# TensorFlow performance

**URL:** <https://forums.developer.nvidia.com/t/tensorflow-performance/72112>\
**Category:** Jetson Nano\
**Created:** [March 28, 2019, 4:42am UTC](https://forums.developer.nvidia.com/t/tensorflow-performance/72112 "2019-03-28T04:42:14Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![clarenceho](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@clarenceho](https://forums.developer.nvidia.com/u/clarenceho)\
**Post date:** [March 28, 2019, 4:42am UTC](https://forums.developer.nvidia.com/t/tensorflow-performance/72112/1 "2019-03-28T04:42:14Z")

</div>

Anyone has experience with TensorFlow on Nano? How is the performance? I just tried a simple script and it runs nearly twice as fast on CPU when compared with running it on CUDA.

I followed the steps to install TensorFlow: [https://docs.nvidia.com/deeplearning/dgx/install-tf-xavier/index.html](https://docs.nvidia.com/deeplearning/dgx/install-tf-xavier/index.html) (similar to the steps mentioned in another topic here)

Then I tried to run this sample, which is modified from from the book “TensorFlow for Deep Learning Companion Code”  
[https://github.com/kitsook/dlwithtf/blob/fix-lost-function/ch3/linear\_regression\_tf.py](https://github.com/kitsook/dlwithtf/blob/fix-lost-function/ch3/linear_regression_tf.py)

Switch Ubuntu to runlevel 3 to free up some memory before the tests.

```auto
sudo systemctl isolate multi-user.target

```

Running the code normally on CUDA, the training part took: 43s:

```auto
(linear_regression_tf.py:8106): Gdk-CRITICAL **: 21:21:38.160: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed

(linear_regression_tf.py:8106): Gdk-CRITICAL **: 21:21:38.164: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-03-27 21:21:41.201561: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency
2019-03-27 21:21:41.202159: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x3f3f6740 executing computations on platform Host. Devices:
2019-03-27 21:21:41.202218: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): <undefined>, <undefined>
2019-03-27 21:21:41.289107: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:965] ARM64 does not support NUMA - returning NUMA node zero
2019-03-27 21:21:41.289392: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x3d4e3d50 executing computations on platform CUDA. Devices:
2019-03-27 21:21:41.289441: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): NVIDIA Tegra X1, Compute Capability 5.3
2019-03-27 21:21:41.289806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: NVIDIA Tegra X1 major: 5 minor: 3 memoryClockRate(GHz): 0.9216
pciBusID: 0000:00:00.0
totalMemory: 3.86GiB freeMemory: 1.94GiB
2019-03-27 21:21:41.289863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-03-27 21:21:42.374481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-27 21:21:42.374562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 
2019-03-27 21:21:42.374592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 
2019-03-27 21:21:42.374771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1518 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)
2019-03-27 21:21:43.782819: I tensorflow/stream_executor/dso_loader.cc:153] successfully opened CUDA library libcublas.so.10.0 locally
Training reulst: W=4.765114, b=2.123778
Time taken for learning: 43.052430391311646
Pearson R^2: 0.994371
RMS: 0.119845

```

Running the same script on CPU by specifying the environment variable to hide CUDA:

```auto
CUDA_VISIBLE_DEVICES="" python3 linear_regression_tf.py

```

It took 25s, nearly half the time, to complete the training.

```auto
(linear_regression_tf.py:8295): Gdk-CRITICAL **: 21:22:35.522: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed

(linear_regression_tf.py:8295): Gdk-CRITICAL **: 21:22:35.526: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-03-27 21:22:38.583032: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency
2019-03-27 21:22:38.584033: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x9774bd0 executing computations on platform Host. Devices:
2019-03-27 21:22:38.584096: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): <undefined>, <undefined>
2019-03-27 21:22:38.613701: E tensorflow/stream_executor/cuda/cuda_driver.cc:300] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2019-03-27 21:22:38.613800: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:148] kernel driver does not appear to be running on this host (clarence-jetson-nano): /proc/driver/nvidia/version does not exist
Training reulst: W=4.765114, b=2.123778
Time taken for learning: 25.613048553466797
Pearson R^2: 0.994371
RMS: 0.119845

```

Maybe the platform is not meant to be used for training?

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [March 28, 2019, 8:30am UTC](https://forums.developer.nvidia.com/t/tensorflow-performance/72112/2 "2019-03-28T08:30:51Z")

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Hi,

Jetson platform is designed for fast inference so it’s not recommended to use for training.  
If you are looking for an AI benchmark for Nano, please check this blog:  
[url][https://devblogs.nvidia.com/jetson-nano-ai-computing/[/url]](https://devblogs.nvidia.com/jetson-nano-ai-computing/%5B/url%5D)

Thanks.

---

<div class="post-metadata">

**Author:** ![nadeemm](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/nadeemm/32/367020_2.png) [@nadeemm](https://forums.developer.nvidia.com/u/nadeemm)\
**Post date:** [October 18, 2021, 5:43pm UTC](https://forums.developer.nvidia.com/t/tensorflow-performance/72112/3 "2021-10-18T17:43:53Z")

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