Accessing Data
There’s a tremendous amount of information available, just for a simple 3-step pipeline that runs locally. This pipeline can run in the cloud on a Dataflow
runner, for example, with minimum changes in configuration.
In this scenario, it’s much easier to use data that’s stored in the database, instead of browsing cloud storage buckets and VMs on a server farm.
From this point on, you can connect to a ML Metadata store either from a direct SQL connection, or by gRPC (via stub or plain old calls). Then, it’s a matter of selecting the kinds of data you want to inspect manually. This could be the schema or the statistics protobuf, for example.
Typically, you only need to access the resource identifiers of the resources. You should be able to access them via only the URI if you’re in the same environment (ex. a notebook inside a GCP Project VM).
Example Use Case
Assume that you’ve got a pipeline running in some interval (or event-based triggering) and, sometimes, you want to view the data statistics of the latest pipeline run in comparison to the previous run.
-
You need the StatisticsGen/statistics
artifacts of 2 different pipeline runs (these are the ExampleStatistics
type, with
type_id
8). These can be found on theArtifact
table. -
You also need access to the artifact from the correct pipeline runs. The
Attribution
table associatescontext_id
withartifact_id
. The only thing missing is to pinpoint the 2context_id
s you need in order to make a simple select query. -
The
Context
table also contains timestamp information. For example, the rowPipeline .2020–07–14T23:45:00.508181.StatisticsGen
has got acontext_id
5.
Context Id 5, corresponds to Artifact Id 3 from the Attribution table. Artifact Id 3 is indeed the Statistics artifact we need.
Fortunately, kubeflow pipelines already do this visualisation automatically
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以上所述就是小编给大家介绍的《A comprehensive ML Metadata walkthrough for Tensorflow Extended》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
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