Metadata-Version: 2.1
Name: nemo-toolkit
Version: 0.11.0
Summary: NEMO core package. Necessary for all collections
Home-page: https://github.com/nvidia/nemo
Author: NVIDIA
Author-email: nemo-toolkit@nvidia.com
Maintainer: NVIDIA
Maintainer-email: nemo-toolkit@nvidia.com
License: Apache2
Download-URL: https://github.com/NVIDIA/NeMo/releases
Description: NeMo (**Ne**ural **Mo**dules) is a toolkit for creating AI applications built around **neural modules**, conceptual blocks of neural networks that take *typed* inputs and produce *typed* outputs.
        
        **NeMo Core** provides the fundamental building blocks for all neural models and NeMo's type system.
        
        Core Principles
        ---------------
        NEMO is built around these principles:
        
        * Neural Module is a block that computes a set of outputs from a set of inputs.
            * Think of it as an abstraction between a layer and a neural network.
            * Examples: pre-trained language model, acoustic model, encoder, decoder, etc.
        * NeMo-based application is a DAG of :class:`NeuralModule<nemo.core.neural_modules.NeuralModule>` objects connected together via **typed** *input* and *output* ports.
            * :class:`NmTensor<nemo.core.neural_types.NmTensor>` objects flow between modules from port to port.
            * *Lazy execution* model. First, user defines activation flow graph. Then, calls *action* (such as training). Actual computation happen only after action is called.
        * The input and output ports of each neural module and :class:`NmTensor<nemo.core.neural_types.NmTensor>` objects all have *Neural Type* - :class:`NeuralType<nemo.core.neural_types.NeuralType>`.
        * :class:`NeuralType<nemo.core.neural_types.NeuralType>` the semantics, axis order, and dimensions of a tensor and more.
        * Domain or module-specific *Callbacks* are used for evaluations and inspecting training performance.
        
        
        Built to run on GPUs
        --------------------
        NeMo Core provides:
        
        * Mixed-precision training using Tensor Cores on NVIDIA's Volta and Turing GPUs
        * Distributed training
        * Distributed evaluation
        
Keywords: deep learning,machine learning,gpu,NLP,NeMo,nvidia,pytorch,torch,tts,speech,language
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: docker
Provides-Extra: test
Provides-Extra: asr
Provides-Extra: cv
Provides-Extra: nlp
Provides-Extra: simple_gan
Provides-Extra: tts
Provides-Extra: all
