right, in which case you want to dynamically change with your code, not with the config file. This is where the Logger.set_default_output_upload come in

If you care about the local destination then you may want to use this 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,
         ...

Are you talking about this: None
It seems to not doing anything aboout the database data ...

some clearml cache folder

oh, looks like I need to empty the Installed Package before enqueue the cloned task

what is the difference between vscode via clearml-session and vscode via remote ssh extension ?

For #2: it's a pull rather than a push system: you need to have a script that do pulling at regular interval and need to keep track what new and what not?

@<1523701868901961728:profile|ReassuredTiger98> I found that you an set the file_server in your local clearml.conf to your own cloud storage. In our case, we use something like this in our clearml.conf:

api {
   file_server: "azure://<account>..../container"
}

All non artifact model are then store in our azure storage. In our self-hosted clearml setup, we don't even have a file server running alltogether

You are using CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL the wrong way

Done: None

Artifact can be anything, that you can use clearml SDK to upload to storage. Which storage is used is defined by your clearml.conf (with its credentials) ClearML web and api server do not store those files

Model is a special artifact: None
Example you have the lineage feature where if you train model B using model A as starting point (aka pre-trained) , and model C from model B, ... The lineage will track modelC was built on...

you should be able to use as many agent as you want.
On the same or different queue

Found a trick to have empty Installed package:
clearml.Task.force_requirements_env_freeze(force=True,requirements_file="/dev/null")
Not sure if this is the right way or not ...

you may want to share your config (with credential redacted) and the full docker compose start up log ?

@<1523701070390366208:profile|CostlyOstrich36>

with

    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'])

    import clearml
    task = clearml.Task.current_task()
    task.get_logger().report_table(title='table example', series='pandas DataFrame', iteration=0, table_plot=df)
    # logger.report_table(title='table example',series='pandas DataFrame',iteration=0,tabl...

I don't have it so I don't know how things are setup and how to pass on credentials in this case

About the caching: how does it work ? ClearML maintain it own cache and monitor if any of you code changes? Even code that get change inside an import ?

ok, so if git commit or uncommit changes differ from previous run, then the cache is "invalidated" and the step will be run again ?

in my case, I set eveything inside the container, including the agent and not using docker mode altogether.
When my container start, it start the agent inside it in "normal" mode

are you using the agent docker mode ?

you should be able to test your credential first using something like rclone or azure-cli

I am more curious about how to migrate all the information stored in the local clearml server to the clearml server in the cloud

i need to do a git clone

You need to do it to test if it works. Clearml-agent will run it itself when it take in a task

do you mean having the ClearML FileServer store on azure blob instead of on the local drive?

Yes, that is what I wanted.

If so, that's not possible. You can however point the fileserver data folder to some mounted folder - if you have something that can create a mount from a filesystem folder to azure blob, it will work (the file server will always treat it as a local file system)

Thanks for confirming that it's the only solution. 👍

Nevermind: None

By default, the File Server is not secured even if Web Login Authentication has been configured. Using an object storage solution that has built-in security is recommended.

My bad

Found the issue: my bad practice for import 😛
You need to import clearml before doing argument parser. Bad way:

import argparse

def handleArgs():

    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--config-file', type=str, default='train_config.yaml',
                        help='train config file')   
    parser.add_argument('--device', type=int, default=0,
                        help='cuda device index to run the training')

    args = parser....

had you made sure that the agent inside GCP VM have access to your repository ? Can you ssh into that VM and try to do a git clone ?

@<1558986867771183104:profile|ShakyKangaroo32> If you just want something to run in regular period, have you consider TaskScheduler: None