| DeepSpeedMicrosoft | RayAnyscale | |||||
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| About
            DeepSpeed is an open source deep learning optimization library for PyTorch.  It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware.  DeepSpeed is optimized for low latency, high throughput training.
DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters.  It can also train up to 13 billion parameters in a single GPU.
DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism. 
             | 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.
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| Audience
        Deep learning model developers
         | Audience
        ML and AI Engineers, Software Developers
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| Pricing
        Free
        
     
            
                Free Version
            
            
         
            
                Free Trial
            
            
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        Free
        
     
            
                Free Version
            
            
         
            
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| Training
            
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                Documentation
            
            
         
            
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| Company InformationMicrosoft Founded: 1975 United States www.deepspeed.ai/ | Company InformationAnyscale Founded: 2019 United States ray.io | |||||
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| Integrations
            
                
    PyTorch
            
            
         
            
                
    Python
            
            
         
            
                
    Amazon EC2 Trn2 Instances
            
            
         
            
                
    Amazon EKS
            
            
         
            
                
    Amazon SageMaker
            
            
         
            
                
    Anyscale
            
            
         
            
                
    Azure Kubernetes Service (AKS)
            
            
         
            
                
    Cake AI
            
            
         
            
                
    Comet LLM
            
            
         
            
                
    Databricks Data Intelligence Platform
            
            
         | Integrations
            
                
    PyTorch
            
            
         
            
                
    Python
            
            
         
            
                
    Amazon EC2 Trn2 Instances
            
            
         
            
                
    Amazon EKS
            
            
         
            
                
    Amazon SageMaker
            
            
         
            
                
    Anyscale
            
            
         
            
                
    Azure Kubernetes Service (AKS)
            
            
         
            
                
    Cake AI
            
            
         
            
                
    Comet LLM
            
            
         
            
                
    Databricks Data Intelligence Platform
            
            
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