Average Ratings 1 Rating
Average Ratings 0 Ratings
Description
Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.
Description
Obtain scores that assess factual accuracy, context retrieval quality, guideline compliance, tonality, among other metrics. Improvement is impossible without measurement. UpTrain consistently evaluates your application's performance against various criteria and notifies you of any declines, complete with automatic root cause analysis. This platform facilitates swift and effective experimentation across numerous prompts, model providers, and personalized configurations by generating quantitative scores that allow for straightforward comparisons and the best prompt selection. Hallucinations have been a persistent issue for LLMs since their early days. By measuring the extent of hallucinations and the quality of the retrieved context, UpTrain aids in identifying responses that lack factual correctness, ensuring they are filtered out before reaching end-users. Additionally, this proactive approach enhances the reliability of responses, fostering greater trust in automated systems.
API Access
Has API
API Access
Has API
Integrations
Activeeon ProActive
Amazon EC2 G5 Instances
Amazon EC2 Trn2 Instances
Amazon S3 Express One Zone
Amazon Web Services (AWS)
Comet LLM
Dataoorts GPU Cloud
Fuzzball
Gemma
Giskard
Integrations
Activeeon ProActive
Amazon EC2 G5 Instances
Amazon EC2 Trn2 Instances
Amazon S3 Express One Zone
Amazon Web Services (AWS)
Comet LLM
Dataoorts GPU Cloud
Fuzzball
Gemma
Giskard
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
PyTorch
Founded
2016
Website
pytorch.org
Vendor Details
Company Name
UpTrain
Country
United States
Website
uptrain.ai/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization