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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

CloudBender

March 7, 2022

trainML

trainML

CloudBender™ lets you connect your on-prem and cloud GPUs to the trainML platform and seamlessly run jobs on any CloudBender enabled system. When you start a notebook or submit a job, CloudBender will automatically select the lowest cost available resource that meets your hardware, cost, data, and security specifications.

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NVIDIA NGC Catalog Integration

December 6, 2021

trainML

trainML

trainML is making it even easier to run any GPU-enabled workload by allowing customers to use job images directly from NVIDIA's NGC Catalog.

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Collaborative Projects for Resource Sharing

September 23, 2021

trainML

trainML

Teams can now create collaborative projects to share access to jobs, datasets, and models.

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Customer Provided Job Environments

August 12, 2021

trainML

trainML

Customers with prebuilt docker images can now use them as the job environment for any job type.

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REST Endpoints for Inference

August 9, 2021

trainML

trainML

The trainML platform has been extended to support deploying models as REST API endpoints. These fully managed endpoints give you the real-time predictions you need for production applications without having to worry about servers, certificates, networking, or web development.

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Automatic Dependency Installation

July 22, 2021

trainML

trainML

trainML jobs now accept lists of packages that will be installed using apt, pip, or conda as part of the job creation process and will automatically install dependencies found in the requirements.txt file in the root of the model code working directory.

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Consolidated Account Billing

July 16, 2021

trainML

trainML

Now your entire team or organization can share a single credit balance managed by a central account.

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Load Model Code Directly From Your Laptop

June 28, 2021

trainML

trainML

You can now start any job type from model code stored on your local computer without committing the code to a git repository. In combination with the trainML CLI, starting a notebook from your local computer is as simple as:

trainml job create notebook --model-dir ~/model-code --data-dir ~/data "My Notebook"
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Start Training Models With One Line

April 1, 2021

trainML

trainML

trainml job create notebook "name"

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RTX 3090 (BFGPU) Instances Now Available

March 29, 2021

trainML

trainML

Enjoy the "big ferocious" performance of NVIDIA's Ampere-based RTX 3090 for less than $1 an hour. Supplies are limited so reserve one while you can.

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