Visual Studio 2017 15.x (RTW and all updates) Table 1 Windows Compiler Support in CUDA 12.3 Update 1 Supported Microsoft Windows ® operating systems: To use CUDA on your system, you will need the following installed:Ī supported version of Linux with a gcc compiler and toolchain This guide will show you how to install and check the correct operation of the CUDA development tools. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus. These cores have shared resources including a register file and a shared memory. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.ĬUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. The CPU and GPU are treated as separate devices that have their own memory spaces. As such, CUDA can be incrementally applied to existing applications. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. Support heterogeneous computation where applications use both the CPU and GPU. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation. Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).ĬUDA was developed with several design goals in mind: Introduction ĬUDA ® is a parallel computing platform and programming model invented by NVIDIA. The installation instructions for the CUDA Toolkit on Microsoft Windows systems. Start off by adding the following to your settings.CUDA Installation Guide for Microsoft Windows We'll create a read-write API for accessing information on the users of our project.Īny global settings for a REST framework API are kept in a single configuration dictionary named REST_FRAMEWORK. Let's take a look at a quick example of using REST framework to build a simple model-backed API. Note that the URL path can be whatever you want. Path('api-auth/', include('rest_framework.urls')) Add the following to your root urls.py file. If you're intending to use the browsable API you'll probably also want to add REST framework's login and logout views. git clone Īdd 'rest_framework' to your INSTALLED_APPS setting. Pip install django-filter # Filtering support Pip install markdown # Markdown support for the browsable API. Install using pip, including any optional packages you want. django-guardian (1.1.1+) - Object level permissions support.django-filter (1.0.1+) - Filtering support.Pygments (2.4.0+) - Add syntax highlighting to Markdown processing.Markdown (3.0.0+) - Markdown support for the browsable API.PyYAML, uritemplate (5.1+, 3.0.0+) - Schema generation support.We highly recommend and only officially support the latest patch release of Many thanks to all our wonderful sponsors, and in particular to our premium backers, Sentry, Stream, Spacinov, Retool, bit.io, PostHog, CryptAPI, FEZTO, and Svix. REST framework commercially we strongly encourage you to invest in itsĬontinued development by signing up for a paid plan.Įvery single sign-up helps us make REST framework long-term financially sustainable. REST framework is a collaboratively funded project. Used and trusted by internationally recognised companies including Mozilla, Red Hat, Heroku, and Eventbrite.Extensive documentation, and great community support.Customizable all the way down - just use regular function-based views if you don't need the more powerful features.Serialization that supports both ORM and non-ORM data sources.Authentication policies including packages for OAuth1a and OAuth2. The Web browsable API is a huge usability win for your developers.Some reasons you might want to use REST framework: Django REST framework is a powerful and flexible toolkit for building Web APIs.
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