The auto magical will register the original model as an artifact, so the model that will be register it the original one, you can upload the model to your task as a model (so it will get a model id, like in this example ) or as a regular artifact (like in this example ).

Hi @<1613344994104446976:profile|FancyOtter74> , I’m getting the same, will keep you posted once a fixed version is out

Hi @<1790552666904989696:profile|GlamorousDog61> ,

Can you update clearml ( pip install -U clearml ) to the latest version?
Are you using add_function_step to add steps to the pipeline controller? if so, did you specify the packages with the packages parameter?

Hi @<1613344994104446976:profile|FancyOtter74> , it's high on our list, will keep you posted once release

Hi @<1774245260931633152:profile|GloriousGoldfish63> , checking it

Can I suggest using the sdk? It will do both, log it to the task and will upload it to any storage wanted, like in this example

Hi @<1747066118549278720:profile|WhoppingToad71> , can you share the use case? You want to upload the file to some storage? Or upload to a task?

Hi @<1523701295830011904:profile|CluelessFlamingo93> , no, all the data will be the same, but the user will be deactivate, and won't have access to the workspace (including the credentials, experiments, artifacts, datasets and all the other parts)

Hi @<1742355077231808512:profile|DisturbedLizard6> , not sure I get that, did you use torch.save (like in here ) or some other command to save the models? When running with the clearml-agent. you have a print of all the configurations at the beginning of the log, can you verify your values are as you configure it?

Additionally, which version of clearml , clearml-agent and `...

Hi @<1669152726245707776:profile|ManiacalParrot65> ,

Yes, you can wrap the separate function with a decorator so the function will run as a separate step in the pipeline, and even can cache the step for multi runs.

You can also add the function without a decorator, as a step to the pipeline with PipelineController.add_function_step() .

You can read about it [here](https://clear.ml/docs/latest/docs/pip...

Hi @<1644147961996775424:profile|HurtStarfish47> ,

ClearML will automatically upload you model with the original name and data, if not mistaken, best.pt is given by default from the train function.

You can rename it after the training and upload it, something like:

import shutil

# Rename best model checkpoint after training
shutil.move("runs/train/my_model/weights/best.pt", "my_model.pt")

# upload with the StorageManager
model_path = "my_model.pt"
# Define your...

Hi @<1745616566117994496:profile|FantasticGorilla16> , it looks like this error coming from the Elastic, can you check the disk space you allocated and check if its full?

Hi @<1691983266761936896:profile|AstonishingOx62> , I think it's an issue related to the schema generated by the sdk, can you try adding _allow_extra_fields_ as True with the same call?

Hi @<1773158059758063616:profile|PanickyParrot17> ,

You can do that with ClearML pipelines . step 1 will be pulling the data, step 2 will store the data, step 3 will create the dataset.
The pipeline controller can have the parameters of which data to pull, the name of the created dataset and more, so you can run it again from the UI and just change the data source

All the steps can run with the clearml agent, and you can also specify using o...

@<1691983266761936896:profile|AstonishingOx62> agreed, will push it forward

@<1774969995759980544:profile|SmoggyGoose12> how do you report the scalars? with tb SummaryWriter?

In the UI, if you click the eye symbol, you have only the monitoring options?

Hi @<1742355077231808512:profile|DisturbedLizard6> ,

Currently, only argparse arguments are supported for clearml-task , click is also support, but for now, with the python sdk.

With the API you can register an artifact to a task, but the upload will be done separately with the ClearML sdk (the sdk wrap the registration and upload, with some other things inside the upload_artifact function).

Hi @<1747428509627715584:profile|CumbersomeDuck6> ,

Can you check if you have such graph in the task, under ARTIFACTS -> state ?

Hi @<1779681046892122112:profile|EnviousHare17> and @<1774969995759980544:profile|SmoggyGoose12> ,

I run this code example:

# ClearML - Example of pytorch with tensorboard>=v1.14
#
from __future__ import print_function

import argparse
import os
from tempfile import gettempdir

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from torch.utils.tensorboard im...

Hi @<1594863230964994048:profile|DangerousBee35> ,

i run

    from clearml import Task
    import pandas as pd
    task = Task.init(project_name='examples', task_name='Artifacts with data_')

    df = pd.DataFrame(
        {
            'num_legs': [2, 4, 8, 0],
            'num_wings': [2, 0, 0, 0],
            'num_specimen_seen': [10, 2, 1, 8]
        },
        index=['falcon', 'dog', 'spider', 'fish']
    )

    # Register Pandas object as artifact to watch
    # (it will be mon...

Hi @<1594863230964994048:profile|DangerousBee35> , I can get reproduce the issue, will keep you posted about it

thanks, checking it

Hi @<1577468611524562944:profile|MagnificentBear85>

Thank you for bringing this up! We’re always excited to see contributions from the community, especially around areas like hyperparameter configuration. We’d be happy to consider a PR if you’re open to working on it! Our team encourages contributions 🙂

Did you check the relevant examples from our docs?
None
[None](https://c...

Hi @<1774245260931633152:profile|GloriousGoldfish63> ,

which clearml version are you using? Can you add a snippet of the code? I tried to reproduce it but didn't succeed.

Hi @<1797800418953138176:profile|ScrawnyCrocodile51> ,

I can understand the confusion here, from the code you shared, the issue is with how you're accessing the artifact properties. When you call .get() on an Artifact object, you're actually invoking the get() method defined in the Artifact class, which returns the artifact's content (in your case, a pandas DataFrame), not a dictionary value.
Instead, you should access the URL property directly:

task.artifacts.get("my-artifac...