-upstream.git
./build_rocm_python3
>>> import tensorflow
>>> print(tensorflow.config.experimental.list_physical_devices('GPU'))
2022-03-10 21:21:13.850863: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:838] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-10 21:21:13.911531: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:838] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-10 21:21:13.911596: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:838] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-03-10 21:21:13.911627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1899] Ignoring visible gpu device (device: 0, name: AMD Radeon RX 590 Series, pci bus id: 0000:27:00.0) with AMDGPU version : gfx803. The supported AMDGPU versions are gfx1030, gfx900, gfx906, gfx908, gfx90a.
[]

then do the 22.tensile-gfx803-1.patch on:

git clone https://github.com/ROCmSoftwarePlatform/Tensile.git
git switch -d rocm-5.0.2

build and install rocblas:

git clone https://github.com/ROCmSoftwarePlatform/rocBLAS.git
    git switch -d rocm-5.0.2
    rm -rf library/src/blas3/Tensile/Logic/asm_full/r9nano*
    CXX=/opt/rocm-5.0.2/bin/hipcc cmake -DAMDGPU_TARGETS=gfx803 -DROCM_PATH=/opt/rocm-5.0.2 -DTensile_LOGIC=asm_full -DTensile_ARCHITECTURE=gfx803 -DTensile_CODE_OBJECT_VERSION=V3 -DCMAKE_BUILD_TYPE=Release -DTensile_TEST_LOCAL_PATH=../../Tensile -DBUILD_WITH_TENSILE_HOST=ON -DTensile_LIBRARY_FORMAT=yaml -DRUN_HEADER_TESTING=OFF -DTensile_COMPILER=hipcc -DHIP_CLANG_INCLUDE_PATH=/opt/rocm-5.0.2/llvm/include -DCPACK_SET_DESTDIR=OFF -DCMAKE_PREFIX_PATH=/opt/rocm-5.0.2 -DCMAKE_INSTALL_PREFIX=rocblas-install -DCPACK_PACKAGING_INSTALL_PREFIX=/opt/rocm-5.0.2 -DCPACK_GENERATOR=DEB -G Ninja ..
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``` -- The CXX compiler identification is Clang 14.0.0 -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: /opt/rocm-5.0.2/bin/hipcc - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done -- Looking for C++ include pthread.h -- Looking for C++ include pthread.h - found -- Performing Test CMAKE_HAVE_LIBC_PTHREAD -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success -- Found Threads: TRUE -- Use hip-clang to build for amdgpu backend -- OS detected is debian -- Performing Test HAVE_gfx803 -- Performing Test HAVE_gfx803 - Success -- Performing Test HAVE_gfx900 -- Performing Test HAVE_gfx900 - Success -- Performing Test HAVE_gfx906:xnack- -- Performing Test HAVE_gfx906:xnack- - Success -- Performing Test HAVE_gfx908:xnack- -- Performing Test HAVE_gfx908:xnack- - Success -- Performing Test HAVE_gfx90a:xnack+ -- Performing Test HAVE_gfx90a:xnack+ - Success -- Performing Test HAVE_gfx90a:xnack- -- Performing Test HAVE_gfx90a:xnack- - Success -- Performing Test HAVE_gfx1010 -- Performing Test HAVE_gfx1010 - Success -- Performing Test HAVE_gfx1011 -- Performing Test HAVE_gfx1011 - Success -- Performing Test HAVE_gfx1012 -- Performing Test HAVE_gfx1012 - Success -- Performing Test HAVE_gfx1030 -- Performing Test HAVE_gfx1030 - Success /bin/python3 -m venv ~/rocm/rocBLAS/build/virtualenv --system-site-packages --clear ~/rocm/rocBLAS/build/virtualenv/bin/python3 -m pip install ~/rocm/Tensile Processing ~/rocm/Tensile Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done' Collecting msgpack Using cached msgpack-1.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (322 kB) Requirement already satisfied: pyyaml in /usr/lib/python3/dist-packages (from Tensile==4.31.0) (5.4.1) Building wheels for collected packages: Tensile Building wheel for Tensile (setup.py): started Building wheel for Tensile (setup.py): finished with status 'done' Created wheel for Tensile: filename=Tensile-4.31.0-py3-none-any.whl size=4544414 sha256=31c2449b59d5a6850f2ff31a393b82dfea64eca86f5e8404ee82772d75e578c3 Stored in directory: /tmp/pip-ephem-wheel-cache-8mgcppg6/wheels/48/8d/c5/ef04e9532161b93e4192d8040bf0ac9ddcb15321e71963e0d3 Successfully built Tensile Installing collected packages: msgpack, Tensile Successfully installed Tensile-4.31.0 msgpack-1.0.3 -- using local Tensile from ~/rocm/Tensile, copied to -- Adding ~/rocm/rocBLAS/build/virtualenv to CMAKE_PREFIX_PATH -- The C compiler identification is GNU 11.2.0 -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: /bin/cc - skipped -- Detecting C compile features -- Detecting C compile features - done -- hip::amdhip64 is SHARED_LIBRARY -- Performing Test HIP_CLANG_SUPPORTS_PARALLEL_JOBS -- Performing Test HIP_CLANG_SUPPORTS_PARALLEL_JOBS - Success -- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version "1.2.11") -- Found LibXml2: /usr/lib/x86_64-linux-gnu/libxml2.so (found version "2.9.13") LLVMObjectYAML_LIBRARY: /opt/rocm-5.0.2/llvm/lib/libLLVMObjectYAML.a -- hip::amdhip64 is SHARED_LIBRARY -- Using AMDGPU_TARGETS: gfx803 -- Tensile script: ~/rocm/rocBLAS/build/virtualenv/lib/python3.9/site-packages/Tensile/bin/TensileCreateLibrary -- Tensile_CREATE_COMMAND: ~/rocm/rocBLAS/build/virtualenv/lib/python3.9/site-packages/Tensile/bin/TensileCreateLibrary;--merge-files;--no-short-file-names;--no-library-print-debug;--code-object-version=V3;--cxx-compiler=hipcc;--library-format=yaml;--architecture=gfx803;~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full;~/rocm/rocBLAS/build/Tensile;HIP -- Tensile_MANIFEST_FILE_PATH: ~/rocm/rocBLAS/build/Tensile/library/TensileManifest.txt '~/rocm/rocBLAS/build/virtualenv/lib/python3.9/site-packages/Tensile/bin/TensileCreateLibrary' '--merge-files' '--no-short-file-names' '--no-library-print-debug' '--code-object-version=V3' '--cxx-compiler=hipcc' '--library-format=yaml' '--architecture=gfx803' '~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full' '~/rocm/rocBLAS/build/Tensile' 'HIP' '--generate-manifest-and-exit' ################################################################################ # Tensile Create Library # Detected local GPU with ISA: gfx803 cap gfx000 gfx803 HasMFMA_bf16_1k 0 0 HasAddLshl 0 0 HasAtomicAdd 0 0 HasCodeObjectV3 0 1 HasDirectToLds 0 1 HasExplicitCO 0 0 HasExplicitNC 0 0 HasLshlOr 0 0 HasMFMA 0 0 HasSMulHi 0 0 MaxLgkmcnt 1 1 MaxVmcnt 0 1 SupportedISA 0 1 SupportedSource 1 1 v_dot2_f32_f16 0 0 v_dot2c_f32_f16 0 0 v_fma_f16 0 0 v_fmac_f16 0 0 v_mac_f16 0 1 v_pk_fma_f16 0 0 v_pk_fmac_f16 0 0 v_fma_f32 0 1 v_fma_mix_f32 0 0 v_fmac_f32 0 0 v_mac_f32 0 1 v_mad_mix_f32 0 0 HasMFMA_f64 0 0 v_dot4_i32_i8 0 0 v_dot4c_i32_i8 0 0 ArchAccUnifiedRegs 0 0 CMPXWritesSGPR 1 1 HasAccCD 0 0 HasEccHalf 0 0 HasWave32 0 0 SeparateVscnt 0 0 Waitcnt0Disabled 0 0 # Found hipcc version 5.0.13601-ded05588 # CodeObjectVersion from TensileCreateLibrary: V3 # CxxCompiler from TensileCreateLibrary: hipcc # Architecture from TensileCreateLibrary: gfx803 # LibraryFormat from TensileCreateLibrary: yaml # LibraryLogicFiles: # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HBH_GB.yaml Reading logic files: Launching 16 threads... Reading logic files: Done. Processing logic data: 100%|██████████| 108/108 [00:01<00:00, 105.98it/s] -- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY -- Performing Test COMPILER_HAS_HIDDEN_VISIBILITY - Success -- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY -- Performing Test COMPILER_HAS_HIDDEN_INLINE_VISIBILITY - Success -- Performing Test COMPILER_HAS_DEPRECATED_ATTR -- Performing Test COMPILER_HAS_DEPRECATED_ATTR - Success -- Configuring done -- Generating done CMake Warning: Manually-specified variables were not used by the project: BUILD_WITH_TENSILE_HOST -- Build files have been written to: ~/rocm/rocBLAS/build ``` 

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``` [0/2] Re-checking globbed directories... [3/248] Generating Tensile Libraries ################################################################################ # Tensile Create Library # Detected local GPU with ISA: gfx803 cap gfx000 gfx803 HasMFMA_bf16_1k 0 0 HasAddLshl 0 0 HasAtomicAdd 0 0 HasCodeObjectV3 0 1 HasDirectToLds 0 1 HasExplicitCO 0 0 HasExplicitNC 0 0 HasLshlOr 0 0 HasMFMA 0 0 HasSMulHi 0 0 MaxLgkmcnt 1 1 MaxVmcnt 0 1 SupportedISA 0 1 SupportedSource 1 1 v_dot2_f32_f16 0 0 v_dot2c_f32_f16 0 0 v_fma_f16 0 0 v_fmac_f16 0 0 v_mac_f16 0 1 v_pk_fma_f16 0 0 v_pk_fmac_f16 0 0 v_fma_f32 0 1 v_fma_mix_f32 0 0 v_fmac_f32 0 0 v_mac_f32 0 1 v_mad_mix_f32 0 0 HasMFMA_f64 0 0 v_dot4_i32_i8 0 0 v_dot4c_i32_i8 0 0 ArchAccUnifiedRegs 0 0 CMPXWritesSGPR 1 1 HasAccCD 0 0 HasEccHalf 0 0 HasWave32 0 0 SeparateVscnt 0 0 Waitcnt0Disabled 0 0 # Found hipcc version 5.0.13601-ded05588 # CodeObjectVersion from TensileCreateLibrary: V3 # CxxCompiler from TensileCreateLibrary: hipcc # Architecture from TensileCreateLibrary: gfx803 # LibraryFormat from TensileCreateLibrary: yaml # LibraryLogicFiles: # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_AlikC_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_BjlkC_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bjlk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_4xi8BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_4xi8BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_BSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_CB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_CB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_DB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_DB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HBH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HSS_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_HSS_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_I8II_BH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_I8II_BH_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_SB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_SB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_ZB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Alik_Bljk_ZB_GB.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HBH.yaml # ~/rocm/rocBLAS/library/src/blas3/Tensile/Logic/asm_full/hip_Cijk_Ailk_Bjlk_HBH_GB.yaml Reading logic files: Launching 16 threads... Reading logic files: Done. Processing logic data: 100%|██████████| 108/108 [00:01<00:00, 105.68it/s] # Writing Custom CMake # Writing Kernels... Generating kernels: Launching 16 threads... Generating kernels: Done. 569it [00:00, 2086152.95it/s] Compiling source kernels: Launching 16 threads... Compiling source kernels: Done. # Kernel Building elapsed time = 414.6 secs # Tensile Library Writer DONE ################################################################################ [248/248] Creating library symlink library/src/librocblas.so.0 library/src/librocblas.so ``` 

    ninja package
    sudo dpkg -i *.deb

turns out same error with or without patch rocblas. I am confuse with the your readme of rocm 5.0 which I shound rebuild, is rocm, tensorflow, or just rocblas? pytorch just build and work with rocm 5.0 apt.

xuhuisheng commented 2 years ago

How about test my built tensorflow_rocm-2.8.0 https://github.com/xuhuisheng/rocm-gfx803 you can install docker and run ubuntu:20.04 image. As you see, the built package is for python38.

From your logs, it said there are gfx1030, gfx900, gfx906, gfx908, gfx90a, no gfx803. RX590 is gfx803, So I guess you didn't compile in the server with rx590. so tensorflow didn't aware that we need gfx803, it used default AMDGPU config.

My suggest is compiling tensorflow_rocm in the server which had rx590. then the script will auto-detect the AMDGPU config. Or you can search the default config from tensorflow-upstream repository and change it to gfx803.

tensorflow_rocm configure should be here: ./third_party/gpus/rocm_configure.bzl, search AMDGPU.

Dachtire commented 2 years ago

I did compile in my pc with rx590, I guess its due to this pr your built should worked as you already test it.

xuhuisheng commented 2 years ago

Em~, It looks like ROCm droped gfx900 and early versions. I suggest you try 2.8 branch: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/tree/r2.8-rocm-enhanced

I will try develop-upstream when I have time.

Dachtire commented 2 years ago

I build the r2.8-rocm-enhanced and run text classification sample without rocblas patch, is this output ok?

CLICK ME

``` 2.8.0 Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz 84131840/84125825 [==============================] - 13s 0us/step 84140032/84125825 [==============================] - 13s 0us/step Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived. Found 25000 files belonging to 2 classes. Using 20000 files for training. 2022-03-11 17:07:28.318495: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.372574: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.372645: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.372908: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 17:07:28.373562: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373644: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373689: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373802: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373854: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373902: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 17:07:28.373934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: AMD Radeon RX 590 Series, pci bus id: 0000:27:00.0 2022-03-11 17:07:28.666117: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Review b'"Pandemonium" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. "Airplane", "The Naked Gun" trilogy, "Blazing Saddles", "High Anxiety", and "Spaceballs" are some of my favorite comedies that spoof a particular genre. "Pandemonium" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\'s all this film has going for it. Geez, "Scream" had more laughs than this film and that was more of a horror film. How bizarre is that?

*1/2 (out of four)' Label 0 Label 0 corresponds to neg Review b"David Mamet is a very interesting and a very un-equal director. His first movie 'House of Games' was the one I liked best, and it set a series of films with characters whose perspective of life changes as they get into complicated situations, and so does the perspective of the viewer.

So is 'Homicide' which from the title tries to set the mind of the viewer to the usual crime drama. The principal characters are two cops, one Jewish and one Irish who deal with a racially charged area. The murder of an old Jewish shop owner who proves to be an ancient veteran of the Israeli Independence war triggers the Jewish identity in the mind and heart of the Jewish detective.

This is were the flaws of the film are the more obvious. The process of awakening is theatrical and hard to believe, the group of Jewish militants is operatic, and the way the detective eventually walks to the final violent confrontation is pathetic. The end of the film itself is Mamet-like smart, but disappoints from a human emotional perspective.

Joe Mantegna and William Macy give strong performances, but the flaws of the story are too evident to be easily compensated." Label 0 Label 0 corresponds to neg Review b'Great documentary about the lives of NY firefighters during the worst terrorist attack of all time.. That reason alone is why this should be a must see collectors item.. What shocked me was not only the attacks, but the"High Fat Diet" and physical appearance of some of these firefighters. I think a lot of Doctors would agree with me that,in the physical shape they were in, some of these firefighters would NOT of made it to the 79th floor carrying over 60 lbs of gear. Having said that i now have a greater respect for firefighters and i realize becoming a firefighter is a life altering job. The French have a history of making great documentary\'s and that is what this is, a Great Documentary.....' Label 1 Label 0 corresponds to neg Label 1 corresponds to pos Found 25000 files belonging to 2 classes. Using 5000 files for validation. Found 25000 files belonging to 2 classes. 2022-03-11 17:07:29.624303: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:29.629857: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:29.673993: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:29.677583: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:31.549365: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Review tf.Tensor(b'Great movie - especially the music - Etta James - "At Last". This speaks volumes when you have finally found that special someone.', shape=(), dtype=string) Label neg Vectorized review (, ) 1287 ---> silent 313 ---> night Vocabulary size: 10000 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, None, 16) 160016 _________________________________________________________________ dropout (Dropout) (None, None, 16) 0 _________________________________________________________________ global_average_pooling1d (Gl (None, 16) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 16) 0 _________________________________________________________________ dense (Dense) (None, 1) 17 ================================================================= Total params: 160,033 Trainable params: 160,033 Non-trainable params: 0 _________________________________________________________________ 2022-03-11 17:07:31.736279: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:31.754635: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/10 2022-03-11 17:07:32.072371: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:32.078437: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 616/625 [============================>.] - ETA: 0s - loss: 0.6645 - binary_accuracy: 0.69462022-03-11 17:07:41.636798: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:41.654155: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:41.732077: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:07:41.736354: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 625/625 [==============================] - 10s 5ms/step - loss: 0.6639 - binary_accuracy: 0.6955 - val_loss: 0.6157 - val_binary_accuracy: 0.7718 Epoch 2/10 625/625 [==============================] - 3s 5ms/step - loss: 0.5505 - binary_accuracy: 0.8002 - val_loss: 0.4998 - val_binary_accuracy: 0.8226 Epoch 3/10 625/625 [==============================] - 3s 5ms/step - loss: 0.4469 - binary_accuracy: 0.8436 - val_loss: 0.4212 - val_binary_accuracy: 0.8472 Epoch 4/10 625/625 [==============================] - 3s 5ms/step - loss: 0.3797 - binary_accuracy: 0.8662 - val_loss: 0.3744 - val_binary_accuracy: 0.8604 Epoch 5/10 625/625 [==============================] - 3s 5ms/step - loss: 0.3362 - binary_accuracy: 0.8783 - val_loss: 0.3455 - val_binary_accuracy: 0.8668 Epoch 6/10 625/625 [==============================] - 3s 5ms/step - loss: 0.3055 - binary_accuracy: 0.8893 - val_loss: 0.3262 - val_binary_accuracy: 0.8718 Epoch 7/10 625/625 [==============================] - 3s 5ms/step - loss: 0.2814 - binary_accuracy: 0.8980 - val_loss: 0.3132 - val_binary_accuracy: 0.8724 Epoch 8/10 625/625 [==============================] - 6s 9ms/step - loss: 0.2624 - binary_accuracy: 0.9047 - val_loss: 0.3033 - val_binary_accuracy: 0.8758 Epoch 9/10 625/625 [==============================] - 3s 5ms/step - loss: 0.2457 - binary_accuracy: 0.9108 - val_loss: 0.2967 - val_binary_accuracy: 0.8776 Epoch 10/10 625/625 [==============================] - 3s 5ms/step - loss: 0.2307 - binary_accuracy: 0.9168 - val_loss: 0.2920 - val_binary_accuracy: 0.8784 2022-03-11 17:08:11.337935: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:11.363725: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:11.369296: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 782/782 [==============================] - 1s 1ms/step - loss: 0.3103 - binary_accuracy: 0.8737 Loss: 0.31032100319862366 Accuracy: 0.8736799955368042 2022-03-11 17:08:27.035043: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:27.040798: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 782/782 [==============================] - 2s 3ms/step - loss: 0.3103 - accuracy: 0.8737 0.8736799955368042 2022-03-11 17:08:29.120644: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.124532: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.126733: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.130666: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.134513: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.136871: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 17:08:29.197412: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. ```

xuhuisheng commented 2 years ago

It is fine. If it didnot need rocblas patch, I guess the cu numbers of RX590 is not equals with RX580. Could you execute /opt/rocm/bin/rocminfo and paste the output? Thank you.

Dachtire commented 2 years ago
CLICK ME

``` ROCk module is loaded ===================== HSA System Attributes ===================== Runtime Version: 1.1 System Timestamp Freq.: 1000.000000MHz Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count) Machine Model: LARGE System Endianness: LITTLE ========== HSA Agents ========== ******* Agent 1 ******* Name: AMD Ryzen 7 2700X Eight-Core Processor Uuid: CPU-XX Marketing Name: AMD Ryzen 7 2700X Eight-Core Processor Vendor Name: CPU Feature: None specified Profile: FULL_PROFILE Float Round Mode: NEAR Max Queue Number: 0(0x0) Queue Min Size: 0(0x0) Queue Max Size: 0(0x0) Queue Type: MULTI Node: 0 Device Type: CPU Cache Info: L1: 32768(0x8000) KB Chip ID: 0(0x0) Cacheline Size: 64(0x40) Max Clock Freq. (MHz): 3700 BDFID: 0 Internal Node ID: 0 Compute Unit: 16 SIMDs per CU: 0 Shader Engines: 0 Shader Arrs. per Eng.: 0 WatchPts on Addr. Ranges:1 Features: None Pool Info: Pool 1 Segment: GLOBAL; FLAGS: FINE GRAINED Size: 65845340(0x3ecb85c) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 2 Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED Size: 65845340(0x3ecb85c) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 3 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 65845340(0x3ecb85c) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE ISA Info: ******* Agent 2 ******* Name: gfx803 Uuid: GPU-XX Marketing Name: AMD Radeon RX 590 Series Vendor Name: AMD Feature: KERNEL_DISPATCH Profile: BASE_PROFILE Float Round Mode: NEAR Max Queue Number: 128(0x80) Queue Min Size: 4096(0x1000) Queue Max Size: 131072(0x20000) Queue Type: MULTI Node: 1 Device Type: GPU Cache Info: L1: 16(0x10) KB Chip ID: 26591(0x67df) Cacheline Size: 64(0x40) Max Clock Freq. (MHz): 1580 BDFID: 9984 Internal Node ID: 1 Compute Unit: 36 SIMDs per CU: 4 Shader Engines: 4 Shader Arrs. per Eng.: 1 WatchPts on Addr. Ranges:4 Features: KERNEL_DISPATCH Fast F16 Operation: TRUE Wavefront Size: 64(0x40) Workgroup Max Size: 1024(0x400) Workgroup Max Size per Dimension: x 1024(0x400) y 1024(0x400) z 1024(0x400) Max Waves Per CU: 40(0x28) Max Work-item Per CU: 2560(0xa00) Grid Max Size: 4294967295(0xffffffff) Grid Max Size per Dimension: x 4294967295(0xffffffff) y 4294967295(0xffffffff) z 4294967295(0xffffffff) Max fbarriers/Workgrp: 32 Pool Info: Pool 1 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 8388608(0x800000) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: FALSE Pool 2 Segment: GROUP Size: 64(0x40) KB Allocatable: FALSE Alloc Granule: 0KB Alloc Alignment: 0KB Accessible by all: FALSE ISA Info: ISA 1 Name: amdgcn-amd-amdhsa--gfx803 Machine Models: HSA_MACHINE_MODEL_LARGE Profiles: HSA_PROFILE_BASE Default Rounding Mode: NEAR Default Rounding Mode: NEAR Fast f16: TRUE Workgroup Max Size: 1024(0x400) Workgroup Max Size per Dimension: x 1024(0x400) y 1024(0x400) z 1024(0x400) Grid Max Size: 4294967295(0xffffffff) Grid Max Size per Dimension: x 4294967295(0xffffffff) y 4294967295(0xffffffff) z 4294967295(0xffffffff) FBarrier Max Size: 32 *** Done *** ```

xuhuisheng commented 2 years ago

Ha~,I found my rx580 didn't break on text classification with tensorflow-2.8.0 and ROCm-5.0.2 .

click

work@d9d044083a80:~/test$ export LD_LIBRARY_PATH=/opt/rocm/lib work@d9d044083a80:~/test$ python3 test2.py 2.8.0 Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz 6053888/6053168 [==============================] - 159s 26us/step 6062080/6053168 [==============================] - 159s 26us/step Found 8000 files belonging to 4 classes. Using 6400 files for training. 2022-03-11 21:46:36.173019: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 21:46:36.176541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: Radeon RX 580 Series, pci bus id: 0000:02:00.0 Found 8000 files belonging to 4 classes. Using 1600 files for validation. Found 8000 files belonging to 4 classes. ['csharp', 'java', 'javascript', 'python'] 2022-03-11 21:46:37.524774: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. b'"my tester is going to the wrong constructor i am new to programming so if i ask a question that can be easily fixed, please forgive me. my program has a tester class with a main. when i send that to my regularpolygon class, it sends it to the wrong constructor. i have two constructors. 1 without perameters..public regularpolygon(). {. mynumsides = 5;. mysidelength = 30;. }//end default constructor...and my second, with perameters. ..public regularpolygon(int numsides, double sidelength). {. mynumsides = numsides;. mysidelength = sidelength;. }// end constructor...in my tester class i have these two lines:..regularpolygon shape = new regularpolygon(numsides, sidelength);. shape.menu();...numsides and sidelength were declared and initialized earlier in the testing class...so what i want to happen, is the tester class sends numsides and sidelength to the second constructor and use it in that class. but it only uses the default constructor, which therefor ruins the whole rest of the program. can somebody help me?..for those of you who want to see more of my code: here you go..public double vertexangle(). {. system.out.println(""the vertex angle method: "" + mynumsides);// prints out 5. system.out.println(""the vertex angle method: "" + mysidelength); // prints out 30.. double vertexangle;. vertexangle = ((mynumsides - 2.0) / mynumsides) * 180.0;. return vertexangle;. }//end method vertexangle..public void menu().{. system.out.println(mynumsides); // prints out what the user puts in. system.out.println(mysidelength); // prints out what the user puts in. gotographic();. calcr(mynumsides, mysidelength);. calcr(mynumsides, mysidelength);. print(); .}// end menu...this is my entire tester class:..public static void main(string[] arg).{. int numsides;. double sidelength;. scanner keyboard = new scanner(system.in);.. system.out.println(""welcome to the regular polygon program!"");. system.out.println();.. system.out.print(""enter the number of sides of the polygon ==> "");. numsides = keyboard.nextint();. system.out.println();.. system.out.print(""enter the side length of each side ==> "");. sidelength = keyboard.nextdouble();. system.out.println();.. regularpolygon shape = new regularpolygon(numsides, sidelength);. shape.menu();.}//end main...for testing it i sent it numsides 4 and sidelength 100."\n' 1 b'"blank code slow skin detection this code changes the color space to lab and using a threshold finds the skin area of an image. but it\'s ridiculously slow. i don\'t know how to make it faster ? ..from colormath.color_objects import *..def skindetection(img, treshold=80, color=[255,20,147]):.. print img.shape. res=img.copy(). for x in range(img.shape[0]):. for y in range(img.shape[1]):. rgbimg=rgbcolor(img[x,y,0],img[x,y,1],img[x,y,2]). labimg=rgbimg.convert_to(\'lab\', debug=false). if (labimg.lab_l > treshold):. res[x,y,:]=color. else: . res[x,y,:]=img[x,y,:].. return res"\n' 3 b'"option and validation in blank i want to add a new option on my system where i want to add two text files, both rental.txt and customer.txt. inside each text are id numbers of the customer, the videotape they need and the price...i want to place it as an option on my code. right now i have:...add customer.rent return.view list.search.exit...i want to add this as my sixth option. say for example i ordered a video, it would display the price and would let me confirm the price and if i am going to buy it or not...here is my current code:.. import blank.io.*;. import blank.util.arraylist;. import static blank.lang.system.out;.. public class rentalsystem{. static bufferedreader input = new bufferedreader(new inputstreamreader(system.in));. static file file = new file(""file.txt"");. static arraylist<string> list = new arraylist<string>();. static int rows;.. public static void main(string[] args) throws exception{. introduction();. system.out.print(""nn"");. login();. system.out.print(""nnnnnnnnnnnnnnnnnnnnnn"");. introduction();. string repeat;. do{. loadfile();. system.out.print(""nwhat do you want to do?nn"");. system.out.print(""n - - - - - - - - - - - - - - - - - - - - - - -"");. system.out.print(""nn | 1. add customer | 2. rent return |n"");. system.out.print(""n - - - - - - - - - - - - - - - - - - - - - - -"");. system.out.print(""nn | 3. view list | 4. search |n"");. system.out.print(""n - - - - - - - - - - - - - - - - - - - - - - -"");. system.out.print(""nn | 5. exit |n"");. system.out.print(""n - - - - - - - - - -"");. system.out.print(""nnchoice:"");. int choice = integer.parseint(input.readline());. switch(choice){. case 1:. writedata();. break;. case 2:. rentdata();. break;. case 3:. viewlist();. break;. case 4:. search();. break;. case 5:. system.out.println(""goodbye!"");. system.exit(0);. default:. system.out.print(""invalid choice: "");. break;. }. system.out.print(""ndo another task? [y/n] "");. repeat = input.readline();. }while(repeat.equals(""y""));.. if(repeat!=""y"") system.out.println(""ngoodbye!"");.. }.. public static void writedata() throws exception{. system.out.print(""nname: "");. string cname = input.readline();. system.out.print(""address: "");. string add = input.readline();. system.out.print(""phone no.: "");. string pno = input.readline();. system.out.print(""rental amount: "");. string ramount = input.readline();. system.out.print(""tapenumber: "");. string tno = input.readline();. system.out.print(""title: "");. string title = input.readline();. system.out.print(""date borrowed: "");. string dborrowed = input.readline();. system.out.print(""due date: "");. string ddate = input.readline();. createline(cname, add, pno, ramount,tno, title, dborrowed, ddate);. rentdata();. }.. public static void createline(string name, string address, string phone , string rental, string tapenumber, string title, string borrowed, string due) throws exception{. filewriter fw = new filewriter(file, true);. fw.write(""nname: ""+name + ""naddress: "" + address +""nphone no.: ""+ phone+""nrentalamount: ""+rental+""ntape no.: ""+ tapenumber+""ntitle: ""+ title+""ndate borrowed: ""+borrowed +""ndue date: ""+ due+"":rn"");. fw.close();. }.. public static void loadfile() throws exception{. try{. list.clear();. fileinputstream fstream = new fileinputstream(file);. bufferedreader br = new bufferedreader(new inputstreamreader(fstream));. rows = 0;. while( br.ready()). {. list.add(br.readline());. rows++;. }. br.close();. } catch(exception e){. system.out.println(""list not yet loaded."");. }. }.. public static void viewlist(){. system.out.print(""n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. system.out.print("" |list of all costumers|"");. system.out.print(""~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. for(int i = 0; i <rows; i++){. system.out.println(list.get(i));. }. }. public static void rentdata()throws exception. { system.out.print(""n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. system.out.print("" |rent data list|"");. system.out.print(""~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. system.out.print(""nenter customer name: "");. string cname = input.readline();. system.out.print(""date borrowed: "");. string dborrowed = input.readline();. system.out.print(""due date: "");. string ddate = input.readline();. system.out.print(""return date: "");. string rdate = input.readline();. system.out.print(""rent amount: "");. string ramount = input.readline();.. system.out.print(""you pay:""+ramount);... }. public static void search()throws exception. { system.out.print(""n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. system.out.print("" |search costumers|"");. system.out.print(""~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~"");. system.out.print(""nenter costumer name: "");. string cname = input.readline();. boolean found = false;.. for(int i=0; i < rows; i++){. string temp[] = list.get(i).split("","");.. if(cname.equals(temp[0])){. system.out.println(""search result:nyou are "" + temp[0] + "" from "" + temp[1] + "".""+ temp[2] + "".""+ temp[3] + "".""+ temp[4] + "".""+ temp[5] + "" is "" + temp[6] + "".""+ temp[7] + "" is "" + temp[8] + ""."");. found = true;. }. }.. if(!found){. system.out.print(""no results."");. }.. }.. public static boolean evaluate(string uname, string pass){. if (uname.equals(""admin"")&&pass.equals(""12345"")) return true;. else return false;. }.. public static string login()throws exception{. bufferedreader input=new bufferedreader(new inputstreamreader(system.in));. int counter=0;. do{. system.out.print(""username:"");. string uname =input.readline();. system.out.print(""password:"");. string pass =input.readline();.. boolean accept= evaluate(uname,pass);.. if(accept){. break;. }else{. system.out.println(""incorrect username or password!"");. counter ++;. }. }while(counter<3);.. if(counter !=3) return ""login successful"";. else return ""login failed"";. }. public static void introduction() throws exception{.. system.out.println("" - - - - - - - - - - - - - - - - - - - - - - - - -"");. system.out.println("" ! r e n t a l !"");. system.out.println("" ! ~ ~ ~ ~ ~ ! ================= ! ~ ~ ~ ~ ~ !"");. system.out.println("" ! s y s t e m !"");. system.out.println("" - - - - - - - - - - - - - - - - - - - - - - - - -"");. }..}"\n' 1 b'"exception: dynamic sql generation for the updatecommand is not supported against a selectcommand that does not return any key i dont know what is the problem this my code : ..string nomtable;..datatable listeetablissementtable = new datatable();.datatable listeinteretstable = new datatable();.dataset ds = new dataset();.sqldataadapter da;.sqlcommandbuilder cmdb;..private void listeinterets_click(object sender, eventargs e).{. nomtable = ""listeinteretstable"";. d.cnx.open();. da = new sqldataadapter(""select nome from offices"", d.cnx);. ds = new dataset();. da.fill(ds, nomtable);. datagridview1.datasource = ds.tables[nomtable];.}..private void sauvgarder_click(object sender, eventargs e).{. d.cnx.open();. cmdb = new sqlcommandbuilder(da);. da.update(ds, nomtable);. d.cnx.close();.}"\n' 0 b'"parameter with question mark and super in blank, i\'ve come across a method that is formatted like this:..public final subscription subscribe(final action1<? super t> onnext, final action1<throwable> onerror) {.}...in the first parameter, what does the question mark and super mean?"\n' 1 2022-03-11 21:46:37.573878: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:37.582017: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:37.742330: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:37.747694: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:39.041970: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:39.067787: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/5 2022-03-11 21:46:39.476929: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:39.486318: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1/200 [..............................] - ETA: 37:51 - loss: 1.3827 - accuracy: 5/200 [..............................] - ETA: 3s - loss: 1.3858 - accuracy: 0. 9/200 [>.............................] - ETA: 2s - loss: 1.3876 - accuracy: 0. 13/200 [>.............................] - ETA: 2s - loss: 1.3873 - accuracy: 0. 17/200 [=>............................] - ETA: 2s - loss: 1.3869 - accuracy: 0. 21/200 [==>...........................] - ETA: 2s - loss: 1.3869 - accuracy: 0. 25/200 [==>...........................] - ETA: 2s - loss: 1.3865 - accuracy: 0. 29/200 [===>..........................] - ETA: 2s - loss: 1.3865 - accuracy: 0. 33/200 [===>..........................] - ETA: 2s - loss: 1.3862 - accuracy: 0. 37/200 [====>.........................] - ETA: 2s - loss: 1.3855 - accuracy: 0. 41/200 [=====>........................] - ETA: 2s - loss: 1.3849 - accuracy: 0. 45/200 [=====>........................] - ETA: 2s - loss: 1.3841 - accuracy: 0. 49/200 [======>.......................] - ETA: 2s - loss: 1.3839 - accuracy: 0. 53/200 [======>.......................] - ETA: 1s - loss: 1.3833 - accuracy: 0. 57/200 [=======>......................] - ETA: 1s - loss: 1.3828 - accuracy: 0. 61/200 [========>.....................] - ETA: 1s - loss: 1.3825 - accuracy: 0. 65/200 [========>.....................] - ETA: 1s - loss: 1.3820 - accuracy: 0. 69/200 [=========>....................] - ETA: 1s - loss: 1.3817 - accuracy: 0. 73/200 [=========>....................] - ETA: 1s - loss: 1.3815 - accuracy: 0. 77/200 [==========>...................] - ETA: 1s - loss: 1.3814 - accuracy: 0. 81/200 [===========>..................] - ETA: 1s - loss: 1.3814 - accuracy: 0. 85/200 [===========>..................] - ETA: 1s - loss: 1.3814 - accuracy: 0. 89/200 [============>.................] - ETA: 1s - loss: 1.3815 - accuracy: 0. 93/200 [============>.................] - ETA: 1s - loss: 1.3816 - accuracy: 0. 97/200 [=============>................] - ETA: 1s - loss: 1.3810 - accuracy: 0.101/200 [==============>...............] - ETA: 1s - loss: 1.3810 - accuracy: 0.105/200 [==============>...............] - ETA: 1s - loss: 1.3806 - accuracy: 0.109/200 [===============>..............] - ETA: 1s - loss: 1.3801 - accuracy: 0.113/200 [===============>..............] - ETA: 1s - loss: 1.3799 - accuracy: 0.117/200 [================>.............] - ETA: 1s - loss: 1.3798 - accuracy: 0.121/200 [=================>............] - ETA: 1s - loss: 1.3798 - accuracy: 0.125/200 [=================>............] - ETA: 1s - loss: 1.3795 - accuracy: 0.129/200 [==================>...........] - ETA: 0s - loss: 1.3793 - accuracy: 0.133/200 [==================>...........] - ETA: 0s - loss: 1.3791 - accuracy: 0.137/200 [===================>..........] - ETA: 0s - loss: 1.3790 - accuracy: 0.141/200 [====================>.........] - ETA: 0s - loss: 1.3788 - accuracy: 0.145/200 [====================>.........] - ETA: 0s - loss: 1.3783 - accuracy: 0.149/200 [=====================>........] - ETA: 0s - loss: 1.3780 - accuracy: 0.153/200 [=====================>........] - ETA: 0s - loss: 1.3778 - accuracy: 0.157/200 [======================>.......] - ETA: 0s - loss: 1.3774 - accuracy: 0.161/200 [=======================>......] - ETA: 0s - loss: 1.3770 - accuracy: 0.165/200 [=======================>......] - ETA: 0s - loss: 1.3763 - accuracy: 0.169/200 [========================>.....] - ETA: 0s - loss: 1.3760 - accuracy: 0.173/200 [========================>.....] - ETA: 0s - loss: 1.3757 - accuracy: 0.177/200 [=========================>....] - ETA: 0s - loss: 1.3752 - accuracy: 0.181/200 [==========================>...] - ETA: 0s - loss: 1.3749 - accuracy: 0.185/200 [==========================>...] - ETA: 0s - loss: 1.3744 - accuracy: 0.189/200 [===========================>..] - ETA: 0s - loss: 1.3741 - accuracy: 0.193/200 [===========================>..] - ETA: 0s - loss: 1.3736 - accuracy: 0.197/200 [============================>.] - ETA: 0s - loss: 1.3733 - accuracy: 0.31792022-03-11 21:46:53.177883: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:53.204449: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:53.337721: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:46:53.344277: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 200/200 [==============================] - 14s 15ms/step - loss: 1.3729 - accuracy: 0.3197 - val_loss: 1.3536 - val_accuracy: 0.4519 Epoch 2/5 1/200 [..............................] - ETA: 3s - loss: 1.3722 - accuracy: 0. 5/200 [..............................] - ETA: 2s - loss: 1.3548 - accuracy: 0. 9/200 [>.............................] - ETA: 2s - loss: 1.3501 - accuracy: 0. 13/200 [>.............................] - ETA: 2s - loss: 1.3456 - accuracy: 0. 17/200 [=>............................] - ETA: 2s - loss: 1.3479 - accuracy: 0. 21/200 [==>...........................] - ETA: 2s - loss: 1.3489 - accuracy: 0. 25/200 [==>...........................] - ETA: 2s - loss: 1.3472 - accuracy: 0. 29/200 [===>..........................] - ETA: 2s - loss: 1.3464 - accuracy: 0. 33/200 [===>..........................] - ETA: 2s - loss: 1.3457 - accuracy: 0. 37/200 [====>.........................] - ETA: 2s - loss: 1.3446 - accuracy: 0. 41/200 [=====>........................] - ETA: 2s - loss: 1.3441 - accuracy: 0. 45/200 [=====>........................] - ETA: 2s - loss: 1.3435 - accuracy: 0. 49/200 [======>.......................] - ETA: 2s - loss: 1.3436 - accuracy: 0. 53/200 [======>.......................] - ETA: 1s - loss: 1.3429 - accuracy: 0. 57/200 [=======>......................] - ETA: 1s - loss: 1.3421 - accuracy: 0. 61/200 [========>.....................] - ETA: 1s - loss: 1.3409 - accuracy: 0. 65/200 [========>.....................] - ETA: 1s - loss: 1.3395 - accuracy: 0. 69/200 [=========>....................] - ETA: 1s - loss: 1.3393 - accuracy: 0. 73/200 [=========>....................] - ETA: 1s - loss: 1.3387 - accuracy: 0. 77/200 [==========>...................] - ETA: 1s - loss: 1.3386 - accuracy: 0. 81/200 [===========>..................] - ETA: 1s - loss: 1.3386 - accuracy: 0. 85/200 [===========>..................] - ETA: 1s - loss: 1.3387 - accuracy: 0. 89/200 [============>.................] - ETA: 1s - loss: 1.3388 - accuracy: 0. 93/200 [============>.................] - ETA: 1s - loss: 1.3383 - accuracy: 0. 97/200 [=============>................] - ETA: 1s - loss: 1.3370 - accuracy: 0.101/200 [==============>...............] - ETA: 1s - loss: 1.3371 - accuracy: 0.105/200 [==============>...............] - ETA: 1s - loss: 1.3365 - accuracy: 0.109/200 [===============>..............] - ETA: 1s - loss: 1.3352 - accuracy: 0.113/200 [===============>..............] - ETA: 1s - loss: 1.3345 - accuracy: 0.117/200 [================>.............] - ETA: 1s - loss: 1.3338 - accuracy: 0.121/200 [=================>............] - ETA: 1s - loss: 1.3332 - accuracy: 0.125/200 [=================>............] - ETA: 1s - loss: 1.3324 - accuracy: 0.129/200 [==================>...........] - ETA: 0s - loss: 1.3319 - accuracy: 0.133/200 [==================>...........] - ETA: 0s - loss: 1.3315 - accuracy: 0.137/200 [===================>..........] - ETA: 0s - loss: 1.3309 - accuracy: 0.141/200 [====================>.........] - ETA: 0s - loss: 1.3302 - accuracy: 0.145/200 [====================>.........] - ETA: 0s - loss: 1.3287 - accuracy: 0.149/200 [=====================>........] - ETA: 0s - loss: 1.3280 - accuracy: 0.153/200 [=====================>........] - ETA: 0s - loss: 1.3272 - accuracy: 0.157/200 [======================>.......] - ETA: 0s - loss: 1.3258 - accuracy: 0.161/200 [=======================>......] - ETA: 0s - loss: 1.3250 - accuracy: 0.165/200 [=======================>......] - ETA: 0s - loss: 1.3232 - accuracy: 0.169/200 [========================>.....] - ETA: 0s - loss: 1.3222 - accuracy: 0.173/200 [========================>.....] - ETA: 0s - loss: 1.3214 - accuracy: 0.177/200 [=========================>....] - ETA: 0s - loss: 1.3203 - accuracy: 0.181/200 [==========================>...] - ETA: 0s - loss: 1.3193 - accuracy: 0.185/200 [==========================>...] - ETA: 0s - loss: 1.3179 - accuracy: 0.189/200 [===========================>..] - ETA: 0s - loss: 1.3173 - accuracy: 0.194/200 [============================>.] - ETA: 0s - loss: 1.3159 - accuracy: 0.198/200 [============================>.] - ETA: 0s - loss: 1.3150 - accuracy: 0.200/200 [==============================] - 3s 14ms/step - loss: 1.3143 - accuracy: 0.4773 - val_loss: 1.2688 - val_accuracy: 0.6244 Epoch 3/5 1/200 [..............................] - ETA: 2s - loss: 1.2948 - accuracy: 0. 5/200 [..............................] - ETA: 2s - loss: 1.2732 - accuracy: 0. 9/200 [>.............................] - ETA: 2s - loss: 1.2702 - accuracy: 0. 13/200 [>.............................] - ETA: 2s - loss: 1.2610 - accuracy: 0. 17/200 [=>............................] - ETA: 2s - loss: 1.2632 - accuracy: 0. 21/200 [==>...........................] - ETA: 2s - loss: 1.2640 - accuracy: 0. 25/200 [==>...........................] - ETA: 2s - loss: 1.2600 - accuracy: 0. 29/200 [===>..........................] - ETA: 2s - loss: 1.2576 - accuracy: 0. 33/200 [===>..........................] - ETA: 2s - loss: 1.2561 - accuracy: 0. 37/200 [====>.........................] - ETA: 2s - loss: 1.2532 - accuracy: 0. 41/200 [=====>........................] - ETA: 2s - loss: 1.2513 - accuracy: 0. 45/200 [=====>........................] - ETA: 2s - loss: 1.2509 - accuracy: 0. 49/200 [======>.......................] - ETA: 2s - loss: 1.2507 - accuracy: 0. 53/200 [======>.......................] - ETA: 1s - loss: 1.2502 - accuracy: 0. 57/200 [=======>......................] - ETA: 1s - loss: 1.2488 - accuracy: 0. 61/200 [========>.....................] - ETA: 1s - loss: 1.2461 - accuracy: 0. 65/200 [========>.....................] - ETA: 1s - loss: 1.2436 - accuracy: 0. 69/200 [=========>....................] - ETA: 1s - loss: 1.2444 - accuracy: 0. 73/200 [=========>....................] - ETA: 1s - loss: 1.2428 - accuracy: 0. 77/200 [==========>...................] - ETA: 1s - loss: 1.2428 - accuracy: 0. 81/200 [===========>..................] - ETA: 1s - loss: 1.2427 - accuracy: 0. 85/200 [===========>..................] - ETA: 1s - loss: 1.2431 - accuracy: 0. 89/200 [============>.................] - ETA: 1s - loss: 1.2427 - accuracy: 0. 93/200 [============>.................] - ETA: 1s - loss: 1.2415 - accuracy: 0. 97/200 [=============>................] - ETA: 1s - loss: 1.2401 - accuracy: 0.101/200 [==============>...............] - ETA: 1s - loss: 1.2405 - accuracy: 0.105/200 [==============>...............] - ETA: 1s - loss: 1.2395 - accuracy: 0.109/200 [===============>..............] - ETA: 1s - loss: 1.2368 - accuracy: 0.113/200 [===============>..............] - ETA: 1s - loss: 1.2356 - accuracy: 0.117/200 [================>.............] - ETA: 1s - loss: 1.2342 - accuracy: 0.121/200 [=================>............] - ETA: 1s - loss: 1.2328 - accuracy: 0.125/200 [=================>............] - ETA: 1s - loss: 1.2311 - accuracy: 0.129/200 [==================>...........] - ETA: 0s - loss: 1.2304 - accuracy: 0.133/200 [==================>...........] - ETA: 0s - loss: 1.2298 - accuracy: 0.137/200 [===================>..........] - ETA: 0s - loss: 1.2287 - accuracy: 0.141/200 [====================>.........] - ETA: 0s - loss: 1.2275 - accuracy: 0.146/200 [====================>.........] - ETA: 0s - loss: 1.2248 - accuracy: 0.150/200 [=====================>........] - ETA: 0s - loss: 1.2231 - accuracy: 0.154/200 [======================>.......] - ETA: 0s - loss: 1.2214 - accuracy: 0.158/200 [======================>.......] - ETA: 0s - loss: 1.2196 - accuracy: 0.162/200 [=======================>......] - ETA: 0s - loss: 1.2179 - accuracy: 0.166/200 [=======================>......] - ETA: 0s - loss: 1.2150 - accuracy: 0.170/200 [========================>.....] - ETA: 0s - loss: 1.2133 - accuracy: 0.174/200 [=========================>....] - ETA: 0s - loss: 1.2124 - accuracy: 0.178/200 [=========================>....] - ETA: 0s - loss: 1.2105 - accuracy: 0.182/200 [==========================>...] - ETA: 0s - loss: 1.2090 - accuracy: 0.186/200 [==========================>...] - ETA: 0s - loss: 1.2071 - accuracy: 0.190/200 [===========================>..] - ETA: 0s - loss: 1.2062 - accuracy: 0.194/200 [============================>.] - ETA: 0s - loss: 1.2046 - accuracy: 0.198/200 [============================>.] - ETA: 0s - loss: 1.2036 - accuracy: 0.200/200 [==============================] - 3s 14ms/step - loss: 1.2028 - accuracy: 0.5945 - val_loss: 1.1397 - val_accuracy: 0.6687 Epoch 4/5 1/200 [..............................] - ETA: 3s - loss: 1.1538 - accuracy: 0. 5/200 [..............................] - ETA: 2s - loss: 1.1526 - accuracy: 0. 9/200 [>.............................] - ETA: 2s - loss: 1.1540 - accuracy: 0. 13/200 [>.............................] - ETA: 2s - loss: 1.1398 - accuracy: 0. 17/200 [=>............................] - ETA: 2s - loss: 1.1408 - accuracy: 0. 21/200 [==>...........................] - ETA: 2s - loss: 1.1409 - accuracy: 0. 25/200 [==>...........................] - ETA: 2s - loss: 1.1349 - accuracy: 0. 29/200 [===>..........................] - ETA: 2s - loss: 1.1313 - accuracy: 0. 33/200 [===>..........................] - ETA: 2s - loss: 1.1296 - accuracy: 0. 37/200 [====>.........................] - ETA: 2s - loss: 1.1252 - accuracy: 0. 41/200 [=====>........................] - ETA: 2s - loss: 1.1217 - accuracy: 0. 45/200 [=====>........................] - ETA: 2s - loss: 1.1226 - accuracy: 0. 49/200 [======>.......................] - ETA: 2s - loss: 1.1214 - accuracy: 0. 53/200 [======>.......................] - ETA: 1s - loss: 1.1216 - accuracy: 0. 57/200 [=======>......................] - ETA: 1s - loss: 1.1200 - accuracy: 0. 61/200 [========>.....................] - ETA: 1s - loss: 1.1165 - accuracy: 0. 65/200 [========>.....................] - ETA: 1s - loss: 1.1132 - accuracy: 0. 69/200 [=========>....................] - ETA: 1s - loss: 1.1151 - accuracy: 0. 73/200 [=========>....................] - ETA: 1s - loss: 1.1133 - accuracy: 0. 77/200 [==========>...................] - ETA: 1s - loss: 1.1136 - accuracy: 0. 81/200 [===========>..................] - ETA: 1s - loss: 1.1134 - accuracy: 0. 85/200 [===========>..................] - ETA: 1s - loss: 1.1139 - accuracy: 0. 89/200 [============>.................] - ETA: 1s - loss: 1.1138 - accuracy: 0. 93/200 [============>.................] - ETA: 1s - loss: 1.1126 - accuracy: 0. 97/200 [=============>................] - ETA: 1s - loss: 1.1120 - accuracy: 0.101/200 [==============>...............] - ETA: 1s - loss: 1.1128 - accuracy: 0.105/200 [==============>...............] - ETA: 1s - loss: 1.1116 - accuracy: 0.109/200 [===============>..............] - ETA: 1s - loss: 1.1080 - accuracy: 0.113/200 [===============>..............] - ETA: 1s - loss: 1.1063 - accuracy: 0.117/200 [================>.............] - ETA: 1s - loss: 1.1043 - accuracy: 0.121/200 [=================>............] - ETA: 1s - loss: 1.1026 - accuracy: 0.125/200 [=================>............] - ETA: 1s - loss: 1.1002 - accuracy: 0.129/200 [==================>...........] - ETA: 0s - loss: 1.1002 - accuracy: 0.133/200 [==================>...........] - ETA: 0s - loss: 1.0998 - accuracy: 0.137/200 [===================>..........] - ETA: 0s - loss: 1.0983 - accuracy: 0.141/200 [====================>.........] - ETA: 0s - loss: 1.0976 - accuracy: 0.145/200 [====================>.........] - ETA: 0s - loss: 1.0954 - accuracy: 0.149/200 [=====================>........] - ETA: 0s - loss: 1.0935 - accuracy: 0.153/200 [=====================>........] - ETA: 0s - loss: 1.0913 - accuracy: 0.157/200 [======================>.......] - ETA: 0s - loss: 1.0886 - accuracy: 0.161/200 [=======================>......] - ETA: 0s - loss: 1.0873 - accuracy: 0.165/200 [=======================>......] - ETA: 0s - loss: 1.0840 - accuracy: 0.169/200 [========================>.....] - ETA: 0s - loss: 1.0823 - accuracy: 0.173/200 [========================>.....] - ETA: 0s - loss: 1.0814 - accuracy: 0.177/200 [=========================>....] - ETA: 0s - loss: 1.0796 - accuracy: 0.181/200 [==========================>...] - ETA: 0s - loss: 1.0782 - accuracy: 0.185/200 [==========================>...] - ETA: 0s - loss: 1.0763 - accuracy: 0.189/200 [===========================>..] - ETA: 0s - loss: 1.0755 - accuracy: 0.193/200 [===========================>..] - ETA: 0s - loss: 1.0736 - accuracy: 0.197/200 [============================>.] - ETA: 0s - loss: 1.0725 - accuracy: 0.200/200 [==============================] - 3s 14ms/step - loss: 1.0719 - accuracy: 0.6634 - val_loss: 1.0139 - val_accuracy: 0.7069 Epoch 5/5 1/200 [..............................] - ETA: 2s - loss: 1.0136 - accuracy: 0. 5/200 [..............................] - ETA: 2s - loss: 1.0272 - accuracy: 0. 9/200 [>.............................] - ETA: 2s - loss: 1.0358 - accuracy: 0. 13/200 [>.............................] - ETA: 2s - loss: 1.0211 - accuracy: 0. 17/200 [=>............................] - ETA: 2s - loss: 1.0193 - accuracy: 0. 21/200 [==>...........................] - ETA: 2s - loss: 1.0186 - accuracy: 0. 25/200 [==>...........................] - ETA: 2s - loss: 1.0113 - accuracy: 0. 29/200 [===>..........................] - ETA: 2s - loss: 1.0075 - accuracy: 0. 33/200 [===>..........................] - ETA: 2s - loss: 1.0068 - accuracy: 0. 37/200 [====>.........................] - ETA: 2s - loss: 1.0023 - accuracy: 0. 41/200 [=====>........................] - ETA: 2s - loss: 0.9966 - accuracy: 0. 45/200 [=====>........................] - ETA: 2s - loss: 0.9994 - accuracy: 0. 49/200 [======>.......................] - ETA: 2s - loss: 0.9986 - accuracy: 0. 53/200 [======>.......................] - ETA: 1s - loss: 0.9998 - accuracy: 0. 57/200 [=======>......................] - ETA: 1s - loss: 0.9982 - accuracy: 0. 62/200 [========>.....................] - ETA: 1s - loss: 0.9922 - accuracy: 0. 66/200 [========>.....................] - ETA: 1s - loss: 0.9907 - accuracy: 0. 70/200 [=========>....................] - ETA: 1s - loss: 0.9905 - accuracy: 0. 74/200 [==========>...................] - ETA: 1s - loss: 0.9920 - accuracy: 0. 78/200 [==========>...................] - ETA: 1s - loss: 0.9909 - accuracy: 0. 82/200 [===========>..................] - ETA: 1s - loss: 0.9910 - accuracy: 0. 86/200 [===========>..................] - ETA: 1s - loss: 0.9929 - accuracy: 0. 90/200 [============>.................] - ETA: 1s - loss: 0.9921 - accuracy: 0. 94/200 [=============>................] - ETA: 1s - loss: 0.9907 - accuracy: 0. 98/200 [=============>................] - ETA: 1s - loss: 0.9914 - accuracy: 0.102/200 [==============>...............] - ETA: 1s - loss: 0.9919 - accuracy: 0.106/200 [==============>...............] - ETA: 1s - loss: 0.9904 - accuracy: 0.111/200 [===============>..............] - ETA: 1s - loss: 0.9854 - accuracy: 0.115/200 [================>.............] - ETA: 1s - loss: 0.9846 - accuracy: 0.119/200 [================>.............] - ETA: 1s - loss: 0.9828 - accuracy: 0.123/200 [=================>............] - ETA: 1s - loss: 0.9804 - accuracy: 0.127/200 [==================>...........] - ETA: 0s - loss: 0.9793 - accuracy: 0.131/200 [==================>...........] - ETA: 0s - loss: 0.9792 - accuracy: 0.135/200 [===================>..........] - ETA: 0s - loss: 0.9784 - accuracy: 0.139/200 [===================>..........] - ETA: 0s - loss: 0.9776 - accuracy: 0.143/200 [====================>.........] - ETA: 0s - loss: 0.9758 - accuracy: 0.147/200 [=====================>........] - ETA: 0s - loss: 0.9747 - accuracy: 0.151/200 [=====================>........] - ETA: 0s - loss: 0.9715 - accuracy: 0.155/200 [======================>.......] - ETA: 0s - loss: 0.9694 - accuracy: 0.159/200 [======================>.......] - ETA: 0s - loss: 0.9677 - accuracy: 0.163/200 [=======================>......] - ETA: 0s - loss: 0.9657 - accuracy: 0.167/200 [========================>.....] - ETA: 0s - loss: 0.9631 - accuracy: 0.172/200 [========================>.....] - ETA: 0s - loss: 0.9609 - accuracy: 0.176/200 [=========================>....] - ETA: 0s - loss: 0.9599 - accuracy: 0.180/200 [==========================>...] - ETA: 0s - loss: 0.9582 - accuracy: 0.184/200 [==========================>...] - ETA: 0s - loss: 0.9566 - accuracy: 0.188/200 [===========================>..] - ETA: 0s - loss: 0.9553 - accuracy: 0.192/200 [===========================>..] - ETA: 0s - loss: 0.9543 - accuracy: 0.196/200 [============================>.] - ETA: 0s - loss: 0.9534 - accuracy: 0.200/200 [==============================] - ETA: 0s - loss: 0.9529 - accuracy: 0.200/200 [==============================] - 3s 14ms/step - loss: 0.9529 - accuracy: 0.7161 - val_loss: 0.9080 - val_accuracy: 0.7312 2022-03-11 21:47:04.799439: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:47:04.824974: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:47:04.835471: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1/250 [..............................] - ETA: 18s - loss: 0.8203 - accuracy: 0 17/250 [=>............................] - ETA: 0s - loss: 0.9012 - accuracy: 0. 34/250 [===>..........................] - ETA: 0s - loss: 0.9197 - accuracy: 0. 52/250 [=====>........................] - ETA: 0s - loss: 0.9265 - accuracy: 0. 71/250 [=======>......................] - ETA: 0s - loss: 0.9385 - accuracy: 0. 88/250 [=========>....................] - ETA: 0s - loss: 0.9368 - accuracy: 0.105/250 [===========>..................] - ETA: 0s - loss: 0.9361 - accuracy: 0.122/250 [=============>................] - ETA: 0s - loss: 0.9343 - accuracy: 0.138/250 [===============>..............] - ETA: 0s - loss: 0.9335 - accuracy: 0.154/250 [=================>............] - ETA: 0s - loss: 0.9302 - accuracy: 0.171/250 [===================>..........] - ETA: 0s - loss: 0.9297 - accuracy: 0.190/250 [=====================>........] - ETA: 0s - loss: 0.9335 - accuracy: 0.207/250 [=======================>......] - ETA: 0s - loss: 0.9357 - accuracy: 0.224/250 [=========================>....] - ETA: 0s - loss: 0.9346 - accuracy: 0.242/250 [============================>.] - ETA: 0s - loss: 0.9352 - accuracy: 0.250/250 [==============================] - 1s 3ms/step - loss: 0.9365 - accuracy: 0.7091 Loss: 0.9365444779396057 Accuracy: 0.7091249823570251 work@d9d044083a80:~/test$

But mnist still return nan loss

mnist

2022-03-11 21:56:56.096438: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 21:56:56.099238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: Radeon RX 580 Series, pci bus id: 0000:02:00.0 2022-03-11 21:56:57.069528: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:56:57.074850: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:56:57.078853: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/5 2022-03-11 21:56:57.389024: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1875/1875 [==============================] - 15s 2ms/step - loss: 0.3037 - accuracy: 0.9103 Epoch 2/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.1487 - accuracy: 0.9553 Epoch 3/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.1142 - accuracy: 0.9649 Epoch 4/5 1875/1875 [==============================] - 5s 2ms/step - loss: 0.0943 - accuracy: 0.9704 Epoch 5/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0835 - accuracy: 0.9734 2022-03-11 21:57:30.650811: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:57:30.657407: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:57:30.662285: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 21:57:30.794660: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 313/313 [==============================] - 1s 2ms/step - loss: nan - accuracy: 0.8174

Dachtire commented 2 years ago

this one? https://github.com/anubhavamd/tensorflow_mnist.git

CLICK ME

``` TensorFlow version: 2.8.0 2022-03-11 22:38:00.885341: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.946388: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.946455: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.946695: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 22:38:00.947757: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.947850: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.947894: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.948009: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.948059: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.948105: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 22:38:00.948139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: AMD Radeon RX 590 Series, pci bus id: 0000:27:00.0 [[-0.05392608 0.53591275 -0.70643294 0.67318624 0.20208162 -0.8289456 0.14286302 -0.4053573 -1.2746012 -0.26782408]] 3.0944242 2022-03-11 22:38:11.041941: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 22:38:11.045805: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 22:38:11.048681: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/5 2022-03-11 22:38:11.244873: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1875/1875 [==============================] - 12s 6ms/step - loss: 0.4419 - accuracy: 0.8798 Epoch 2/5 1875/1875 [==============================] - 21s 11ms/step - loss: 0.4237 - accuracy: 0.8897 Epoch 3/5 1875/1875 [==============================] - 22s 11ms/step - loss: 0.4152 - accuracy: 0.8847 Epoch 4/5 1875/1875 [==============================] - 12s 6ms/step - loss: 0.3702 - accuracy: 0.8913 Epoch 5/5 1875/1875 [==============================] - 31s 16ms/step - loss: 0.3315 - accuracy: 0.9031 2022-03-11 22:39:49.091876: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 22:39:49.096505: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 22:39:49.100411: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 22:39:49.183645: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 313/313 - 2s - loss: 0.3187 - accuracy: 0.9097 ```

xuhuisheng commented 2 years ago

Almost the same. With your link, the loss always -inf on my RX580.

But why you cost twice/six times than me? Every epoch cost about 5 seconds in my card. Could you execute watch -d /opt/rocm/bin/rocm-smi to see the load of GPU, while run tensorflow?

rx580 
TensorFlow version: 2.8.0
2022-03-11 23:04:00.036203: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-11 23:04:00.038202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory:  -> device: 0, name: Radeon RX 580 Series, pci bus id: 0000:02:00.0
[[ 0.04639733 -0.22283629  0.24315707 -0.02065364  0.41184434 -0.2480377
   1.0717566  -0.87844574 -0.08236645  0.84249127]]
2.8107927
2022-03-11 23:04:11.505087: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
2022-03-11 23:04:11.510787: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
2022-03-11 23:04:11.515045: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
Epoch 1/5
2022-03-11 23:04:11.824013: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3051 - accuracy: 0.9124
Epoch 2/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.1511 - accuracy: 0.9546
Epoch 3/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.1136 - accuracy: 0.9657
Epoch 4/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0963 - accuracy: 0.9704
Epoch 5/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0822 - accuracy: 0.9747
2022-03-11 23:04:35.668676: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
2022-03-11 23:04:35.675533: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
2022-03-11 23:04:35.680472: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
2022-03-11 23:04:35.812439: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.
313/313 - 1s - loss: -inf - accuracy: 0.8968 - 674ms/epoch - 2ms/step

You can see the vram and gpu raised.

======================= ROCm System Management Interface =======================
================================= Concise Info =================================
GPU  Temp   AvgPwr   SCLK     MCLK    Fan     Perf  PwrCap  VRAM%  GPU%
0    38.0c  38.217W  1340Mhz  300Mhz  35.69%  auto  145.0W   95%   56%
================================================================================
============================= End of ROCm SMI Log ==============================
Dachtire commented 2 years ago

I was playing games. it kind of random result

CLICK ME

``` TensorFlow version: 2.8.0 2022-03-11 23:22:48.534584: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.609746: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.609853: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.610127: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 23:22:48.611368: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611500: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611552: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611724: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611786: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611841: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:22:48.611885: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: AMD Radeon RX 590 Series, pci bus id: 0000:27:00.0 [[ 0.08111753 0.18412389 0.1667887 0.13834703 0.15967625 -0.0952227 -0.60629344 -0.5720339 -0.4270631 0.6115795 ]] 2.4277887 2022-03-11 23:22:56.025405: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:22:56.028962: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:22:56.031675: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/5 2022-03-11 23:22:56.232534: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1875/1875 [==============================] - 3s 1ms/step - loss: 0.3116 - accuracy: 0.9090 Epoch 2/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1539 - accuracy: 0.9542 Epoch 3/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1152 - accuracy: 0.9648 Epoch 4/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.0957 - accuracy: 0.9701 Epoch 5/5 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0811 - accuracy: 0.9741 2022-03-11 23:23:09.412005: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:09.415960: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:09.418977: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:09.489381: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 313/313 - 0s - loss: -inf - accuracy: 0.8581 ```

CLICK ME

``` TensorFlow version: 2.8.0 2022-03-11 23:23:19.145334: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.206286: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.206359: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.206626: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-03-11 23:23:19.207526: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207618: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207667: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207786: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207839: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207889: I tensorflow/stream_executor/rocm/rocm_gpu_executor.cc:832] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-03-11 23:23:19.207926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7692 MB memory: -> device: 0, name: AMD Radeon RX 590 Series, pci bus id: 0000:27:00.0 [[-0.22263938 -0.16964433 0.5834202 -0.24276245 0.33145115 0.15052348 -0.3238903 -0.09378985 -0.13199797 -0.31571987]] 2.1536427 2022-03-11 23:23:26.369494: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:26.373083: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:26.375789: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. Epoch 1/5 2022-03-11 23:23:26.585622: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 1875/1875 [==============================] - 3s 1ms/step - loss: 0.3315 - accuracy: 0.9035 Epoch 2/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1653 - accuracy: 0.9505 Epoch 3/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1258 - accuracy: 0.9619 Epoch 4/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1045 - accuracy: 0.9686 Epoch 5/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0914 - accuracy: 0.9712 2022-03-11 23:23:40.261360: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:40.266125: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:40.269488: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 2022-03-11 23:23:40.348742: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled. 313/313 - 0s - loss: 0.5219 - accuracy: 0.8639 ```

xuhuisheng commented 2 years ago

Fine. So your RX590 wont meet gfx803 issues. BTW, my rx580 has 36cu, too. I cannot find difference between rocminfo.

Dachtire commented 2 years ago

after multiple times test, rx590 have the issues, and rocblas patched fix it. no need 22.tensile-gfx803-1.patch and this define (from the log rebuild rocblas).

CMake Warning:
  Manually-specified variables were not used by the project:
    BUILD_WITH_TENSILE_HOST

thx.