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<div class="col-md-12">
            
    <h2 id="the-mapr-data-platform">THE MAPR SANDBOX FOR HADOOP</h2>
    <p>The MapR Sandbox for Hadoop is a fully-functional single-node cluster that provides data scientists, developers, and other DataOps stakeholders a safe environment in which to explore MapR’s core data storage for files, tables, and streams, plus ecosystem components for Hadoop, HBase, Hive, Hue, Kafka, Pig, Spark, and more.</p>
    <p><h2 id="the-mapr-data-platform">THE MAPR SANDBOX FOR APACHE DRILL</h2></p>
    <p>The MapR Sandbox with Drill is a fully functional single-node cluster that can be used to get an overview of <a href="/products/apache-drill/">Apache Drill</a> in the MapR data platform. Data scientists, developers, and other DataOps stakeholders can use this sandbox environment to get a feel for the power and capabilities of Drill by performing various types of queries outlined in the <a href="http://drill.apache.org/docs/getting-to-know-the-drill-sandbox/">Drill tutorial.</a></p>
<h2 id

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    3  cat /opt/mapr/conf/mapr-clusters.conf
    4  ping localhost
    5  ping localhost.localdomain
    6  jps
    7  hadoop fs -ls /
    8  id mapr
    9  sudo su mapr
   10  maprlogin print
   11  maprlogin password
   12  passwd

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Create a docker network to bridge containers

docker network create mynetwork

Start StreamSets like this:

docker run -it -p 18630:18630 -d --name sdc --network mynetwork streamsets/datacollector

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?><!--
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

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apiVersion: v1
kind: ConfigMap
metadata:
  name: dsr-configmap
  namespace: idownard-cluster
data:
  MAPR_CLUSTER: idownard-cluster
  MAPR_CLDB_HOSTS: 10.24.1.7
  MAPR_HS_HOST: 10.24.1.7
  MAPR_CONTAINER_USER: mapr

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apiVersion: v1
kind: Pod
metadata:
  name: dsr-kube
  labels:
      app: dsr-svc
spec:
  containers:
  - name: dsr
    imagePullPolicy: Always

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enmac:private-kubernetes idownard$ kubectl get pods -n idownard-cluster
NAME                                 READY     STATUS      RESTARTS   AGE
admincli-975b9897d-rvsnv             1/1       Running     0          1h
cldb-0                               1/1       Running     0          1h
dataaccessgateway-5bcdcb4d7c-sgxsd   1/1       Running     5          1h
kafkarest-69984c5dcf-n22pw           1/1       Running     5          1h
ldap-0                               1/1       Running     0          1h
mapr-init-6mxvq                      0/1       Completed   0          1h
maprgateway-0                        1/1       Running     5          1h
mastgateway-6f94c7fd-lr7lm           1/1       Running     5          1h

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enmac:kubeflow-codelab idownard$ gcloud config set project mapr-demos


Updated property [core/project].


Updates are available for some Cloud SDK components.  To install them,
please run:
  $ gcloud components update

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def main(_):
  channel = grpc.insecure_channel(FLAGS.server)
  stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
  # Send request
  with open(FLAGS.image, 'rb') as f:
    # See prediction_service.proto for gRPC request/response details.
    data = f.read()
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'model'
    # request.model_spec.signature_name = 'predict_images'

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def export_model(sess, keys, architecture, saved_model_dir):
  if architecture == 'inception_v3':
    input_tensor = 'DecodeJpeg/contents:0'
  elif architecture.startswith('mobilenet_'):
    input_tensor = 'input:0'
  else:
    raise ValueError('Unkonwn architecture', architecture)
  in_image = sess.graph.get_tensor_by_name(input_tensor)
  inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}