Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
GPUs excel at swiftly transferring data but suffer from limited locality of reference due to their relatively small caches, which makes them better suited for scenarios that involve heavy computation on small datasets rather than light computation on large ones. Consequently, the networks optimized for GPU architecture tend to run in layers sequentially to maximize the throughput of their computational pipelines (as illustrated in Figure 1 below). To accommodate larger models, given the GPUs' restricted memory capacity of only tens of gigabytes, multiple GPUs are often pooled together, leading to the distribution of models across these units and resulting in a convoluted software framework that must navigate the intricacies of communication and synchronization between different machines. In contrast, CPUs possess significantly larger and faster caches, along with access to extensive memory resources that can reach terabytes, allowing a typical CPU server to hold memory equivalent to that of dozens or even hundreds of GPUs. This makes CPUs particularly well-suited for a brain-like machine learning environment, where only specific portions of a vast network are activated as needed, offering a more flexible and efficient approach to processing. By leveraging the strengths of CPUs, machine learning systems can operate more smoothly, accommodating the demands of complex models while minimizing overhead.
Description
Favored by developers, businesses, and open-source initiatives alike, Nx ensures that whether your workspace hosts one project or a multitude, your CI remains quick and your workspace is easy to manage. This next-generation build system offers exceptional support for mono repos along with robust integrations that simplify scalability. Utilizing advanced methods like distributed task execution and computation caching, Nx guarantees that your CI times stay rapid as you continue to expand your workspace. It intelligently identifies previously executed computations, allowing for the restoration of files and terminal outputs from its cache. With its smart, automated task distribution across multiple machines, Nx maximizes parallelization and optimizes CPU usage during CI processes. You can also share your local computation cache with your team and CI systems, enhancing overall efficiency. After all, nothing beats the speed of avoiding unnecessary task execution. Additionally, Nx’s project graph analysis enables it to compare against a baseline, effectively pinpointing which projects have undergone changes, thereby streamlining your workflow even further. This capability not only improves project management but also enhances the team's productivity as they focus on impactful work.
API Access
Has API
API Access
Has API
Integrations
.NET
Angular
Cypress
DFMPro
Docker
Facebook
GitHub
Go
Jest
JetBrains Hub
Integrations
.NET
Angular
Cypress
DFMPro
Docker
Facebook
GitHub
Go
Jest
JetBrains Hub
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Neural Magic
Founded
2018
Country
United States
Website
neuralmagic.com
Vendor Details
Company Name
Nx
Website
nx.dev/
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Build Automation
Automated Testing
Build Cache
Build Management Tools
Build Metrics
Change Only Compiling
Debugging Tools
Dependency Management
IDE Compatibility
Parallel Testing
Plugin Library
Source Code Management
Version Conflict Resolution