Hi all! We're using the MLflow integration to log our experiments, however, when we're testing steps/functions we don't really need or want to log things to MLflow. Is there a way to disable MLflow in a testing context, i.e., the calls to `log_params()` don't do anything? At the moment, we're checking for an `active_run` and if that exists, the parameters are logged.

Hi, guys, what is optimal way of passing custom input params to the zenml pipeline?

Hey ! when running a pipeline on a remote stack (remote zenml server + gcp stack) it seems the image builder doesn't get the right requirements. How does zenml finds the right components to install and how could i force them ?

Hi! How can supply timeout and retry in step config? settings/ extra? not clear from the code/docs

Hi, i haven’t used zenml myself for some time, so now I’m wondering. Is the ZenML board able to show some tracking information per pipeline run? Specifically I’m interested in all the specific artefact store locations used by specific run… (aka some kind of model version tracking)

Hello, colleagues, another question while migrating from 0.21.1 to 0.31.0. So far reproduced only for pandas output, I have pyarrow in dependencies, however I receive following exception(we have custom artifact store implementation for Alluxio) ```╭───────────────────── Traceback (most recent call last) ──────────────────────╮ │ /tmp/pyarrow/_parquet.pyx:1714 in pyarrow._parquet.ParquetWriter.__cinit__ │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/_parquet.pyx' │ │ │ │ /usr/local/lib/python3.8/site-packages/pyarrow/util.py:93 in _stringify_path │ │ │ │ 90 │ except AttributeError: │ │ 91 │ │ pass │ │ 92 │ │ │ ❱ 93 │ raise TypeError("not a path-like object") │ │ 96 def product(seq): │ ╰──────────────────────────────────────────────────────────────────────────────╯ TypeError: not a path-like object During handling of the above exception, another exception occurred: │ /usr/local/lib/python3.8/site-packages/zenml/orchestrators/step_launcher.py: │ │ 380 in _run_step_without_step_operator │ │ │ │ 377 │ │ │ output_artifact_uris: The output artifact URIs of the curr │ │ 378 │ │ """ │ │ 379 │ │ runner = StepRunner(step=self._step, stack=self._stack) │ │ ❱ 380 │ │ runner.run( │ │ 381 │ │ │ input_artifacts=input_artifacts, │ │ 382 │ │ │ output_artifact_uris=output_artifact_uris, │ │ 383 │ │ │ step_run_info=step_run_info, │ │ │ │ /usr/local/lib/python3.8/site-packages/zenml/orchestrators/step_runner.py:12 │ │ 2 in run │ │ │ │ 119 │ │ # Store and publish the output artifacts of the step function. │ │ 120 │ │ output_annotations = parse_return_type_annotations(spec.annota │ │ 121 │ │ output_data = self._validate_outputs(return_values, output_ann │ │ ❱ 122 │ │ output_artifacts = self._store_output_artifacts( │ │ 123 │ │ │ output_data=output_data, │ │ 124 │ │ │ output_artifact_uris=output_artifact_uris, │ │ 125 │ │ │ output_materializers=output_materializers, │ │ │ │ /usr/local/lib/python3.8/site-packages/zenml/orchestrators/step_runner.py:37 │ │ 4 in _store_output_artifacts │ │ │ │ 371 │ │ │ │ artifact_store_id=artifact_store_id, │ │ 372 │ │ │ ) │ │ 373 │ │ │ output_artifacts[output_name] = output_artifact │ │ ❱ 374 │ │ │ materializer_class(uri).save(return_value) │ │ 375 │ │ return output_artifacts │ │ 376 │ │ │ │ /usr/local/lib/python3.8/site-packages/zenml/materializers/pandas_materializ │ │ er.py:130 in save │ │ │ │ 127 │ │ │ │ 128 │ │ if self.pyarrow_exists: │ │ 129 │ │ │ with fileio.open(self.parquet_path, mode="wb") as f: │ │ ❱ 130 │ │ │ │ df.to_parquet(f, compression=COMPRESSION_TYPE) │ │ 131 │ │ else: │ │ 132 │ │ │ with fileio.open(self.csv_path, mode="wb") as f: │ │ 133 │ │ │ │ df.to_csv(f, index=True) │ │ │ │ /usr/local/lib/python3.8/site-packages/pandas/util/_decorators.py:211 in │ │ wrapper │ │ │ │ 208 │ │ │ │ │ raise TypeError(msg) │ │ 209 │ │ │ │ else: │ │ 210 │ │ │ │ │ kwargs[new_arg_name] = new_arg_value │ │ ❱ 211 │ │ │ return func(*args, **kwargs) │ │ 212 │ │ │ │ 213 │ │ return cast(F, wrapper) │ │ 214 │ │ │ │ /usr/local/lib/python3.8/site-packages/pandas/core/frame.py:2975 in │ │ to_parquet │ │ │ │ 2972 │ │ """ │ │ 2973 │ │ from pandas.io.parquet import to_parquet │ │ 2974 │ │ │ │ ❱ 2975 │ │ return to_parquet( │ │ 2976 │ │ │ self, │ │ 2977 │ │ │ path, │ │ 2978 │ │ │ engine, │ │ │ │ /usr/local/lib/python3.8/site-packages/pandas/io/parquet.py:430 in │ │ to_parquet │ │ │ │ 427 │ │ │ 428 │ path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path │ │ 429 │ │ │ ❱ 430 │ impl.write( │ │ 431 │ │ df, │ │ 432 │ │ path_or_buf, │ │ 433 │ │ compression=compression, │ │ │ │ /usr/local/lib/python3.8/site-packages/pandas/io/parquet.py:204 in write │ │ │ │ 201 │ │ │ │ ) │ │ 202 │ │ │ else: │ │ 203 │ │ │ │ # write to single output file │ │ ❱ 204 │ │ │ │ self.api.parquet.write_table( │ │ 205 │ │ │ │ │ table, path_or_handle, compression=compression, ** │ │ 206 │ │ │ │ ) │ │ 207 │ │ finally: │ │ │ │ /usr/local/lib/python3.8/site-packages/pyarrow/parquet/core.py:2964 in │ │ write_table │ │ │ │ 2961 │ row_group_size = kwargs.pop('chunk_size', row_group_size) │ │ 2962 │ use_int96 = use_deprecated_int96_timestamps │ │ 2963 │ try: │ │ ❱ 2964 │ │ with ParquetWriter( │ │ 2965 │ │ │ │ where, table.schema, │ │ 2966 │ │ │ │ filesystem=filesystem, │ │ 2967 │ │ │ │ version=version, │ │ │ │ /usr/local/lib/python3.8/site-packages/pyarrow/parquet/core.py:966 in │ │ __init__ │ │ │ │ 963 │ │ │ sink = where │ │ 964 │ │ self._metadata_collector = options.pop('metadata_collector', │ │ 965 │ │ engine_version = 'V2' │ │ ❱ 966 │ │ self.writer = _parquet.ParquetWriter( │ │ 967 │ │ │ sink, schema, │ │ 968 │ │ │ version=version, │ │ 969 │ │ │ compression=compression, │ │ │ │ /tmp/pyarrow/_parquet.pyx:1716 in pyarrow._parquet.ParquetWriter.__cinit__ │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/_parquet.pyx' │ │ │ │ /tmp/pyarrow/io.pxi:1807 in pyarrow.lib.get_writer │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/io.pxi' │ │ │ │ /tmp/pyarrow/io.pxi:214 in pyarrow.lib.NativeFile.get_output_stream │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/io.pxi' │ │ │ │ /tmp/pyarrow/io.pxi:228 in pyarrow.lib.NativeFile._assert_writable │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/io.pxi' │ │ │ │ /tmp/pyarrow/io.pxi:219 in pyarrow.lib.NativeFile._assert_open │ │ │ │ [Errno 2] No such file or directory: '/tmp/pyarrow/io.pxi' │ ╰──────────────────────────────────────────────────────────────────────────────╯ ValueError: I/O operation on closed file Runtime execution graph. Only steps that are currently running or have already completed are shown.```

Hi all, in the quickstart tutorial, I see the inference pipeline can retrieve the latest model trained via the training pipeline via the `model_deployer.find_model_server` call. Is this functionality generalizable to regular artifacts (instead of model deployer)? Say I have a training pipeline that produces a model that’s stored in the artifact store, is there a way for my inference pipeline to get the latest version of that model from the artifact store?

Hi all! I've installed zenml server to gcp (gke cluster) with gcp minimal stack recipe (version 0.32.0). I'm currently working on updates to seldon integration, hence I'm using 0.32.1 version of zenml client (from the development branch). Whenever I connect to the server and try interacting with it in any way I get a version mismatch warning and then `DoesNotExistException: Endpoint does not exist.` Is there an easy way of installing/updating a version of the server to match what I currently have locally?

OperationalError: (sqlite3.OperationalError) no such table: workspace [SQL: SELECT workspace.id, workspace.created, workspace.updated, workspace.name, workspace.description FROM workspace WHERE workspace.name = ?] [parameters: ('default',)] (Background on this error at: )

[ZenML Stack Recipe] Hi all ! I am trying to use a stack recipe to setup a simple GCP stack. Already have the account and billing info ready. I pulled the vertex-ai recipe with zenml cli, configured (i think) the and values.tfvars.json files. But when I try to deploy the stack using the zenml CLI i get the following error: (see in thread)