Yes, i run it locally,
its even before i run my script, i run the cell:
task = Task.init(project_name="***2", task_name="***3")It happens only with .ipynb, if my experiment is in .py i don’t have this error.

yes

the code is running on a jupyter server, and it works. the only code that creates this error is the clearml line

` import plotly.express as px
from clearml import Task
import pandas as pd
df = pd.DataFrame({'a': ['val1', 'val1', 'val1', 'val1', 'val1', 'val1', 'val1', 'val1', 'val1', 'val1', 'val2', 'val2', 'val2', 'val2', 'val2', 'val2'],
'b':['group_a', 'group_a', 'group_a', 'group_a', 'group_b', 'group_b', 'group_b', 'group_b', 'group_b', 'group_b', 'group_a', 'group_a', 'group_a', 'group_a', 'group_b', 'group_b']})
task = Task.init(**{"project_name":"test",
"ta...

CostlyOstrich36 this code will show you the first issue of headline does over the chart facet col header

Hi, any updates ? CostlyOstrich36

SweetBadger76 CostlyOstrich36 after trying to run the same code (and ignoring the warning) i get a different error:
ValueError: HyperParameter type <class ‘clearml.automation.parameters.ParameterSet’> not supported yet with OptimizerBOHB
I looked at the “OptimizerOptuna” code, (clearml/automation/optuna/optuna.py) and i saw that the paramset is really not suppurted.
Which optimizer support parameterset?

make sure you use the same version as i am

plotly 5.11.0

second problem:
fig = px.imshow([[1,2], [3,4]], labels=dict(x="Predicted value", y="Real value"), x=['a', 'b'], y=['a','b'], title="Predicted and real values" ) fig.update_xaxes(side="top") clearml_logger.report_plotly('table test 2', series=f"table test 2 ", iteration=0, figure=fig) task.close()in this example you will see ...

and when you compare with oter (same tasks) the clearml title goes over the axis labels

clearml 1.9.0
clearml-agent 1.5.1

Thanks! I added an issue

parameter_optimization_space = [{
type = “ParameterSet”
name = null
values = [{
General/data_module = “”
General/model = “”
General/homogeneous = true
General/model_kwargs/pooling_flow = “source_to_target”
General/trainer_kwargs/epochs = 2
}]
},
{
type = “UniformParameterRange”
name = “General/data_module_kwargs/”
min_value = 0.001
max_value = 0.005
step_size = 0.001
include_max = true
},
{
...

Is it enough ? CostlyOstrich36

Can you maybe provide me an example of how to use the parameterset?

` hyper_task = Task.init(project_name="***",
task_name="hyper-param-tuning",
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False)

optimizer = HyperParameterOptimizer(
# specifying the task to be optimized, task must be in system already so it can be cloned
base_task_id=task.id,
# setting the hyper-parameters to optimize
hyper_parameters=[
ParameterSet([{"General/data_module": "", "General/model": "*", "Ge...