Visual Studio Code Numpy

Posted : admin | On 02-05-2021



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  1. Install Numpy
  2. Visual Studio Code Numpy Dll
  • Python Importing numpy prevents Visual Studio Code from running 'Hello world' script. I've run into a really bizarre bug using Visual Studio Code with a Python script. I've been using it without any problem for months, but now suddenly the code isn't running when I click the run button in the top right hand corner. I'm not getting any.
  • Install Visual Studio Code. Download and install Visual Studio Code from official website. Now, you can write your code in VS Code and run the code in command line. You can also open command line in VS Code by Ctrl + `. If you want to set up a debug environment for Python, please follow this official tutorial. There are two tips.

Visual Studio Tools for AI can be installed on Windows 64-bit operating systems.

Install Visual Studio Tools for AI

This extension works with Visual Studio 2015 and Visual Studio 2017, Community edition or higher.

You can download the tools from the Visual Studio Marketplace, or from within Visual Studio:

Previous step: Run code in the debugger. The Python developer community has produced thousands of useful packages that you can incorporate into your own projects. Visual Studio provides a UI to manage packages in your Python environments. View environments. Select the View Other Windows Python Environments menu command. Importing Numpy prevents Visual Studio Code from running 'Hello world' script I've run into a really bizarre bug using Visual Studio Code with a Python script. I've been using it without any problem for months, but now suddenly the code isn't running when I click the run button in the top right hand corner. For this demonstration, we will use the package NumPy. NumPy is used for generating multi dimensional array objects used for scientific calculation. Here is a simple program using NumPy package. Import numpy as np a = np.arange(6) print(a) Let us run this program in the debug mode. Create a break point at the first line of the code.

  1. Select Tools > Extensions and Updates.

  2. In the Extensions and Updates dialog box, select Online on the left-hand side.

  3. In the search box in the upper right-hand corner, type or enter 'tools for ai'.

  4. Select Visual Studio Tools for AI from the results.

  5. Select Download.

Prepare your local machine

Before training deep learning models on your local computer, make sure you have the applicable prerequisites installed. This includes making sure you have the latest drivers and libraries for your NVIDIA GPU (if you have one). Also ensure you've installed Python and Python libraries such as NumPy, SciPy, and appropriate deep learning frameworks such as Microsoft Cognitive Toolkit (CNTK), TensorFlow, Caffe2, MXNet, Keras, Theano, PyTorch, and Chainer, that you plan to use in your project.

Note

Software introduction in the following subsections is excerpted from their homepages.

NVIDIA GPU driver

Deep learning frameworks take advantage of NVIDIA GPU to let machines learn at a speed, accuracy, and scale towards true artificial intelligence. If your computer has NVIDIA GPU cards, see NVIDIA Driver Downloads or try an operating system update to install the latest driver.

CUDA

CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the GPU. Currently, CUDA Toolkit 8.0 is required by deep learning frameworks.

To install CUDA

  • Visit this site, download CUDA, and install it.
  • Make sure to install the CUDA runtime libraries, and then add CUDA binary path to the %PATH% or $Path environment variable.
  • On Windows, this path is 'C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0bin' by default.

cuDNN

cuDNN (CUDA Deep Neural Network library) is a GPU-accelerated library of primitives for deep neural networks by NVIDIA. cuDNN v6 is required by latest deep learning frameworks.

To install cuDNN:

  • Visit NVIDIA Developer to download and install the latest package.
  • Ensure to add the directory containing cuDNN binary to the %PATH% or $Path environment variable.
  • On Windows, you can copy cudnn64_6.dll to 'C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0bin'.

Note

Previous deep learning frameworks such as CNTK 2.0 and TensorFlow 1.2.1 need cuDNN v5.1. However, you can install multiple cuDNN versions together.

Python

Python has been the primary programming language for deep learning applications. 64-bit Python distribution is required, and Python 3.5.4 is recommended for the best compatibility.

To install Python on Windows

  • We suggest installing the Python launcher for yourself only, and add Python to the %PATH% environment variable.
  • Ensure to install pip, which is the package management system to install and manage software packages written in Python.

Deep learning frameworks rely on pip for their own installation.

Then, we need to verify whether Python 3.5 is installed correctly, and upgrade pip to the latest version by executing the following commands in a terminal:

Code

  • Windows

  • macOS

Python on Visual Studio

Python is fully supported in Visual Studio through extensions.Learn more about install Python for Visual Studio Tools for more details.

NumPy and SciPy

  • NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

  • SciPy (pronounced 'Sigh Pie') is open-source software for mathematics, science, and engineering, depending on NumPy. Starting from version 1.0.0, SciPy now has official prebuilt wheel package for Windows.

To install NumPy and SciPy, run the following command in a terminal:

Note

Conda

The above command upgrades existing old or unofficial (e.g. third-party packages from http://www.lfd.uci.edu/~gohlke/pythonlibs/ for Windows) NumPy and SciPy to the latest official ones.

Microsoft Cognitive Toolkit (CNTK)

The Microsoft Cognitive Toolkit is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. CNTK supports both Python and BrainScript programming languages.

To install CNTK Python package, see how to install CNTK.

TensorFlow

TensorFlow is an open-source software library for numerical computation using data flow graphs. Refer to here for detailed installation.

Note

As of version 1.2, TensorFlow no longer provides GPU support for macOS.

Caffe2

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.

Currently, there's no prebuilt Caffe2 python wheel package available.

Install Numpy

Visit here to build from source code.

MXNet

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mixsymbolic and imperative programming to maximize efficiency and productivity.

To install MXNet, run the following command in a terminal:

  • With GPU

  • Without GPU

Keras

Keras is a high-level neural networks API, written in Python, that's capable of running on top of CNTK, TensorFlow, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

To install Keras, run the following command in a terminal:

Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

To install Theano, run the following command in a terminal:

PyTorch

Visual Studio Code Numpy Dll

PyTorch is a python package that provides two high-level features:

  • Tensor computation (like numpy) with strong GPU acceleration
  • Deep Neural Networks built on a tape-based autograd system

To install PyTorch, run the following command in a terminal:

  • Windows

    There's no official wheel package yet. You can download a third-party package from Anaconda or University of California.

    • Decompress it to your home directory, for example, C:Userstestpytorch.

    • Add C:UserstestpytorchLibsite-packages to the %PYTHONPATH% environment variable.

  • macOS

    Note

    macOS binaries don't support CUDA, install from source if CUDA is needed

  • Linux

    Note

    This single package supports both GPU and CPU.

Finally, install torchvision on non-Windows:

Chainer

Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (also known as dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks.

To enable CUDA support, install CuPy:

Note

On Windows, you need the 2015 version of Visual Studio or the Microsoft Visual C++ Build Tools to compile CuPy with CUDA 8.0.

To install Chainer, run the following command in a terminal: