Ubuntu 20.04 CUDA 11.0 tfjs-node-gpu 2.0.1 Could not load dynamic library Error, Skipping registering GPU devices

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TensorFlow.js version

2.0.1

Browser version

Describe the problem or feature request

i installed CUDA 11 on Ubuntu 20 using these apt clean; apt update; apt purge cuda; apt purge nvidia-*; apt autoremove; apt install cuda install using this https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=2004&target_type=deblocal run post install - https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions

Trying to run a sample using GPU getting this error : node index.js

Overriding the gradient for ‘Max’ Overriding the gradient for ‘OneHot’ Overriding the gradient for ‘PadV2’ Overriding the gradient for ‘SpaceToBatchND’ Overriding the gradient for ‘SplitV’ node-pre-gyp info This Node instance does not support builds for N-API version 5 node-pre-gyp info This Node instance does not support builds for N-API version 6 node-pre-gyp info This Node instance does not support builds for N-API version 5 node-pre-gyp info This Node instance does not support builds for N-API version 6 2020-08-03 21:38:11.446282: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2020-08-03 21:38:11.486608: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3492985000 Hz 2020-08-03 21:38:11.487958: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x40053a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-08-03 21:38:11.487999: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2020-08-03 21:38:11.488794: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2020-08-03 21:38:11.505048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-08-03 21:38:11.505923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: TITAN RTX major: 7 minor: 5 memoryClockRate(GHz): 1.77 pciBusID: 0000:42:00.0 2020-08-03 21:38:11.505985: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcudart.so.10.0’; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.506014: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcublas.so.10.0’; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.506040: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcufft.so.10.0’; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.506065: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcurand.so.10.0’; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.506090: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcusolver.so.10.0’; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.506114: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘libcusparse.so.10.0’; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory 2020-08-03 21:38:11.508703: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2020-08-03 21:38:11.508718: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices… 2020-08-03 21:38:11.587020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-08-03 21:38:11.587051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2020-08-03 21:38:11.587056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N

Code to reproduce the bug / link to feature request

$ nvcc --version nvcc: NVIDIA ® Cuda compiler driver Copyright © 2005-2020 NVIDIA Corporation Built on Thu_Jun_11_22:26:38_PDT_2020 Cuda compilation tools, release 11.0, V11.0.194 Build cuda_11.0_bu.TC445_37.28540450_0

$ cat /etc/*-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=20.04 DISTRIB_CODENAME=focal DISTRIB_DESCRIPTION=“Ubuntu 20.04.1 LTS” NAME=“Ubuntu” VERSION=“20.04.1 LTS (Focal Fossa)” ID=ubuntu ID_LIKE=debian PRETTY_NAME=“Ubuntu 20.04.1 LTS” VERSION_ID=“20.04” HOME_URL=“https://www.ubuntu.com/” SUPPORT_URL=“https://help.ubuntu.com/” BUG_REPORT_URL=“https://bugs.launchpad.net/ubuntu/” PRIVACY_POLICY_URL=“https://www.ubuntu.com/legal/terms-and-policies/privacy-policy” VERSION_CODENAME=focal UBUNTU_CODENAME=focal

$ node -v v11.15.0

$more index.js

// const tf = require(‘@tensorflow/tfjs’); // const tf = require(‘@tensorflow/tfjs-node’);

// Optional Load the binding: // Use ‘@tensorflow/tfjs-node-gpu’ if running with GPU. const tf = require(‘@tensorflow/tfjs-node-gpu’);

// Train a simple model: const model = tf.sequential(); model.add(tf.layers.dense({ units: 100, activation: ‘relu’, inputShape: [10] })); model.add(tf.layers.dense({ units: 1, activation: ‘linear’ })); model.compile({ optimizer: ‘sgd’, loss: ‘meanSquaredError’ });

const xs = tf.randomNormal([100, 10]); const ys = tf.randomNormal([100, 1]);

model.fit(xs, ys, { epochs: 100, callbacks: { onEpochEnd: (epoch, log) => console.log(Epoch ${epoch}: loss = ${log.loss}) } });

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[jackz314]

TensorFlow core has supported CUDA 11 for a while now, which I verified by testing out the Python version. However, I’m still having the same problem (Could not load dynamic library ‘libcuxxx.so.10.0’) with tfjs. Is this still not supported?

[proj56578]

any idea when cuda 11 will be supported ? i read on one of the threads here , in 2.4 ? if that’s true , can you pls share a timeframe ?

On Aug 6, 2020, at 10:19 AM, Rajeshwar Reddy T [e-mail] wrote:

yes ,please try with 10.

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