yep i am working on it - i have something that i suspect not to work as expected. nothing sure though
for the step that reports the model :
`
@PipelineDecorator.component(return_values=['res'],
parents=['step_one'],
cache=False,
monitor_models=['mymodel'])
def step_two():
import torch
from clearml import Task
import torch.nn as nn
class nn_model(nn.Module):
def init(self):
super(nn_model, self).init()
self.encoder = nn.Sequential(
nn.Linear(28 * 28, 256),
nn.ReLU(True),
)
def forward(self, x):
x = self.encoder(x)
return x
def save(self, path):
torch.save(self.state_dict(), path)

mymodel = nn_model()
mymodel.save('./mymodel.pth') `