What is PyTorch and what are its top alternatives?
PyTorch is an open-source machine learning library that offers high flexibility and speed through its dynamic computation graph. Key features include easy model deployment, support for complex computations, and a vast library of pre-trained models and tools. However, PyTorch has limitations in terms of memory utilization and efficiency in large-scale production environments.
- TensorFlow: TensorFlow is an open-source machine learning library that offers scalability, flexibility, and a vast ecosystem of tools and support. Key features include distributed computing, production-ready APIs, and built-in visualizations. Pros include strong community support and extensive documentation, while cons may include a steeper learning curve compared to PyTorch.
- MXNet: MXNet is a deep learning framework known for its efficiency, scalability, and flexibility. Key features include multi-language support, optimized performance, and seamless integration with other libraries like NumPy. Pros include fast execution speed and seamless integration with AWS services, while cons may include a less user-friendly interface.
- Keras: Keras is a high-level neural networks API that is easy to use, fast, and easily expandable. Key features include modularity, simplicity, and support for both convolutional and recurrent networks. Pros include user-friendly syntax and seamless integration with TensorFlow, while cons may include limited customization compared to PyTorch.
- Caffe: Caffe is a deep learning framework known for its speed, scalability, and modularity. Key features include GPU acceleration, pre-trained models, and a rich repository of community-built models. Pros include fast model training and deployment, while cons may include less flexibility compared to PyTorch.
- Chainer: Chainer is a powerful, flexible, and intuitive deep learning framework that supports dynamic computation graphs. Key features include easy model prototyping, seamless debugging, and support for both CPU and GPU computing. Pros include easy-to-use syntax and dynamic graph creation, while cons may include less community support compared to PyTorch.
- Theano: Theano is a deep learning library known for its efficient mathematical operations and optimization capabilities. Key features include symbolic expressions, GPU acceleration, and automatic differentiation. Pros include fast computation speed and seamless integration with NumPy, while cons may include a lack of new updates and support compared to PyTorch.
- TensorLayer: TensorLayer is a deep learning library built on top of TensorFlow that offers simplicity, flexibility, and extensibility. Key features include high-level APIs, flexible model construction, and support for both beginners and advanced users. Pros include easy model building and training, while cons may include limited community support compared to PyTorch.
- Gluon: Gluon is a deep learning interface that offers flexibility, simplicity, and high performance. Key features include symbolic and imperative hybridization, user-friendly APIs, and support for both beginners and experts. Pros include ease of use and seamless integration with MXNet, while cons may include limited advanced features compared to PyTorch.
- Caffe2: Caffe2 is a lightweight deep learning framework that offers scalability, portability, and efficiency. Key features include mobile deployment, distributed computing, and seamless integration with PyTorch. Pros include high performance and efficient deployment, while cons may include a smaller community compared to PyTorch.
- Microsoft Cognitive Toolkit (CNTK): CNTK is a deep learning framework known for its scalability, speed, and support for both commercial and research applications. Key features include distributed computing, efficient training algorithms, and seamless integration with Azure services. Pros include fast model training speed and strong support for production environments, while cons may include a steeper learning curve compared to PyTorch.
Top Alternatives to PyTorch
TensorFlowTensorFlow 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. ...
KerasDeep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...
Caffe2Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile. ...
MXNetA 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. ...
TorchIt is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. ...
scikit-learnscikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. ...
ChainerIt is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. ...
NumPyBesides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...
PyTorch alternatives & related posts
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
- Documentation not very helpful2
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
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- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
- Hard to debug4
related Keras posts
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.
- Mobile deployment1
- Open Source1
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- User friendly2
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Torch
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- Scientific computing25
- Easy19
- Limited2
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Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?
Hi, I wanted to jump into Machine Learning.
I first tried brain.js, but its capabilities are very limited and it abstracts most concepts of ML away. I've tried TensorFlow, but it's very hard for me to understand the concepts.
Now, I thought about trying NumPy or scikit-learn, but I don't really know much about ML, but still want to use 100% Power of ML.
What do you recommend me to use as a beginner in ML?
Also do you know any good tutorials which explain how ML works and how to implement it in a given framework (ideal in german)?
Thanks for your attention & help :D
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- Great for data analysis10
- Faster than list4
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Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?