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16:34:10.445410: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

16:34:10.444913: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 16:34:10.443339: 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: AVX2 FMA 16:34:10.386060: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 16:34:10.385631: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 16:34:10.381327: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero Np.testing.assert_array_equal(input_tensor_read, ds_read) # fails with ~11% diff.įile "/srv/2D3Dreg/deeplearning/tf_issue/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 930, in assert_array_equalĪssert_array_compare(operator._eq_, x, y, err_msg=err_msg,įile "/srv/2D3Dreg/deeplearning/tf_issue/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 840, in assert_array_compare 14:10:09.596141: I tensorflow/compiler/mlir/mlir_graph_optimization_:185] None of the MLIR Optimization Passes are enabled (registered 2) 14:10:09.586072: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:09.585487: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:09.584861: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:09.000204: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:08.999644: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:08.999018: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 14:10:08.998427: 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: AVX2 AVX512F FMA 14:10:08.997667: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:08.997055: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 14:10:08.987749: I tensorflow/stream_executor/cuda/cuda_gpu_:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero Np.testing.assert_array_equal(plain_tensor, ds_tensor) # fails with ~11% diff. Plain_tensor = tf_read_and_resize_image(im_filename)ĭs = tf._tensor_slices()Īssert plain_tensor.dtype = ds_tensor.dtype = "float32"Īssert plain_tensor.shape = ds_tensor.shape = (224, 224, 3) Image = tf.code_jpeg(image_string, channels=3) From PIL import tf_read_and_resize_image(filename):