from clearml import Task
task = Task.init(project_name='ClearML test', task_name='test 10')

AgitatedDove14 quick question, do I need to alter the name of the dict file I save with torch.save for each run? Or is clearml able to version it out?

now that I know the logging is working, it must be something dumb I've overlooked in my code

this is in the code but still no logged model

torch.save(model.state_dict(), " http://test_cnn.pt ")

thanks for your help!

Hi RoundMole15 ! Are you able to see a model logged when you run this simple example?
` from clearml import Task
import torch.nn.functional as F
import torch.nn as nn
import torch
class TheModelClass(nn.Module):
def init(self):
super(TheModelClass, self).init()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x

Initialize model

model = TheModelClass()
task = Task.init(project_name='ClearML test', task_name='test 10')
torch.save(model.state_dict(), "test_cnn.pt") `

You're welcome! Feel free to write here again if you believe this might be a ClearML problem

how does your model look like?

Hi RoundMole15

What exactly triggers the "automagic" logging of the model and weights?

framework save call, for example torch.save or joblib.save

I've pulled my simple test project out of jupyter lab and the same problem still exists,

What is "the same problem" ?

AgitatedDove14 other things are logging, I'm using task.connect to log hyperparameters logger to log the loss

"the same problem" is I posted above but I was using jupyter lab, I simplified hoping the problem was with jupyter

it's as simple as yours,-- class myCNN(nn.Module):

hmmm, yes that worked

RoundMole15 how does the Task.init look like?

You should alter the name (or else the model will be overwritten)