Training jobs' worker log output can now be viewed centrally from the proxiML platform in real-time. Keep an eye on all your job workers' training progress at the same time, so you can stop them early if they are no longer making progress.

proxiML customers can now select from a variety of popular public datasets when starting a notebook or training jobs. There is no storage cost for using these datasets, no matter how many jobs you attach them to.

The proxiML 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.

The proxiML platform now allows customers to store datasets permanently and reuse those datasets for as many notebook and training jobs as desired.

You can now run proxiML serverless deep learning training jobs on model code hosted in private git repositories that support SSH authentication.

proxiML training jobs can now run on data stored in Google Cloud Storage and upload their results to the same or another bucket. GCP access credentials can also be attached to notebooks and training job workers to provide them with easy access to other GCP services.

proxiML training jobs can now run on data directly from your local computer and upload their results back without using any cloud intermediary. If you already have the data set on your local computer and want to avoid the repetitive cycle of uploading and downloading from cloud storage, this storage type is for you.

The HTTP input data type is now available for proxiML training jobs. This option is ideal for publicly available dataset that are hosted on public HTTP or FTP servers. If you were previously using wget/curl in your training scripts to download data, this option is for you. Additionally, if you specify a path to an archive as the input storage path, the archive will automatically be extracted before being attached to the workers.