Updating references to the project repository to kubeflow/pipelines.

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/googleprivate/ml.svg?token=JjfzFsYGxZwkHvXFCpwt&branch=master)](https://travis-ci.com/googleprivate/ml)
+[![Build Status](https://travis-ci.com/kubeflow/pipelines.svg?token=JjfzFsYGxZwkHvXFCpwt&branch=master)](https://travis-ci.com/kubeflow/pipelines)
 
 # ML Pipeline Services - Overview
 
@@ -112,14 +112,14 @@ gcloud container clusters create $CLUSTER_NAME \
 ```
 Here we choose the cloud-platform scope so the cluster can invoke GCP APIs. You can find all the options for creating a cluster in [here](https://cloud.google.com/sdk/gcloud/reference/container/clusters/create). 
 
-Next, grant your user account permission to create new cluster roles. This step is necessary because installing ML Pipelines Services inlcudes installing a few [clusterroles](https://github.com/googleprivate/ml/search?utf8=%E2%9C%93&q=clusterrole+path%3Aml-pipeline%2Fml-pipeline&type=). 
+Next, grant your user account permission to create new cluster roles. This step is necessary because installing ML Pipelines Services inlcudes installing a few [clusterroles](https://github.com/kubeflow/pipelines/search?utf8=%E2%9C%93&q=clusterrole+path%3Aml-pipeline%2Fml-pipeline&type=). 
 ```bash
 kubectl create clusterrolebinding ml-pipeline-admin-binding --clusterrole=cluster-admin --user=$(gcloud config get-value account)
 ```
 
 ## Deploy ML Pipeline Services and Kubeflow 
 
-Go to [release page](https://github.com/googleprivate/ml/releases) to find a version of ML Pipeline Services. Deploy the ML Pipeline Services and Kubeflow to your cluster.
+Go to [release page](https://github.com/kubeflow/pipelines/releases) to find a version of ML Pipeline Services. Deploy the ML Pipeline Services and Kubeflow to your cluster.
 
 For example:
 ```bash
@@ -166,7 +166,7 @@ If you are using Cloud Shell, you could view the UI by open the [web preview](ht
 If you are using local console instead of Cloud Shell, you can access the ML pipeline UI at [localhost:8080/pipeline](http://localhost:8080/pipeline).
 
 ## Run your first TFJob pipeline
-See the following authoring guide on how to compile your python pipeline code into workflow tar file. Then, follow [README.md](https://github.com/googleprivate/ml/blob/master/samples/kubeflow-tf/README.md) to deploy your first TFJob pipeline.  
+See the following authoring guide on how to compile your python pipeline code into workflow tar file. Then, follow [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/kubeflow-tf/README.md) to deploy your first TFJob pipeline.  
 
 ## Uninstall
 To uninstall ML pipeline, download the bootstrapper file and change the arguments to the deployment job.
@@ -189,7 +189,7 @@ then create job using the updated YAML by running ```kubectl create -f bootstrap
 
 # ML Pipeline Services - Authoring Guideline
 
-For more details, see [README.md](https://github.com/googleprivate/ml/blob/master/samples/README.md).
+For more details, see [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/README.md).
 
 ## Setup
 * Create a python3 environment.

backend/Dockerfile

@@ -7,7 +7,7 @@ RUN curl -fsSL -o /bin/dep https://github.com/golang/dep/releases/download/v0.5.
 ADD https://github.com/golang/dep/releases/download/v0.5.0/dep-linux-amd64 /usr/bin/dep
 RUN chmod +x /usr/bin/dep
 
-WORKDIR /go/src/github.com/googleprivate/ml
+WORKDIR /go/src/github.com/kubeflow/pipelines
 COPY . .
 
 # Needed for github.com/mattn/go-sqlite3
@@ -22,7 +22,7 @@ ENV COMMIT_SHA=${COMMIT_SHA}
 WORKDIR /bin
 
 COPY --from=builder /bin/apiserver /bin/apiserver
-COPY --from=builder /go/src/github.com/googleprivate/ml/third_party/license.txt /bin/license.txt
+COPY --from=builder /go/src/github.com/kubeflow/pipelines/third_party/license.txt /bin/license.txt
 COPY backend/src/apiserver/config/ /config
 COPY backend/src/apiserver/samples/ /samples
 

backend/Dockerfile.persistenceagent

@@ -8,7 +8,7 @@ RUN curl -fsSL -o /bin/dep https://github.com/golang/dep/releases/download/v0.5.
 ADD https://github.com/golang/dep/releases/download/v0.5.0/dep-linux-amd64 /usr/bin/dep
 RUN chmod +x /usr/bin/dep
 
-WORKDIR /go/src/github.com/googleprivate/ml
+WORKDIR /go/src/github.com/kubeflow/pipelines
 COPY . .
 
 # Needed for github.com/mattn/go-sqlite3
@@ -19,7 +19,7 @@ FROM alpine
 WORKDIR /bin
 
 COPY --from=builder /bin/persistence_agent /bin/persistence_agent
-COPY --from=builder /go/src/github.com/googleprivate/ml/third_party/license.txt /bin/license.txt
+COPY --from=builder /go/src/github.com/kubeflow/pipelines/third_party/license.txt /bin/license.txt
 RUN chmod +x /bin/persistence_agent
 
 CMD persistence_agent \

backend/Dockerfile.scheduledworkflow

@@ -7,7 +7,7 @@ RUN curl -fsSL -o /bin/dep https://github.com/golang/dep/releases/download/v0.5.
 ADD https://github.com/golang/dep/releases/download/v0.5.0/dep-linux-amd64 /usr/bin/dep
 RUN chmod +x /usr/bin/dep
 
-WORKDIR /go/src/github.com/googleprivate/ml
+WORKDIR /go/src/github.com/kubeflow/pipelines
 COPY . .
 
 # Needed for github.com/mattn/go-sqlite3
@@ -18,7 +18,7 @@ FROM alpine
 WORKDIR /bin
 
 COPY --from=builder /bin/controller /bin/controller
-COPY --from=builder /go/src/github.com/googleprivate/ml/third_party/license.txt /bin/license.txt
+COPY --from=builder /go/src/github.com/kubeflow/pipelines/third_party/license.txt /bin/license.txt
 RUN chmod +x /bin/controller
 
 CMD /bin/controller -alsologtostderr=true

backend/src/crd/hack/update-codegen.sh

@@ -27,6 +27,6 @@ CODEGEN_PKG=${SCRIPT_ROOT}/../../../../../../k8s.io/code-generator
 echo "CODEGEN_PKG is $CODEGEN_PKG"
 
 ${CODEGEN_PKG}/generate-groups.sh "deepcopy,client,informer,lister" \
-  github.com/googleprivate/ml/backend/src/crd/pkg/client github.com/googleprivate/ml/backend/src/crd/pkg/apis \
+  github.com/kubeflow/pipelines/backend/src/crd/pkg/client github.com/kubeflow/pipelines/backend/src/crd/pkg/apis \
   scheduledworkflow:v1alpha1 \
   --go-header-file ${SCRIPT_ROOT}/hack/custom-boilerplate.go.txt

developer_guide.md

@@ -1,6 +1,6 @@
 # ML Pipeline Development Guideline
 
-This document describes the development guideline to contribute to ML pipeline project. Please check the [main page](https://github.com/googleprivate/ml/blob/master/README.md) for instruction on how to deploy a ML pipeline system.
+This document describes the development guideline to contribute to ML pipeline project. Please check the [main page](https://github.com/kubeflow/pipelines/blob/master/README.md) for instruction on how to deploy a ML pipeline system.
 
 ## ML pipeline deployment
 
@@ -17,9 +17,9 @@ The docker container accepts various parameters to customize your deployment.
 - **--report_usage** whether to report usage for the deployment
 - **--uninstall** to uninstall everything.
 
-See [bootstrapper.yaml](https://github.com/googleprivate/ml/blob/master/bootstrapper.yaml) for examples on how to pass in parameter.
+See [bootstrapper.yaml](https://github.com/kubeflow/pipelines/blob/master/bootstrapper.yaml) for examples on how to pass in parameter.
 
-Alternatively, you can use [deploy.sh](https://github.com/googleprivate/ml/blob/master/ml-pipeline/deploy.sh) if you want to interact with Ksonnet directly.
+Alternatively, you can use [deploy.sh](https://github.com/kubeflow/pipelines/blob/master/ml-pipeline/deploy.sh) if you want to interact with Ksonnet directly.
 To deploy, run the script locally.
 ```bash
 $ ml-pipeline/deploy.sh
@@ -101,7 +101,7 @@ pip install ./dsl-compiler/ --upgrade && python ./dsl-compiler/tests/main.py
 ## Integration test
 
 ### API server
-Check [this](https://github.com/googleprivate/ml/blob/master/test/apiserver/README.md) page for more details.
+Check [this](https://github.com/kubeflow/pipelines/blob/master/test/apiserver/README.md) page for more details.
 
 ## E2E test
 TODO: Add instruction

frontend/server/package.json

@@ -22,12 +22,12 @@
   },
   "repository": {
     "type": "git",
-    "url": "git+https://github.com/googleprivate/ml.git"
+    "url": "git+https://github.com/kubeflow/pipelines.git"
   },
   "author": "",
   "license": "",
   "bugs": {
-    "url": "https://github.com/googleprivate/ml/issues"
+    "url": "https://github.com/kubeflow/pipelines/issues"
   },
-  "homepage": "https://github.com/googleprivate/ml#readme"
+  "homepage": "https://github.com/kubeflow/pipelines#readme"
 }

ml-pipeline/deploy.sh

@@ -120,7 +120,7 @@ echo "Initialized ksonnet APP completed successfully"
 # an known issue: https://github.com/ksonnet/ksonnet/issues/232, we are working around by creating
 # a symbolic links in ./vendor and manually modifying app.yaml
 # when the repo is public we can do following:
-# ks registry add ml-pipeline github.com/googleprivate/ml/tree/master/ml-pipeline
+# ks registry add ml-pipeline github.com/kubeflow/pipelines/tree/master/ml-pipeline
 # ks pkg install ml-pipeline/ml-pipeline
 BASEDIR=$(cd $(dirname "$0") && pwd)
 ln -s ${BASEDIR} ${APP_DIR}/vendor/ml-pipeline

ml-pipeline/ml-pipeline/parts.yaml

@@ -26,10 +26,10 @@
    ],
    "repository": {
       "type": "git",
-      "url": "https://github.com/googleprivate/ml"
+      "url": "https://github.com/kubeflow/pipelines"
    },
    "bugs": {
-      "url": "https://github.com/googleprivate/ml/issues"
+      "url": "https://github.com/kubeflow/pipelines/issues"
    },
    "keywords": [
       "ml-pipeline"

samples/README.md

@@ -71,7 +71,7 @@ Install [docker](https://www.docker.com/get-docker).
 
 ### Step One: Create A Container For Each Component
 In most cases, you need to create your own container image that includes your program. You can find container 
-building examples from [here](https://github.com/googleprivate/ml/blob/master/components)(in the directory, go to any subdirectory and then go to “containers” directory).
+building examples from [here](https://github.com/kubeflow/pipelines/blob/master/components)(in the directory, go to any subdirectory and then go to “containers” directory).
 
 If your component creates some outputs to be fed as inputs to the downstream components, each output has 
 to be a string and needs to be written to a separate local text file by the container image. 
@@ -155,4 +155,4 @@ args go first and keyword args go next.
 should all be of that type. The default values will show up in the Pipeline UI but can be overwritten.
 
 
-See an example [here](https://github.com/googleprivate/ml/blob/master/samples/xgboost-spark/xgboost-training-cm.py).
+See an example [here](https://github.com/kubeflow/pipelines/blob/master/samples/xgboost-spark/xgboost-training-cm.py).

samples/basic/README.md

@@ -1,5 +1,5 @@
 ## Compile
-Follow [README.md](https://github.com/googleprivate/ml/blob/master/samples/README.md) to install the compiler and 
+Follow [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/README.md) to install the compiler and 
 compile the sample python into workflow yaml. 
 
 "sequential.yaml" is pre-generated for referencing purpose.

samples/kubeflow-tf/README.md

@@ -12,7 +12,7 @@ Note: The trainer depends on KubeFlow API Version v1alpha2.
 
 ## Compiling the pipeline template
 
-Follow [README.md](https://github.com/googleprivate/ml/blob/master/samples/README.md) to install the compiler and then run the following command to compile the pipeline:
+Follow [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/README.md) to install the compiler and then run the following command to compile the pipeline:
 
 ```bash
 dsl-compile --py kubeflow-training-classification.py --output kubeflow-training-classification.tar.gz
@@ -29,17 +29,17 @@ The pipeline will require one argument:
 ## Components Source
 
 Preprocessing:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/dataflow/tft), 
-  [container](https://github.com/googleprivate/ml/tree/master/components/dataflow/containers/tft)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/containers/tft)
 
 Training:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/kubeflow/launcher), 
-  [container](https://github.com/googleprivate/ml/tree/master/components/kubeflow/container/launcher)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/container/launcher)
 
 Prediction:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/dataflow/predict), 
-  [container](https://github.com/googleprivate/ml/tree/master/components/dataflow/containers/predict)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/predict), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/containers/predict)
 
 Confusion Matrix:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/local/evaluation), 
-  [container](https://github.com/googleprivate/ml/tree/master/components/local/containers/confusion_matrix)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/local/evaluation), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/local/containers/confusion_matrix)

samples/resnet-cmle/README.md

@@ -8,7 +8,7 @@ Training and serving uses Google Cloud Machine Learning Engine. So [Cloud Machin
 given project. 
 
 ## Compile
-Follow [README.md](https://github.com/googleprivate/ml/blob/master/samples/README.md) to install the compiler and 
+Follow [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/README.md) to install the compiler and 
 compile your python sample into workflow yaml.
 
 ## Deploy
@@ -25,13 +25,13 @@ bucket: A Google storage bucket to store results.
 ## Components Source
 
 Preprocessing:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/resnet) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/containers/preprocess)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/resnet) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/containers/preprocess)
 
 Training:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/resnet) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/containers/train)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/resnet) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/containers/train)
 
 Deployment:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/resnet) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/resnet-cmle/containers/deploy)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/resnet) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/resnet-cmle/containers/deploy)

samples/tfma/README.md

@@ -31,7 +31,7 @@ Instructions for enabling that can be found [here](https://cloud.google.com/endp
 
 ## Compiling the pipeline template
 
-Follow [README.md](https://github.com/googleprivate/ml/blob/master/samples/README.md) to install the compiler and then run the following to compile the pipeline:
+Follow [README.md](https://github.com/kubeflow/pipelines/blob/master/samples/README.md) to install the compiler and then run the following to compile the pipeline:
 
 ```bash
 dsl-compile --py taxi-cab-classification-pipeline.py --output taxi-cab-classification-pipeline.tar.gz
@@ -49,17 +49,17 @@ The pipeline will require two arguments:
 ## Components Source
 
 Preprocessing:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/dataflow/tft) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/dataflow/containers/tft)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/containers/tft)
 
 Training:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/kubeflow/launcher) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/kubeflow/container/launcher)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/container/launcher)
 
 Analysis:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/dataflow/tfma) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/dataflow/containers/tfma)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tfma) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/containers/tfma)
 
 Prediction:
-  [source code](https://github.com/googleprivate/ml/tree/master/components/dataflow/predict) 
-  [container](https://github.com/googleprivate/ml/tree/master/components/dataflow/containers/predict)
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/predict) 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/containers/predict)