Frequently asked questions


No, Daeploy enters the project when a trained model already exists.  Daeploy creates an application around the trained model and makes the model a part of the production-ready application.

Daeploy supports all major ML frameworks, for instance

  • PyTorch
  • TensorFlow
  • Keras

And many more!

First of all, thank you for wanting to report the bug and improving Daeploy!

We have a public issue tracker on GitHub, please follow the instructions here.

First of all, thank you for showing interest in Daeploy!

We have a public issue tracker and a public development board on GitHub, you find it here.

Ps, for the latest news we recommend you to join our community on Slack!


No! Daeploy works without internet access.

First, you need to pull the Daeploy Manager image from here from a computer with internet access. You then need to save the image as a tar file using docker save.  The tar file can then be transferred to the wanted machine/server using SCP or a similar tool.

When the machine has the Daeploy Manager image, we recommend following our documentation for how to configure the manager for the production.  You find the relevant documentation here.

Yes! You can use Daeploy to on any machine or virtual machine, be it on premise or on the cloud. Our only requirement is Docker and access to Docker daemon.