Update tfbatchpredict.md (#1517)

* Update tfbatchpredict.md

- Add Alpha notice to Batch Predict Documentation Page
- Added link to Batch Predict Project
- Added Alpha feature note that Batch Predict is not supported in Kubeflow >0.6 and code has not been updated in over a year.

* Remove outdated docs

Update tfbatchpredict.md

* Update tfbatchpredict.md

fix case

content/docs/components/serving/tfbatchpredict.md

 +++
 title = "TensorFlow Batch Predict"
-description = "Batch prediction for TensorFlow models"
+description = "See Kubeflow [v0.6 docs](https://v0-6.kubeflow.org/docs/components/serving/tfbatchpredict/) for batch prediction with TensorFlow models"
 weight = 60
 +++
 
-## Kubeflow Batch Predict
-
-Kubeflow batch-predict allows users to run predict jobs over a trained
-TensorFlow model in SavedModel format in a batch mode. It is
-[apache-beam](https://beam.apache.org/)-based and currently runs with a local
-runner on a single node in a Kubernetes cluster.
-
-
-## Run a TensorFlow Batch Predict Job
-
-**Note:** Before running a job, you should have [deployed kubeflow to your cluster](#deploy-kubeflow).
-
-To run batch prediction, we create a Kubernetes job to run beam.  Kubeflow provides a [ksonnet prototype](https://github.com/kubeflow/kubeflow/blob/{{< params "githubbranch" >}}/kubeflow/examples/prototypes/tf-batch-predict.jsonnet) suitable for you to to generate a component which you can then customize for your jobs.
-
-### Create the component
-
-```
-MY_BATCH_PREDICT_JOB=my_batch_predict_job
-GCP_CREDENTIAL_SECRET_NAME=user-gcp-sa
-INPUT_FILE_PATTERNS=gs://my_data_bucket/my_file_pattens
-MODEL_PATH=gs://my_model_bucket/my_model
-OUTPUT_RESULT_PREFIX=gs://my_data_bucket/my_result_prefix
-OUTPUT_ERROR_PREFIX=gs://my_data_bucket/my_error_prefix
-BATCH_SIZE=4
-INPUT_FILE_FORMAT=my_format
-
-ks registry add kubeflow-git github.com/kubeflow/kubeflow/tree/${VERSION}/kubeflow
-ks pkg install kubeflow-git/examples
-
-ks generate tf-batch-predict ${MY_BATCH_PREDICT_JOB}
-  --gcpCredentialSecretName=${GCP_CREDENTIAL_SECRET_NAME} \
-  --inputFilePatterns=${INPUT_FILE_PATTERNS} \
-  --inputFileFormat=${INPUT_FILE_FORMAT} \
-  --modelPath=${MODEL_PATH} \
-  --outputResultPrefix=${OUTPUT_RESULT_PREFIX} \
-  --outputErrorPrefix=${OUTPUT_ERROR_PREFIX} \
-  --batchSize=${BATCH_SIZE}
-```
-
-The supported parameters and their usage:
-
-  * **inputFilePatterns** The list of input files or file patterns, separated by commas.
-
-  * **inputFileFormat** One of the following values: json, tfrecord, and tfrecord_gzip.
-
-  * **modelPath** The path containing the model files in SavedModel format.
-
-  * **batchSize** Number of prediction instances in one batch. This largely
-    depends on how many instances can be held and processed simultaneously in the
-    memory of your machine.
-
-  * **outputResultPrefix** Output path to save the prediction results.
-
-  * **outputErrorPrefix** Output path to save the prediction errors.
-
-  * **numGpus** Number of GPUs to use per machine.
-
-  * **gcpCredentialSecretName** Secret name if used on GCP. Only needed for running the jobs in GKE in order to output results to GCS.
-
-You can set or update values for optional parameters after generating the
-component. For example, you can set the modelPath to a new value (e.g. to test
-out another model) or set the output to another gcs location (e.g. in order not
-to overwrite the results from previous
-runs). For example:
-
-```
-ks param set --env=default ${MY_BATCH_PREDICT_JOB} modelPath gs://my_new_bucket/my_new_model
-ks param set --env=default ${MY_BATCH_PREDICT_JOB} outputResultPrefix gs://my_new_bucket/my_new_output
-```
-
-
-### Use GPUs
-
-To use GPUs your cluster must be configured to use GPUs.
-
-  * Nodes must have GPUs attached.
-  * The Kubernetes cluster must recognize the `nvidia.com/gpu` resource type.
-  * GPU drivers must be installed on the cluster.
-  * For more information:
-      * [Kubernetes instructions for scheduling GPUs](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/)
-      * [GKE instructions](https://cloud.google.com/kubernetes-engine/docs/concepts/gpus)
-
-When all the conditions above are satisfied, you should set the number of GPUs to a positive integer. For example:
-
-```
-ks param set --env=default ${MY_BATCH_PREDICT_JOB} numGpus 1
-```
-
-This way, the batch-predict job will use a GPU version of docker image and add appropriate
-configuration to start the kubernetes job.
-
-
-### Submit the job
-
-```
-export KF_ENV=default
-ks apply ${KF_ENV} -c ${MY_BATCH_PREDICT_JOB_NAME}
-```
-
-The `KF_ENV` environment variable represents a conceptual deployment environment 
-such as development, test, staging, or production, as defined by 
-ksonnet. For this example, we use the `default` environment.
-You can read more about Kubeflow's use of ksonnet in the Kubeflow 
-[ksonnet component guide](/docs/components/ksonnet/).
-
-You should see that a job is started to provision the batch-predict docker image.
-Then a pod starts to run the job.
-
-```
-kubectl get pods
-kubectl logs -f ${POD_NAME}
-```
-
-You can check the state of the pod to determine if a job is running,
-failed, or completed. Once it is completed, you can check
-the result output location to see if any sensible results are generated. If
-anything goes wrong, check the error output location where the error message is
-stored.
-
-### Delete the job
-
-```
-ks delete ${KF_ENV} -c ${MY_BATCH_PREDICT_JOB_NAME}
-```
+[Batch predict](https://github.com/kubeflow/batch-predict) is not supported in Kubeflow versions greater than 0.6.