Neural Magic vs. Ray Comparison


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About GPUs bring data in and out quickly, but have little locality of reference because of their small caches. They are geared towards applying a lot of compute to little data, not little compute to a lot of data. The networks designed to run on them therefore execute full layer after full layer in order to saturate their computational pipeline (see Figure 1 below). In order to deal with large models, given their small memory size (tens of gigabytes), GPUs are grouped together and models are distributed across them, creating a complex and painful software stack, complicated by the need to deal with many levels of communication and synchronization among separate machines. CPUs, on the other hand, have large, much faster caches than GPUs, and have an abundance of memory (terabytes). A typical CPU server can have memory equivalent to tens or even hundreds of GPUs. CPUs are perfect for a brain-like ML world in which parts of an extremely large network are executed piecemeal, as needed.	About Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.
Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Companies doing AI and ML development	Audience ML and AI Engineers, Software Developers
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Not Supported Free Trial Not Supported	Pricing Free Open source. Consumption-based. Free Version Supported Free Trial Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software

Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Supported Live Online Supported In Person Supported
Company Information Neural Magic Founded: 2018 United States neuralmagic.com	Company Information Anyscale Founded: 2019 United States ray.io
Alternatives Sharky Neural Network SharkTime Software	Alternatives Anyscale
Automaton AI	Horovod
Google Deep Learning Containers Google	Determined AI
Neural Designer Artelnics	Neuralhub
Fabric for Deep Learning (FfDL) IBM View All	Automaton AI View All
Categories Artificial Intelligence Supported Deep Learning Supported Machine Learning Supported Neural Network Supported	Categories Deep Learning Supported Machine Learning Supported ML Model Deployment Supported

Integrations Amazon EC2 Trn2 Instances Not Supported Amazon EKS Not Supported Amazon SageMaker Not Supported Amazon Web Services (AWS) Not Supported Anyscale Not Supported Dask Not Supported Databricks Not Supported Feast Not Supported Flyte Not Supported Google Cloud Platform Not Supported Google Kubernetes Engine (GKE) Not Supported Kubernetes Not Supported LanceDB Not Supported MLflow Not Supported PyTorch Not Supported Python Not Supported TensorFlow Not Supported Ultralytics Supported Union Cloud Not Supported io.net Not Supported Show More Integrations View All 1 Integration	Integrations Amazon EC2 Trn2 Instances Supported Amazon EKS Supported Amazon SageMaker Supported Amazon Web Services (AWS) Supported Anyscale Supported Dask Supported Databricks Supported Feast Supported Flyte Supported Google Cloud Platform Supported Google Kubernetes Engine (GKE) Supported Kubernetes Supported LanceDB Supported MLflow Supported PyTorch Supported Python Supported TensorFlow Supported Ultralytics Not Supported Union Cloud Supported io.net Supported Show More Integrations View All 22 Integrations
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