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features
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support

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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 

Screenshots View All

Screenshots View All

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 

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SubQ

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