trainML Documentation
  • Docs
  • Tutorials
  • Blog
  • Login/Signup

›All Blog Posts

All Blog Posts

  • 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

Major UI Overhaul and Direct Notebook Access

September 1, 2020

trainML

trainML

The trainML platform experience has been redesigned to make it easier to manage notebooks, training jobs, and datasets independently. Additionally, Notebooks are now directly access from the web interface instead of launched through the connection utility.

User Interface Changes

An administrative side-panel navigation bar has been added to better organize the different functions of the trainML platform. Notebooks, training jobs, and datasets now have their own pages, and a new Home page contains information about the resources currently configured in your system as well as links to other useful information to get started with the platform. The job archive page has been added where you can view details about terminated jobs. Additionally, links to the billing and account setting pages are now easily accessible from the side-panel navigation.

The notebook, training job, and dataset pages now provide additional information about each item in grid form. You are able select multiple items simultaneously and perform operations on multiple jobs at once. If an option is greyed out during a multi-select, that means at least one of the selected items is not in the correct state for that action. For example, if you have 4 notebook jobs running and which to stop all fo them, you can select all 4 and click the Stop button. However, if you have 3 running job and 1 stopped job, you will not be able to click stop if all 4 jobs are selected. Certain actions, like Edit are only allowed on single items.

Direct Notebook Access

Notebook instances previously required the connection utility in order to access the Notebook. Now, you simply click the Open button on the notebook job grid and it will open the notebook instance in a new browser tab. This means users can access Notebooks from any operating system with no additional dependency installation. The connection utility is still required for certain advanced features like local data storage or allowing job workers to interact with databases and other services on your local computer.

Tweet
Recent Posts
  • User Interface Changes
  • Direct Notebook Access
trainML Documentation
Docs
Getting StartedTutorials
Legal
Privacy PolicyTerms of Use
Copyright © 2022 trainML, LLC, All rights reserved