Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
DeepSpeed is an open-source library focused on optimizing deep learning processes for PyTorch. Its primary goal is to enhance efficiency by minimizing computational power and memory requirements while facilitating the training of large-scale distributed models with improved parallel processing capabilities on available hardware. By leveraging advanced techniques, DeepSpeed achieves low latency and high throughput during model training.
This tool can handle deep learning models with parameter counts exceeding one hundred billion on contemporary GPU clusters, and it is capable of training models with up to 13 billion parameters on a single graphics processing unit. Developed by Microsoft, DeepSpeed is specifically tailored to support distributed training for extensive models, and it is constructed upon the PyTorch framework, which excels in data parallelism. Additionally, the library continuously evolves to incorporate cutting-edge advancements in deep learning, ensuring it remains at the forefront of AI technology.
Description
The Large-Scale Optimizer™ is a collaborative creation by Michael Best, who is a Professor Emeritus in the Department of Combinatorics and Optimization at the University of Waterloo, and Jivendra Kale, the President of Financiometrics Inc. This remarkable quadratic optimizer is designed for the swift construction of long-only, long-short, and market-neutral portfolios that can encompass thousands of assets, allowing for effective risk management in relation to a standard or benchmark portfolio. Additionally, it serves as a tool for asset allocation that employs Markowitz mean-variance analysis principles. This version of the Large-Scale Optimizer™ is an unrestricted edition that can be acquired either as an application or as a subroutine library that can be integrated into your software. Utilizing an advanced active set method, which has been further refined through the implementation of penalty function techniques, the Large-Scale Optimizer™ achieves significant speed enhancements to ensure the attainment of a true global optimal solution for extensive, real-world portfolio optimization challenges, even when variable transaction costs are present. This unique capability makes it an essential tool for financial analysts and portfolio managers seeking to optimize their investment strategies efficiently.
API Access
Has API
No
API Access
Has API
No
Integrations
Axolotl
Yes
Cake AI
Yes
Comet LLM
Yes
Nurix
Yes
PyTorch
Yes
Python
Yes
Integrations
Axolotl
No
Cake AI
No
Comet LLM
No
Nurix
No
PyTorch
No
Python
No
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
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
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
www.deepspeed.ai/
Vendor Details
Company Name
Financiometrics
Website
www.financiometrics.com
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
Product Features
Financial Risk Management
Compliance Management
No
Credit Risk Management
No
For Hedge Funds
Yes
Liquidity Analysis
No
Loan Portfolio Management
No
Market Risk Management
No
Operational Risk Management
No
Portfolio Management
Yes
Portfolio Modeling
No
Risk Analytics Benchmarks
No
Stress Tests
No
Value At Risk Calculation
No