Ray

Ray

Anyscale
+
+

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About

Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.

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
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Companies in need of a solution to train ML models quickly and cost effectively

Audience

ML and AI Engineers, Software Developers

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

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
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Amazon
Founded: 1994
United States
aws.amazon.com/sagemaker/train/

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Alternatives

Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow
Amazon EC2 Trn2 Instances
Anyscale
Apache Airflow
CodeGPT
Feast
Flyte
Google Cloud Platform
Google Kubernetes Engine (GKE)
Hugging Face
Kubernetes
LanceDB
MLflow
NVIDIA NeMo Megatron
Snowflake
Union Cloud
io.net

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow
Amazon EC2 Trn2 Instances
Anyscale
Apache Airflow
CodeGPT
Feast
Flyte
Google Cloud Platform
Google Kubernetes Engine (GKE)
Hugging Face
Kubernetes
LanceDB
MLflow
NVIDIA NeMo Megatron
Snowflake
Union Cloud
io.net
Claim Amazon SageMaker Model Training and update features and information
Claim Amazon SageMaker Model Training and update features and information
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Claim Ray and update features and information