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Description
Hinode serves as a cloud workspace designed specifically for creators working with AI-generated images and videos. This platform efficiently manages your files, applications, and provides a shared directory for models and datasets, ensuring continuity between sessions. When extensive GPU resources are required for computations, you can link a powerful machine such as an NVIDIA L4 with 24 GB of VRAM, L40S with 48 GB, or the RTX PRO 6000 boasting 96 GB, all accompanied by RAM options ranging from 32 to 256 GB. Upon launching, these machines automatically load your previously saved workspace, which means all the necessary tools are readily available for your use.
Accessing your projects is seamless through a streamed Ubuntu desktop available in the Hinode app for Mac, Windows, and Linux, or via Chrome and Edge without needing to install anything. Additionally, it supports SSH connections through the hinode command-line tool, allows access via VS Code Remote-SSH, and connects through an MCP interface for Claude and other assistants. Installing applications like ComfyUI, PyTorch, JupyterLab, Ollama, vLLM, AI Toolkit, Axolotl, and several others is a simple one-click process, enhancing the overall user experience and productivity. Furthermore, the platform continually evolves, incorporating new tools and features to meet the demands of its users.
Description
TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.
API Access
Has API
API Access
Has API
Pricing Details
$0.29/h/workspace
Free Trial
Free Version
Pricing Details
Free
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
Hinode
Founded
2026
Country
United States
Website
hinode.run
Vendor Details
Company Name
TorchMetrics
Country
United States
Website
torchmetrics.readthedocs.io/en/stable/
Product Features
Product Features
Application Development
Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development