Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
IBM Spectrum Symphony® software provides robust management solutions designed for executing compute-heavy and data-heavy distributed applications across a scalable shared grid. This powerful software enhances the execution of numerous parallel applications, leading to quicker outcomes and improved resource usage. By utilizing IBM Spectrum Symphony, organizations can enhance IT efficiency, lower infrastructure-related expenses, and swiftly respond to business needs. It enables increased throughput and performance for analytics applications that require significant computational power, thereby expediting the time it takes to achieve results. Furthermore, it allows for optimal control and management of abundant computing resources within technical computing environments, ultimately reducing expenses related to infrastructure, application development, deployment, and overall management of large-scale projects. This all-encompassing approach ensures that businesses can efficiently leverage their computing capabilities while driving growth and innovation.
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.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Amazon EC2 Trn2 Instances
No
Amazon SageMaker
No
Amazon Web Services (AWS)
No
Anyscale
No
Apache Airflow
No
Azure Kubernetes Service (AKS)
No
Dask
No
Feast
No
Flyte
No
Google Cloud Platform
No
Integrations
Amazon EC2 Trn2 Instances
Yes
Amazon SageMaker
Yes
Amazon Web Services (AWS)
Yes
Anyscale
Yes
Apache Airflow
Yes
Azure Kubernetes Service (AKS)
Yes
Dask
Yes
Feast
Yes
Flyte
Yes
Google Cloud Platform
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
Free
Open source. Consumption-based.
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
www.ibm.com/products/analytics-workload-management
Vendor Details
Company Name
Anyscale
Founded
2019
Country
United States
Website
ray.io
Product Features
Cloud Cost Management
Cost Reduction Optimization
No
Dashboard
No
Data Import/Export
No
Data Storage
No
Data Visualization
No
Resource Usage Reporting
No
Roles / Permissions
No
Spend and Cost Reporting
No
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No