Installation

General Remarks

  • There are two different variations of TensorFlow that you might wish to install, depending on whether you would like TensorFlow to run on your CPU or GPU, namely TensorFlow CPU and TensorFlow GPU. I will proceed to document both and you can choose which one you wish to install.

  • If you wish to install both TensorFlow variants on your machine, ideally you should install each variant under a different (virtual) environment. If you attempt to install both TensorFlow CPU and TensorFlow GPU, without making use of virtual environments, you will either end up failing, or when we later start running code there will always be an uncertainty as to which variant is being used to execute your code.

  • To ensure that we have no package conflicts and/or that we can install several different versions/variants of TensorFlow (e.g. CPU and GPU), it is generally recommended to use a virtual environment of some sort. For the purposes of this tutorial we will be creating and managing our virtual environments using Anaconda, but you are welcome to use the virtual environment manager of your choice (e.g. virtualenv).

Install Anaconda Python 3.7 (Optional)

Although having Anaconda is not a requirement in order to install and use TensorFlow, I suggest doing so, due to it’s intuitive way of managing packages and setting up new virtual environments. Anaconda is a pretty useful tool, not only for working with TensorFlow, but in general for anyone working in Python, so if you haven’t had a chance to work with it, now is a good chance.

Windows

Linux

  • Go to https://www.anaconda.com/download/

  • Download Anaconda Python 3.7 version for Windows

  • Run the downloaded executable (.exe) file to begin the installation. See here for more details.

  • (Optional) In the next step, check the box “Add Anaconda to my PATH environment variable”. This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors.

  • Go to https://www.anaconda.com/download/

  • Download Anaconda Python 3.7 version for Linux

  • Run the downloaded bash script (.sh) file to begin the installation. See here for more details.

  • When prompted with the question “Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home/<user>/.bashrc ?”, answer “Yes”. If you enter “No”, you must manually add the path to Anaconda or conda will not work.

TensorFlow Installation

As mentioned in the Remarks section, there exist two generic variants of TensorFlow, which utilise different hardware on your computer to run their computationally heavy Machine Learning algorithms.

  1. The simplest to install, but also in most cases the slowest in terms of performance, is TensorFlow CPU, which runs directly on the CPU of your machine.

  2. Alternatively, if you own a (compatible) Nvidia graphics card, you can take advantage of the available CUDA cores to speed up the computations performed by TensorFlow, in which case you should follow the guidelines for installing TensorFlow GPU.

TensorFlow CPU

Getting setup with an installation of TensorFlow CPU can be done in 3 simple steps.

Important

The term Terminal will be used to refer to the Terminal of your choice (e.g. Command Prompt, Powershell, etc.)

Create a new Conda virtual environment (Optional)

  • Open a new Terminal window

  • Type the following command:

    conda create -n tensorflow_cpu pip python=3.7

  • The above will create a new virtual environment with name tensorflow_cpu

  • Now lets activate the newly created virtual environment by running the following in the Terminal window:

    activate tensorflow_cpu

Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.:

(tensorflow_cpu) C:\Users\sglvladi>

Install TensorFlow CPU for Python

  • Open a new Terminal window and activate the tensorflow_cpu environment (if you have not done so already)

  • Once open, type the following on the command line:

    pip install --ignore-installed --upgrade tensorflow==1.14

  • Wait for the installation to finish

Test your Installation

  • Open a new Terminal window and activate the tensorflow_cpu environment (if you have not done so already)

  • Start a new Python interpreter session by running:

    python

  • Once the interpreter opens up, type:

    >>> import tensorflow as tf

  • If the above code shows an error, then check to make sure you have activated the tensorflow_cpu environment and that tensorflow_cpu was successfully installed within it in the previous step.

  • Then run the following:

    >>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()

  • Once the above is run, if you see a print-out similar (or identical) to the one below, it means that you could benefit from installing TensorFlow by building the sources that correspond to you specific CPU. Everything should still run as normal, but potentially slower than if you had built TensorFlow from source.

    2019-02-28 11:59:25.810663: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

  • Finally, run the following:

    >>> print(sess.run(hello))
b'Hello, TensorFlow!'

TensorFlow GPU

The installation of TensorFlow GPU is slightly more involved than that of TensorFlow CPU, mainly due to the need of installing the relevant Graphics and CUDE drivers. There’s a nice Youtube tutorial (see here), explaining how to install TensorFlow GPU. Although it describes different versions of the relevant components (including TensorFlow itself), the installation steps are generally the same with this tutorial.

Before proceeding to install TensorFlow GPU, you need to make sure that your system can satisfy the following requirements:

Prerequisites

Nvidia GPU (GTX 650 or newer)

CUDA Toolkit v10.0

CuDNN 7.6.5

Anaconda with Python 3.7 (Optional)

Install CUDA Toolkit

Windows

Linux

Follow this link to download and install CUDA Toolkit 10.0.

Follow this link to download and install CUDA Toolkit 10.0 for your Linux distribution.

Install CUDNN

Windows

Linux

Environment Setup

Windows

Linux

  • Go to Start and Search “environment variables”

  • Click “Edit the system environment variables”. This should open the “System Properties” window

  • In the opened window, click the “Environment Variables…” button to open the “Environment Variables” window.

  • Under “System variables”, search for and click on the Path system variable, then click “Edit…”

  • Add the following paths, then click “OK” to save the changes:

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.0\libnvvp

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.0\extras\CUPTI\libx64

    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.0\cuda\bin

As per Section 7.1.1 of the CUDA Installation Guide for Linux, append the following lines to ~/.bashrc:

# CUDA related exports
export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Update your GPU drivers (Optional)

If during the installation of the CUDA Toolkit (see Install CUDA Toolkit) you selected the Express Installation option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers.

Create a new Conda virtual environment

  • Open a new Terminal window

  • Type the following command:

    conda create -n tensorflow_gpu pip python=3.7

  • The above will create a new virtual environment with name tensorflow_gpu

  • Now lets activate the newly created virtual environment by running the following in the Anaconda Promt window:

    activate tensorflow_gpu

Once you have activated your virtual environment, the name of the environment should be displayed within brackets at the beggining of your cmd path specifier, e.g.:

(tensorflow_gpu) C:\Users\sglvladi>

Install TensorFlow GPU for Python

  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

  • Once open, type the following on the command line:

    pip install --upgrade tensorflow-gpu==1.14

  • Wait for the installation to finish

Test your Installation

  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

  • Start a new Python interpreter session by running:

    python

  • Once the interpreter opens up, type:

    >>> import tensorflow as tf

  • If the above code shows an error, then check to make sure you have activated the tensorflow_gpu environment and that tensorflow_gpu was successfully installed within it in the previous step.

  • Then run the following:

    >>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()

  • Once the above is run, you should see a print-out similar (but not identical) to the one bellow:

    2019-11-25 07:20:32.415386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2019-11-25 07:20:32.449116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1070 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-11-25 07:20:32.455223: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-25 07:20:32.460799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-25 07:20:32.464391: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-11-25 07:20:32.472682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1070 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-11-25 07:20:32.478942: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-25 07:20:32.483948: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-25 07:20:33.181565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-25 07:20:33.185974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-11-25 07:20:33.189041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-11-25 07:20:33.193290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6358 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)

  • Finally, run the following:

    >>> print(sess.run(hello))
b'Hello, TensorFlow!'

TensorFlow Models Installation

Now that you have installed TensorFlow, it is time to install the models used by TensorFlow to do its magic.

Install Prerequisites

Building on the assumption that you have just created your new virtual environment (whether that’s tensorflow_cpu, tensorflow_gpu or whatever other name you might have used), there are some packages which need to be installed before installing the models.

Prerequisite packages

Name

Tutorial version-build

pillow

6.2.1-py37hdc69c19_0

lxml

4.4.1-py37h1350720_0

jupyter

1.0.0-py37_7

matplotlib

3.1.1-py37hc8f65d3_0

opencv

3.4.2-py37hc319ecb_0

pathlib

1.0.1-cp37

The packages can be installed using conda by running:

conda install <package_name>(=<version>) <package_name>(=<version>) ... <package_name>(=<version>)

where <package_name> can be replaced with the name of the package, and optionally the package version can be specified by adding the optional specifier =<version> after <package_name>. For example, to simply install all packages at their latest versions you can run:

conda install pillow lxml jupyter matplotlib opencv cython

Alternatively, if you don’t want to use Anaconda you can install the packages using pip:

pip install <package_name>(==<version>) <package_name>(==<version>) ... <package_name>(==<version>)

but you will need to install opencv-python instead of opencv.

Downloading the TensorFlow Models

Note

To ensure compatibility with the chosen version of Tensorflow (i.e. 1.14.0), it is generally recommended to use one of the Tensorflow Models releases, as they are most likely to be stable. Release v1.13.0 is the last unofficial release before v2.0 and therefore is the one used here.

  • Create a new folder under a path of your choice and name it TensorFlow. (e.g. C:\Users\sglvladi\Documents\TensorFlow).

  • From your Terminal cd into the TensorFlow directory.

  • To download the models you can either use Git to clone the TensorFlow Models v.1.13.0 release inside the TensorFlow folder, or you can simply download it as a ZIP and extract it’s contents inside the TensorFlow folder. To keep things consistent, in the latter case you will have to rename the extracted folder models-r1.13.0 to models.

  • You should now have a single folder named models under your TensorFlow folder, which contains another 4 folders as such:

TensorFlow
└─ models
    ├── official
    ├── research
    ├── samples
    └── tutorials

Protobuf Installation/Compilation

The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be downloaded and compiled.

This should be done as follows:

  • Head to the protoc releases page

  • Download the latest protoc-*-*.zip release (e.g. protoc-3.11.0-win64.zip for 64-bit Windows)

  • Extract the contents of the downloaded protoc-*-*.zip in a directory <PATH_TO_PB> of your choice (e.g. C:\Program Files\Google Protobuf)

  • Extract the contents of the downloaded protoc-*-*.zip, inside C:\Program Files\Google Protobuf

  • Add <PATH_TO_PB> to your Path environment variable (see Environment Setup)

  • In a new Terminal 1, cd into TensorFlow/models/research/ directory and run the following command:

    # From within TensorFlow/models/research/
protoc object_detection/protos/*.proto --python_out=.

Important

If you are on Windows and using Protobuf 3.5 or later, the multi-file selection wildcard (i.e *.proto) may not work but you can do one of the following:

Windows Powershell

Command Prompt

# From within TensorFlow/models/research/
Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}

# From within TensorFlow/models/research/
for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
1

NOTE: You MUST open a new Terminal for the changes in the environment variables to take effect.

Adding necessary Environment Variables

  1. Install the Tensorflow\models\research\object_detection package by running the following from Tensorflow\models\research:

    # From within TensorFlow/models/research/
pip install .

  2. Add research/slim to your PYTHONPATH:

Windows

Linux

  • Go to Start and Search “environment variables”

  • Click “Edit the system environment variables”. This should open the “System Properties” window

  • In the opened window, click the “Environment Variables…” button to open the “Environment Variables” window.

  • Under “System variables”, search for and click on the PYTHONPATH system variable,

    • If it exists then click “Edit…” and add <PATH_TO_TF>\TensorFlow\models\research\slim to the list

    • If it doesn’t already exist, then click “New…”, under “Variable name” type PYTHONPATH and under “Variable value” enter <PATH_TO_TF>\TensorFlow\models\research\slim

  • Then click “OK” to save the changes:

The Installation docs suggest that you either run, or add to ~/.bashrc file, the following command, which adds these packages to your PYTHONPATH:

# From within tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:<PATH_TO_TF>/TensorFlow/models/research/slim

where, in both cases, <PATH_TO_TF> replaces the absolute path to your TensorFlow folder. (e.g. <PATH_TO_TF> = C:\Users\sglvladi\Documents if TensorFlow resides within your Documents folder)

COCO API installation (Optional)

The pycocotools package should be installed if you are interested in using COCO evaluation metrics, as discussed in Evaluating the Model (Optional).

Windows

Linux

Run the following command to install pycocotools with Windows support:

pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

Note that, according to the package’s instructions, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from here.

Download cocoapi to a directory of your choice, then make and copy the pycocotools subfolder to the Tensorflow/models/research directory, as such:

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <PATH_TO_TF>/TensorFlow/models/research/

Note

The default metrics are based on those used in Pascal VOC evaluation.

  • To use the COCO object detection metrics add metrics_set: "coco_detection_metrics" to the eval_config message in the config file.

  • To use the COCO instance segmentation metrics add metrics_set: "coco_mask_metrics" to the eval_config message in the config file.

Test your Installation

  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

  • cd into TensorFlow\models\research\object_detection and run the following command:

    # From within TensorFlow/models/research/object_detection
jupyter notebook

  • This should start a new jupyter notebook server on your machine and you should be redirected to a new tab of your default browser.

  • Once there, simply follow sentdex’s Youtube video to ensure that everything is running smoothly.

  • When done, your notebook should look similar to the image bellow:

    alternate text

Important

  1. If no errors appear, but also no images are shown in the notebook, try adding %matplotlib inline at the start of the last cell, as shown by the highlighted text in the image bellow:

alternate text

  1. If Python crashes when running the last cell, have a look at the Terminal window you used to run jupyter notebook and check for an error similar (maybe identical) to the one below:

    2018-03-22 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000).  If using a binary install, upgrade your CuDNN library to match.  If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.

    • If the above line is present in the printed debugging, it means that you have not installed the correct version of the cuDNN libraries. In this case make sure you re-do the Install CUDNN step, making sure you instal cuDNN v7.6.5.

LabelImg Installation

There exist several ways to install labelImg. Below are 3 of the most common.

Use precompiled binaries (Easy)

Precompiled binaries for both Windows and Linux can be found here .

Installation is the done in three simple steps:

  1. Inside you TensorFlow folder, create a new directory, name it addons and then cd into it.

  2. Download the latest binary for your OS from here. and extract its contents under Tensorflow/addons/labelImg.

  3. You should now have a single folder named addons\labelImg under your TensorFlow folder, which contains another 4 folders as such:

TensorFlow
├─ addons
│   └── labelImg
└─ models
    ├── official
    ├── research
    ├── samples
    └── tutorials

  1. labelImg can then be run as follows:

# From within Tensorflow/addons/labelImg
labelImg
# or
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Build from source (Hard)

The steps for installing from source follow below.

1. Download labelImg

  • Inside you TensorFlow folder, create a new directory, name it addons and then cd into it.

  • To download the package you can either use Git to clone the labelImg repo inside the TensorFlow\addons folder, or you can simply download it as a ZIP and extract it’s contents inside the TensorFlow\addons folder. To keep things consistent, in the latter case you will have to rename the extracted folder labelImg-master to labelImg. 2

  • You should now have a single folder named addons\labelImg under your TensorFlow folder, which contains another 4 folders as such:

TensorFlow
├─ addons
│   └── labelImg
└─ models
    ├── official
    ├── research
    ├── samples
    └── tutorials
2

The latest repo commit when writing this tutorial is 8d1bd68.

2. Install dependencies and compiling package

  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

  • cd into TensorFlow\addons\labelImg and run the following commands:

    Windows

    Linux

    conda install pyqt=5
pyrcc5 -o libs/resources.py resources.qrc

    sudo apt-get install pyqt5-dev-tools
sudo pip install -r requirements/requirements-linux-python3.txt
make qt5py3

3. Test your installation

  • Open a new Terminal window and activate the tensorflow_gpu environment (if you have not done so already)

  • cd into TensorFlow\addons\labelImg and run the following command:

    # From within Tensorflow/addons/labelImg
python labelImg.py
# or
python  labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]