MXNet vs Pythia
Overview
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Detailed Comparison
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. | A modular framework for supercharging vision and language research built on top of PyTorch. |
Lightweight;Portable;Flexible distributed/Mobile deep learning; | 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 |
Statistics | |
Stacks 49 | Stacks 0 |
Followers 81 | Followers 8 |
Votes 2 | Votes 0 |
Pros & Cons | |
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Integrations | |
What are some alternatives to MXNet, Pythia?
TensorFlow
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scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.
Keras
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Kubeflow
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TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
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Streamlit
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MLflow
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H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
