LeaderGPU Integrations

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Definitive functions are the heart of extensible programming. Python supports keyword arguments, mandatory and optional arguments, as well as arbitrary argument lists. It doesn't matter if you are a beginner or an expert programmer, Python is easy to learn. Python is easy to learn, whether you are a beginner or an expert in other languages. These pages can be a helpful starting point to learn Python programming. The community hosts meetups and conferences to share code and much more. The documentation for Python will be helpful and the mailing lists will keep in touch. The Python Package Index (PyPI), hosts thousands of third-party Python modules. Both Python's standard library and the community-contributed modules allow for endless possibilities.

C++ is a simple language with clear expressions. ...), but once one knows the meaning of such characters it can be even more schematic and clear than other languages that rely more on English words. C++'s simplified input/output interface and incorporation of the standard library of templates make data manipulation and communication much easier than in C. It is a programming model in which each component is treated as an object. This replaces or complements the structured programming paradigm that focuses on procedures and parameters.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.

Software is what transforms a vehicle into a smart machine. Open source software stack NVIDIA DRIVE™, enables developers to quickly build and deploy a variety state-of the-art AV applications. This includes perception, localization, mapping, driver monitoring, planning and control, driver monitoring and natural language processing. DRIVE OS, the foundation of the DRIVE SoftwareStack, is the first secure operating system for accelerated computation. It includes NvMedia to process sensor input, NVIDIACUDA®, libraries for parallel computing implementations that are efficient, NVIDIA TensorRT™ for real time AI inference, as well as other tools and modules for accessing hardware engines. NVIDIA DriveWorks®, a SDK that provides middleware functions over DRIVE OS, is essential for autonomous vehicle development. These include the sensor abstraction layer (SAL), sensor plugins, data recorder and vehicle I/O support.