Orin Nano TF cuDNN error

Orin Nano w/JetPack 5.1.2 which included CUDA11.4 and cuDNN 8.6 according to jtop.

Installed the tensorflow package 2.12.0+nv23.6 from https://developer.download.nvidia.com/compute/redist/jp/v512.

Basic tensorflow operations work fine, report the GPU device, etc.

However, anything that requires cuDNN fails with:

2024-02-14 18:11:02.216299: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:429] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
2024-02-14 18:11:02.216522: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:434] Error retrieving driver version: NOT_FOUND: could not find kernel module information in driver version file contents: "NVRM version: NVIDIA UNIX Open Kernel Module for aarch64  35.4.1  Release Build  (buildbrain@mobile-u64-6422-d7000)  Tue Aug  1 12:45:41 PDT 2023
GCC version:  gcc version 9.3.0 (Buildroot 2020.08) 
2024-02-14 18:11:02.216689: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at conv_ops_fused_impl.h:624 : UNIMPLEMENTED: DNN library is not found.

The tensorflow page indicates 2.12 requires CUDA 11.8 and cuDNN 8.6 so I followed the info in another post to upgrade CUDA to 11.8, rebooted, but the same error remains.


The package is a custom Jetson version built with JetPack 5.1.2.
Does it work with the default JetPack setting?

If not, could you share the script that reproduces this issue?



So, woke up this morning, turned on the device, and re-ran the script before posting and… it worked. Sorry for the false alarm. Here was the script for reference:

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

x = np.random.normal(size=(100, 28, 28, 1)).astype(np.float32)
y = np.zeros([100, 10], dtype=np.float32)
y[:, 1] = 1.

train_ds = tf.data.Dataset.from_tensor_slices((x, y)).shuffle(buffer_size=100).batch(32)
num_classes = 10

model = Sequential([
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.Dense(128, activation='relu'),
history = model.fit(

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