PyPI vs. Python vs. TFLearn vs. VisionPro Deep Learning Comparison Chart

PyPI is the official repository for Python software packages, hosting hundreds of thousands of projects that developers can publish and users can discover and install. It supports both source distributions (“sdists”) and pre-built binary “wheels”, allowing packages to include native extensions for different platforms. Projects on PyPI consist of multiple releases, each of which can include various files for different operating systems or Python versions. Metadata for each package includes things like version number, dependencies, licensing, classifiers, description (including rendering Markdown or reStructuredText), and other information that tools like pip use to resolve, download, and install the correct package. PyPI provides search and filtering based on package metadata, letting users find what they need via keywords, compatibility, or other package attributes.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks.