沧州建设厅网站,越南注册公司流程和费用,怎么利用网站做cpa推广,网站怎么做排查修复这里写自定义目录标题 jax 不识别gpu错误信息最终解决方案 倒腾过程jaxlib version jax 不识别gpu
以下内容在 x86 linux cudagpu 测试通过
错误信息
jax-0.4.25 jaxlib-0.4.25 不识别gpu: RuntimeError: Unknown backend: ‘gpu’ requested, but no platforms that are in… 这里写自定义目录标题 jax 不识别gpu错误信息最终解决方案 倒腾过程jaxlib version jax 不识别gpu
以下内容在 x86 linux cudagpu 测试通过
错误信息
jax-0.4.25 jaxlib-0.4.25 不识别gpu: RuntimeError: Unknown backend: ‘gpu’ requested, but no platforms that are instances of gpu are present. Platforms present are: cpu
最终解决方案
根据cudnn版本和py版本去 https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 查对于的jaxlib版本来进行安装。
倒腾过程
pip install --upgrade “jax[cuda]” 会装jax-0.2.22 jaxlib-0.4.25
jax-0.2.22引入就报错。ImportError: cannot import name ‘pocketfft’ from ‘jaxlib’
pip uninstall jax jaxlib
https://pypi.org/project/jax/ pip install -U “jax[cuda12_pip]” -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 装完就是 jax-0.4.25 jaxlib-0.4.25cuda12.cudnn89
jax.devices() 会爆下面的错误 CUDA backend failed to initialize: Found cuDNN version 8400, but JAX was built against version 8906, 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_LEVEL0 and rerun for more info.) https://blog.csdn.net/Papageno_Xue/article/details/125754893
不存在 /usr/local/cuda-12.1/include/cudnn_version.h,/usr/local/cuda/include/cudnn_version.h 显示是840和上面提示的错误信息一直
这里确定了cudaa 版本8400,然后我是py39
jaxlib version
https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 里没有 0.4.2584的cudnn84以下有的82的但是是cuda11直接 pip install -U “jax[cuda11_pip]” -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 的话装的是jaxlib0.4.25cuda11.cudnn86根据 py39cudaa82 查看得知最高 jaxlib0.4.7
pip install jax0.4.7 jaxlib0.4.7cuda11.cudnn82 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html import jax jax.devices()[StreamExecutorGpuDevice(id0, process_index0, slice_index0), StreamExecutorGpuDevice(id1, process_index0, slice_index0), StreamExecutorGpuDevice(id2, process_index0, slice_index0), StreamExecutorGpuDevice(id3, process_index0, slice_index0)]
