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Oops! Something went wrong 😔 to Transition to Enterprise Model

In the fall of 2018, acquired and transformed it into, the one-click deep learning platform to help advance medical AI research. Since then, we rewrote the data science analysis platform for greater scalability of projects, and the data science courses have been used thousands of times.

As the platform and its usage continues to evolve, we've decided to move to an Enterprise model. This means that as of August 31, 2019, the service will no longer be available to individual participants.

What does this mean for me? From now through the end of August you will continue to have access to your instances with support. During this time, make sure to download your data, models and complete your current work. As of August 31, 2019 the service and your data will no longer be accessible on the platform.

How can I download my data? You can download your data in three suggested steps. Here we use Google Cloud as a primary example of another Cloud provider, but you could use AWS, Dropbox, or Github as well.

If you do not already have one, sign up for cloud provider account. *Note Google Cloud will typically give you a $300 credit for first-time signup.

Install Cloud SDK via the "versioned archives" and use the package for linux-64bit ie. google-cloud-sdk-x.x.x-linux-x86_64.tar.gz" to download, extract and install. View the “how-to” article here.

Finally, use gsutil to copy the file from/to crestle/gcloud ref. You can find the “how-to” article here. Note that if you have a large number of files to transfer you might want to use the gsutil -m option, to perform a parallel (multi-threaded/multi-processing) copy:

$ gsutil -m cp -r directory_to_copy gs://my-bucket

Are there alternative services I can use? Other resources include Colab and SageMaker, which are independently owned and not affiliated with You can learn more about these options on

Thank you for being part of the community. We encourage you to stay in touch with through our various open source projects such as Federated Learning for privacy and data sharing. Check out our work on GitHub.

If you have any questions, please email

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