Hi @<1529633475710160896:profile|ThickChicken87> , I would suggest opening developer tools (F12) and observing what api calls go out when going over the experiment object. This way you can replicate the api calls to pull all the relevant data. I'd suggest reading more here - None

We use task.export_task() and a hacked version to get console log:

def save_console_log(task: clearml.Task, fs, remote_path, number_of_reports=10000):
    from clearml.backend_api.services import events
    from clearml.backend_api import Session

    # Stollen from Task.get_reported_console_output()
    if Session.check_min_api_version('2.9'):
        request = events.GetTaskLogRequest(
            task=task.id,
            order='asc',
            navigate_earlier=True,
            batch_size=number_of_reports)
    else:
        request = events.GetTaskLogRequest(
            task=task.id,
            order='asc',
            from_='tail',
            batch_size=number_of_reports)

    res = task.send(request)
    response = res.wait()
    if not response.ok() or not response.response_data.get('events'):
        print("Warning: No Clearml Console log retrieved")
        return

    logs = response.response_data['events']
    json_data = json.dumps(logs, indent=2)
    print(f"Saving ClearML task console logs to {remote_path}")
    with fs.open(remote_path, "w") as json_file:
        json_file.write(json_data)

Great suggestions, thanks guys

not sure how for debug sample and scalars ....
But theorically, with the above, one should be able to fully reproduce a run