PyTorch vs Datatron Comparison | StackShare | StackShare

Datatron vs PyTorch

Overview

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Datatron

Stacks0

Followers10

Votes0

PyTorch vs Datatron: What are the differences?

PyTorch: A deep learning framework that puts Python first. 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; Datatron: Production AI Model Management at Scale. Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

PyTorch and Datatron can be categorized as "Machine Learning" tools.

PyTorch is an open source tool with 35.8K GitHub stars and 9.01K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

Advice on PyTorch, Datatron

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Comments

Detailed Comparison

PyTorch	Datatron
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.	Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.
Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system	Explore models built and uploaded by your Data Science team, all from one centralized repository; Create and scale model deployments in just a few clicks. Deploy models developed in any framework or language; Make better business decisions to save your team time and money. Monitor model performance and detect model decay as it happens; Spend less time on model validation, bias detection, and internal audit processes. Go from model development to internal auditing to production faster than ever; Manage multivariate models through A/B testing for live inference and batch tasks; Apply business logic to your model prediction results. Create workflows for your models using multiple sources and languages
Statistics
GitHub Stars 94.7K	GitHub Stars -
GitHub Forks 25.8K	GitHub Forks -
Stacks 1.6K	Stacks 0
Followers 1.5K	Followers 10
Votes 43	Votes 0
Pros & Cons
Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop	No community feedback yet
Integrations
Python	TensorFlow scikit-learn H2O

What are some alternatives to PyTorch, Datatron?

Heroku

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Clever Cloud

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Google App Engine

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Red Hat OpenShift

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AWS Elastic Beanstalk

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Render

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Hasura

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TensorFlow

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Cloud 66

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Jelastic

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