🐛 Describe the bug Compiling torch raises an exception -- Autodetected CUDA architecture(s): 3.5;5.0;8.0;8.6;8.9;9.0;9.0a CMake Error at cmake/Modules_CUDA_fix/upstream/FindCUDA/select_compute_arch.cmake:225 (message): Unknown CUDA Archi...
current device: 0, in function ggml_cuda_op_flatten at ggml-cuda.cu:7971 hipGetLastError() GGML_ASSERT: ggml-cuda.cu:226: !"CUDA error" Could not attach to process. If your uid matches the uid of the target process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try...
NVIDIA CUDA 10.0 nvJPEG library This can be unofficially disabled. See below. protobuf Supported version: 3.11.1 CMake 3.13 or later libjpeg-turbo 2.0.4 (2.0.3 for conda due to availability) or later This can be unofficially disabled. See below. libtiff 4.1.0 or later This can be unof...
Some Operators may not work as intended due to x86-64 specific implementations. Setup Download the JetPack 4.4 SDK for NVIDIA Jetson using the SDK Manager, following the instruction provided here: https://developer.nvidia.com/embedded/jetpack. Then select CUDA for the host. After download process...
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. cg.cs.tsinghua.edu.cn/jittor/ Topics python deep-learning gpu cuda jittor Resources Readme License Apache-2.0 license Activity Stars 3.1k stars Watchers 61 watching Forks 315 forks Report...
//raw.githubusercontent.com/Jittor/jittor/master/script/install.sh|with_clang=1 bash#install with g++ and cudawget -O - https://raw.githubusercontent.com/Jittor/jittor/master/script/install.sh|with_gcc=1 with_cuda=1 bash#install with g++wget -O - https://raw.githubusercontent.com/...
CUDA/cuDNN version No response GPU model and memory No response Current behavior? I am trying to compile the tf lite benchmark tool on MacOS. For this I ran the following commands on tf 2.15 and master git clone https://github.com/tensorflow/tensorflow.git tensorflow ...
Optional Step 4: Enable CUDA Using CUDA in Jittor is very simple, Just setup environment valuenvcc_path #replace this var with your nvcc locationexportnvcc_path="/usr/local/cuda/bin/nvcc"#run a simple cuda testpython3.7 -m jittor.test.test_cuda ...
OPERATORS -D__CUDA_NO_HALF_CONVERSIONS_ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -w -std=c++14 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS_...
Using CUDA in Jittor is very simple, Just setup environment valuenvcc_path if the test is passed, your can use Jittor with CUDA by settinguse_cudaflag. importjittorasjtjt.flags.use_cuda=1 Optional Step 5: Test Resnet18 training To check the integrity of Jittor, you can run Resnet18 tra...