Fabric for Deep Learning (FfDL) vs. Ray Comparison


Fabric for Deep Learning (FfDL) IBM	Ray Anyscale	+	+
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About Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.	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 Neural networks experts looking for a solution to run deep-learning frameworks as a service on Kubernetes	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 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 Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Supported Live Online Supported In Person Supported
Company Information IBM Founded: 1911 United States developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/	Company Information Anyscale Founded: 2019 United States ray.io
Alternatives Google Cloud Deep Learning VM Image Google	Alternatives Anyscale
AWS Deep Learning AMIs Amazon	Horovod
Deeplearning4j	Determined AI
Horovod	Neuralhub
Google Deep Learning Containers Google View All	Automaton AI View All
Categories Deep Learning Supported Neural Network Supported	Categories Deep Learning Supported Machine Learning Supported ML Model Deployment Supported

Integrations Kubernetes Supported PyTorch Supported TensorFlow Supported Amazon EC2 Trn2 Instances Not Supported Amazon SageMaker Not Supported Amazon Web Services (AWS) Not Supported Apache Airflow Not Supported Azure Kubernetes Service (AKS) Not Supported Caffe Supported Dask Not Supported Feast Not Supported Flyte Not Supported Google Cloud Platform Not Supported LanceDB Not Supported MLflow Not Supported Python Not Supported Snowflake Not Supported Torch Supported Union Cloud Not Supported io.net Not Supported Show More Integrations View All 5 Integrations	Integrations Kubernetes Supported PyTorch Supported TensorFlow Supported Amazon EC2 Trn2 Instances Supported Amazon SageMaker Supported Amazon Web Services (AWS) Supported Apache Airflow Supported Azure Kubernetes Service (AKS) Supported Caffe Not Supported Dask Supported Feast Supported Flyte Supported Google Cloud Platform Supported LanceDB Supported MLflow Supported Python Supported Snowflake Supported Torch Not Supported Union Cloud Supported io.net Supported Show More Integrations View All 22 Integrations
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