Search Results for "numpy python 3.4"
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Showing 11 open source projects for "numpy python 3.4"
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AIOHTTP
Asynchronous HTTP client/server framework for asyncio and Python
...The main change is dropping yield from support and using async/await everywhere. Farewell, Python 3.4. You often want to send some sort of data in the URL’s query string. If you were constructing the URL by hand, this data would be given as key/value pairs in the URL after a question mark, e.g. httpbin.org/get?key=val. Requests allows you to provide these arguments as a dict, using the params keyword argument. aiohttp internally performs URL canonicalization before sending request. -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new... -
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Jittor
Jittor is a high-performance deep learning framework
...Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. ... -
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IVY
The Unified Machine Learning Framework
Take any code that you'd like to include. For example, an existing TensorFlow model, and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization etc. Choose any backend framework which should be used under the hood, for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs. DeepMind releases an... -
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WALKOFF
A flexible, easy to use, automation framework
...Act faster with WALKOFF by integrating the capabilities you already own to dynamically respond on your terms to your fast-moving environment. Drag and drop workflow editor. Sharable apps and workflows. Deploy on Windows or Linux. Python 2.7 and 3.4+. Scale to your needs. Plug-and-play integration of almost anything with easy-to-develop apps. Send workflow data to custom visual interfaces to act with confidence. Harness the power of automation on your terms. An active community is essential for our success. Help us to develop and promote a common integration format that would enable the seamless deployment of WALKOFF apps across participating platforms. -
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Aglyph
Aglyph is a Dependency Injection framework for Python.
Aglyph is a Dependency Injection framework for Python, supporting type 2 (setter) and type 3 (constructor) injection. Aglyph runs on CPython (http://www.python.org/) 2.7 and 3.4+, and on recent versions of the PyPy (http://pypy.org/>),Jython (http://www.jython.org/), IronPython (http://ironpython.net/), and Stackless Python (http://www.stackless.com/) variants. Aglyph can assemble "prototype" components (a new instance is created every time), "singleton" components (the same instance is returned every time), "borg" components (a new instance is created every time, but all instances of the same class share the same internal state), and "weakref" components (the same instance is returned as long as there is at least one "live" reference to the instance in the running application). ... -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
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. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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REST framework JWT Auth
JSON Web Token Authentication support for Django REST Framework
JSON Web Token Authentication support for Django REST Framework. This package provides JSON Web Token Authentication support for Django REST framework. Unlike some more typical uses of JWTs, this module only generates authentication tokens that will verify the user who is requesting one of your DRF protected API resources. The actual request parameters themselves are not included in the JWT claims which means they are not signed and may be tampered with. You should only expose your API... -
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PyGObject for Windows
All-In-One PyGI/PyGObject for Windows Installer
Cross-platform python dynamic bindings of GObject-based libraries for Windows 32-bit and 64-bit. -
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10
Django Autocomplete Light
A fresh approach to autocomplete implementations
A fresh approach to autocomplete implementations, specially for Django. Python 2.7, 3.4, Django 2.0+ support (Django 1.11 (LTS), is supported until django-autocomplete-light-3.2.10), Django (multiple) choice support, Django (multiple) model choice support, Django generic foreign key support (through django-querysetsequence), Django generic many to many relation support (through django-generic-m2m and django-gm2m). -
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Shovel Library
Simple graphics, keyboard and mouse library with a C interface
.... === Functions include === * Window creation * 32-bit RGBA bitmap creation * Fast software based drawing routines (pixels, lines, text etc) * Mouse and keyboard input === Details === * Written in C * Python bindings provided * Permissive BSD licence * Win32 version currently. Linux and Mac planned. === Performance === Running on Windows XP on an Intel Core i3 530 (3.4 GHz): * Putpixel - 31 million per second * Rectangle fill - 11 billion pixels per second * Text render - 11 million characters per second (8 point, fixed width font)
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