Adding Custom ClearML Logger Callbacks option through config.yaml file

What is the best way to add a custom logger to the config.yaml file to be consumed by the for the rllib train file config.yaml command

I am using a custom logger called ClearMLLogger

So, assuming I start with an example in the tune_examples folder, such as cartpole-ppo.yaml and I want to use my defined ClearMLLogger class from the config file

Example Loggers available look like there are already integrations for them

Can you provide the following information?

  1. An example of how the callbacks section of my cartpole.yaml should look to utilize the ClearMLLogger class I defined.
  2. Another example for the current supported callbacks e.g. MLFlow also in the cartpole-ppo.yaml (Even with the available loggers, I dont get an idea how to set them up from the config.yaml file.)

Here is my clearml_logger file

from typing import Dict, List
import os
import json
from clearml import Task, Logger
from ray.tune.logger import LoggerCallback

class ClearMLLogger(LoggerCallback):
“”“Custom ClearML logger interface”“”

def __init__(self, project_name: str, task_name: str, auto_connect_frameworks: Dict):
    self._trial_tasks = {}
    self._project_name = project_name
    self._task_name = task_name
    self._auto_connect_frameworks = auto_connect_frameworks

def log_trial_start(self, trial: "Trial"):
    task = Task.init(
    self._trial_tasks[trial] = task

def log_trial_result(self, iteration: int, trial: "Trial", result: Dict):
    if trial in self._trial_tasks:
        task = self._trial_tasks[trial]
        logger = task.get_logger()
        for key, value in result.items():
            if isinstance(value, (int, float)):
                logger.report_scalar(title=key, series="result", value=value, iteration=iteration)

def on_trial_complete(self, iteration: int, trials: List["Trial"], trial: "Trial", **info):
    if trial in self._trial_tasks:
        task = self._trial_tasks[trial]
        del self._trial_tasks[trial]

Lets start with the cart pole-ppo.,yaml: ray/rllib/tuned_examples/ppo/cartpole-ppo.yaml at master · ray-project/ray · GitHub
Which works for me.

My updated version to use callbacks (used the 0s to specify the number of indents for each line)
0 env: CartPole-v1
0 run: PPO
0 stop:
00 sampler_results/episode_reward_mean: 150
00 timesteps_total: 100000
0 config:
# Works for both torch and tf2.
00 framework: torch
00 gamma: 0.99
00 lr: 0.0003
00 num_workers: 1
00 num_sgd_iter: 6
00 vf_loss_coeff: 0.01
00 model:
000 fcnet_hiddens: [32]
000 fcnet_activation: linear
000 vf_share_layers: true
00 callbacks: clearml_logger.ClearMLLogger
00 project_name: “ClearML Project Name”
00 task_name: “Clearml Task name”
00 auto_connect_frameworks:
000 tensorboard: true
000 tfdefines: false

Error Summary:
ValueError: Could not deserialize the given classpath module=clearml_logger.ClearMLLogger into a valid python class! Make sure you have all necessary pip packages installed and all custom
modules are in your PYTHONPATH env variable.

Also: Note that the rllib train —help shows that —config should be a json file, but that does not work really