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Answered
Hi Guys, I Am Having Some Trouble Running Some Training Scripts With The Agent Functionality:

Hi guys, I am having some trouble running some training scripts with the agent functionality:
https://stackoverflow.com/questions/73279794/catboostclearml-error-if-loss-function-is-logloss-then-class-weights-shoul
Does anyone know a solution or is able to help understand what is happening?

  
  
Posted one year ago
Votes Newest

Answers 16


UnsightlyHorse88 & ShallowGoldfish8 , can you please provide a code snippet to play with?

  
  
Posted one year ago

We also disabled the auto_connect_framework for catboost, but still the same thing

  
  
Posted one year ago

Hi ShallowGoldfish8 , what versions of ClearML & ClearML-Agent are you using?

  
  
Posted one year ago

CLEARML-AGENT version 1.3.0 CL-server 1.6.0 clearml==1.6.2

  
  
Posted one year ago

UnsightlyHorse88 , do you know?

  
  
Posted one year ago

` from importlib.machinery import EXTENSION_SUFFIXES
import catboost
from clearml import Task, Logger, Dataset

import lightgbm as lgb
import numpy as np
import pandas as pd
import dask.dataframe as dd
import matplotlib.pyplot as plt

MODELS = {
'catboost': {
'model_class': catboost.CatBoostClassifier,
'file_extension': 'cbm'
},
'lgbm': {
'model_class': lgb.LGBMClassifier,
'file_extension': 'txt'
}
}

class ModelTrainer():
def init(self):
pass

@staticmethod
def train(X_train, y_train, X_val, y_val, model, fit_params):
    model.fit(
        X_train, y_train,
        eval_set=(X_val, y_val),
        **fit_params
    )
    return model

def load_dataset(
    self,
    dataset_name,
    features,
    id_cols=None,
    target='target',
    dataset_project=''
):
    dataset_path = Dataset.get(
        dataset_name=dataset_name, dataset_project=dataset_project
    ).get_local_copy()

    dataset = dd.read_parquet(dataset_path).compute()
    if id_cols:
        return dataset[features], dataset[target], dataset[id_cols]
    else:
        return dataset[features], dataset[target]

def run(
    self, exp_params,
    model_params, fit_params, reference_task_params, dataset_params
):
    model_name = exp_params['model_name']
    exp_identifier = exp_params['identifier']
    print(f"starting session - {exp_identifier}")

    # initialize ClearML task
    task = Task.init(
        project_name="RecSys",
        task_name=f"model_training - {exp_identifier}",
        output_uri=True
    )
    task.add_tags(['template'])
    task.connect(fit_params, 'fit_params')
    task.connect(model_params, 'model_params')
    task.connect(exp_params, 'exp_params')
    task.connect(reference_task_params, 'reference_task_params')
    task.connect(dataset_params, 'dataset_params')

    self.model = MODELS[model_name]['model_class'](**model_params)

    reference_task = Task.get_task(
        project_name='RecSys',
        task_name=reference_task_params['name']
    )

    columns = reference_task.artifacts[
        reference_task_params['features_articafact_name']
    ].get()

    features = [c for c in columns if c not in ID_COLS + [TARGET]]
    task.upload_artifact('features', features)
    task.upload_artifact('ID_COLS', ID_COLS)
    task.upload_artifact('target', TARGET)

    print('number of features to load: ', len(features), '\n features: ')
    print(features)

    print("loading train data")
    # load train data
    X_train, y_train, id_cols_train = self.load_dataset(
        dataset_name=dataset_params['train_name'],
        features=features,
        id_cols=ID_COLS,
        dataset_project=dataset_params['project_name'],
    )

    print("loading validation data")
    # load validation data
    X_val, y_val, id_cols_val = self.load_dataset(
        dataset_name=dataset_params['validation_name'],
        features=features,
        id_cols=ID_COLS,
        dataset_project=dataset_params['project_name'],
    )

    logger = task.get_logger()

    print('training model')
    # train model
    model = self.model
    model.fit(
        X_train, y_train,
        eval_set=(X_val, y_val),
        **fit_params
    )

    print('evaluating model')
    # evaluate model with train data
    self.evaluation_metrics(
        y_train,
        np.array([p[1] for p in model.predict_proba(X_train)]),
        "train", logger, ids_idx=id_cols_train
    )


    task.close()

if name == 'main':
rfs = ModelTrainer()
model_params = {
"loss_function": "Logloss",
"eval_metric": "AUC",
"class_weights": {0: 1, 1: 60},
"learning_rate": 0.1
}
fit_params = {
"early_stopping_rounds": 20,
"plot": True
}
reference_task_params = {
'name': 'upload_features',
'features_articafact_name': 'features_list'
}
dataset_params = {
'train_name': 'classifier train',
'validation_name': '_classifier validation',
'test_name': 'classifier test',
'project_name': '',
}
experiment_params = {
'model_name': 'catboost',
'identifier': 'catboost_remote_v0'
}
rfs.run(
experiment_params,
model_params, fit_params,
reference_task_params, dataset_params
) `

  
  
Posted one year ago

Simplified a little bit and removed private parameters, but thats pretty much the code. We did not try with toy examples, since that was already done with the example pipelines when we implemented and the model training itself is quite simple basic there already (only few hyperparameters set)

  
  
Posted one year ago

That would make sense, although clearml, at least on UI, shows the deeper level of the nested dict as a int, as one would expect

  
  
Posted one year ago

oooohhh.. you mean the key of the nested dict, that would make a lot of sense

  
  
Posted one year ago

I will try the suggested edit here

  
  
Posted one year ago

When we enqueue the task using the web-ui we have the above error

ShallowGoldfish8 I think I understand the issue,
basically I think the issue is:
task.connect(model_params, 'model_params')Since this is a nested dict:
model_params = { "loss_function": "Logloss", "eval_metric": "AUC", "class_weights": {0: 1, 1: 60}, "learning_rate": 0.1 }The class_weights is stored as a String key, but catboost expects "int" key, hence it fails.
One option would be to remove the task.connect(model_params, 'model_params'')
Another hack (until we fix it) would be to do:
task.connect(model_params, 'model_params') model_params["class_weights"] = { 0: model_params["class_weights"].get("0", model_params["class_weights"].get(0)) 1: model_params["class_weights"].get("1", model_params["class_weights"].get(1)) }wdyt?

  
  
Posted one year ago

Martin, if you want, feel free to add your answer in the stackoverflow so that I can mark it as a solution.

Will do 🙂 give me 5

  
  
Posted one year ago

Martin, if you want, feel free to add your answer in the stackoverflow so that I can mark it as a solution.

  
  
Posted one year ago

Done 🙂

  
  
Posted one year ago

It worked!

  
  
Posted one year ago

When we use clearml-session to create a debug session and run the code from jupyter lab( inside the container) the training script run just fine.

When we enqueue the task using the web-ui we have the above error

  
  
Posted one year ago