Jetson nano tensorflow 2.4.0

Autonomous Machines Jetson & Embedded Systems Jetson Nano
,
#1

i have installed jetpack v 4.5.1
and i installed tensorflow from https://developer.download.nvidia.com/compute/redist/jp/v45/tensorflow/tensorflow-2.4.0+nv21.4-cp36-cp36m-linux_aarch64.whl
with all needed dependencies
i just tried a small test:

import numpy as np
import tensorflow as tf

D = tf.convert_to_tensor(np.array([[1., 2., 3.], [-3., -7., -1.], [0., 5., -2.]]))
print(tf.linalg.det(D))
...
and this stops with 
tensorflow/core/kernels/linalg/determinant_op_gpu.cu.cc:136] Non-OK-status: GpuLaunchKernel( DeterminantFromPivotedLUKernel<Scalar, false>, config.block_count, config.thread_per_block, 0, device.stream(), config.virtual_thread_count, n, lu_factor.data(), pivots, nullptr, output.data()) status: Internal: too many resources requested for launch

i tried several things, for days now, and always the same error: GpuLaunchKernel  status: Internal: too many resources requested for launch

is there a workaround or a other tensorflow version
#3

Hi,

This is a known issue and we are still checking this.

Is TensorFlow 1…15.x acceptable for you?
If yes, please give it a try.

Thanks.

#5

hi,
thanks for the quick response
i’ll give tf1 a try, but i have to reprogram all my codes and models
so is there a plan when this issue with tf2 will be fixed, or when a working release of tf2 is released

thanks

#6

Hi,

We can reproduce this issue in our environment.
And it’s checking the details with our internal team.

Will share more information with you later.
Thanks.