Work with Remote Targets

DL Workbench can collect performance data not only on the machine on which you run it, but also on other machines in your local network. This helps when you cannot run the DL Workbench on a machine due to security or network issues or because it is impossible to install Docker. If this is the case, run the DL Workbench on another machine and collect performance data on a remote machine in your local network.

When connected to a remote machine, you can currently use a limited set of DL Workbench features:

Feature Supported
Single and group inference Yes
(HDDL plugin is not supported)
INT8 calibration Yes
Winograd algorithmic tuning No
Accuracy measurements No
Performance comparison between models on local and remote machines Yes
Deployment package creation No

Follow the steps below to profile your model on a remote target:

  1. Set up the target machine
  2. Register the remote target in the DL Workbench
  3. Profile on the remote machine

NOTE: Working with machines in your local network is not available when you run the DL Workbench in the Intel® DevCloud for the Edge.

See Also