# Framework PyTorch and TensorFlow are two widely used frameworks in the field of deep learning. **PyTorch**, a product of Facebook’s AI Research lab (FAIR), has attracted many researchers and developers with features such as high compatibility with Python, ease of use, and providing advanced capabilities for building neural networks. **TensorFlow**, developed by the Google Brain team, is one of the most popular frameworks in the field of machine learning. This framework, with features such as support for distributed computations, a variety of tools for visualization, and the ability to use different hardware such as GPUs and TPUs, has maintained its place among users. The **NVIDIA TAO Toolkit** is an open-source software that simplifies and accelerates the creation of customized and enterprise-ready AI models for computer vision applications. [It uses transfer learning, vision transformers, AutoML, and cloud-native technology to deliver high accuracy and performance on any platform](https://developer.nvidia.com/tao-toolkit)[1](https://developer.nvidia.com/tao-toolkit). The **Triton Inference Server** (formerly known as TensorRT Inference Server or Triton) is a server software that standardizes the deployment and execution of AI models across every workload. [The server supports trained AI models from many frameworks, including TensorFlow, TensorRT, PyTorch, and ONNX](https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/)[2](https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.binaexperts.com/train/framework.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.