* Components - Added support for Dataflow in TFX components
To use Dataflow, pass beam_pipeline_args to a component.
```
transformer_op(
...,
beam_pipeline_args = [
'--runner=DataflowRunner',
'--experiments=shuffle_mode=auto',
'--project=' + project_id,
'--temp_location=' + gcs_bucket + '/tmp'),
'--region=' + gcp_region,
'--disk_size_gb=50',
],
)
```
These components use URI-based I/O since TFX with Beam's DataflowRunner only supports GCS URIs for inputs and outputs. With URI-based IO, the user must specify all output URIs themselves (e.g. `CsvEampleGen(..., output_examples_uri=...)`). Do not forget to do so. The `kfp.dsl.EXECUTION_ID_PLACEHOLDER` object can help construct execution-unique URIs, but if the component has multiple URIs, you will need to add some prefixes that are different for each output.
There is a bug in TFX+Beam which prevents using DataflowRunner, but these componenct contain a workaround. The workaround can be removed when the fixed verson of TFX is released ddb01c0242
* Added the TFX on KFP Dataflow sample
* Updated the README.md file
* Enabled the blessing output of the Evaluator
The Evaluator does not always write to that URI, but for components with URI-based I/O this does not matter.
* Fixed the indent in YAML
* Addressed the review feedback
* Updated the sample after the component changes
* Fixed the Dataflow casing in the sample name
* Using channel_utils.unwrap_channel_dict
* Updated the sample pipeline
* Sjortened the .get expressions
* Updated the sample
fa3b3043c6