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Multiple dead/broken links fixed see #1184 (#1185)

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butuzov authored and k8s-ci-robot committed Oct 1, 2019
1 parent ebe2adc commit a51773643d2a7d6b4b272161d555b857d98b51d4
@@ -63,9 +63,8 @@ To run the notebook in your Kubeflow cluster:

1. Follow the guide to
[setting up your Jupyter notebooks in Kubeflow](/docs/notebooks/setup/).
1. Go to the [`demo` notebook on
GitHub](https://github.com/kubeflow/metadata/blob/master/sdk/python/sample/demo.ipynb).
1. Download the notebook code by opening the **Raw** view of the file, then
1. Go to the [`demo` notebook on GitHub](https://github.com/kubeflow/metadata/blob/master/sdk/python/sample/demo.ipynb).
1. Download the notebook code by opening the **Raw** view of the file, then
right-clicking on the content and saving the file locally as `demo.ipynb`.
1. Go back to your Jupyter notebook server in the Kubeflow UI. (If you've
moved away from the notebooks section in Kubeflow, click
@@ -190,8 +190,8 @@ Notebook, e.g. we can create `Go` functions if we need performance/concurrency f

Some useful function example Notebooks:

- [TensorFlow Serving function](https://github.com/v3io/tutorials/blob/master/demos/image-classification/infer.ipynb)
- [TensorFlow Serving function](https://github.com/v3io/tutorials/blob/master/demos/image-classification/02-infer.ipynb)
- [Predictive Infrastructure Monitoring (Scikit Learn)](https://github.com/v3io/tutorials/blob/master/demos/netops/04-infer.ipynb)
- [Twitter Feed NLP](https://github.com/v3io/tutorials/blob/master/demos/stocks/read-tweets.ipynb)
- [Real-time Stock data reader](https://github.com/v3io/tutorials/blob/master/demos/stocks/read-stocks.ipynb)
- [Twitter Feed NLP](https://github.com/v3io/tutorials/blob/master/demos/stocks/04-read-tweets.ipynb)
- [Real-time Stock data reader](https://github.com/v3io/tutorials/blob/master/demos/stocks/03-read-stocks.ipynb)

@@ -69,7 +69,7 @@ like this:


#### Define and view metrics
See istio [doc](https://istio.io/docs/tasks/telemetry/metrics-logs.html).
See istio [doc](https://istio.io/docs/tasks/telemetry/).

#### Expose Grafana dashboard behind ingress/IAP

@@ -46,15 +46,15 @@ kubectl create -f deploy/

## Creating an MPI Job

You can create an MPI job by defining an `MPIJob` config file. See [TensorFlow benchmark example](https://github.com/kubeflow/mpi-operator/blob/master/examples/tensorflow-benchmarks.yaml) config file for launching a multi-node TensorFlow benchmark training job. You may change the config file based on your requirements.
You can create an MPI job by defining an `MPIJob` config file. See [TensorFlow benchmark example](https://github.com/kubeflow/mpi-operator/blob/master/examples/v1alpha1/tensorflow-benchmarks.yaml) config file for launching a multi-node TensorFlow benchmark training job. You may change the config file based on your requirements.

```
cat examples/tensorflow-benchmarks.yaml
cat examples/v1alpha1/tensorflow-benchmarks.yaml
```
Deploy the `MPIJob` resource to start training:

```
kubectl create -f examples/tensorflow-benchmarks.yaml
kubectl create -f examples/v1alpha1/tensorflow-benchmarks.yaml
```

## Monitoring an MPI Job
@@ -316,7 +316,7 @@ spec:
restartPolicy: OnFailure
```

Follow TensorFlow's [instructions](https://www.tensorflow.org/tutorials/using_gpu)
Follow TensorFlow's [instructions](https://www.tensorflow.org/guide/gpu)
for using GPUs.

## Monitoring your job
@@ -108,7 +108,7 @@ Below are some examples of `KfDef` configuration files:

* [kfctl_k8s_istio.yaml](https://github.com/kubeflow/kubeflow/blob/master/bootstrap/config/kfctl_k8s_istio.yaml)
to install Kubeflow on an existing Kubernetes cluster.
* [kfctl_gcp_basic_auth.yaml](https://github.com/kubeflow/kubeflow/blob/master/bootstrap/config/kfctl_gcp_basic_auth.yaml)
* [kfctl_gcp_basic_auth.yaml](https://github.com/kubeflow/manifests/blob/master/kfdef/kfctl_gcp_basic_auth.yaml)
to set up a Google Kubernetes Engine (GKE) cluster with Kubeflow using basic
authentication.

@@ -195,7 +195,7 @@ Notes:

1. These are the minimum recommended settings on the VM created by minikube for kubeflow deployment. You are free to adjust them **higher** based on your host machine
capabilities and workload requirements.
1. Using certain hypervisors might require you to set --vm-driver option [specifying the driver](https://github.com/kubernetes/minikube/blob/{{< params "githubbranch" >}}/docs/drivers.md)
1. Using certain hypervisors might require you to set --vm-driver option [specifying the driver](https://github.com/kubernetes/minikube/blob/master/docs/drivers.md)
you want to use.

In case, you have the default minikube VM already created (following detailed installation instructions), please use the following to update the VM.
@@ -41,12 +41,12 @@ in the Kubeflow Pipelines sample repository.
This section assumes that you have already created a program to perform the
task required in a particular step of your ML workflow. For example, if the
task is to train an ML model, then you must have a program that does the
training, such as the program that
[trains an XGBoost model](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/train/src/train.py).
training, such as the program that
[trains an XGBoost model](https://github.com/kubeflow/pipelines/blob/master/components/deprecated/dataproc/train/src/train.py).

Create a [Docker](https://docs.docker.com/get-started/) container image that
packages your program. See the
[Docker file](https://github.com/kubeflow/pipelines/blob/master/components/dataproc/train/Dockerfile)
Create a [Docker](https://docs.docker.com/get-started/) container image that
packages your program. See the
[Docker file](https://github.com/kubeflow/pipelines/blob/master/components/deprecated/dataproc/train/Dockerfile)
for the example XGBoost model training program mentioned above. You can also
examine the generic
[`build_image.sh`](https://github.com/kubeflow/pipelines/blob/master/components/build_image.sh)

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