我或多或少遵循这个示例，使用我自己的数据集将光线调谐超参数库与拥抱脸转换器库集成。
这是我的脚本：
import ray
from ray import tune
from ray.tune import CLIReporter
from ray.tune.examples.pbt_transformers.utils import download_data, \
build_compute_metrics_fn
from ray.tune.schedulers import PopulationBasedTraining
from transformers import glue_tasks_num_labels, AutoConfig, \
AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
def get_model():
# tokenizer = AutoTokenizer.from_pretrained(model_name, additional_special_tokens = ['[CHARACTER]'])
model = ElectraForSequenceClassification.from_pretrained('google/electra-small-discriminator', num_labels=2)
model.resize_token_embeddings(len(tokenizer))
return model
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
acc = accuracy_score(labels, preds)
return {
'accuracy': acc,
'f1': f1,
'precision': precision,
'recall': recall
}
training_args = TrainingArguments(
"electra_hp_tune",
report_to = "wandb",
learning_rate=2e-5, # config
do_train=True,
do_eval=True,
evaluation_strategy="epoch",
load_best_model_at_end=True,
num_train_epochs=2, # config
per_device_train_batch_size=16, # config
per_device_eval_batch_size=16, # config
warmup_steps=0,
weight_decay=0.1, # config
logging_dir="./logs",
)
trainer = Trainer(
model_init=get_model,
args=training_args,
train_dataset=chunked_encoded_dataset['train'],
eval_dataset=chunked_encoded_dataset['validation'],
compute_metrics=compute_metrics
)
tune_config = {
"per_device_train_batch_size": 32,
"per_device_eval_batch_size": 32,
"num_train_epochs": tune.choice([2, 3, 4, 5])
}
scheduler = PopulationBasedTraining(
time_attr="training_iteration",
metric="eval_acc",
mode="max",
perturbation_interval=1,
hyperparam_mutations={
"weight_decay": tune.uniform(0.0, 0.3),
"learning_rate": tune.uniform(1e-5, 2.5e-5),
"per_device_train_batch_size": [16, 32, 64],
})
reporter = CLIReporter(
parameter_columns={
"weight_decay": "w_decay",
"learning_rate": "lr",
"per_device_train_batch_size": "train_bs/gpu",
"num_train_epochs": "num_epochs"
},
metric_columns=[
"eval_f1", "eval_loss", "epoch", "training_iteration"
])
from ray.tune.integration.wandb import WandbLogger
trainer.hyperparameter_search(
hp_space=lambda _: tune_config,
backend="ray",
n_trials=10,
scheduler=scheduler,
keep_checkpoints_num=1,
checkpoint_score_attr="training_iteration",
progress_reporter=reporter,
name="tune_transformer_gr")
最后一次函数调用（对 trainer.hyperparameter_search）是引发错误的时间。错误信息是：
AttributeError：模块“pickle”没有属性“PickleBuffer”
这是完整的堆栈跟踪：
AttributeError Traceback（最近一次调用最后一次）
在 () 8 checkpoint_score_attr="training_iteration", 9 progress_reporter=reporter, ---> 10 name="tune_transformer_gr")
14帧
/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in hyperparameter_search(self, hp_space, compute_objective, n_trials, direction, backend, hp_name, **kwargs) 1666 1667
run_hp_search = run_hp_search_optuna if backend == HPSearchBackend.OPTUNA 否则 run_hp_search_ray -> 1668 best_run = run_hp_search(self, n_trials, direction, **kwargs) 1669 1670 self.hp_search_backend = None/usr/local/lib/python3.7/dist-packages/transformers/integrations.py in run_hp_search_ray(trainer, n_trials, direction, **kwargs) 231 232 analysis = ray.tune.run(--> 233 ray.tune .with_parameters(_objective, local_trainer=trainer), 234 config=trainer.hp_space(None), 235 num_samples=n_trials,
/usr/local/lib/python3.7/dist-packages/ray/tune/utils/trainable.py in with_parameters(trainable, **kwargs) 294 prefix = f"{str(trainable)}_" 295 for k, v in kwargs.items(): --> 296 parameter_registry.put(prefix + k, v) 297 298 trainable_name = getattr(trainable, " name ", "tune_with_parameters")
/usr/local/lib/python3.7/dist-packages/ray/tune/registry.py in put(self, k, v) 160 self.to_flush[k] = v 161 if ray.is_initialized(): -- > 162 self.flush() 163 164 def get(self, k):
/usr/local/lib/python3.7/dist-packages/ray/tune/registry.py in flush(self) 169 def flush(self): 170 for k, v in self.to_flush.items(): -- > 171 self.references[k] = ray.put(v) 172 self.to_flush.clear() 173
/usr/local/lib/python3.7/dist-packages/ray/_private/client_mode_hook.py in wrapper(*args, **kwargs) 45 if client_mode_should_convert(): 46 return getattr(ray, func.name ) (* args, **kwargs) ---> 47 return func(*args, **kwargs) 48 49 return wrapper
/usr/local/lib/python3.7/dist-packages/ray/worker.py in put(value)
1512 with profiling.profile("ray.put"): 1513 try: -> 1514 object_ref = worker.put_object(值）1515 除了 ObjectStoreFullError：1516 logger.info（/usr/local/lib/python3.7/dist-packages/ray/worker.py 在 put_object(self, value, object_ref) 259 "插入一个 ObjectRef") 260 --> 261 serialized_value = self.get_serialization_context()。 serialize(value) 262 # 这必须是我们构造这个 python 263 # ObjectRef 的第一个地方，因为在
/usr/local/lib/python3.7/dist-packages/ray/serialization.py in serialize(self, value) 322 return RawSerializedObject(value) 323 else: --> 324 return self._serialize_to_msgpack(value)
/usr/local/lib/python3.7/dist-packages/ray/serialization.py in _serialize_to_msgpack(self, value) 302 metadata = ray_constants.OBJECT_METADATA_TYPE_PYTHON 303 pickle5_serialized_object =
--> 304 self._serialize_to_pickle5(metadata, python_objects) 305 else : 306 pickle5_serialized_object = 无/usr/local/lib/python3.7/dist-packages/ray/serialization.py in _serialize_to_pickle5(self, metadata, value) 262 例外为 e: 263 self.get_and_clear_contained_object_refs() --> 264 最终引发 e 265： 266 self.set_out_of_band_serialization（）
/usr/local/lib/python3.7/dist-packages/ray/serialization.py in _serialize_to_pickle5(self, metadata, value) 259 self.set_in_band_serialization() 260 inband = pickle.dumps(--> 261 value, protocol= 5、buffer_callback=writer.buffer_callback) 262 Exception as e: 263 self.get_and_clear_contained_object_refs()
/usr/local/lib/python3.7/dist-packages/ray/cloudpickle/cloudpickle_fast.py 在转储（obj，protocol，buffer_callback）71 文件中，protocol=protocol，buffer_callback=buffer_callback 72）---> 73 cp。转储(obj) 74 返回文件.getvalue() 75
/usr/local/lib/python3.7/dist-packages/ray/cloudpickle/cloudpickle_fast.py in dump(self, obj) 578 def dump(self, obj): 579 try: --> 580 return Pickler.dump( self, obj) 581 除了 RuntimeError as e: 582 if "recursion" in e.args[0]:
/usr/local/lib/python3.7/dist-packages/pyarrow/io.pxi 在 pyarrow.lib.Buffer 中。reduce_ex ()
AttributeError：模块“pickle”没有属性“PickleBuffer”
我的环境设置：
- 我正在使用 Google Colab
- 平台：Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python版本：3.7.10
- 变形金刚版本：4.6.1
- 射线版本：1.3.0
我试过的：
- 更新泡菜
- 安装并导入pickle5作为pickle
- 确保我的直接目录中没有名为“pickle”的 python 文件
这个错误来自哪里，我该如何解决？