ML Metadata (MLMD) is a library developed by Google for recording and retrieving metadata associated with ML developer and data scientist workflows. MLMD is an integral part of TensorFlow Extended (TFX).
Questions tagged [mlmd]
6 questions
9
votes
2 answers
Data stored in MLMD in TensorFlow TFX
As far as I understand, TensorFlow uses MLMD to record and retrieve metadata associated with workflows. This may include:
results of pipeline components
metadata about artifacts generated through the components of the pipelines
metadata about…

Josh
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1
vote
1 answer
ml_metadata.errors.AlreadyExistsError: Given node already exists
I ran into a problem using TFX, MLMD, and Apache-Airflow as the orchestrator. Local-dag-runner, provided by TFX, works fine, resulting in distinct artifacts for each pipeline component run. The problem arises when airflow is used as the…

Parham Davari
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vote
1 answer
Dependencies issue while installing model card toolkit
Model Card --> Model Card toolkit
I want to install a model card toolkit in my python virtual environment through this command:
pip install model-card-toolkit
and I am facing this below issue, I have tried many times with different combinations…

Muhammad Hassan
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- 8
0
votes
1 answer
Tensorflow: How to add a property in execution object in MLMD MetadataStore?
I'm using the MLMD MetadataStore to manage the data pipelines and I need to add an execution property in MLMD to get this property later.
I'm trying add with this:
from ml_metadata.proto import metadata_store_pb2
from ml_metadata.metadata_store…

natielle
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0 answers
How to start MLMD gRPC server?
I am doing a POC with the MLMD library. I want to expose a grpc/REST service on the MLMD schemas.
I am referring this official guide.
https://www.tensorflow.org/tfx/guide/mlmd#use_mlmd_with_a_remote_grpc_server
But I couldn't start the server the…

user2789165
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0
votes
1 answer
How to get the uri of the current pipeline's artifact
Consider the following pipeline:
example_gen = tfx.components.ImportExampleGen(input_base=_dataset_folder)
statistics_gen = tfx.components.StatisticsGen(examples=example_gen.outputs['examples'])
schema_gen = tfx.components.SchemaGen(
…

Mehran
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