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
<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 |
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 |
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
<?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 |
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 |
apiVersion: v1 | |
kind: Pod | |
metadata: | |
name: dsr-kube | |
labels: | |
app: dsr-svc | |
spec: | |
containers: | |
- name: dsr | |
imagePullPolicy: Always |
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 |
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 |
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' |
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)} |