Hi @sushreebarsa according the compatibility table, all the tensorflow doesnot support cuda11.3 since the latest supported version is cuda 11.2.ngam commented Feb 17, 2022 • edited @MinWang1997 anything compiled with cuda=11.2 should work with anything cuda>=11.2, see more here : https://...
Make sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table. TFCUDA 2.1.0 - 2.2.010.1 1.13.1 - 2.010.0 1.5.0 - 1.12.09.0 If you have above configuration and usingWindowsplatform - ...
Compatibility Table 1. TensorFlow compatibility with NVIDIA containers and Jetpack TensorFlow Version 2.12.0 2.11.0 2.10.1 NVIDIA TensorFlow Container 23.06, 23.05, 23.04 23.03, 23.02, 23.01 22.12 JetPack Version 5.1.x 5.0.2 TensorFlow For Jetson Platform SWE-SWDOCTFX-001-RELN _v001 ...
Release 20.12 is based on NVIDIA CUDA 11.1.1, which requires NVIDIA Driver release 455 or later. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 418.xx, 440.30, or 450.xx. The CUDA driver's compatibility package only...
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Fixed OpenSSL compatibility by avoiding EVP_MD_CTX_destroy. Added bounds checking to printing deprecation warnings. Upgraded CUDA dependency to 10.0 To build with Android NDK r14b, add "#include <linux/compiler.h>" to android-ndk-r14b/platforms/android-14/arch-*/usr/include/linux/futex.h Remo...
CPU、GPU通用算法开发和训练基础镜像,预置AI引擎TensorFlow2.1 CPU/GPU 是是 tensorflow1.13-cuda10.0-cudnn7-ubuntu18.04 GPU通用算法开发和训练基础镜像,预置AI引擎TensorFlow1.13.1 GPU 是是 conda3-ubuntu18 来自:帮助中心 查看更多 → 共105条 1 2 3 4 5 内容专区 代码托管服务作用_ 软件开发生产线...
TensorFlow 1.7 may be the last time we support Cuda versions below 8.0. Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported version. TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0. Starting with TensorFlow 1.8 release, 6.0 will be the minimum ...
ForCUDAToolkit<=7.5do: 代码语言:javascript 复制 $ sudo apt-getinstall libcupti-dev 代码语言:javascript 复制 ***[OPTIONAL]**For optimized inferencing performance,you can also installNVIDIATensorRT3.0\.For details,see[NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-...
Again the RTX3080 is doing very well with mixed precision fp16. I expect this number to improve with a new driver and some CUDA patches. There is a dramatic improvement for the RTX Titan at fp16 1082 img/sec vs 653 img/sec from the older testing!