What is Pythia?
A modular framework for supercharging vision and language research built on top of PyTorch.
Pythia is a tool in the Machine Learning Tools category of a tech stack.
Pythia is an open source tool with GitHub stars and GitHub forks. Here’s a link to Pythia's open source repository on GitHub
Pythia Integrations
Python, TensorFlow, PyTorch, Torch, and Caffe are some of the popular tools that integrate with Pythia. Here's a list of all 9 tools that integrate with Pythia.
Pythia's Features
- Model Zoo
- Multi-Tasking
- Datasets: Includes support for various datasets built-in including VQA, VizWiz, TextVQA and VisualDialog
- Modules: Provides implementations for many commonly used layers in vision and language domain
- Distributed: Support for distributed training based on DataParallel as well as DistributedDataParallel
- Unopinionated: Unopinionated about the dataset and model implementations built on top of it
- Customization: Custom losses, metrics, scheduling, optimizers, tensorboard
- suits all your custom needs
Pythia Alternatives & Comparisons
What are some alternatives to Pythia?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.