Tensorflow Visual Studio

1. Tensorflow Visual Studio Code Windows
2. Tensorflow Visual Studio
3. Visual Studio Code

Visual Studio Code (VSCode) is a free code editor, which runs on the macOS, Linux, and Windows operating systems. It has elegent tooling support which supports Python & C++ development, visual debugging, integration with git and many more interesting features. Owing to the ease of use and extension management, it is a great editor for TensorFlow IO development. However, some effort is necessaryto configure it properly. Since VSCode configuration is very flexible, it allows developers to compile project using bazel and run the code under Python and C++ debuggers. The base tool setup might differ based on the operation systems, but the configuration approach should be similar.

Extensions

To install an extension click the extensions view icon (Extensions) on the Sidebar, or use the shortcut Ctrl+Shift+X. Then searh for keyword below.

• C/C++ - Official C++ extension from Microsoft
• Python - Official Python extension from Microsoft
• Python Extension Pack - another useful extension for Python development

Compiling projects

TensorFlow IO is compiled using bazel build command:

See project README file for details on how to setup development environment in Ubuntu.--compilation_mode dbg flag here indicates that produced binary should have debug symbols.Once you can compile project from command line, you can also configure VSCode to be able to invoke same command.

Open View->Command Pallete (Ctrl+Shift+P) and start typing: 'Tasks: Configure Build Task'.If you are doing this for the first time, editor is going to suggest creating tasks.json file.Once you have it, paste the following json:

Now, you can press Ctrl+Shift+B, and VSCode is going to use the command above to build the project.It uses its own terminal window, where all links are clickable. So when a compilation error occurs, you open the corresponding file and navigate to the line by just clicking on the link in the terminal window.

Visual Studio Installer. Use Visual Studio Installer to install Python with Visual Studio. Modify your Visual Studio Version, check Python development and click on the Modify button. Python will be installed to 'C: Program Files (x86) Microsoft Visual Studio Shared'. Add to path environment variable. Jul 26, 2019 I work on the Visual Studio C Compiler team. We are interested in helping TensorFlow upgrade to Visual Studio 2019 while also making sure the best compiler switches are being used. We have seen significant throughput and code generation improvements between the Visual Studio 2017 and the 2019 compilers across various projects. Visual Studio Code + Anaconda + Tensorflow + Debugging. Download & install Anaconda; Follow the steps in the following link to setup an environment with Tensorflow.

Hoping to use tensorflow function, (CNN deep learning function) to classify images with c coding in visual studio 2017. Nov 13, 2017 Open solution and train model Launch Visual Studio and select File Open Project/Solution. Select the Tensorflow Examples folder from the samples repository downloaded and open the TensorflowExamples.sln file. Find the MNIST project in Solution Explorer, right-click and select Set as StartUp.

Debugging projects

Debugging Python code is trivial, follow official documentation to figure out how to configure VSCode to enable that: https://code.visualstudio.com/docs/python/debugging

However, debugging C++ code requires GDB to be installed on your system. If you have a bq_sample_read.py python script that uses tensorflow-io library and is normally executed in the follow manner:

You can execute it under GDB using the following:

If the application crashes in the C++ code phase, you can run backtrace in GDB console to get the stacktrace of the error.

VSCode also has GDB debugger support. It allows adding breakpoints, observe values of variables and step through the code in a step by step manner. To add debug configuration press the Debug View icon (Debug) on the Sidebar, or use the shortcut Ctrl+Shift+D. Here, press the little down arrow next to the play button and select 'Add Configuration..'. It will now create a launch.json file, to which, please add the following config:

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If everything is configured correctly, you should be able to do Run -> Start Debugging (F5) or Run -> Run Without Debugging (Ctrl + F5). This will run your code under debugger:

In order to further simplify the debugging experience, you can configure GDB to skip standard C++ libraries. This allows you to ignore the code that you don't care about. To do this, create a ~/.gdbinit file with the following content:

Formatting files

You can always reformat C++ or Python file by Right Click -> Format Document (Ctrl + Shift + I), but VSCode uses a different style convention. Luckily, it is easy to change.

For Python formatting, see https://donjayamanne.github.io/pythonVSCodeDocs/docs/formatting/

For C++ formatting, do the following:

• Go to Preferences -> Settings
• Search for 'C_Cpp.clang_format_fallbackStyle'
• Modify the file:setting.json file directly by adding the following content

TensorFlow 2 packages are available

• tensorflow —Latest stable release with CPU and GPU support (Ubuntu and Windows)
• tf-nightly —Preview build (unstable) . Ubuntu and Windows include GPU support .

Older versions of TensorFlow

Surfshark vpn openvpn chrome. For TensorFlow 1.x, CPU and GPU packages are separate:

• tensorflow1.15 —Release for CPU-only
• tensorflow-gpu1.15 —Release with GPU support (Ubuntu and Windows)

System requirements

• Python 3.6–3.8
  • Python 3.8 support requires TensorFlow 2.2 or later.
• pip 19.0 or later (requires manylinux2010 support)
• Ubuntu 16.04 or later (64-bit)
• macOS 10.12.6 (Sierra) or later (64-bit) (no GPU support)
  • macOS requires pip 20.3 or later
• Windows 7 or later (64-bit)
• Raspbian 9.0 or later
• GPU support requires a CUDA®-enabled card (Ubuntu and Windows)

Hardware requirements

• Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.
• Read the GPU support guide to set up a CUDA®-enabled GPU card on Ubuntu or Windows.

1. Install the Python development environment on your system

Check if your Python environment is already configured:

If these packages are already installed, skip to the next step.
Otherwise, install Python , the pip package manager , and venv :

Ubuntu

Tensorflow Visual Studio Code Windows

macOS

Install using the Homebrew package manager:

Windows

Install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019 . Starting with the TensorFlow 2.1.0 version, the msvcp140_1.dll file is required from this package (which may not be provided from older redistributable packages). The redistributable comes with Visual Studio 2019 but can be installed separately:

1. Go to the Microsoft Visual C++ downloads ,
2. Scroll down the page to the Visual Studio 2015, 2017 and 2019 section.
3. Download and install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 for your platform.

Make sure long paths are enabled on Windows.

Install the 64-bit Python 3 release for Windows (select pip as an optional feature).

Raspberry Pi

Requirements for the Raspbian operating system:

Other

2. Create a virtual environment (recommended)

Python virtual environments are used to isolate package installation from the system.

Ubuntu / macOS

Create a new virtual environment by choosing a Python interpreter and making a ./venv directory to hold it:

Activate the virtual environment using a shell-specific command:

When the virtual environment is active, your shell prompt is prefixed with (venv) .

Install packages within a virtual environment without affecting the host system setup. Start by upgrading pip :

And to exit the virtual environment later:

Windows

Create a new virtual environment by choosing a Python interpreter and making a .venv directory to hold it:

Activate the virtual environment:

Install packages within a virtual environment without affecting the host system setup. Start by upgrading pip :

And to exit the virtual environment later:

Conda

While the TensorFlow provided pip package is recommended, a community-supported Anaconda package is available. To install, read the Anaconda TensorFlow guide .

3. Install the TensorFlow pip package

Choose one of the following TensorFlow packages to install from PyPI :

For Python formatting, see https://donjayamanne.github.io/pythonVSCodeDocs/docs/formatting/

For C++ formatting, do the following:

• Go to Preferences -> Settings
• Search for 'C_Cpp.clang_format_fallbackStyle'
• Modify the file:setting.json file directly by adding the following content

TensorFlow 2 packages are available

• tensorflow —Latest stable release with CPU and GPU support (Ubuntu and Windows)
• tf-nightly —Preview build (unstable) . Ubuntu and Windows include GPU support .

Older versions of TensorFlow

Surfshark vpn openvpn chrome. For TensorFlow 1.x, CPU and GPU packages are separate:

• tensorflow1.15 —Release for CPU-only
• tensorflow-gpu1.15 —Release with GPU support (Ubuntu and Windows)

System requirements

• Python 3.6–3.8
  • Python 3.8 support requires TensorFlow 2.2 or later.
• pip 19.0 or later (requires manylinux2010 support)
• Ubuntu 16.04 or later (64-bit)
• macOS 10.12.6 (Sierra) or later (64-bit) (no GPU support)
  • macOS requires pip 20.3 or later
• Windows 7 or later (64-bit)
• Raspbian 9.0 or later
• GPU support requires a CUDA®-enabled card (Ubuntu and Windows)

Hardware requirements

• Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.
• Read the GPU support guide to set up a CUDA®-enabled GPU card on Ubuntu or Windows.

1. Install the Python development environment on your system

Check if your Python environment is already configured:

If these packages are already installed, skip to the next step.
Otherwise, install Python , the pip package manager , and venv :

Ubuntu

Tensorflow Visual Studio Code Windows

macOS

Install using the Homebrew package manager:

Windows

Install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019 . Starting with the TensorFlow 2.1.0 version, the msvcp140_1.dll file is required from this package (which may not be provided from older redistributable packages). The redistributable comes with Visual Studio 2019 but can be installed separately:

1. Go to the Microsoft Visual C++ downloads ,
2. Scroll down the page to the Visual Studio 2015, 2017 and 2019 section.
3. Download and install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 for your platform.

Make sure long paths are enabled on Windows.

Install the 64-bit Python 3 release for Windows (select pip as an optional feature).

Raspberry Pi

Requirements for the Raspbian operating system:

Other

2. Create a virtual environment (recommended)

Python virtual environments are used to isolate package installation from the system.

Ubuntu / macOS

Create a new virtual environment by choosing a Python interpreter and making a ./venv directory to hold it:

Activate the virtual environment using a shell-specific command:

When the virtual environment is active, your shell prompt is prefixed with (venv) .

Install packages within a virtual environment without affecting the host system setup. Start by upgrading pip :

And to exit the virtual environment later:

Windows

Create a new virtual environment by choosing a Python interpreter and making a .venv directory to hold it:

Activate the virtual environment:

Install packages within a virtual environment without affecting the host system setup. Start by upgrading pip :

And to exit the virtual environment later:

Conda

While the TensorFlow provided pip package is recommended, a community-supported Anaconda package is available. To install, read the Anaconda TensorFlow guide .

3. Install the TensorFlow pip package

Choose one of the following TensorFlow packages to install from PyPI :

• tensorflow —Latest stable release with CPU and GPU support (Ubuntu and Windows) .
• tf-nightly —Preview build (unstable) . Ubuntu and Windows include GPU support .
• tensorflow1.15 —The final version of TensorFlow 1.x.

Virtual environment install

Verify the install:

System install

Verify the install:

Package location

Tensorflow Visual Studio

A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on your Python version.

Visual Studio Code