Como criar e implantar soluções de machine learning na Vertex AI: laboratório com desafio avaliações

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INFO:google.cloud.aiplatform.pipeline_jobs:Creating PipelineJob --------------------------------------------------------------------------- _InactiveRpcError Traceback (most recent call last) File ~/.local/lib/python3.10/site-packages/google/api_core/grpc_helpers.py:72, in _wrap_unary_errors.<locals>.error_remapped_callable(*args, **kwargs) 71 try: ---> 72 return callable_(*args, **kwargs) 73 except grpc.RpcError as exc: File /opt/conda/lib/python3.10/site-packages/grpc/_channel.py:1181, in _UnaryUnaryMultiCallable.__call__(self, request, timeout, metadata, credentials, wait_for_ready, compression) 1175 ( 1176 state, 1177 call, 1178 ) = self._blocking( 1179 request, timeout, metadata, credentials, wait_for_ready, compression 1180 ) -> 1181 return _end_unary_response_blocking(state, call, False, None) File /opt/conda/lib/python3.10/site-packages/grpc/_channel.py:1006, in _end_unary_response_blocking(state, call, with_call, deadline) 1005 else: -> 1006 raise _InactiveRpcError(state) _InactiveRpcError: <_InactiveRpcError of RPC that terminated with: status = StatusCode.ALREADY_EXISTS details = "Pipeline job with the specified ID already exists." debug_error_string = "UNKNOWN:Error received from peer ipv4:142.250.31.95:443 {grpc_message:"Pipeline job with the specified ID already exists.", grpc_status:6, created_time:"2024-11-21T13:23:10.574384397+00:00"}" > The above exception was the direct cause of the following exception: AlreadyExists Traceback (most recent call last) Cell In[51], line 1 ----> 1 vertex_pipelines_job.run() File /opt/conda/lib/python3.10/site-packages/google/cloud/aiplatform/pipeline_jobs.py:331, in PipelineJob.run(self, service_account, network, reserved_ip_ranges, sync, create_request_timeout) 309 """Run this configured PipelineJob and monitor the job until completion. 310 311 Args: (...) 327 Optional. The timeout for the create request in seconds. 328 """ 329 network = network or initializer.global_config.network --> 331 self._run( 332 service_account=service_account, 333 network=network, 334 reserved_ip_ranges=reserved_ip_ranges, 335 sync=sync, 336 create_request_timeout=create_request_timeout, 337 ) File /opt/conda/lib/python3.10/site-packages/google/cloud/aiplatform/base.py:863, in optional_sync.<locals>.optional_run_in_thread.<locals>.wrapper(*args, **kwargs) 861 if self: 862 VertexAiResourceNounWithFutureManager.wait(self) --> 863 return method(*args, **kwargs) 865 # callbacks to call within the Future (in same Thread) 866 internal_callbacks = [] File /opt/conda/lib/python3.10/site-packages/google/cloud/aiplatform/pipeline_jobs.py:367, in PipelineJob._run(self, service_account, network, reserved_ip_ranges, sync, create_request_timeout) 339 @base.optional_sync() 340 def _run( 341 self, (...) 346 create_request_timeout: Optional[float] = None, 347 ) -> None: 348 """Helper method to ensure network synchronization and to run 349 the configured PipelineJob and monitor the job until completion. 350 (...) 365 Optional. The timeout for the create request in seconds. 366 """ --> 367 self.submit( 368 service_account=service_account, 369 network=network, 370 reserved_ip_ranges=reserved_ip_ranges, 371 create_request_timeout=create_request_timeout, 372 ) 374 self._block_until_complete() 376 # AutoSxS view model evaluations

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Fixed now

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