Traceback (most recent call last):


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/dataframe.py", line 3523, in table_part

values[name] = df.evaluate(name)


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/dataframe.py", line 5165, in evaluate

self.map_reduce(assign, lambda *_: None, expressions, ignore_filter=False, selection=selection, pre_filter=use_filter, info=True, to_numpy=False)


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/dataframe.py", line 306, in map_reduce

return self._delay(delay, task)


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/dataframe.py", line 1428, in _delay

self.execute()


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/dataframe.py", line 293, in execute

self.executor.execute()


 "/Users/yonatanalexander/opt/anaconda3/envs/xds/lib/python3.7/site-packages/vaex/execution.py", line 127, in execute

raise RuntimeError("nested execute call")
RuntimeError: nested execute call

Traceback (most recent call last):


 "/home/ubuntu/anaconda3/lib/python3.8/concurrent/futures/thread.py", line 57, in run

result = self.fn(*self.args, **self.kwargs)


 "/home/ubuntu/ParlAI/parlai/chat_service/core/world_runner.py", line 128, in _world_fn

return self._run_world(task, world_name, agents)


 "/home/ubuntu/ParlAI/parlai/chat_service/core/world_runner.py", line 95, in _run_world

world = world_generator(self.opt, agents)


 "/home/ubuntu/ParlAI/parlai/tasks/blended_skill_talk/worlds.py", line 142, in generate_world

agent = create_agent(opt, requireModelExists=True)


 "/home/ubuntu/ParlAI/parlai/core/agents.py", line 402, in create_agent

model = create_agent_from_opt_file(opt)


 "/home/ubuntu/ParlAI/parlai/core/agents.py", line 355, in create_agent_from_opt_file

return model_class(opt_from_file)


 "/home/ubuntu/ParlAI/parlai/core/torch_generator_agent.py", line 475, in __init__

super().__init__(opt, shared)


 "/home/ubuntu/ParlAI/parlai/core/torch_agent.py", line 679, in __init__

super().__init__(opt, shared)


 "/home/ubuntu/ParlAI/parlai/core/agents.py", line 69, in __init__

self.opt = copy.deepcopy(opt)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 153, in deepcopy

y = copier(memo)


 "/home/ubuntu/ParlAI/parlai/core/opt.py", line 71, in __deepcopy__

memo = Opt({k: copy.deepcopy(v) for k, v in self.items()})


 "/home/ubuntu/ParlAI/parlai/core/opt.py", line 71, in <dictcomp>

memo = Opt({k: copy.deepcopy(v) for k, v in self.items()})


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 146, in deepcopy

y = copier(x, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 230, in _deepcopy_dict

y[deepcopy(key, memo)] = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 146, in deepcopy

y = copier(x, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 230, in _deepcopy_dict

y[deepcopy(key, memo)] = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 172, in deepcopy

y = _reconstruct(x, memo, *rv)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 270, in _reconstruct

state = deepcopy(state, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 146, in deepcopy

y = copier(x, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 230, in _deepcopy_dict

y[deepcopy(key, memo)] = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 172, in deepcopy

y = _reconstruct(x, memo, *rv)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 296, in _reconstruct

value = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 172, in deepcopy

y = _reconstruct(x, memo, *rv)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 270, in _reconstruct

state = deepcopy(state, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 146, in deepcopy

y = copier(x, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 230, in _deepcopy_dict

y[deepcopy(key, memo)] = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 172, in deepcopy

y = _reconstruct(x, memo, *rv)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 296, in _reconstruct

value = deepcopy(value, memo)


 "/home/ubuntu/anaconda3/lib/python3.8/copy.py", line 153, in deepcopy

y = copier(memo)


 "/home/ubuntu/anaconda3/lib/python3.8/site-packages/torch-1.7.0-py3.8-linux-x86_64.egg/torch/nn/parameter.py", line 32
, in __deepcopy__

result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
RuntimeError: CUDA out of memory. Tried to allocate 40.00 MiB (GPU 0; 15.78 GiB total capacity; 14.32 GiB already allocated;
1.50 MiB free; 14.58 GiB reserved in total by PyTorch)
