Overview

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Skyl.ai

Stacks0

Followers7

Votes0

PyTorch vs Skyl.ai: What are the differences?

## Comparison between PyTorch and Skyl.ai

PyTorch and Skyl.ai both serve as deep learning frameworks, but they differ in several key areas that impact their usability and functionality.

1. **Ease of Use**: PyTorch is known for its user-friendly interface, allowing developers to experiment and build models with ease. On the other hand, Skyl.ai offers a more automated approach, providing pre-built models and pipelines to minimize the coding required, making it ideal for beginners and those looking for quick solutions.

2. **Community Support**: PyTorch has a large and active community, ensuring a wealth of resources, tutorials, and discussions. Skyl.ai, although emerging, may have limited community support in comparison, impacting the availability of help and updates.

3. **Deployment Options**: PyTorch offers flexibility in deployment on various platforms such as cloud servers, mobile devices, and edge devices. Skyl.ai, in contrast, offers a more streamlined deployment process, focusing on simplifying the deployment to cloud environments for quick and efficient model deployment.

4. **Model Customization**: PyTorch allows for extensive model customization, catering to the specific needs and requirements of developers. Skyl.ai, while providing pre-built models, may have limited options for customization, limiting its adaptability for complex tasks.

5. **Integration with Other Libraries**: PyTorch seamlessly integrates with popular libraries like NumPy and SciPy, enhancing its functionality and versatility. Skyl.ai may have limitations in integration capabilities with external libraries, potentially restricting the complexity of projects that can be undertaken.

6. **Cost Factors**: PyTorch, being an open-source framework, is cost-effective for development and deployment. In contrast, Skyl.ai's pricing model may vary based on usage and features, potentially impacting the overall cost of implementing models and solutions.

In Summary, PyTorch and Skyl.ai differ in ease of use, community support, deployment options, model customization, integration with other libraries, and cost considerations, catering to diverse needs within the deep learning community.

Advice on PyTorch, Skyl.ai

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	Skyl.ai
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.	Build & deploy ML models faster on unstructured data. No specialized skills required. Easy-to-use & scalable SaaS platform.
Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system	Collect, label, and visualize unstructured data; Guided modules to upload, clean, label, and visualize unstructured data; Create & train models automatically; Train models without coding using our ready-made, fine tuned, state-of-the-art neural network architecture; Monitor model performance and iterate in minutes; Monitor data collection, labeling, training, and performance of deployed models in real-time
Statistics
GitHub Stars 94.7K	GitHub Stars -
GitHub Forks 25.8K	GitHub Forks -
Stacks 1.6K	Stacks 0
Followers 1.5K	Followers 7
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	No integrations available

What are some alternatives to PyTorch, Skyl.ai?

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.