With the recent major release of PyTorch 2.0, new trainML job environments are available.

Start building your own ChatGPT-like applications with popular open source Large Language Models like GPT-J, GPT-NeoX, and BloomZ.

CloudBender™ Device Configurations have been expanded to allow attaching public and private Checkpoints to edge inference devices.

Checkpoints and Datasets are now supported output destinations for trainML Training and Inference jobs.

Checkpoints can now be created directly from public or private Hugging Face models.

Get started building models even faster by using Public Checkpoints.

trainML now supports the creation and use of Checkpoints to store immutable versions of large model weight files.

Google Drive files can now be downloaded directly into trainML Datasets and Models.

Analytics providers can now run their models directly on the trainML deployments of their customers. This allows the analytics provider to maintain and protect their intellectual property while providing analytics services inside their customers' secure, private infrastructure.

Run real-time inference workloads on NVIDIA Jetson fully managed by CloudBender™.