Paperspace vs Torch
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. | It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines. |
A powerful N-dimensional array;
Lots of routines for indexing, slicing, transposing;
Amazing interface to C, via LuaJIT;
Linear algebra routines;
Neural network, and energy-based models;
Numeric optimization routines;
Fast and efficient GPU support;
Embeddable, with ports to iOS and Android backends | Intelligent alert;
Two-factor authentication;
Share drives;
Unlimited power;
Multiple monitors;
Remote access;
Simple management. |
Statistics | |
GitHub Stars 9.1K | GitHub Stars - |
GitHub Forks 2.4K | GitHub Forks - |
Stacks 355 | Stacks 4 |
Followers 61 | Followers 20 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Torch, Paperspace?
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.
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
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
NanoNets
Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
Inferrd
It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.
