Installation

TensorFlow Installation

Install TensorFlow pip package

  • Open a new Terminal window

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

    pip install --upgrade tensorflow

  • Verify the install:

    python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

    Once the above is run, you should see a print-out similar to the one bellow:

    2020-06-22 19:20:32.614181: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-06-22 19:20:32.620571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-06-22 19:20:35.027232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-06-22 19:20:35.060549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-22 19:20:35.074967: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-06-22 19:20:35.084458: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found
2020-06-22 19:20:35.094112: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2020-06-22 19:20:35.103571: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2020-06-22 19:20:35.113102: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2020-06-22 19:20:35.123242: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found
2020-06-22 19:20:35.140987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-06-22 19:20:35.146285: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1598] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2020-06-22 19:20:35.162173: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-22 19:20:35.178588: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x15140db6390 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-22 19:20:35.185082: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-06-22 19:20:35.191117: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-22 19:20:35.196815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]
tf.Tensor(1620.5817, shape=(), dtype=float32)

Install CUDA libraries (Optional)

Although using a GPU to run TensorFlow is not necessary, the computational gains are substantial. Therefore, if your machine is equipped with a compatible CUDA-enabled GPU, it is recommended to follow the steps listed below to install the relevant libraries necessary to enable TensorFlow to make use of your GPU.

By default, when TensorFlow is run it will attempt to register compatible GPU devices. If this fails, TensorFlow will resort to running on the platform’s CPU. This can also be observed in the printout shown in the previous section, under the “Verify the install” bullet-point, where there are a number of messages which report missing library files (e.g. Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found).

In order for TensorFlow to run on your GPU, the following requirements must be met:

Prerequisites
Nvidia GPU (GTX 650 or newer)
CUDA Toolkit v10.1
CuDNN 7.6.5

Install CUDA Toolkit

Windows
Linux
  • Follow this link to download and install CUDA Toolkit 10.1
  • Installation instructions can be found here
  • Follow this link to download and install CUDA Toolkit 10.1 for your Linux distribution.
  • Installation instructions can be found here

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\v10.1\bin
    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp
    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\libx64
    • <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v10.1\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-10.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Verify the install

Important

A new terminal window must be opened for the changes to the Environmental variables to take effect!!

As before, run the following command in a new Terminal window:

python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Once the above is run, you should see a print-out similar to the one bellow:

2020-06-22 20:24:31.355541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-06-22 20:24:33.650692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-06-22 20:24:33.686846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-22 20:24:33.697234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-06-22 20:24:33.747540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-06-22 20:24:33.787573: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-06-22 20:24:33.810063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-06-22 20:24:33.841474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-06-22 20:24:33.862787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-06-22 20:24:33.907318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-06-22 20:24:33.913612: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-06-22 20:24:33.918093: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-22 20:24:33.932784: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2382acc1c40 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-22 20:24:33.939473: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-06-22 20:24:33.944570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:02:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2020-06-22 20:24:33.953910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-06-22 20:24:33.958772: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-06-22 20:24:33.963656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-06-22 20:24:33.968210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-06-22 20:24:33.973389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-06-22 20:24:33.978058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-06-22 20:24:33.983547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-06-22 20:24:33.990380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-06-22 20:24:35.338596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-22 20:24:35.344643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0
2020-06-22 20:24:35.348795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N
2020-06-22 20:24:35.353853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6284 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:02:00.0, compute capability: 6.1)
2020-06-22 20:24:35.369758: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2384aa9f820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-06-22 20:24:35.376320: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070 Ti, Compute Capability 6.1
tf.Tensor(122.478485, shape=(), dtype=float32)

Notice from the lines highlighted above that the library files are now “Successfully opened” and a debugging message is presented to confirm that TensorFlow has successfully “Created TensorFlow device”.

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.

TensorFlow Models Installation

Downloading the TensorFlow Models

  • 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 repository inside the TensorFlow folder, or you can simply download it as a ZIP and extract its contents inside the TensorFlow folder. To keep things consistent, in the latter case you will have to rename the extracted folder models-master to models.
  • You should now have a single folder named models under your TensorFlow folder, which contains another 3 folders as such:
TensorFlow
└─ models
    ├── community
    ├── official
    ├── research
    └── ...

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 TesnorFlow folder. (e.g. <PATH_TO_TF> = C:\Users\sglvladi\Documents if TensorFlow resides within your Documents folder)

Test your Installation

  • Open a new Terminal window

  • Install jupyter (if not done so already) by running:

    pip install jupyter

  • Download this notebook and paste it inside TensorFlow\models\research\object_detection.

  • 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