Ray 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.
Ray Alternatives
TensorFlow
Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.
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Azure Machine Learning
Streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with diverse, efficient tools for swiftly constructing, training, and deploying machine learning models. Speed up market readiness and enhance team collaboration through top-notch MLOps—akin to DevOps but tailored for machine learning. Foster innovation on a secure and trusted platform that prioritizes responsible machine learning practices. Cater to all skill levels by offering both code-first approaches and user-friendly drag-and-drop designers, alongside automated machine learning options. Leverage comprehensive MLOps functionalities that seamlessly integrate into current DevOps workflows and oversee the entire ML lifecycle effectively. Emphasize responsible ML practices, ensuring model interpretability and fairness, safeguarding data through differential privacy and confidential computing, while maintaining oversight of the ML lifecycle with audit trails and datasheets. Furthermore, provide exceptional support for a variety of open-source frameworks and programming languages, including but not limited to MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, making it easier for teams to adopt best practices in their machine learning projects. With these capabilities, organizations can enhance their operational efficiency and drive innovation more effectively.
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AWS Neuron
It enables high-performance training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, which are powered by AWS Trainium. For deploying models, the system offers efficient and low-latency inference capabilities on Amazon EC2 Inf1 instances that utilize AWS Inferentia and on Inf2 instances based on AWS Inferentia2. With the Neuron software development kit, users can seamlessly leverage popular machine learning frameworks like TensorFlow and PyTorch, allowing for the optimal training and deployment of machine learning models on EC2 instances without extensive code modifications or being locked into specific vendor solutions. The AWS Neuron SDK, designed for both Inferentia and Trainium accelerators, integrates smoothly with PyTorch and TensorFlow, ensuring users can maintain their existing workflows with minimal adjustments. Additionally, for distributed model training, the Neuron SDK is compatible with libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and usability in various ML projects. This comprehensive support makes it easier for developers to manage their machine learning tasks efficiently.
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Amazon SageMaker
Amazon SageMaker is a comprehensive service that empowers developers and data scientists to efficiently create, train, and deploy machine learning (ML) models with ease. By alleviating the burdens associated with the various stages of ML processes, SageMaker simplifies the journey towards producing high-quality models.
In contrast, conventional ML development tends to be a complicated, costly, and iterative undertaking, often compounded by the lack of integrated tools that support the entire machine learning pipeline. As a result, practitioners are forced to piece together disparate tools and workflows, leading to potential errors and wasted time. Amazon SageMaker addresses this issue by offering an all-in-one toolkit that encompasses every necessary component for machine learning, enabling quicker production times while significantly reducing effort and expenses. Additionally, Amazon SageMaker Studio serves as a unified, web-based visual platform that facilitates all aspects of ML development, granting users comprehensive access, control, and insight into every required procedure. This streamlined approach not only enhances productivity but also fosters innovation within the field of machine learning.
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Pricing
Pricing Starts At:
Free
Pricing Information:
Open source. Consumption-based.
Free Version:
Yes
Free Trial:
Yes
Company Details
Company:
Anyscale
Year Founded:
2019
Headquarters:
United States
Website:
ray.io
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Product Details
Platforms
SaaS
Windows
Mac
Linux
On-Premises
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Training Docs
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Online Support
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