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Description
JAX is a specialized Python library tailored for high-performance numerical computation and research in machine learning. It provides a familiar NumPy-like interface, making it easy for users already accustomed to NumPy to adopt it. Among its standout features are automatic differentiation, just-in-time compilation, vectorization, and parallelization, all of which are finely tuned for execution across CPUs, GPUs, and TPUs. These functionalities are designed to facilitate efficient calculations for intricate mathematical functions and expansive machine-learning models. Additionally, JAX seamlessly integrates with various components in its ecosystem, including Flax for building neural networks and Optax for handling optimization processes. Users can access extensive documentation, complete with tutorials and guides, to fully harness the capabilities of JAX. This wealth of resources ensures that both beginners and advanced users can maximize their productivity while working with this powerful library.
Description
You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.
API Access
Has API
API Access
Has API
Integrations
Python
TensorFlow
AWS EC2 Trn3 Instances
Amazon EKS
Amazon SageMaker
Dask
Feast
Flower
Flyte
Grain
Integrations
Python
TensorFlow
AWS EC2 Trn3 Instances
Amazon EKS
Amazon SageMaker
Dask
Feast
Flower
Flyte
Grain
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
JAX
Country
United States
Website
docs.jax.dev/en/latest/
Vendor Details
Company Name
Anyscale
Founded
2019
Country
United States
Website
ray.io
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization