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support

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Description

A comprehensive platform designed for reproducible and scalable applications in Machine Learning and Deep Learning. Explore the array of features and products that support the leading platform for managing data science workflows today. Polyaxon offers an engaging workspace equipped with notebooks, tensorboards, visualizations, and dashboards. It facilitates team collaboration, allowing members to share, compare, and analyze experiments and their outcomes effortlessly. With built-in version control, you can achieve reproducible results for both code and experiments. Polyaxon can be deployed in various environments, whether in the cloud, on-premises, or in hybrid setups, ranging from a single laptop to container management systems or Kubernetes. Additionally, you can easily adjust resources by spinning up or down, increasing the number of nodes, adding GPUs, and expanding storage capabilities as needed. This flexibility ensures that your data science projects can scale effectively to meet growing demands.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
Google Cloud Platform Yes 
Kubernetes Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Anyscale No 
Caffe Yes 
Dask No 
Feast No 
Flyte No 
Google Kubernetes Engine (GKE) No 
Keras Yes 
LanceDB No 
MLflow No 
Microsoft Azure Yes 
Python No 
Union Cloud No 
io.net No 

Integrations

Amazon Web Services (AWS) Yes 
Google Cloud Platform Yes 
Kubernetes Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Anyscale Yes 
Caffe No 
Dask Yes 
Feast Yes 
Flyte Yes 
Google Kubernetes Engine (GKE) Yes 
Keras No 
LanceDB Yes 
MLflow Yes 
Microsoft Azure No 
Python Yes 
Union Cloud Yes 
io.net Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source. Consumption-based.
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Polyaxon

Website

polyaxon.com

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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