我正在尝试为计算机视觉模型建立一个SM管道。数据是存储在S3存储桶中的图像。我使用ScriptProcessor完成了预处理，现在正在尝试构建估算器。预处理工作正常。但是估计器部分给了我TypeError：Join类型的对象不是JSON可序列化的：错误

from sagemaker.tensorflow import TensorFlow

output_config = preprocessing_job_description["ProcessingOutputConfig"]
for output in output_config["Outputs"]:
    if output["OutputName"] == "train_data":
        preprocessed_training_data = output["S3Output"]["S3Uri"]
    if output["OutputName"] == "valid_data":
        preprocessed_test_data = output["S3Output"]["S3Uri"]

s3_train = "s3://bucketname/image_data/train/"
s3_val = "s3://bucketname/image_data/val/"

tf_estimator = TensorFlow(entry_point="train.py",
                          sagemaker_session=sess,
                          role=role,
                          instance_count=1, 
                          instance_type="ml.m5.xlarge",
                          # output_path = "/opt/ml/processing/output",
                          model_dir="s3://bucketname/image_data/output",
                          py_version='py37',
                          framework_version='2.4', 
                          hyperparameters={'epochs': epochs,
                                           'learning_rate': learning_rate, 
                                           'train_batch_size': 64,
                                          },
                          metric_definitions=metrics_definitions,
                          script_mode=True,
                          max_run=7200 # max 2 hours * 60 minutes seconds per hour * 60 sec per minutes
                         )

tf_estimator.fit({"train": preprocessed_training_data})

这给了我以下错误：
TypeError Traceback（most recent call last）in 36）37---> 38 tf_estimator. fit（{"train "：preprocessed_training_data}）39 #tf_estimator. fit（{"train "：s3_train}）
/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline_context. py in wrapper（* args，*kwargs）207 return self_instance. sagemaker_session. context 208--> 209 return run_func（ args，**kwargs）210 211 return wrapper
/opt/conda/lib/python3.7/site-packages/sagemaker/www.example.com in fit（self，inputs，wait，logs，job_name，experiment_config）976 self._prepare_for_training（job_name = job_name）977--> 978 self. latest_training_job =_TrainingJob. start_new（self，inputs，experiment_config）979 self. jobs. append（self. latest_training_job）980 if wait：estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config) 976 self._prepare_for_training(job_name=job_name) 977 --> 978 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config) 979 self.jobs.append(self.latest_training_job) 980 if wait:
/opt/conda/lib/python3.7/site-packages/sagemaker/www.example.com in start_new（cls，estimator，inputs，experiment_config）1806 estimator.py in start_new(cls, estimator, inputs, experiment_config) 1806
train_args = cls._get_train_args（estimator，inputs，experiment_config）1807-> 1808 estimator. sagemaker_session. train（**train_args）1809 1810返回cls（estimator. sagemaker_session，estimator._current_job_name）
/opt/conda/lib/python3.7/site-packages/sagemaker/www.example.com in trainsession.pydebug_hook_config，tensorboard_output_config，enable_sagemaker_metrics，profiler_rule_configs，profiler_config，environment，retry_strategy）592 encrypt_inter_container_traffic = encrypt_inter_container_traffic，593 use_spot_instances = use_spot_instances，--> 594 checkpoint_s3_uri = checkpoint_s3_uri，595 checkpoint_local_path = checkpoint_local_path，596 experiment_config = experiment_config， debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics, profiler_rule_configs, profiler_config, environment, retry_strategy) 592 encrypt_inter_container_traffic=encrypt_inter_container_traffic, 593 use_spot_instances=use_spot_instances, --> 594 checkpoint_s3_uri=checkpoint_s3_uri, 595 checkpoint_local_path=checkpoint_local_path, 596 experiment_config=experiment_config,
地址：深圳市宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区宝安区session.py in _intercept_create_request(self, request, create, func_name) 4201 """ 4202 region = self.boto_session.region_name -> 4203 sts_client = self.boto_session.client( 4204 "sts", region_name=region, endpoint_url=sts_regional_endpoint(region) 4205 )
/opt/conda/lib/python3.7/site-packages/sagemaker/www.example.com in submit（request）589 enable_network_isolation = enable_network_isolation，590 image_uri = image_uri，--> 591 algorithm_arn = algorithm_arn，592 encrypt_inter_container_traffic = encrypt_inter_container_traffic，593 use_spot_instances = use_spot_instances，session.py in submit(request) 589 enable_network_isolation=enable_network_isolation, 590 image_uri=image_uri, --> 591 algorithm_arn=algorithm_arn, 592 encrypt_inter_container_traffic=encrypt_inter_container_traffic, 593 use_spot_instances=use_spot_instances,
/opt conda/lib/python3.7/json/init. py in dumps（obj，skipkeys，确保_ascii，check_circular，allow_nan，cls，indent，separators，default，sort_keys，kw）236 check_circular = check_circular，allow_nan = allow_nan，indent = indent，237 separators = separators，default = default，sort_keys = sort_keys，--> 238kw）. encode（obj）239 240
/opt/conda/lib/python3.7/json/www.example.com in encode（self，o）199 chunks = self. iterencode（o，_one_shot = True）200 if not isinstance（chunks，（list，tuple））：encoder.py in encode(self, o) 199 chunks = self.iterencode(o, _one_shot=True) 200 if not isinstance(chunks, (list, tuple)): --> 201 chunks = list(chunks) 202 return ''.join(chunks) 203
/opt/conda/lib/python3.7/json/www.example.com in_iterencode（o，_current_indent_level）429 yield from_iterencode_list（o，_current_indent_level）430 elif isinstance（o，dict）：encoder.py433如果标记不是None： --> 431 yield from _iterencode_dict(o, _current_indent_level) 432 else: 433 if markers is not None:
/opt/conda/lib/python3.7/json/www.example.com in_iterencode_dict（dct，_current_indent_level）403 else：encoder.py407_current_indent_level-= 1 404 chunks = _iterencode(value, _current_indent_level) --> 405 yield from chunks 406 if newline_indent is not None: 407 _current_indent_level -= 1
/opt/conda/lib/python3.7/json/www.example.com in_iterencode_dict（dct，_current_indent_level）403 else：encoder.py407_current_indent_level-= 1 404 chunks = _iterencode(value, _current_indent_level) --> 405 yield from chunks 406 if newline_indent is not None: 407 _current_indent_level -= 1
/opt/conda/lib/python3.7/json/www.example.com in_iterencode（o，_current_indent_level）436 raise ValueError（“检测到循环引用”）437 marks [markerid]= o--> 438 〇 =_default（o）439 yield from_iterencode（o，_current_indent_level）440如果marks不为None：encoder.py in _iterencode(o, _current_indent_level) 436 raise ValueError("Circular reference detected") 437 markers[markerid] = o --> 438 o = _default(o) 439 yield from _iterencode(o, _current_indent_level) 440 if markers is not None:
/opt/conda/lib/python3.7/json/www.example.com in default（self，o）177 178 """--> 179 raise TypeError（f'Object of type {o.class.name}'180 f'is not JSON serializable'）181 encoder.py in default(self, o) 177 178 """ --> 179 raise TypeError(f'Object of type {o.class.name} ' 180 f'is not JSON serializable') 181
TypeError：Join类型的对象不是JSON可序列化的
我试着改变我为估计量给出的所有参数。有时启用，有时禁用。- -> 594 checkpoint_s3_uri = checkpoint_s3_uri，如果这是原点，我也试过给出它。
不知道我哪里搞砸了。我在用

sagemaker 2.94.0
Python3 Data Science kernel
boto3 '1.24.8'