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TypeError: cannot pickle '_thread.RLock' object
I'm follow this tutorials: creating-a-production-ready-llm-powered-agent-with-zenml
I have changed
Here is full logs
/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/langchain/chat_models/__init__.py:31: LangChainDeprecationWarning: Importing chat models from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead:
`from langchain_community.chat_models import ChatOpenAI`.
To install langchain-community run `pip install -U langchain-community`.
warnings.warn(
/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/langchain/_api/module_import.py:120: LangChainDeprecationWarning: Importing CohereEmbeddings from langchain.embeddings is deprecated. Please replace deprecated imports:
>> from langchain.embeddings import CohereEmbeddings
with new imports of:
>> from langchain_community.embeddings import CohereEmbeddings
warn_deprecated(
/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/langchain/_api/module_import.py:120: LangChainDeprecationWarning: Importing FAISS from langchain.vectorstores is deprecated. Please replace deprecated imports:
>> from langchain.vectorstores import FAISS
with new imports of:
>> from langchain_community.vectorstores import FAISS
warn_deprecated(
/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/langchain/_api/module_import.py:120: LangChainDeprecationWarning: Importing UnstructuredURLLoader from langchain.document_loaders is deprecated. Please replace deprecated imports:
>> from langchain.document_loaders import UnstructuredURLLoader
with new imports of:
>> from langchain_community.document_loaders import UnstructuredURLLoader
warn_deprecated(
[1;35mRunning pipeline with config: agent_config.yaml[0m
[1;35mInitiating a new run for the pipeline: [0m[1;36mzenml_agent_creation_pipeline[1;35m.[0m
[1;35mReusing registered pipeline version: [0m[1;36m(version: 3)[1;35m.[0m
[1;35mNew model version [0m[1;36m14[1;35m was created.[0m
[1;35mModels can be viewed in the dashboard using ZenML Cloud. Sign up for a free trial at https://www.zenml.io/cloud/[0m
[1;35mExecuting a new run.[0m
[1;35mUsing user: [0m[1;36madmin[1;35m[0m
[1;35mUsing stack: [0m[1;36mdefault[1;35m[0m
[1;35m artifact_store: [0m[1;36mdefault[1;35m[0m
[1;35m orchestrator: [0m[1;36mdefault[1;35m[0m
[1;35mDashboard URL: http://localhost:8080/workspaces/default/pipelines/df01067d-49e1-43c7-83e6-640e0a722d05/runs/abad422f-532d-4e87-8298-51068fce3f26/dag[0m
[1;35mCaching [0m[1;36mdisabled[1;35m explicitly for [0m[1;36murl_scraper[1;35m.[0m
[1;35mStep [0m[1;36murl_scraper[1;35m has started.[0m
[1;35mStep [0m[1;36murl_scraper[1;35m has finished in [0m[1;36m0.784s[1;35m.[0m
[1;35mStep [0m[1;36mweb_url_loader[1;35m has started.[0m
[1;35mReading document from string ...[0m
[1;35mReading document ...[0m
[1;35mImplicitly linking artifact [0m[1;36moutput[1;35m to model [0m[1;36mzenml_agent[1;35m version [0m[1;36m14[1;35m.[0m
[1;35mStep [0m[1;36mweb_url_loader[1;35m has finished in [0m[1;36m10.697s[1;35m.[0m
[1;35mStep [0m[1;36mindex_generator[1;35m has started.[0m
/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `CohereEmbeddings` was deprecated in LangChain 0.0.30 and will be removed in 0.3.0. An updated version of the class exists in the langchain-cohere package and should be used instead. To use it run `pip install -U langchain-cohere` and import as `from langchain_cohere import CohereEmbeddings`.
warn_deprecated(
[1;35mLoading faiss with AVX2 support.[0m
[1;35mSuccessfully loaded faiss with AVX2 support.[0m
[33mNo materializer is registered for type [0m[1;36m<class 'langchain_community.vectorstores.faiss.FAISS'>[33m, so the default Pickle materializer was used. Pickle is not production ready and should only be used for prototyping as the artifacts cannot be loaded when running with a different Python version. Please consider implementing a custom materializer for type [0m[1;36m<class 'langchain_community.vectorstores.faiss.FAISS'>[33m according to the instructions at https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types[0m
[31mFailed to run step [0m[1;36mindex_generator[31m.[0m
[31mcannot pickle '_thread.RLock' object[0m
Traceback (most recent call last):
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py", line 230, in launch
self._run_step(
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py", line 442, in _run_step
self._run_step_without_step_operator(
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orchestrators/step_launcher.py", line 517, in _run_step_without_step_operator
runner.run(
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orchestrators/step_runner.py", line 250, in run
output_artifact_ids = self._store_output_artifacts(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orchestrators/step_runner.py", line 624, in _store_output_artifacts
artifact = save_artifact(
^^^^^^^^^^^^^^
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/artifacts/utils.py", line 183, in save_artifact
materializer_object.save(data)
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/materializers/cloudpickle_materializer.py", line 114, in save
cloudpickle.dump(data, fid)
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/cloudpickle/cloudpickle_fast.py", line 57, in dump
).dump(obj)
^^^^^^^^^
File "/media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/cloudpickle/cloudpickle_fast.py", line 602, in dump
return Pickler.dump(self, obj)
^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot pickle '_thread.RLock' object
[31mPipeline run [0m[1;36mzenml_agent_creation_pipeline-2024_05_14-01_06_59_775139[31m failed.[0m
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /media/manhdt4/C69EA17C9EA166211/zenml/zenml-projects/llm-agents/run.py:62 in <module> │
│ │
│ 59 │
│ 60 │
│ 61 if __name__ == "__main__": │
│ ❱ 62 │ main() │
│ 63 │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/click/core.p │
│ y:1130 in __call__ │
│ │
│ 1127 │ │
│ 1128 │ def __call__(self, *args: t.Any, **kwargs: t.Any) -> t.Any: │
│ 1129 │ │ """Alias for :meth:`main`.""" │
│ ❱ 1130 │ │ return self.main(*args, **kwargs) │
│ 1131 │
│ 1132 │
│ 1133 class Command(BaseCommand): │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/click/core.p │
│ y:1055 in main │
│ │
│ 1052 │ │ try: │
│ 1053 │ │ │ try: │
│ 1054 │ │ │ │ with self.make_context(prog_name, args, **extra) as ctx: │
│ ❱ 1055 │ │ │ │ │ rv = self.invoke(ctx) │
│ 1056 │ │ │ │ │ if not standalone_mode: │
│ 1057 │ │ │ │ │ │ return rv │
│ 1058 │ │ │ │ │ # it's not safe to `ctx.exit(rv)` here! │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/click/core.p │
│ y:1404 in invoke │
│ │
│ 1401 │ │ │ echo(style(message, fg="red"), err=True) │
│ 1402 │ │ │
│ 1403 │ │ if self.callback is not None: │
│ ❱ 1404 │ │ │ return ctx.invoke(self.callback, **ctx.params) │
│ 1405 │ │
│ 1406 │ def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]: │
│ 1407 │ │ """Return a list of completions for the incomplete value. Looks │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/click/core.p │
│ y:760 in invoke │
│ │
│ 757 │ │ │
│ 758 │ │ with augment_usage_errors(__self): │
│ 759 │ │ │ with ctx: │
│ ❱ 760 │ │ │ │ return __callback(*args, **kwargs) │
│ 761 │ │
│ 762 │ def forward( │
│ 763 │ │ __self, __cmd: "Command", *args: t.Any, **kwargs: t.Any # noqa: B902 │
│ │
│ /media/manhdt4/C69EA17C9EA166211/zenml/zenml-projects/llm-agents/run.py:58 in main │
│ │
│ 55 │ if config: │
│ 56 │ │ pipeline_args["config_path"] = os.path.join(config_folder, config) │
│ 57 │ │
│ ❱ 58 │ zenml_agent_creation_pipeline.with_options(**pipeline_args)() │
│ 59 │
│ 60 │
│ 61 if __name__ == "__main__": │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/new/pi │
│ pelines/pipeline.py:1397 in __call__ │
│ │
│ 1394 │ │ │ return self.entrypoint(*args, **kwargs) │
│ 1395 │ │ │
│ 1396 │ │ self.prepare(*args, **kwargs) │
│ ❱ 1397 │ │ return self._run(**self._run_args) │
│ 1398 │ │
│ 1399 │ def _call_entrypoint(self, *args: Any, **kwargs: Any) -> None: │
│ 1400 │ │ """Calls the pipeline entrypoint function with the given arguments. │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/new/pi │
│ pelines/pipeline.py:758 in _run │
│ │
│ 755 │ │ │ │ │ │ "`zenml up`." │
│ 756 │ │ │ │ │ ) │
│ 757 │ │ │ │
│ ❱ 758 │ │ │ deploy_pipeline( │
│ 759 │ │ │ │ deployment=deployment_model, stack=stack, placeholder_run=run │
│ 760 │ │ │ ) │
│ 761 │ │ │ if run: │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/new/pi │
│ pelines/run_utils.py:148 in deploy_pipeline │
│ │
│ 145 │ │ │ # placeholder run to stay in the database │
│ 146 │ │ │ Client().delete_pipeline_run(placeholder_run.id) │
│ 147 │ │ │
│ ❱ 148 │ │ raise e │
│ 149 │ finally: │
│ 150 │ │ constants.SHOULD_PREVENT_PIPELINE_EXECUTION = previous_value │
│ 151 │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/new/pi │
│ pelines/run_utils.py:136 in deploy_pipeline │
│ │
│ 133 │ previous_value = constants.SHOULD_PREVENT_PIPELINE_EXECUTION │
│ 134 │ constants.SHOULD_PREVENT_PIPELINE_EXECUTION = True │
│ 135 │ try: │
│ ❱ 136 │ │ stack.deploy_pipeline(deployment=deployment) │
│ 137 │ except Exception as e: │
│ 138 │ │ if ( │
│ 139 │ │ │ placeholder_run │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/stack/ │
│ stack.py:853 in deploy_pipeline │
│ │
│ 850 │ │ Returns: │
│ 851 │ │ │ The return value of the call to `orchestrator.run_pipeline(...)`. │
│ 852 │ │ """ │
│ ❱ 853 │ │ return self.orchestrator.run(deployment=deployment, stack=self) │
│ 854 │ │
│ 855 │ def _get_active_components_for_step( │
│ 856 │ │ self, step_config: "StepConfiguration" │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/base_orchestrator.py:175 in run │
│ │
│ 172 │ │ environment = get_config_environment_vars(deployment=deployment) │
│ 173 │ │ │
│ 174 │ │ try: │
│ ❱ 175 │ │ │ result = self.prepare_or_run_pipeline( │
│ 176 │ │ │ │ deployment=deployment, stack=stack, environment=environment │
│ 177 │ │ │ ) │
│ 178 │ │ finally: │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/local/local_orchestrator.py:78 in prepare_or_run_pipeline │
│ │
│ 75 │ │ │ │ │ step_name, │
│ 76 │ │ │ │ ) │
│ 77 │ │ │ │
│ ❱ 78 │ │ │ self.run_step( │
│ 79 │ │ │ │ step=step, │
│ 80 │ │ │ ) │
│ 81 │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/base_orchestrator.py:195 in run_step │
│ │
│ 192 │ │ │ step=step, │
│ 193 │ │ │ orchestrator_run_id=self.get_orchestrator_run_id(), │
│ 194 │ │ ) │
│ ❱ 195 │ │ launcher.launch() │
│ 196 │ │
│ 197 │ @staticmethod │
│ 198 │ def requires_resources_in_orchestration_environment( │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/step_launcher.py:230 in launch │
│ │
│ 227 │ │ │ │ logger.info(f"Step `{self._step_name}` has started.") │
│ 228 │ │ │ │ if execution_needed: │
│ 229 │ │ │ │ │ try: │
│ ❱ 230 │ │ │ │ │ │ self._run_step( │
│ 231 │ │ │ │ │ │ │ pipeline_run=pipeline_run, │
│ 232 │ │ │ │ │ │ │ step_run=step_run_response, │
│ 233 │ │ │ │ │ │ ) │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/step_launcher.py:442 in _run_step │
│ │
│ 439 │ │ │ │ │ step_run_info=step_run_info, │
│ 440 │ │ │ │ ) │
│ 441 │ │ │ else: │
│ ❱ 442 │ │ │ │ self._run_step_without_step_operator( │
│ 443 │ │ │ │ │ pipeline_run=pipeline_run, │
│ 444 │ │ │ │ │ step_run=step_run, │
│ 445 │ │ │ │ │ step_run_info=step_run_info, │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/step_launcher.py:517 in _run_step_without_step_operator │
│ │
│ 514 │ │ │ output_artifact_uris: The output artifact URIs of the current step. │
│ 515 │ │ """ │
│ 516 │ │ runner = StepRunner(step=self._step, stack=self._stack) │
│ ❱ 517 │ │ runner.run( │
│ 518 │ │ │ pipeline_run=pipeline_run, │
│ 519 │ │ │ step_run=step_run, │
│ 520 │ │ │ input_artifacts=input_artifacts, │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/step_runner.py:250 in run │
│ │
│ 247 │ │ │ │ │ │ │ is_enabled_on_step=step_run_info.config.enable_artifact_visu │
│ 248 │ │ │ │ │ │ │ is_enabled_on_pipeline=step_run_info.pipeline.enable_artifac │
│ 249 │ │ │ │ │ │ ) │
│ ❱ 250 │ │ │ │ │ │ output_artifact_ids = self._store_output_artifacts( │
│ 251 │ │ │ │ │ │ │ output_data=output_data, │
│ 252 │ │ │ │ │ │ │ output_artifact_uris=output_artifact_uris, │
│ 253 │ │ │ │ │ │ │ output_materializers=output_materializers, │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/orches │
│ trators/step_runner.py:624 in _store_output_artifacts │
│ │
│ 621 │ │ │ # Get full set of tags │
│ 622 │ │ │ tags = step_context.get_output_tags(output_name) │
│ 623 │ │ │ │
│ ❱ 624 │ │ │ artifact = save_artifact( │
│ 625 │ │ │ │ name=artifact_name, │
│ 626 │ │ │ │ data=return_value, │
│ 627 │ │ │ │ materializer=materializer_class, │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/artifa │
│ cts/utils.py:183 in save_artifact │
│ │
│ 180 │ # Save the artifact to the artifact store │
│ 181 │ data_type = type(data) │
│ 182 │ materializer_object.validate_type_compatibility(data_type) │
│ ❱ 183 │ materializer_object.save(data) │
│ 184 │ │
│ 185 │ # Save visualizations of the artifact │
│ 186 │ visualizations: List[ArtifactVisualizationRequest] = [] │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/zenml/materi │
│ alizers/cloudpickle_materializer.py:114 in save │
│ │
│ 111 │ │ # save data │
│ 112 │ │ filepath = os.path.join(self.uri, DEFAULT_FILENAME) │
│ 113 │ │ with self.artifact_store.open(filepath, "wb") as fid: │
│ ❱ 114 │ │ │ cloudpickle.dump(data, fid) │
│ 115 │ │
│ 116 │ def _save_python_version(self) -> None: │
│ 117 │ │ """Saves the Python version used to materialize the artifact.""" │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/cloudpickle/ │
│ cloudpickle_fast.py:57 in dump │
│ │
│ 54 │ │ """ │
│ 55 │ │ CloudPickler( │
│ 56 │ │ │ file, protocol=protocol, buffer_callback=buffer_callback │
│ ❱ 57 │ │ ).dump(obj) │
│ 58 │ │
│ 59 │ def dumps(obj, protocol=None, buffer_callback=None): │
│ 60 │ │ """Serialize obj as a string of bytes allocated in memory │
│ │
│ /media/manhdt4/C69EA17C9EA166211/miniconda3/envs/zenml/lib/python3.11/site-packages/cloudpickle/ │
│ cloudpickle_fast.py:602 in dump │
│ │
│ 599 │ │
│ 600 │ def dump(self, obj): │
│ 601 │ │ try: │
│ ❱ 602 │ │ │ return Pickler.dump(self, obj) │
│ 603 │ │ except RuntimeError as e: │
│ 604 │ │ │ if "recursion" in e.args[0]: │
│ 605 │ │ │ │ msg = ( │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: cannot pickle '_thread.RLock' object
How can I fix?
