trainML Endpoints are now even more flexibile with the ability to bring-your-own-web-server.

Persistent Notebooks can now be compressed to free up space from deleted files stored in previous job runs.

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.

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.

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

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

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.

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.

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

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"