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A Linux shell script that creates a virtual python 3 environment with all libraries pre-installed in it to be able to use the TensorFlow Object Detection API features

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TODAv3

TODAv3 is a Linux shell script that creates a virtual python 3 environment with all libraries pre-installed in it to be able to use the TensorFlow Object Detection API features.

Installation

1)Download the createTensorflowVenv.sh file.

2)Give execute permissions to the downloaded script with command

chmod +x /path/to/createTensorflowVenv.sh

3)Install Protoc. Detailed instillation guide is here (This step requires sudo operations; therefore, it is the best way to do it with your own hands to avoid future mistakes.)

For Mac OSX:

brew install protobuf

For Linux:

PROTOC_ZIP=protoc-3.7.1-linux-x86_64.zip
curl -OL https://github.com/google/protobuf/releases/download/v3.7.1/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local include/*
rm -f $PROTOC_ZIP

Important Note:

Respect to the TensorFlow official installation page, before starting installation (If you are going to install GPU version. There is no requirement for CPU version )you should satisfy some requirements(CUDA Toolkit, cuDNN etc.). You should check these requirements from the link above.

Usage

Script accepts 2 arguments.

1- Path with folder name to create

2- cpu or gpu selection respect to your system

If you want to use TensorFlow(CPU):

./createTensorflowVenv.sh /path/to/myNewProject cpu

If you want to use TensorFlow(GPU):

./createTensorflowVenv.sh /path/to/myNewProject gpu

After instillation the project structure should be like:

-> myNewProject
         ----->cocoapi
         ----->models
         ----->venv

Note: The installed project size is near to 1.8 GB .

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

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A Linux shell script that creates a virtual python 3 environment with all libraries pre-installed in it to be able to use the TensorFlow Object Detection API features

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