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support

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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.

Description

Our accelerator hardware is specifically crafted to enhance the performance and efficiency of deep learning, while prioritizing usability for developers. SynapseAI aims to streamline the development process by providing support for widely-used frameworks and models, allowing developers to work with the tools they are familiar with and prefer. Essentially, SynapseAI and its extensive array of tools are tailored to support deep learning developers in their unique workflows, empowering them to create projects that align with their preferences and requirements. Additionally, Habana-based deep learning processors not only safeguard existing software investments but also simplify the process of developing new models, catering to both the training and deployment needs of an ever-expanding array of models that shape the landscape of deep learning, generative AI, and large language models. This commitment to adaptability and support ensures that developers can thrive in a rapidly evolving technological environment.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
PyTorch Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Anyscale Yes 
Apache Airflow Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Feast Yes 
Flyte Yes 
Google Cloud Platform Yes 
Google Kubernetes Engine (GKE) Yes 
LanceDB Yes 
Python Yes 
Snowflake Yes 
Union Cloud Yes 
io.net Yes 

Integrations

Amazon Web Services (AWS) Yes 
PyTorch Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Anyscale No 
Apache Airflow No 
Azure Kubernetes Service (AKS) No 
Dask No 
Databricks No 
Feast No 
Flyte No 
Google Cloud Platform No 
Google Kubernetes Engine (GKE) No 
LanceDB No 
Python No 
Snowflake No 
Union Cloud No 
io.net No 

Pricing Details

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

Pricing Details

No price information available.
Free Trial No 
Free Version 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 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
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 Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

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

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Vendor Details

Company Name

Habana Labs

Founded

2016

Country

Israel

Website

habana.ai/training-software/

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 

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 

Alternatives

Alternatives