![]() The upgrade automatically removes the existing TensorFlow and installs the stated version.ģ. If you are using a Notebook environment, run the following command and restart the kernel when the installation completes: !pip install -upgrade tensorflow= When choosing, make sure the version is compatible with the Python release. Set the version to a lower number than the currently installed release. Downgrade TensorFlow to a lower version by running: pip3 install -upgrade tensorflow= Check the currently installed TensorFlow version: pip3 show tensorflowĢ. Older versions have vulnerability issues, so be cautious when downgrading.ġ. The best practice for TensorFlow downgrade is to use the latest version of Python and TensorFlow. Lastly, check the upgraded version by running: pip3 show tensorflow How to Downgrade TensorFlow The install automatically removes the old version along with the dependencies and installs the newer upgrade.Ĥ. For the notebook environment, use the following command and restart the kernel after completion: !pip install -upgrade tensorflow= ![]() If the release is incompatible, the version will not install. Make sure to select a version compatible with your Python release. Upgrade TensorFlow to a newer version with: pip3 install -upgrade tensorflow= The command shows information about the package, including the version.ģ. Check the currently installed TensorFlow version: pip3 show tensorflow To upgrade TensorFlow to a newer version:Ģ. Python 3.8 works with TensorFlow 2.2 and later releases.Python 3.9 works with TensorFlow 2.5 and later releases.This isn't a hard rule, but in most of the cases you will get the best performance with quantization aware training and running inference with ARMNN.Note: Depending on the Python version, only specific TensorFlow releases are available: You should change the command instead to python3 -m pip install tensorflow-macos and it will work. Using Arm NN in a custom C/C++ application To run tflite Inference with eIQ on the board, there are currently two options: Convert to tflite post training without quantization (set converter.post_training_quantize = False in the previous post) Convert to tflite and quantize model post training - check this post: For embedded Systems, it is recommended to convert the tensorflow model to tflite. Or you can start with a pre-trained model and use transfer learning to specialize it for your use case - check this community post: ĮIQ handles the Inference. Let me ask is there any way for tensorflow to work on python3?Įxactly, training can be done on the host PC (like ubuntu) with python tensorflow 1.12.ĭepending on what you wan to achieve with your application, there is also the option to use a pre-trained model: Tensorflow Models TensorFlow Lite models . I also tried to install tensorflow through bazel as shown in the link below but it failed at build bazel. ![]() I tried "pip3 install tensorflow" but pip3 install tensorflowĮRROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)ĮRROR: No matching distribution found for tensorflow ImportError: No module named 'tensorflow' Type "help", "copyright", "credits" or "license" for more information. I need to install tensorflow for python3 run on the imx8MM EVK board.Īfter reference the document about NXP eIQ™ Machine Learning at: "", I was installed tensorflow, running benchmark and building example from sources successfully.īut "python3 import tensorflow as tf" had the python3
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