![]() To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 11:50:29.264401: I tensorflow/core/platform/cpu_feature_:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 11:50:29.247104: I tensorflow/stream_executor/cuda/cuda_:169] retrieving CUDA diagnostic information for host: DESKTOP-LKK3I7H 11:50:29.234980: W tensorflow/stream_executor/cuda/cuda_:269] failed call to cuInit: UNKNOWN ERROR (303) 11:50:29.225694: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'nvcuda.dll' dlerror: nvcuda.dll not found 11:50:25.622571: I tensorflow/stream_executor/cuda/cudart_:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 11:50:25.612224: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'cudart64_110.dll' dlerror: cudart64_110.dll not found If you getting tensor in output, than latest TensorFlow version is installed successfully. Python -c "import tensorflow as tf print(tf.reduce_sum(tf.random.normal()))" ![]() 11:48:40.033709: I tensorflow/stream_executor/cuda/cudart_:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. We are still missing Windows builds for TensorFlow (CPU & CUDA, unfortunately) and would love the community to help us out with that. You can use TensorFlow to train and deploy deep neural networks for image recognition, natural language processing, recurrent neural networks, and other machine learning applications. 11:48:40.023881: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'cudart64_110.dll' dlerror: cudart64_110.dll not found Currently (november 2020), TensorFlow 2.3 is supported with Cuda 10.1 To install this environment the recommended method is: conda create -n tfenv. With the TensorFlow builds in place, conda-forge now has CUDA-enabled builds for PyTorch and Tensorflow, the two most popular deep learning libraries. The TensorFlow conda environment is an ecosystem of tools and libraries to create state-of-the-art machine learning models. Python -c "import tensorflow as tf print(tf._version_)"
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