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[Doc] Update permission requirement in README #2422

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Oct 29, 2019
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[Doc] Update permission requirement in README #2422

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178fbcd
add tfx permission guide
Oct 17, 2019
51c7000
add xgboost guide
Oct 17, 2019
f3063c5
add a direct link to dataproc API
Oct 18, 2019
6731f4d
Merge branch 'master' of https://github.com/kubeflow/pipelines into f…
Oct 24, 2019
24d3718
Add tip
Oct 24, 2019
13eb78e
Add title for parity. Merge with master
Oct 25, 2019
73aa49a
Update README.md
Oct 28, 2019
7451fa5
Update and apply suggestions
Oct 28, 2019
a967c2c
Fix
Oct 28, 2019
89ad614
Update README.md
IronPan Oct 29, 2019
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23 changes: 23 additions & 0 deletions manifests/gcp_marketplace/guide.md
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ gcloud projects add-iam-policy-binding $PROJECT_ID \
--member=serviceAccount:$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com \
--role=roles/storage.admin

# Note that you can not bind multiple roles in one line.
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gcloud projects add-iam-policy-binding $PROJECT_ID \
--member=serviceAccount:$SA_NAME@$PROJECT_ID.iam.gserviceaccount.com \
--role=roles/ml.admin
Expand Down Expand Up @@ -98,3 +99,25 @@ as `Service Account User`. The Google Service Account is [Compute Engine default
- Please also add your account as `Project Viewer` via [IAM](https://console.cloud.google.com/iam-admin/iam).

For simplicity but not good for security, adding as `Project Editor` also can work.

### Pipeline steps got insufficient permission
If you see an error message stating that the pipeline got insufficient
permissions, for example:

```
Error executing an HTTP request: HTTP response code 403 with body '{
"error": {
"errors": [
{
"domain": "global",
"reason": "insufficientPermissions",
"message": "Insufficient Permission"
}
],
"code": 403,
"message": "Insufficient Permission"
}
}
```
please make sure following the procedure in [credential setup](#gcp-service-account-credentials). IAM configuration and/or
API enabling might take up to 5 mins to propagate.
13 changes: 8 additions & 5 deletions samples/contrib/parameterized_tfx_oss/README.md
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@@ -1,4 +1,4 @@
# Parameterized TFX pipeline sample
# Overview

[Tensorflow Extended (TFX)](https://github.com/tensorflow/tfx) is a Google-production-scale machine
learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines
Expand All @@ -9,15 +9,19 @@ This sample demonstrates how to author a ML pipeline in TFX and run it on a KFP
Please refer to inline comments for the purpose of each step.

In order to successfully compile this sample, you'll need to have a TFX installation at HEAD.
First, you can clone their repo and run `python setup.py install` from `tfx/tfx`.
First, you can clone their repo and run `python setup.py install` from `tfx/`.
The image used in the pipeline is specified as `tfx_image` in the
`KubeflowDagRunnerConfig`. Currently we're using our own patched version of TFX image containing visualization support.
List of officially released nightly build image available can be found [here](https://hub.docker.com/r/tensorflow/tfx/tags)).

After that, running
`python3 chicago_taxi_pipeline_simple.py` compiles the TFX pipeline into KFP pipeline package.
This pipeline requires google storage permission to run.

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# Permission

This pipeline requires Google Cloud Storage permission to run.
If KFP was deployed through K8S marketplace, please follow instructions in [the guideline](https://github.com/kubeflow/pipelines/blob/master/manifests/gcp_marketplace/guide.md#gcp-service-account-credentials)
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to make sure the service account has `storage.admin` role.

## Caveats

Expand All @@ -28,8 +32,7 @@ objects `dsl.PipelineParam` and appending them to the `KubeflowDagRunner._params
KubeflowDagRunner can correctly identify those pipeline parameters and interpret them as Argo
placeholder correctly when compilation. However, this parameterization approach is a hack and
we do not have plan for long-term support. Instead we're working with TFX team to support
pipeline parameterization using their [RuntimeParameter](https://github.com/tensorflow/tfx/blob/46bb4f975c36ea1defde4b3c33553e088b3dc5b8/tfx/orchestration/data_types.py#L108).

pipeline parameterization using their [RuntimeParameter](https://github.com/tensorflow/tfx/blob/46bb4f975c36ea1defde4b3c33553e088b3dc5b8/tfx/orchestration/data_types.py#L108).
### Known issues
* This approach only works for string-typed quantities. For example, you cannot parameterize
`num_steps` of `Trainer` in this way.
Expand Down
7 changes: 5 additions & 2 deletions samples/core/xgboost_training_cm/README.md
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Expand Up @@ -12,8 +12,11 @@ or not.

## Requirements

Preprocessing uses Google Cloud DataProc. Therefore, you must enable the [DataProc API](https://cloud.google.com/endpoints/docs/openapi/enable-api) for the given GCP project.

Preprocessing uses Google Cloud DataProc. Therefore, you must enable the
[Cloud Dataproc API](https://pantheon.corp.google.com/apis/library/dataproc.googleapis.com?q=dataproc) for the given GCP project. This is the
general [guideline](https://cloud.google.com/endpoints/docs/openapi/enable-api) for enabling GCP APIs.
If KFP was deployed through K8S marketplace, please follow instructions in [the guideline](https://github.com/kubeflow/pipelines/blob/master/manifests/gcp_marketplace/guide.md#gcp-service-account-credentials)
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to make sure the service account used has the role `storage.admin` and `dataproc.admin`.
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Probably also add the API enabled requirement, such as "enable the DataProc API for the given GCP project"

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We can have a direct link to enabling that API.

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Add a link to the API page.
@Ark-kun are you saying there is a direct link that can enable the API within one-click? If so that'll be better.

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When you try running a Dataproc or Dataflow component, that link is in the error log.

Here is the Dataflow link: https://console.cloud.google.com/apis/api/dataflow.googleapis.com/overview?project=1234567


## Compile

Expand Down