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  • CloudBender
  • NVIDIA NGC Catalog Integration
  • Collaborative Projects for Resource Sharing
  • Customer Provided Job Environments
  • REST Endpoints for Inference
  • Automatic Dependency Installation
  • Consolidated Account Billing
  • Load Model Code Directly From Your Laptop
  • Start Training Models With One Line
  • RTX 3090 (BFGPU) Instances Now Available
  • Build Full Machine Learning Pipelines with trainML Inference Jobs
  • Store Training Results Directly on the trainML Platform
  • Dataset Viewing
  • Stay Modern with Python 3.8 Job Environments
  • Downloadable Log Extracts for Jobs and Datasets
  • Automate Training with the trainML Python SDK
  • trainML Jobs on Google Cloud Platform Instances
  • Spawn Training Jobs Directly From Notebooks
  • Easy Notebook Forking For Rapid Experimentation
  • Making Datasets More Flexible and Expanding Environment Options
  • Kaggle Datasets and API Integration
  • Centralized, Real-Time Training Job Worker Monitoring
  • Free to Use Public Datasets
  • Major UI Overhaul and Direct Notebook Access
  • Load Data Once, Reuse Infinitely
  • Serverless Deep Learning On Private Git Repositories
  • Google Cloud Storage Integration Released
  • Skip the Cloud Data Transfers with Local Storage
  • Web (HTTP/FTP) Data Downloads Plus Auto-Extraction of Archives

Dataset Viewing

February 23, 2021

trainML

trainML

You can now view summary details of the contents of a created dataset from the user interface.

From the dataset or dashboard, click the dataset name to view the details page. In the center of the details page, you will see the folder structure of the files in the dataset with the first level expanded. Each folder lists the total number of items (files and directories) in each folder as well as the folders total size (including all subdirectories). Files and their names are not shown. If a folder has subfolders, it will be formatted as a link. Clicking on the folder name will expand or collapse the folder.

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