ptrblck:

after removing the federated learning code


Thank you for your help! So the version without the federated learning set up is working perfectly. This is a stack trace I get from the logs of the workers:
ERROR base_events.py(l:1615, p:10310) - Task exception was never retrieved
future: <Task finished coro=<WebsocketServerWorker._producer_handler() done, defined at /home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/workers/websocket_server.py:95> exception=RuntimeError(“forward() is missing value for argument ‘inputs’. Declaration: forward(ClassType self, Tensor inputs, Tensor outputs) -> (Tensor)”)>
Traceback (most recent call last):
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/workers/websocket_server.py”, line 113, in _producer_handler
response = self._recv_msg(message)
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/workers/websocket_server.py”, line 124, in _recv_msg
return self.recv_msg(message)
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/workers/base.py”, line 292, in recv_msg
response = self._message_routermsg_type
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/workers/base.py”, line 412, in execute_command
response = getattr(_self, command_name)(*args, **kwargs)
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/federated/federated_client.py”, line 87, in fit
return self._fit(model=model, dataset_key=dataset_key, loss_fn=loss_fn)
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/syft/federated/federated_client.py”, line 121, in _fit
loss = loss_fn(target=target, pred=output)
File “/home/niki/miniconda3/envs/orchestrator/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 541, in call
result = self.forward(*input, **kwargs)
RuntimeError: forward() is missing value for argument ‘inputs’. Declaration: forward(ClassType self, Tensor inputs, Tensor outputs) -> (Tensor)