针对你遇到的错误“unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute”,这通常是由于JAX库与其依赖的CUDA环境之间存在不兼容或配置错误所导致的。以下是一些具体的解决步骤,你可以按照这些步骤逐一排查和解决问题: 确认CUDA和cuDNN的正确安装与版本匹配: 首先,确保你的系统中已...
Unable to initialize backend 'cuda': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig' how can i solve this problem? 👍 sumowi Jun 5, 2023 • edited Copy link Dharmendra04commentedJul 15, 2023 Hi, I tried your way and installed jax gpu version using pip install --up...
pip install --upgrade jax==0.4.19 jaxlib==0.4.19+cuda12.cudnn89 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html When I run my python script, which is basically a program that runs whisper-jax I’m getting this message: CUDA backend failed to ini...
will fail withCUDA backend failed to initialize: Found cuDNN version 8500, but JAX was built against version 8600, which is newer. The copy of cuDNN that is installed must be at least as new as the version against which JAX was built. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more...
INFO:absl:Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with...
试着设置一下环境变量NCCL_P2P_DISABLE=1，看看能不能解决
Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. 1. 同样可能是由于cudnn版本对应错误,可重新修改cudnn,即找到对应版本cudnn,重复安装流程第二步。
PyTorch splits its backend into two shared libraries: a CPU library and a CUDA ... 分享63 cuda吧 勤奋的陈大饼 求大佬们指教关于深度学习GPU加速我安装了cuda 10.2版本,想试下用GPU训练下数字识别,结果报错,求各位大佬指教RuntimeError: Cannot initialize CUDA without ATen_cuda library. PyTorch splits ...
Release Notes 1.1. 11.8 Release New Unified Debugger backend A new debugger backend named the Unified Debugger (UD) has been introduced on Linux platforms with this release. UD is supported across multiple platforms including both Windows and Linux. The UD should mostly be transparent to ...
raise Exception("Failed to initialize the CUDA driver API") report_used_memory(msg="after cuinit") device = 0 # Create contexts on device 0 contexts = [] for i in range(3): context = ctypes.c_void_p() result = cuda.cuCtxCreate(ctypes.byref(context), 0, device) ...