continua a ser nossa escolha de framework de aprendizado de m¨¢quina (ML). A maioria de nossas equipes prefere o PyTorch ao TensorFlow. O PyTorch exp?e o funcionamento interno do ML, algo que o TensorFlow oculta, facilitando a depura??o. Com grafos computacionais din?micos, a otimiza??o do modelo ¨¦ muito mais f¨¢cil se comparada a qualquer outro framework de ML. A ampla disponibilidade de trabalhos de pesquisa sobre e sobre a facilidade de implementa??o fazem o PyTorch se destacar. Quando se trata de ML com grafos, o ¨¦ um ecossistema mais maduro e nossas equipes tiveram ¨®timas experi¨ºncias com ele. O PyTorch gradualmente tamb¨¦m preencheu lacunas no tocante a implanta??o e dimensionamento de modelos; nossas equipes usaram o para oferecer modelos pr¨¦-treinados com sucesso em produ??o, por exemplo. Com muitas equipes adotando o PyTorch como padr?o para suas necessidades de aprendizagem profunda de ponta a ponta, recomendamos alegremente a ado??o do PyTorch.
Nossos times continuam a usar e apreciar o framework de aprendizado de m¨¢quina , e v¨¢rios times preferem PyTorch a TensorFlow. O PyTorch exp?e o funcionamento interno de ML que o TensorFlow oculta, facilitando a depura??o, al¨¦m de conter constru??es com as quais as pessoas programadoras est?o familiarizadas, como ciclos e a??es. As vers?es recentes melhoraram o desempenho do PyTorch, e o usamos com sucesso em projetos de produ??o.
is a complete rewrite of the machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. ThoughtWorks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow¡¯s performance for production-scale training and classification.
is a complete rewrite of the machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. ThoughtWorks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow¡¯s performance for production-scale training and classification.
