PyPI 官网下载 | nemo_toolkit-0.11.0b1.tar.gz

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