https://github.com/qengineering/tensorflow-lite-raspberry-pi_64-bit

TensorFlow Lite installation wheels for Raspberry Pi 64 OS
https://github.com/qengineering/tensorflow-lite-raspberry-pi_64-bit

aarch64 armv8 cpp deep-learning installation-wheel jetson-nano linux pip3 python python3 raspberry-pi raspberry-pi-64-os tensorflow-lite wheel wheels whl

Last synced: 3 months ago
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TensorFlow Lite installation wheels for Raspberry Pi 64 OS

• Host: GitHub
• URL: https://github.com/qengineering/tensorflow-lite-raspberry-pi_64-bit
• Owner: Qengineering
• License: bsd-3-clause
• Created: 2022-10-15T16:08:04.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-02T12:17:49.000Z (over 2 years ago)
• Last Synced: 2025-01-26T03:45:46.098Z (5 months ago)
• Topics: aarch64, armv8, cpp, deep-learning, installation-wheel, jetson-nano, linux, pip3, python, python3, raspberry-pi, raspberry-pi-64-os, tensorflow-lite, wheel, wheels, whl
• Homepage: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html
• Size: 54.4 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TensorFlow Lite for Raspberry Pi and Jetson Nano

![output image]( https://qengineering.eu/github/Tensor-flow_Lite_icon.webp )



Here you find the C++ installation tar.gz files as mentioned in our [guide](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html).


Please see this page on how to install TensorFlow Lite C++.



For convenience reasons, we also placed the Python3 wheels.
 

Note, the provided wheels are ***not*** suitable for C++ programming, only the tar files are.



Both C++ and Python support NEON and XNNPACK.


-----------------

## Roadmap.

Select your Python3 version depending on your **64-bit** operating system.

| Python3 | Operating system  | TF 2.10.0 | TF 2.9.1 | TF 2.8.0 | TF 2.7.0 |

| ---------- | --------------------- | :---------------:  | :----------------: | :---------------: | :---------------: |

| Python 3.6 | Jetson Nano 4.6       |                    |                    |                   |:heavy_check_mark: |

| Python 3.7 | Raspberry Pi **Buster**   | :heavy_check_mark: | :heavy_check_mark: |:heavy_check_mark: |:heavy_check_mark: |

| Python 3.8 | Ubuntu 20.04          | :heavy_check_mark: | :heavy_check_mark: |:heavy_check_mark: |:heavy_check_mark: |

| Python 3.9 | Raspberry Pi **Bullseye** | :heavy_check_mark: | :heavy_check_mark: |:heavy_check_mark: |:heavy_check_mark: |

| Python 3.10 | Ubuntu 22.04         | :heavy_check_mark: | :heavy_check_mark: |:heavy_check_mark: |:heavy_check_mark: |

----------------------

![output image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find TensorFlow Lite with other frameworks and deep-learning examples on our [SD-image](https://github.com/Qengineering/RPi-image)



### TensorFlow Lite

The TensorFlow Lite versions support NEON and XNNPack.


Please notice the provided wheels are ***not*** suitable for C++ programming!


Only for the Python3 installation of TensorFlow Lite on your machine.


### Jetson Nano

TensorFlow Lite, intended for mobile devices, does not support CUDA.


You will have the regular aarch64 installation on your Nano.


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