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· 4 min read

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.

· 5 min read

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.

· 3 min read

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.

· 3 min read

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"