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features
design
support

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Write a Review

Description

Neuralhub is a platform designed to streamline the process of working with neural networks, catering to AI enthusiasts, researchers, and engineers who wish to innovate and experiment in the field of artificial intelligence. Our mission goes beyond merely offering tools; we are dedicated to fostering a community where collaboration and knowledge sharing thrive. By unifying tools, research, and models within a single collaborative environment, we strive to make deep learning more accessible and manageable for everyone involved. Users can either create a neural network from the ground up or explore our extensive library filled with standard network components, architectures, cutting-edge research, and pre-trained models, allowing for personalized experimentation and development. With just one click, you can construct your neural network while gaining a clear visual representation and interaction capabilities with each component. Additionally, effortlessly adjust hyperparameters like epochs, features, and labels to refine your model, ensuring a tailored experience that enhances your understanding of neural networks. This platform not only simplifies the technical aspects but also encourages creativity and innovation in AI development.

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

Integrations

Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Anyscale Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Feast Yes 
Flyte Yes 
Google Kubernetes Engine (GKE) Yes 
Kubernetes Yes 
LanceDB Yes 
MLflow Yes 
PyTorch Yes 
Python Yes 
Snowflake Yes 
TensorFlow 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 No 
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 No 
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

Neuralhub

Website

neuralhub.ai/

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

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 

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 

Alternatives

Alternatives

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