autogluon | AutoGluon : AutoML for Image , Text , and Tabular Data | Machine Learning library

def get_args():
    parser = argparse.ArgumentParser("OneShot_cifar_Experiments_Configuration")
    parser.add_argument('--signal', type=str, default='different_hpo', help='describe:glboal_hpo/')
    parser.add_argument('--different-hpo', action='sto  

cd src/Supernet_cifar
python3 train.py --num-classes 10 --signal different_hpo --different-hpo --num-trials 16 --total-iters 7800 --batch-size 64 --block 4 --lr-range "0.01,0.2" --wd-range "4e-5,5e-3"

python3 train.py --num-classes 10 --signal glboa  

  library("mlr3")
  # Instantiate Learner
  lrn = LearnerClassifKerasFF$new()

  # Set Learner Hyperparams
  lrn$param_set$values$epochs = 50
  lrn$param_set$values$layer_units = 12

  # Train and Predict
  lrn$train(tsk("iris"))
  lrn$predict(tsk("i  

# Disclaimer! The script here is partially based on
# https://github.com/nyu-mll/jiant/blob/master/scripts/download_glue_data.py
# and
# https://github.com/nyu-mll/jiant/blob/master/scripts/download_superglue_data.py
import os
import shutil
import ar  

import abc
import os
import pandas as pd
from autogluon.multimodal.constants import (
    BINARY,
    MULTICLASS,
    REGRESSION,
    ACC,
    RMSE,
    CATEGORICAL,
    NUMERICAL,
)
from autogluon.multimodal.utils import download


# TODO: release t  

import argparse
from autogluon.multimodal import MultiModalPredictor
from datasets import load_dataset

from time import time
import os
import pandas as pd

PAWS_TASKS = ["en", "de", "es", "fr", "ja", "ko", "zh"]


def tasks_to_id(pawsx_tasks):
    i