I want to use AutoML Library Autogluon with Paperspace IPU/Kaggle TPU instance for specification reasons (big RAM, big space, and fast training time). For IPU, when I try to fit the Autogluon predictor class, the library only recognizes the available IPU but not using it. How to make the Autogluon use the IPU? For TPU, I have not yet tried it because somewhat I could not import the Autogluon library. Last for GPU, Currently, from what I tried, Autogluon could use the available GPU but I don't want to use it because of performance reasons.
Predictor fit output with IPU instance example:
predictor.fit(
train_data=train_data,
hyperparameters={
'model.hf_text.checkpoint_name': 'xlm-roberta-base'
}
)
Output:
Global seed set to 123
/usr/local/lib/python3.8/dist-packages/autogluon/multimodal/utils/environment.py:96: UserWarning: Only CPU is detected in the instance. This may result in slow speed for MultiModalPredictor. Consider using an instance with GPU support.
warnings.warn(
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: True, using: 0 IPUs
HPU available: False, using: 0 HPUs
/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py:1777: UserWarning: IPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='ipu', devices=4)`.
rank_zero_warn(
| Name | Type | Params
-------------------------------------------------------------------
0 | model | HFAutoModelForTextPrediction | 278 M
1 | validation_metric | Accuracy | 0
2 | loss_func | CrossEntropyLoss | 0
-------------------------------------------------------------------
278 M Trainable params
0 Non-trainable params
278 M Total params
1,112.190 Total estimated model params size (MB)
Importing Autogluon library with TPU instance:
import os
import numpy as np
import warnings
import pandas as pd
from IPython.display import display, Image
import json
# Auto Exploratory Data Analysis
from pandas_profiling import ProfileReport
# AutoML
from autogluon.core.utils.loaders import load_zip
from autogluon.multimodal import MultiModalPredictor
from autogluon.multimodal.data.infer_types import infer_column_types
from autogluon.tabular import TabularPredictor
from autogluon.features.generators import AutoMLPipelineFeatureGenerator
from autogluon.tabular import FeatureMetadata
pd.set_option('display.max_columns', None)
np.random.seed(123)
Output:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /opt/conda/lib/python3.7/site-packages/transformers/utils/import_utils.py:1063 in _get_module │
│ │
│ 1060 │ │
│ 1061 │ def _get_module(self, module_name: str): │
│ 1062 │ │ try: │
│ ❱ 1063 │ │ │ return importlib.import_module("." + module_name, self.__name__) │
│ 1064 │ │ except Exception as e: │
│ 1065 │ │ │ raise RuntimeError( │
│ 1066 │ │ │ │ f"Failed to import {self.__name__}.{module_name} because of the followin │
│ │
│ /opt/conda/lib/python3.7/importlib/__init__.py:127 in import_module │
│ │
│ 124 │ │ │ if character != '.': │
│ 125 │ │ │ │ break │
│ 126 │ │ │ level += 1 │
│ ❱ 127 │ return _bootstrap._gcd_import(name[level:], package, level) │
│ 128 │
│ 129 │
│ 130 _RELOADING = {} │
│ <frozen importlib._bootstrap>:1006 in _gcd_import │
│ <frozen importlib._bootstrap>:983 in _find_and_load │
│ <frozen importlib._bootstrap>:967 in _find_and_load_unlocked │
│ <frozen importlib._bootstrap>:677 in _load_unlocked │
│ <frozen importlib._bootstrap_external>:728 in exec_module │
│ <frozen importlib._bootstrap>:219 in _call_with_frames_removed │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/modeling_tf_utils.py:39 in <module> │
│ │
│ 36 from tensorflow.python.keras.saving import hdf5_format │
│ 37 │
│ 38 from huggingface_hub import Repository, list_repo_files │
│ ❱ 39 from keras.saving.hdf5_format import save_attributes_to_hdf5_group │
│ 40 from transformers.utils.hub import convert_file_size_to_int, get_checkpoint_shard_files │
│ 41 │
│ 42 from . import DataCollatorWithPadding, DefaultDataCollator │
│ │
│ /opt/conda/lib/python3.7/site-packages/keras/__init__.py:21 in <module> │
│ │
│ 18 [keras.io](https://keras.io). │
│ 19 """ │
│ 20 from keras import distribute │
│ ❱ 21 from keras import models │
│ 22 from keras.engine.input_layer import Input │
│ 23 from keras.engine.sequential import Sequential │
│ 24 from keras.engine.training import Model │
│ │
│ /opt/conda/lib/python3.7/site-packages/keras/models/__init__.py:18 in <module> │
│ │
│ 15 """Keras models API.""" │
│ 16 │
│ 17 │
│ ❱ 18 from keras.engine.functional import Functional │
│ 19 from keras.engine.sequential import Sequential │
│ 20 from keras.engine.training import Model │
│ 21 │
│ │
│ /opt/conda/lib/python3.7/site-packages/keras/engine/functional.py:26 in <module> │
│ │
│ 23 │
│ 24 import tensorflow.compat.v2 as tf │
│ 25 │
│ ❱ 26 from keras import backend │
│ 27 from keras.dtensor import layout_map as layout_map_lib │
│ 28 from keras.engine import base_layer │
│ 29 from keras.engine import base_layer_utils │
│ │
│ /opt/conda/lib/python3.7/site-packages/keras/backend.py:32 in <module> │
│ │
│ 29 import numpy as np │
│ 30 import tensorflow.compat.v2 as tf │
│ 31 │
│ ❱ 32 from keras import backend_config │
│ 33 from keras.distribute import distribute_coordinator_utils as dc │
│ 34 from keras.engine import keras_tensor │
│ 35 from keras.utils import control_flow_util │
│ │
│ /opt/conda/lib/python3.7/site-packages/keras/backend_config.py:33 in <module> │
│ │
│ 30 │
│ 31 │
│ 32 @keras_export("keras.backend.epsilon") │
│ ❱ 33 @tf.__internal__.dispatch.add_dispatch_support │
│ 34 def epsilon(): │
│ 35 │ """Returns the value of the fuzz factor used in numeric expressions. │
│ 36 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
AttributeError: module 'tensorflow.compat.v2.__internal__' has no attribute 'dispatch'
The above exception was the direct cause of the following exception:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /tmp/ipykernel_248/1088848273.py:13 in <module> │
│ │
│ [Errno 2] No such file or directory: '/tmp/ipykernel_248/1088848273.py' │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/__init__.py:6 in <module> │
│ │
│ 3 except ImportError: │
│ 4 │ pass │
│ 5 │
│ ❱ 6 from . import constants, data, models, optimization, predictor, utils │
│ 7 from .predictor import AutoMMPredictor, MultiModalPredictor │
│ 8 from .utils import download │
│ 9 │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/optimization/__init__.py:1 in │
│ <module> │
│ │
│ ❱ 1 from . import lit_module, utils │
│ 2 │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/optimization/lit_module.py:14 in │
│ <module> │
│ │
│ 11 │
│ 12 from ..constants import AUTOMM, LM_TARGET, LOGITS, T_FEW, TEMPLATE_LOGITS, WEIGHT │
│ 13 from ..data.mixup import MixupModule, multimodel_mixup │
│ ❱ 14 from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_l │
│ 15 │
│ 16 logger = logging.getLogger(AUTOMM) │
│ 17 │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/optimization/utils.py:57 in <module> │
│ │
│ 54 │ ROOT_MEAN_SQUARED_ERROR, │
│ 55 │ SPEARMANR, │
│ 56 ) │
│ ❱ 57 from ..utils import MeanAveragePrecision │
│ 58 from .losses import MultiNegativesSoftmaxLoss, SoftTargetCrossEntropy │
│ 59 from .lr_scheduler import ( │
│ 60 │ get_cosine_schedule_with_warmup, │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/utils/__init__.py:39 in <module> │
│ │
│ 36 from .log import LogFilter, apply_log_filter, make_exp_dir │
│ 37 from .map import MeanAveragePrecision │
│ 38 from .matcher import compute_semantic_similarity, convert_data_for_ranking, create_siame │
│ ❱ 39 from .metric import compute_ranking_score, compute_score, get_minmax_mode, infer_metrics │
│ 40 from .misc import logits_to_prob, shopee_dataset, tensor_to_ndarray │
│ 41 from .mmcv import CollateMMCV, send_datacontainers_to_device, unpack_datacontainers │
│ 42 from .model import create_fusion_model, create_model, list_timm_models, modify_duplicate │
│ │
│ /opt/conda/lib/python3.7/site-packages/autogluon/multimodal/utils/metric.py:7 in <module> │
│ │
│ 4 import warnings │
│ 5 from typing import Dict, List, Optional, Tuple, Union │
│ 6 │
│ ❱ 7 import evaluate │
│ 8 import numpy as np │
│ 9 from sklearn.metrics import f1_score │
│ 10 │
│ │
│ /opt/conda/lib/python3.7/site-packages/evaluate/__init__.py:29 in <module> │
│ │
│ 26 │
│ 27 del version │
│ 28 │
│ ❱ 29 from .evaluator import ( │
│ 30 │ Evaluator, │
│ 31 │ ImageClassificationEvaluator, │
│ 32 │ QuestionAnsweringEvaluator, │
│ │
│ /opt/conda/lib/python3.7/site-packages/evaluate/evaluator/__init__.py:29 in <module> │
│ │
│ 26 │
│ 27 from .base import Evaluator │
│ 28 from .image_classification import ImageClassificationEvaluator │
│ ❱ 29 from .question_answering import QuestionAnsweringEvaluator │
│ 30 from .text_classification import TextClassificationEvaluator │
│ 31 from .token_classification import TokenClassificationEvaluator │
│ 32 │
│ │
│ /opt/conda/lib/python3.7/site-packages/evaluate/evaluator/question_answering.py:22 in <module> │
│ │
│ 19 │
│ 20 │
│ 21 try: │
│ ❱ 22 │ from transformers import Pipeline, PreTrainedModel, PreTrainedTokenizer, TFPreTraine │
│ 23 │ │
│ 24 │ TRANSFORMERS_AVAILABLE = True │
│ 25 except ImportError: │
│ <frozen importlib._bootstrap>:1032 in _handle_fromlist │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/utils/import_utils.py:1053 in __getattr__ │
│ │
│ 1050 │ │ if name in self._modules: │
│ 1051 │ │ │ value = self._get_module(name) │
│ 1052 │ │ elif name in self._class_to_module.keys(): │
│ ❱ 1053 │ │ │ module = self._get_module(self._class_to_module[name]) │
│ 1054 │ │ │ value = getattr(module, name) │
│ 1055 │ │ else: │
│ 1056 │ │ │ raise AttributeError(f"module {self.__name__} has no attribute {name}") │
│ │
│ /opt/conda/lib/python3.7/site-packages/transformers/utils/import_utils.py:1068 in _get_module │
│ │
│ 1065 │ │ │ raise RuntimeError( │
│ 1066 │ │ │ │ f"Failed to import {self.__name__}.{module_name} because of the followin │
│ 1067 │ │ │ │ f" traceback):\n{e}" │
│ ❱ 1068 │ │ │ ) from e │
│ 1069 │ │
│ 1070 │ def __reduce__(self): │
│ 1071 │ │ return (self.__class__, (self._name, self.__file__, self._import_structure)) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Failed to import transformers.modeling_tf_utils because of the following error (look up to see its
traceback):
module 'tensorflow.compat.v2.__internal__' has no attribute 'dispatch'
