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?

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

Hi @<1747428509627715584:profile|CumbersomeDuck6> ,

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

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 @<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 @<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...

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...

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 @<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?

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).

Do you get any errors from the container? Do you have any limitations? Can you share the docker run command (the first output in the log)

Hi @<1835851157679902720:profile|CumbersomeBluewhale64> , can you try running this one?

from clearml import Task

# create an dataset experiment
task = Task.init(project_name="examples", task_name="my task")
task.set_base_docker("python:3.9-slim")

# only create the task, we will actually execute it later
task.execute_remotely(queue_name=<placeholder>, clone=False)

print("hello world")

Dont forget to change the queue name

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

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

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

thanks, checking it

Hi @<1797800424254738432:profile|FlatHippopotamus76> ,

You can control the pip installation and setting the env with CLEARML_AGENT_SKIP_PIP_VENV_INSTALL and CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL env vars ( examples here )

Do you have python on this container? for running the clearml-agent

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...

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

Hi @<1523708920831414272:profile|SuperficialDolphin93> ,

I would use Kibana to investigate the documents.

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 @<1861218295315697664:profile|FlutteringLobster45> ,

poetry is supported (all the information can be found here ), just need to configure the agent to work with it.
If you want to run the Task inside a container, this is also supported, just need to run the agent in docker mode .

Hi @<1742355077231808512:profile|DisturbedLizard6> ,

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

👍 , can you check it with the latest version?

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?
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