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Expires on March 31, 2018

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Simply shop from March 1, 2018 to March 31, 2018, choose to pay with PayPal at checkout and your offer will be automatically applied to your purchase. Terms apply.

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"Eligible Participant": Open only to residents of any one (1) of the fifty (50) United States or the District of Columbia who: (1) are eighteen (18) years of age or older; and (2) are holders of a U.S. personal, premier, or business PayPal account in good standing ("Valid Account"); and (3) "receive an authorized email containing the invitation to participate in the offer (eligibility for/those who receive such an email will be determined solely by PayPal).""Eligible Purchase(s)": Purchases made in USD using a Valid Account. Eligible Purchases do not include: (1) send/receive money transactions (including those marked as a "Goods and Services" payments), (2) charitable donations, (3) purchases made using Pay After Delivery, PayPal.me, PayPal Cash card, and PayPal Debit MasterCard®, (4) PayPal transaction fees "Offer Period": Starts at 5:00:00 a.m. Eastern Time ("ET") on March 1, 2018 and ends at 11:59:00 p.m. ET on March 31, 2018. "Reward": $5 USD voucher that will be viewable in the "Offers" section of the Eligible Participant's PayPal account for personal and premier accounts. For business accounts, the Reward will only be visible during checkout and on the purchase transaction receipt(s). How to Redeem: For certain offers as described in the invitation for the offer, Eligible Participants must activate the offer prior to redemption by following the steps in the invitation. Once received and activated (if applicable), the Reward will be applied automatically to the Eligible Participant's next Eligible Purchase made during the Offer Period in accordance with these terms. Use of the Reward will be reflected on the Eligible Participant's PayPal receipt and/or in the transaction details of his/her PayPal account. There is a limit of one (1) Reward per Valid Account.Miscellaneous: Redemptions are final, will not be returned, and are subject to review and verification. The Reward has no cash value and cannot be redeemed for cash or withdrawn from the Eligible Participant's PayPal account, except in PayPal's sole discretion. If item(s) purchased using a Reward are returned and the Offer Period has expired, PayPal will first refund the cash spent in the form of a credit to Eligible Participant's PayPal balance and purchase amount in excess of the cash spent (i.e., the value of the Reward) will not be refunded. If the Offer Period has not expired, the value of the Reward will be returned to the Offers section of the PayPal account and will be available for use by the account holder. PayPal may provide an alternate reward of equal value if it is unable for any reason to fulfill the Reward. PayPal reserves the right to cancel, suspend or modify this offer in part or in its entirety at any time without notice, for any reason in its sole discretion. Similar offers may run at the same time; qualification for this offer does not constitute qualification for any other offer. PayPal is not responsible and/or liable for any lost, stolen, late, incomplete, illegible, interrupted, delayed, or misdirected e-mail, Reward, or offer-related materials or correspondence or if any participant's e-mail address, Valid Account, or other contact information does not work, is deleted, or is changed without participant giving prior written notice to PayPal. Offer is void where prohibited, if Eligible Purchases are not completed through legitimate channels, or if any offer-related materials are counterfeit, altered, defective, tampered with or irregular in any way. Certain offers may not be transferable. Any questions relating to the offer will be resolved in PayPal's sole discretion and its decisions related to the offer will be final and binding.

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How to redeem

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Business Development Representative Diffbot -

Business Development RepresentativeDiffbot -

https://www.indeed.com/viewjob?jk=363d9d2a6aad465f&q=Technical+Sales+Engineer&l=Silicon+Valley%2C+CA&tk=1c8n12gosbv5rc8j&from=web&vjs=3

About Diffbot  At Diffbot, we believe that access to structured information will be the critical resource for the coming wave of intelligent applications—for everything from new app experiences, to search assistants, to enterprise business intelligence. That’s why our team of world-class AI engineers is building a machine that can autonomously synthesize the world’s largest database of structured information -- now, we’re looking for those that want to join us on this mission.  Business Development Representative (BDR)  The Business Development Representative (BDR) at Diffbot is the first line of customer identification and qualification for the Data Solutions team. This role helps qualify inbound and prospect outbound leads for the company’s sales process -- BDRs are innately curious about how AI will revolutionize the nature of information processing across all industries in the future. They are comfortable speaking the language of all of the various stakeholders in a large organization: ranging from the developer up to the CEO, to learn about how they use data and educate them about how our technology can make their workflows more efficient.  Role Responsibilities  Lead prospecting targeted outbound accounts for new opportunities; respond to and qualify inbound leads for potential customers Meet and exceed set monthly opportunity generation goals Communicate professionally and build relationships with C-level and VP level contacts at companies across various industries Role Requirements  Ideally 1-2+ years of experience in B2B software sales Strong and clear communicator, good at managing relationships with a variety of stakeholders: technical, product, and business buyers Experience using Salesforce CRM BS / BA degree Preferred Skills  Exposure to web development -- can speak the language of APIs, JSON, REST and have some knowledge of a front-end language such as Python, Ruby, etc Experience communicating with and selling to C-level and VP level contacts at companies across various industries Be able to work independently in an entrepreneurial, start-up environment Be able to understand and successfully promote technical offerings and solutions Passion and curiosity in how AI, machine learning, big data will revolutionize all industries Goal driven, proactive, outgoing and energetic individual To apply, please submit: A cover letter introducing yourself (describe your interest and area of speciality) Your resume, with your LinkedIn profile and website How did you hear about Diffbot?

Sr. Sales Engineer USA | #1249 Software Placement Group -

Sr. Sales Engineer USA | #1249Software Placement Group -

https://www.indeed.com/viewjob?jk=19d7a1adf010bd93&tk=1c8n12gosbv5rc8j&from=serp&vjs=3

REQUIRED SKILLS: Experience supporting enterprise software sales cycles with large organizations ($5 Billion in revenue and above)  Excellent communication skills including the ability to deliver compelling presentations Customer-focused work habits and attention to detail Open and proactive approach to interpersonal communication Ability to quickly learn new products and technical environments Ability to systematically assess and accurately diagnose software-related problems Creativity and Imagination the ability to think out-side the box Excellent time-management skill TECHNICAL BACKGROUND: 5+ years development experience with core infrastructure technologies including operating systems (Linux, Windows), relational databases (e.g. Oracle, MySQL, PostgreSQL) 3+ years of experience implementing or supporting data warehouse and business intelligence technologies At least one-year experience working with a data integration (ETL) tool Comfortable working in the Linux shell Solid understanding of Java architectures Practical working knowledge of web application development tools and components such as XML, CSS, HTML, JDBC, JavaScript, SQL and shell scripting Experience with security systems and LDAP a plus Java programming experience a plus

Sales Engineer Ozen Engineering, Inc. -

Sales EngineerOzen Engineering, Inc. -

https://www.indeed.com/cmp/ANSYS,-Inc./jobs/Sales-Engineer-7991a8d40a9d9b00?sjdu=QwrRXKrqZ3CNX5W-O9jEvdWaFExGg97Dg5JRAWGCkoDraCQppGMX-yPfH2i0xGVTb-SWwwsrEAbZZf_2FrFKLw&tk=1c8n12gosbv5rc8j&vjs=3

JOB SUMMARY The Sales Engineer is responsible for all sales activities, from lead generation, lead qualification through close in an assigned territory or set of accounts, and is responsible for maximizing campaign efforts, account penetration, sales growth and profitability. The position sells ANSYS software products and services directly to end users primarily via face-to-face contact, phone and email. Sales Engineers will be located in Ozen Engineering, Inc. offices in Sunnyvale, CA. Ozen Engineering is an ANSYS channel partner and distributor in N. California. ESSENTIAL FUNCTIONS Perform research on target territory and/or accounts, identify contacts, determine potential and create and qualify leads Perform sales activities of products and services through telephone, email, and face-to-face contact via discovery calls and cold calling Establish, qualify, develop and maintain business relationships with current and prospective customers Drive centralized competitive campaigns to build pipeline, close business and exceed sales goals Ensure that sales pipeline remains full with qualified prospects, document information and provide accurate pipeline and forecast for new sales and renewal business Develop clear and effective written proposals/quotations for current and prospective customers Learn and remain knowledgeable of the ANSYS products and solutions, take certification tests Coordinate lead generation and sales efforts with assigned channel partner’s field sales, marketing, sales management, and pre and post-sale technical team Other job related duties may be assigned by management from time to time JOB MINIMUM REQUIREMENTS AND QUALIFICATIONS Bachelor’s degree in engineering or technical field. Strong work ethic, great communication skills, good listener, persuasive, self-motivated, collaborative, confident, intelligent and competitive Highly proficient computer skills including office productivity tools, email efficiency, CRM and social media OTHER INFORMATION Minimum of 1 year of experience in field or inside sales is preferred Demonstrated awareness and understanding of simulation software industry Ability to maintain an upbeat and positive attitude at all times Must demonstrate leadership qualities and maintain the highest ethical standards Job Type: Full-time Required education: Bachelor's

Sales Engineer, Google Cloud

Sales Engineer, Google Cloud https://www.indeed.com/viewjob?jk=09208c8c819dcdc5&tk=1c8n12gosbv5rc8j&from=serp&vjs=3 Google  2,115 reviews - Remote locations may be available in the following states: Alaska, Indiana, Michigan, Minnesota, Missouri, Ohio or Wisconsin When leading companies choose Google Cloud it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.  You're a Sales Engineer with excellent technical, communication, and organizational skills, and previous experience in cloud platform and infrastructure products, search products, content/document management repository systems, and SQL-based relational databases.  In this role, you will work hand-in-hand with the Sales team to introduce Google Cloud to our customers. You will help prospective customers and partners understand the power of Google Cloud, explaining technical features and problem-solving any potential roadblocks.  Google Cloud helps millions of employees and organizations empower their employees, serve their customers, and build what’s next for their business — all with technology built in the cloud. Our products are engineered for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. And our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life.  Responsibilities Work with the team to identify and qualify business opportunities, identify key customer technical objections and develop the strategy to resolve technical blockers. Own the technical relationship with Google’s customers, including managing product and solution briefings, proof-of-concept work, and the coordination of additional technical resources. Work hands-on with customers to demonstrate and prototype Google Cloud product integrations in customer/partner environments. Recommend integration strategies, enterprise architectures, platforms and application infrastructure required to successfully implement a complete solution using best practices on Google Cloud. Travel to customer sites, conferences, and other related events as required. Qualifications Minimum qualifications: BA/BS degree in Computer Science or equivalent practical experience. Experience serving in the capacity of a technical sales engineer in a cloud computing environment or equivalent experience in a customer-facing role (including working as a member of a professional services or systems engineering team). Experience with Big Data, PaaS, and IaaS technologies. Experience presenting and delivering technical pitches. Preferred qualifications: Master's degree in Computer Science or other technical field. Experience with networking concepts including: routing, VPNs, load balancers, and firewalls. Experience in and understanding of data and information management - especially as it relates to big data trends and issues within businesses. Experience architecting and developing software or infrastructure for scalable, distributed systems. Ability to quickly learn, understand, and work with new emerging technologies, methodologies and solutions in the cloud/IT technology space. 30+ days ago - Save Job  - Original Job Apply Now Other Jobs You May Like Software Engineer EverString-San Mateo, CA 17 days ago Easily apply Associate Sales Engineer (Full Time) - United... Cisco-San Jose, CA Cisco Systems -  6 days ago Sales Engineer, Application Development, Goog... Google-Mountain View, CA 13 days ago Cloud Engineer OSI Engineering, Inc.-Mountain View, CA 11 hours ago Easily apply Cloud Engineer Axelon Services Corporation-Mountain View, CA 1 day ago See More Recommended Jobs-9 new Sales Engineer jobs in Mountain View, CA Jobs at Google in Mountain View, CA Sales Engineer salaries in Mountain View, CA

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Technical Sales Etron Technology America, Inc. -

Technical SalesEtron Technology America, Inc. -

https://www.indeed.com/cmp/Etron-Technology/jobs/Technical-Sales-21424836a9ca986b?sjdu=QwrRXKrqZ3CNX5W-O9jEvUzVNu4SdAfeVRWoZazBs03633ioYZXgwSdDJ8f15oanDu7agHTWCjK70I_TwHeMmw&tk=1c8n12gosbv5rc8j&vjs=3

Job Descriptions: Promptly handle customer inquiries and manage project by executing and following company online systems Plan and manage sales activities with assigned customer accounts Present quotations and negotiate term & conditions with customers Interface with Customers, Factory and Design Engineers on production and prototype orders Work with manager to develop and execute strategic sales & marketing plan Coordinate customer visits, and periodic meetings at customers facilities Support the sales organization with new business opportunities Basic Requirements: BS/BA degree or equivalent experience; Electrical Engineering or Computer Engineering background preferred Excellent verbal, written communications and presentation skills Demonstrate initiative and be proactive on follow-up, organizational and time management skills Attention to details Self-starter, problem-solver, works well independently with a minimum of supervision Minimum 2 years + working in sales Proficiency on M/S Office (PowerPoint, Excel, Word) Must have a flexible work schedule as frequent communication with Asia is required 10% ~ 25% business travel required Preferred Qualifications: Past experience working in semiconductor or IC design houses Past working experience in the Image Sensor, Video or Graphic processing fields USB Type-C and power Delivery experience is a plus Job Type: Full-time

MailChimp Master List

Combine Lists to a Master List That Uses Groups Updated: Feb 1, 2018 Copy Article URL Simplify your list management when you combine your lists into a master list that uses groups to organize your subscribers. Groups help you target campaigns to maximize subscriber engagement, and reduce the need for duplicate subscribers across lists. In this article, you'll learn how to back up your existing lists and create your master list with groups. Before you begin, decide which of your existing lists you want to use as your master list, or create a new list to use. Before You Start Here are some things to know before you begin this process. In order to complete this process, you'll need to have two or more subscriber lists in MailChimp that you can consolidate to one master list. This process is not mandatory but can help organize your list management by keeping all your subscribers in one place and can potentially reduce your monthly bill if you currently have duplicate subscribers across lists. This process involves four major tasks: back up your existing lists, create groups in your master list, combine your lists, and then clean your new master list. It's important that you complete all four tasks. When you combine your lists, you're only combining subscriber data. Any data associated with your old lists, like subscriber campaign activity, is not brought over to your master list when you combine. This data will continue to live in your old lists unless you choose to delete them. Back Up Your Lists All list actions are permanent and can't be undone, so before you create your new list, you need to back up your existing lists. Keeping a backup is a safeguard, in case you change your mind or something goes wrong. For each list you want to combine to a master list, you need to export your contacts. To export all list contacts, follow these steps. Navigate to the Lists page. Click the title of the list you want to export. Click the Export List button to download a CSV file of your contacts. When the list has finished exporting, click Export as CSV. We'll automatically separate your contacts into separate files for subscribed, cleaned, and unsubscribed addresses. click export as csv Repeat the above steps for each of the lists you're working with. If you're using an existing list as your master list, be sure to also export that list for backup. Create Groups in Your Master List To add groups to your master list, follow these steps. Navigate to the Lists page. Click the master list's drop-down menu, and choose Manage contacts. click manage contacts Click Groups. Click Create Groups to expand the group options. Select the As checkboxes option. In the Group category field, type a title or category for your groups. Input a Group name that corresponds to each of your original lists. Click Add Group if you need more Group names and click Save when you're done. Combine Your Lists to Your Master List There are two different ways to combine your existing lists into your master list. You can use the Combine feature or import your exported CSV files into a single list. Before You Combine Match the field labels between your old lists and your master list before you combine. When you combine lists, information stored in the list you're moving your subscribers from will overwrite existing data in the list you're adding to. While field labels need to match, merge tags don't. If it's going to be difficult to make sure your field labels match, consider combining lists by importing from a CSV. Importing subscribers to groups is an additive-only process. When you combine lists that contain groups, we will add subscribers to any group they're in across those lists, but we don't remove subscribers from groups. Note MailChimp requires that lists must be retained for seven days after a campaign or automation email is sent. If you've recently sent to one of your lists, you won't be able to combine it until after the seven day period. Combining your lists results in an independent master list intended to replace your existing lists. It does not connect your master lists to your existing lists, so changes made to your old list will not sync with your master list. Combine Using the Combine Feature To combine lists already in your account with the Combine feature, follow the steps below. The Combine feature moves active subscribers from one list into the second list as brand new subscribers. It does not transfer your unsubscribed and cleaned addresses. From the Lists page, click the drop-down menu for the list you want to add to your master list and select Combine lists. click combine lists Use the drop-down menu to choose the name of your master list and check any groups you want to add these subscribers to. Then, click Next. Type CONFIRM into the pop-up modal, and click Combine Lists. Repeat this process for each of the lists you want to combine with your master list. When you've combined all of your lists, remove your cleaned and unsubscribed addresses. Combine Lists from a CSV File To import the the backup CSV files you made, follow the steps below. With the CSV method, subscribers will remain in your old list until you delete them or until you delete your old list. To import the backup CSV files you made, follow the steps below. Navigate to the Lists page, click the drop-down menu for your master list, and select Import. Select CSV or tab-delimited text file, then click Next. import from csv or text file Click Browse to choose the list file you exported. Click the I understand that my billing plan may be automatically upgraded checkbox. Your account will only be upgraded if your list import causes your subscriber count to exceed your current billing plan. Remove your subscribers from your old list to avoid duplicate subscribers across lists. Click Next to move on to the Import Subscribers step and match the columns in your list import to the columns in your MailChimp list. Click Save for the Email Address column. We automatically match the column in your import that contains email addresses to the required Email Address field in your list. Save button on Email Address column. On the second column, click Save, or click the drop-down menu to select a different field. To create a new column in your list, choose New Column Name. Example of new column added during matching with Phone as the column name. The editing pane for each column includes the current column name, field type, and example data from your list, for reference. You can click Skip to move over a column and exclude it from the final import. This will not remove a column that already exists in your list. Repeat step 7 for all unmatched columns. You must match or skip all columns to complete your import. We'll display a success message when you've either matched or skipped all your columns. Success message displayed when all your columns have been matched. Click Next to review your final import settings. On the You're all set to import screen, review the settings for your import. We'll display the method you chose for your import and the number of columns you matched. You can click Details to view which columns you matched to fields in your list, or Edit to make changes. If you have groups set up in your list, you'll have the options to import your subscribers to one of your groups. Click Import. Repeat this for each list you combine into your master list. When you're done, remove your cleaned and unsubscribed addresses. Note MailChimp requires that lists must be retained for seven days after a campaign or automation email is sent. If you've recently sent to one of your lists, you won't be able to combine it until after the seven day period. Remove Unsubscribed and Cleaned Addresses If you had subscribers who were on multiple lists, you might have multiple or different data for them. To help keep your list healthy, follow the steps below to remove your unsubscribed and cleaned addresses for each list you combined. Open the unsubscribed/cleaned CSV export files on your computer using a spreadsheet program. From the Lists page, click the drop-down menu for your master list and choose Manage contacts. click manage contacts Click Unsubscribe people. Copy all your unsubscribed and cleaned email addresses from the CSV files, and paste them into the Unsubscribe People screen, then click Unsubscribe. Repeat these steps for each list you combined to remove cleaned and unsubscribed addresses. Delete Your Old Lists When you're finished removing your cleaned and unsubscribed addresses from your master list, follow the steps below to delete your old lists from your account. Save all your exported CSV files in case you need your original lists again later. This process also deletes all list stats. If you want to combine your lists but don't want to delete your list stats, you could remove your subscribers from your old lists, but leave the list intact. Navigate to the Lists page. Check the box next to the list you want to delete. Click Delete. Note MailChimp requires that lists must be retained for seven days after a campaign or automation email is sent. If you've recently sent to one of your lists, you won't be able to combine it until after the seven day period. Be sure to update any signup form URLs, and pop-up, embedded, or hosted forms to reflect your master list. Was this article helpful? Anything else we can do to improve our site? Related Articles Create a New List Technical Support Free accounts do not have access to email and chat support. You can upgrade your account for full access to technical support. Back to top ©2001-2018 All Rights Reserved. MailChimp® is a registered trademark of The Rocket Science Group.

Dan Bikle CS101a Setting up and Using Google Cloud Platform

Feb 15:

This week we will study some Web Resources which train us to use Google GCP which is a competitor of Amazon AWS:

https://codelabs.developers.google.com/cpb100

**
Feb 18:

This week we will study some Web Resources which train us to use Google GCP which is a competitor of Amazon AWS:

https://codelabs.developers.google.com/cpb100

This will be a repeat of the Feb 15 Meetup.

**
Feb 24:

This week we will study some Web Resources which train us to use Google GCP which is a competitor of Amazon AWS:

http://cs101.us

https://codelabs.developers.google.com/cpb100

**
Feb 25:

This week we will study some Web Resources which train us to use Google GCP which is a competitor of Amazon AWS:

http://cs101.us

https://codelabs.developers.google.com/cpb100

**
Mar 2:

LOCATION: 333 W. Maude [Maude and Mathilda] #110

This week we will study some Web Resources which train us to use Google GCP which is a competitor of Amazon AWS:

http://cs101.us

https://codelabs.developers.google.com/cpb100

If you want to sign up for a free GCP account, bring your laptop, gmail-account, and a credit card.

The WIFI at this location is EXCELLENT.

We offer: Table, Chair, Power, and WIFI.

This 2 hour Meetup is timed to start at 5pm Friday so you can attend Free-Training rather than sit in traffic.

**

This is the MEAT of what Dan Bikle is teaching:
https://cs101.herokuapp.com/lessons

https://cs101.herokuapp.com/lessons/lesson001

Computer Science 101 cs101.us About Blog Contact Links Lessons

Lesson 001 [ Google GCP ] I have taught this lesson several times now. Most of this lesson is done with a browser. I offer tips for success. Use Google-Chrome browser. Dedicate a new browser window to this lesson. If you need a browser to check news or e-mail, do it in another window. Get a new window with: File -> New Window Why? Because you want to avoid working in a window with many tabs. On a piece of paper, write down a useful tab-organization: Tab 1: cs101.us Tab 2: codelabs Tab 3: github Tab 4: GCP 1 Tab 5: GCP Cloud-Shell Tab 6: DataLab Tab 7: GCP 3 I started this lesson by creating a new Chrome-window. I created 7 tabs in that window. In tab 1 I loaded this URL: http://cs101.us In tab 2 I loaded this URL: https://codelabs.developers.google.com/cpb100 In tab 3 I loaded this URL: https://github.com/GoogleCloudPlatform In tab 4 I loaded this URL: https://console.cloud.google.com In tabs 5,6,7 I had done nothing (yet). After I created the 7 tabs, my Chrome-window looked like this: I clicked on tab 2, the codelabs-tab. I continued this lesson by studying the URL listed below: https://codelabs.developers.google.com/cpb100 On that page I assume the correct ordering of some links is listed below: https://codelabs.developers.google.com/codelabs/cpb100-free-trial https://codelabs.developers.google.com/codelabs/cpb100-compute-engine https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql https://codelabs.developers.google.com/codelabs/cpb100-dataproc https://codelabs.developers.google.com/codelabs/cpb100-datalab https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset https://codelabs.developers.google.com/codelabs/cpb100-tensorflow https://codelabs.developers.google.com/codelabs/cpb100-translate-api https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat The above links are discussed below: free-trial compute-engine cloud-storage cloud-sql dataproc datalab bigquery-dataset tensorflow translate-api serverless-ingest distributed-landsat Free-Trial Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cpb100-free-trial I saw a simple path through 4 pages. The third page had a noteworthy URL I used to get $300 of cloud-credit from Google: I loaded the URL into the GCP-tab: https://console.developers.google.com/freetrial I logged in using my gmail account. It worked well; I gave it my credit-card info. It gave me $300 of cloud-credit from Google. I hope that you can also get that credit. Next, I visited the URL listed below: https://console.cloud.google.com/projectcreate I used that page to create a project called cpb100. Google needed 4 minutes to create project cpb100. Although I wanted the name to be cp100, Google added some text to the end of the name: cpb100-195004 Google created a URL for the project: https://console.cloud.google.com/home/activity?project=cpb100-195004 In the upper right I saw a blue-button called "UPGRADE". I clicked the button. Next, I accepted the upgrade offer because I wanted access to services required by future training sessions. Then I verified that the "hamburger-link" in the upper-left behaved well when I clicked it. It did behave well and I was ready to move on. In the codelabs-tab, I returned to the URL listed below: https://codelabs.developers.google.com/cpb100 [top] Compute-Engine Next, in the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-compute-engine I quickly clicked through the first two links in that page and landed at this URL: https://codelabs.developers.google.com/codelabs/cpb100-compute-engine/#2 It asked me to click the upper-left-hamburger-link in my project page. I went to the GCP-tab and did that. Then I followed the advice to click the "Compute Engine" link on the left: https://console.cloud.google.com/compute I saw this: I clicked the "Create" button. It served me a form from this URL: https://console.cloud.google.com/compute/instancesAdd I saw this: I clicked radio-button: "Allow full access to all Cloud APIs" I deviated from the tutorial and clicked the link: "SSH keys" Note: If you are on windows, you can get ssh from the URL listed below: https://git-scm.com/download/win My intent was to ease my ability to ssh into the GCP-Instance from a shell in my laptop. The text I mouse-copied into the GCP SSH-key-form came from this file: ~/.ssh/id_rsa.pub It looks like this: ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC8cbLxnaqPPznHz9DgMq xg38LPxRTRT1qjyQH0cGEHHtHVYHgPdCNvW++0ArCuJVxiQ7fx XvK2cYThurYozSkI6uwxVSPBoJgsLmLPvhc+JshDHi7SgtWl4b 8JZlnL5dMPQNo61p/qGmqZpKxXYJanY0zN4WnB17vlnVFhXL2j 3U3YKvifIC8a6gRKitG+XFGmj5sZKbJuqbnfhD93ytcRGV+rEM VipYAl2XBs27K0VGwK+u3NOOerWXjRrgqIo9Frk7C4rps/dMYd 56QKxnVumr24TUJ0TlymsCYkhD9qDHyJHxGTyN5BAzUpryphd7 QDLZn+Rdrm4Ssu8/jclLPH dan@ubu42.net Notice the last line. The last line asks GCP to create a Linux account named "dan" in the new Linux instance. I saw this: Next, I clicked the blue "Create" button. GCP started the instance in about 1 minute. I saw the corresponding IP address: 35.229.101.148 I saw this: I tried to login with this shell command: ssh 35.229.101.148 It failed. I used a shell command which asked to login as "dan". ssh dan@35.229.101.148 The above command worked perfectly. If you do not know how to start a shell on your laptop, you should click the SSH-link in your GCP-tab: Next, I returned to the codelabs-tab; I studied the instructions in this page: https://codelabs.developers.google.com/codelabs/cpb100-compute-engine/#4 I returned to my shell-window. I followed instructions I had just read: sudo apt-get update sudo apt-get -y -qq install git git --version The above shell commands worked perfectly in my new GCP instance. In the codelabs-tab, I returned to the URL listed below: https://codelabs.developers.google.com/cpb100 That ended my interaction with the CPB100-node I call: "Compute-Engine". [top] Cloud-Storage Next, in the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage I studied the page there and clicked through until I landed at this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#2 I verified that I could ssh into the GCP instance I had created earlier. I saw this: After the instance served me a shell prompt, I issued a shell command: git clone https://github.com/GoogleCloudPlatform/training-data-analyst That command worked well. I ran more shell commands: cd training-data-analyst/CPB100/lab2b less ingest.sh bash ingest.sh head earthquakes.csv In the codelabs-tab, I clicked the next node in the page and landed on this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#3 I read the content on that page. Next, I clicked the github-tab. I visited the URL below: https://github.com/GoogleCloudPlatform/datalab-samples/blob/master/basemap/earthquakes.ipynb I studied the above page. I returned to the shell. I ran two shell commands inside the GCP instance: bash install_missing.sh python transform.py The last command resulted in some warnings from Python but I am confident it worked. I noticed a new file which python created: dan@instance-1:~/training-data-analyst/CPB100/lab2b$ ls -latr total 632 -rw-r--r-- 1 dan dan 751 Feb 13 08:22 earthquakes.htm -rw-r--r-- 1 dan dan 637 Feb 13 08:22 commands.sh -rwxr-xr-x 1 dan dan 3074 Feb 13 08:22 transform.py drwxr-xr-x 2 dan dan 4096 Feb 13 08:22 scheduled -rwxr-xr-x 1 dan dan 680 Feb 13 08:22 install_missing.sh -rwxr-xr-x 1 dan dan 759 Feb 13 08:22 ingest.sh drwxr-xr-x 8 dan dan 4096 Feb 13 08:22 .. -rw-r--r-- 1 dan dan 297347 Feb 13 08:43 earthquakes.csv drwxr-xr-x 3 dan dan 4096 Feb 13 08:56 . -rw-r--r-- 1 dan dan 313185 Feb 13 08:56 earthquakes.png dan@instance-1:~/training-data-analyst/CPB100/lab2b$ dan@instance-1:~/training-data-analyst/CPB100/lab2b$ dan@instance-1:~/training-data-analyst/CPB100/lab2b$ I copied the PNG file to my laptop and looked at it: In the codelabs-tab, I clicked the next node in the page and landed on this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#4 I clicked on the GCP-tab. I visited the URL below: https://cloud.google.com/ I hovered the button: "GO TO CONSOLE". I made note of its href-URL: https://console.cloud.google.com I clicked the button. It sent me to a page I call: "Dashboard". I clicked the upper-left-hamburger-link and from there clicked: "Storage". It served me a page with blue-button: "Create bucket". I clicked "Create bucket". It served me a form asking me to name a new bucket. I called my new bucket: cs101feb2018 In retrospect, I can see now that was a poor naming choice. Now I know to create buckets with shorter names. At the bottom of the form I clicked the blue "Create" button. I saw evidence that it worked. It served me a page, called "Browser", prompting me to upload files into the bucket. The page reminds me of the page served to me by AWS when I work with S3. I saw this: In the codelabs-tab, I returned to the cpb100 page: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#4 I clicked the next node in the page and landed on this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#5 I returned to the shell prompt of my GCP instance and type a command: gsutil cp earthquakes.* gs://cs101feb2018 I am confident it worked. I verified by returning to the "Browser" page in the GCP-tab and inspecting the contents of cs101feb2018. It listed three files: earthquakes.csv earthquakes.htm earthquakes.png I saw this: In the codelabs-tab, I returned to the cpb100 page: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#5 I clicked the next node in the page and landed on this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#6 I returned to the "Browser" page in the GCP-tab. I checked the boxes to "Share publicly" the three files in cs101feb2018. I list the resulting URLs below: https://storage.cloud.google.com/cs101feb2018/earthquakes.csv https://storage.cloud.google.com/cs101feb2018/earthquakes.htm https://storage.cloud.google.com/cs101feb2018/earthquakes.png Using a laptop-shell, I verified that the public could see earthquakes.png cd /tmp wget https://storage.cloud.google.com/cs101feb2018/earthquakes.png I tried to use an incognito-browser to see earthquakes.png The google server asked me to authenticate. Google should not force me to login to gmail in order to see publicly-shared content. Based on this "feature", AWS-S3 is clearly superior to GCP-Storage. In the codelabs-tab, I returned to the cpb100 page: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#6 I clicked the next node in the page and landed on this URL: https://codelabs.developers.google.com/codelabs/cpb100-cloud-storage/#7 I followed the instructions in that page to delete my GCP instance. I saw this: In the codelabs-tab, I returned to the URL listed below: https://codelabs.developers.google.com/cpb100 That ended my interaction with the CPB100-node I call: "Cloud-Storage". [top] Cloud-Sql This sub-lesson has many steps and offers many opportunities to get lost. Good Luck! In the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#0 I studied the next page in sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#1 I studied the next page in sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#2 In the GCP-tab, I visited this URL: https://console.cloud.google.com I found the tiny "Cloud-Shell" button near the upper-right. I hovered it to see: "Activate Google Cloud Shell". I saw this: I clicked it. A dark window appeared at the bottom. After 15 seconds, a white shell prompt appeared in the window. I tried some shell commands and saw this: Welcome to Cloud Shell! Type "help" to get started. cs101@cpb100-195004:~$ id uid=1000(cs101) gid=1000(cs101) groups=1000(cs101),4(adm),27(sudo),999(docker) cs101@cpb100-195004:~$ df -h Filesystem Size Used Avail Use% Mounted on none 25G 19G 5.0G 79% / tmpfs 853M 0 853M 0% /dev tmpfs 853M 0 853M 0% /sys/fs/cgroup /dev/sda1 25G 19G 5.0G 79% /etc /dev/sdb1 4.8G 11M 4.6G 1% /home shm 64M 0 64M 0% /dev/shm cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ ls -la total 24 drwxr-xr-x 2 cs101 cs101 4096 Feb 13 12:06 . drwxr-xr-x 4 root root 4096 Feb 13 12:04 .. -rw------- 1 cs101 cs101 9 Feb 13 12:06 .bash_history -rw-r--r-- 1 cs101 cs101 220 May 15 2017 .bash_logout -rw-r--r-- 1 cs101 cs101 3564 Feb 9 16:03 .bashrc -rw-r--r-- 1 cs101 cs101 675 May 15 2017 .profile lrwxrwxrwx 1 cs101 cs101 38 Feb 13 12:04 README-cloudshell.txt -> /google/devshell/README-cloudshell.txt cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ ifconfig docker0: flags=4099<UP,BROADCAST,MULTICAST> mtu 1500 inet 172.18.0.1 netmask 255.255.0.0 broadcast 0.0.0.0 ether 02:42:77:65:a7:42 txqueuelen 0 (Ethernet) RX packets 0 bytes 0 (0.0 B) RX errors 0 dropped 0 overruns 0 frame 0 TX packets 0 bytes 0 (0.0 B) TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0 eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST> mtu 1460 inet 172.17.0.2 netmask 255.255.0.0 broadcast 0.0.0.0 ether 02:42:ac:11:00:02 txqueuelen 0 (Ethernet) RX packets 366 bytes 105824 (103.3 KiB) RX errors 0 dropped 0 overruns 0 frame 0 TX packets 326 bytes 54222 (52.9 KiB) TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0 lo: flags=73<UP,LOOPBACK,RUNNING> mtu 65536 inet 127.0.0.1 netmask 255.0.0.0 loop txqueuelen 0 (Local Loopback) RX packets 49 bytes 3813 (3.7 KiB) RX errors 0 dropped 0 overruns 0 frame 0 TX packets 49 bytes 3813 (3.7 KiB) TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0 cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ I popped that shell window so it had its own tab. I moved that tab so it was tab #5, the tab after GCP-tab. After that, GCP issued an error. I sense that it some kind of GCP-bug: It is unfortunate that GCP lacks reliability. The error suggested that I send feedback. So I did that and halted my activity in this lab for a day. After 24 hours, I continued. In GCP-tab I started a new Cloud-Shell. I move the shell to its own tab (tab #5). Cloud-Shell stopped issuing errors. In the codelabs-tab, I returned my attention to this page: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#2 In the Cloud-Shell-tab, I typed a shell command which looked familiar: git clone https://github.com/GoogleCloudPlatform/training-data-analyst That worked well; I entered another shell command: cd training-data-analyst/CPB100/lab3a I entered another shell command: cat cloudsql/table_creation.sql I saw some SQL syntax: CREATE DATABASE IF NOT EXISTS recommendation_spark; USE recommendation_spark; DROP TABLE IF EXISTS Recommendation; DROP TABLE IF EXISTS Rating; DROP TABLE IF EXISTS Accommodation; CREATE TABLE IF NOT EXISTS Accommodation ( id varchar(255), title varchar(255), location varchar(255), price int, rooms int, rating float, type varchar(255), PRIMARY KEY (ID) ); CREATE TABLE IF NOT EXISTS Rating ( userId varchar(255), accoId varchar(255), rating int, PRIMARY KEY(accoId, userId), FOREIGN KEY (accoId) REFERENCES Accommodation(id) ); CREATE TABLE IF NOT EXISTS Recommendation ( userId varchar(255), accoId varchar(255), prediction float, PRIMARY KEY(userId, accoId), FOREIGN KEY (accoId) REFERENCES Accommodation(id) ); I can see in the above syntax that the SQL script declares three tables and their columns: Accommodation id varchar(255), title varchar(255), location varchar(255), price int, rooms int, rating float, type varchar(255), Rating userId varchar(255), accoId varchar(255), rating int, Recommendation userId varchar(255), accoId varchar(255), prediction float, I see two relationships in the above syntax: An Accommodation has 0 or more Ratings An Accommodation has 0 or more Recommendations The tables might support these scenarios: When a user rates a house, the app adds a row to Rating. When admin adds a house for rent, the app adds a row to Accommodation. When ML-app predicts a user rating for a house, ML-app adds a row to Recommendation. In the Cloud-Shell-tab I ran another command and collected the output: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ head cloudsql/*.csv ==> cloudsql/accommodation.csv <== 1,Comfy Quiet Chalet,Vancouver,50,3,3.1,cottage 2,Cozy Calm Hut,London,65,2,4.1,cottage 3,Agreable Calm Place,London,65,4,4.8,house 4,Colossal Quiet Chateau,Paris,3400,16,2.7,castle 5,Homy Quiet Shack,Paris,50,1,1.1,cottage 6,Pleasant Quiet Place,Dublin,35,5,4.3,house 7,Vast Peaceful Fortress,Seattle,3200,24,1.9,castle 8,Giant Quiet Fortress,San Francisco,3400,12,4.1,castle 9,Giant Peaceful Palace,London,1500,20,3.5,castle 10,Sizable Calm Country House,Auckland,650,9,4.9,mansion ==> cloudsql/rating.csv <== 10,1,1 18,1,2 13,1,1 7,2,2 4,2,2 13,2,3 19,2,2 12,2,1 11,2,1 1,2,2 cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ I see in the above output that we have accommodations and ratings but no recommendations yet. I returned to the codelabs-tab. I navigated to the next page in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#3 I returned to the Cloud-Shell-tab. I ran another command: gsutil cp cloudsql/* gs://cs101feb2018/sql/ It was very slow; it needed 60 seconds to finish: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ gsutil cp cloudsql/* gs://cs101feb2018/sql/ Copying file://cloudsql/accommodation.csv [Content-Type=text/csv]... Copying file://cloudsql/rating.csv [Content-Type=text/csv]... Copying file://cloudsql/table_creation.sql [Content-Type=application/x-sql]... - [3 files][ 14.2 KiB/ 14.2 KiB] Operation completed over 3 objects/14.2 KiB. cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ I visited the GCP-tab. I verified success of the above command by visiting an appropriate URL: https://console.cloud.google.com/storage/browser/cs101feb2018/sql/ I returned to the codelabs-tab. I navigated to the next page in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#4 I studied that page. The page listed 10 steps. To start step 1, clicked the GCP-tab and I visited this URL: https://console.cloud.google.com I clicked the upper-left-hamburger-link and from there clicked: "SQL". It served me a page describing: "Cloud SQL Instances" Cloud SQL instances are fully managed relational: MySQL databases PostgreSQL databases I saw this: I clicked the blue: "Create instance" button. In the next page I picked the MySQL radio-button. I clicked the blue: "Next" button. I saw this: I clicked the blue: "Choose Second Generation" button. The next page landed me in a field: "Instance ID". I entered string: "rentals". I entered root password: "root". I saw this: I clicked the blue "Create" button. GCP served me a page with information about my new db instance: MySQL 2nd Gen 5.7 IP: 104.197.219.252 Instance connection name: cpb100-195004:us-central1:rentals I saw this: Next I clicked the Cloud-Shell-tab. I ran shell commands: cd ~/training-data-analyst/CPB100/lab3a bash find_my_ip.sh I saw this: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ bash find_my_ip.sh 35.227.172.20 cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ I ran shell commands: cd ~/training-data-analyst/CPB100/lab3a bash authorize_cloudshell.sh I saw this: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cat authorize_cloudshell.sh #!/bin/bash gcloud sql instances patch rentals \ --authorized-networks `wget -qO - http://ipecho.net/plain`/32 cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ bash authorize_cloudshell.sh When adding a new IP address to authorized networks, make sure to also include any IP addresses that have already been authorized. Otherwise, they will be overwritten and de-authorized. Do you want to continue (Y/n)? y The following message will be used for the patch API method. {"project": "cpb100-195004", "name": "rentals", "settings": {"ipConfiguration": {"authorizedNetworks": [{"value": "35.227.172.20/32"}]}}} Patching Cloud SQL instance...done. Updated [https://www.googleapis.com/sql/v1beta4/projects/cpb100-195004/instances/rentals]. cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ The intent of the above syntax is to grant networking permission from MySQL to Cloud-Shell. If you have problems getting permissions granted, GCP allows you to grant permissions in a web page. A clue to do that is described below: In the GCP-tab, visit this URL: https://console.cloud.google.com/sql Then look for a link for the MySQL instance. Under that link find the authorization form. I saw this: Enough about that; back to the lesson. In the codelabs-tab, I navigated to the next page in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#5 I studied that page. The page listed 4 steps. To start step 1, in the GCP-tab, I navigated to the URL listed below: https://console.cloud.google.com/sql I clicked the link called "rentals" which is the name of the Instance I created earlier. I clicked "Import" at the top. GCP served a page: "Import data from Cloud Storage" I saw this: I clicked the button named: "Browse". GCP served a link named: "cs101feb2018" which is the name of the bucket I created earlier. I clicked the "cs101feb2018" link. GCP showed three files and a folder named "sql" in that bucket. I clicked "table_creation.sql" in that folder. I saw this: GCP activated a blue button named "Import". I saw this: I clicked the button. GCP got busy for 10 seconds and then indicated success with a green-check-mark. I saw this: In the codelabs-tab, I navigated to the next page in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#6 I studied that page. The page listed 3 steps. To start step 1, in the GCP-tab, I navigated to the URL listed below: https://console.cloud.google.com/sql I clicked the link called "rentals" which is the name of the Instance I created earlier. I clicked "Import" at the top. I browsed to accommodation.csv and selected it. GCP served me a page: "Import data from Cloud Storage" I selected radio-button: "CSV". I selected database: "recommendation_spark". In the next field, I entered string: "Accommodation". GCP responded by activating the blue "Import" button. I saw this: I clicked the button. GCP quickly served me a page named "Instance details". Quickly after that it served a green-check-mark to indicate success. I clicked "Import" at the top. I browsed to rating.csv and selected it. GCP served me a page: "Import data from Cloud Storage" I selected radio-button: "CSV". I selected database: "recommendation_spark". In the "Table" field, I entered string: "Rating". GCP responded by activating the blue "Import" button. I clicked the button. GCP quickly served me a page named "Instance details". Quickly after that it served a green-check-mark to indicate success. In the codelabs-tab, I visited the next page in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-cloud-sql/#7 I studied that page. The page contained 5 steps. To start step 1, in the GCP-tab, I visited this page: https://console.cloud.google.com/sql I used that page to get the IP address of the MySQL instance: 104.197.219.252 Next, in the Cloud-Shell-tab, I entered a shell command: mysql --host=104.197.219.252 --user=root --password I saw this: cs101@cpb100-195004:~$ mysql --host=104.197.219.252 --user=root --password Enter password: ERROR 2003 (HY000): Can't connect to MySQL server on '104.197.219.252' (110 "Connection timed out") cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ The above error was caused because Cloud-Shell had "lost" its IP address. So, GCP gave it a new IP address. It was up to me tell MySQL about this problem. I tried these shell commands: cd ~/training-data-analyst/CPB100/lab3a bash find_my_ip.sh bash authorize_cloudshell.sh mysql --host=104.197.219.252 --user=root --password I saw this: cs101@cpb100-195004:~$ mysql --host=104.197.219.252 --user=root --password Enter password: ERROR 2003 (HY000): Can't connect to MySQL server on '104.197.219.252' (110 "Connection timed out") cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cd ~/training-data-analyst/CPB100/lab3a cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ bash find_my_ip.sh 35.203.155.130 cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ bash authorize_cloudshell.sh When adding a new IP address to authorized networks, make sure to also include any IP addresses that have already been authorized. Otherwise, they will be overwritten and de-authorized. Do you want to continue (Y/n)? y The following message will be used for the patch API method. {"project": "cpb100-195004", "name": "rentals", "settings": {"ipConfiguration": {"authorizedNetworks": [{"value": "35.203.155.130/32"}]}}} Patching Cloud SQL instance...done. Updated [https://www.googleapis.com/sql/v1beta4/projects/cpb100-195004/instances/rentals]. cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3a$ mysql --host=104.197.219.252 --user=root --password Enter password: Welcome to the MariaDB monitor. Commands end with ; or \g. Your MySQL connection id is 899 Server version: 5.7.14-google-log (Google) Copyright (c) 2000, 2017, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MySQL [(none)]> MySQL [(none)]> MySQL [(none)]> At the MySQL prompt I entered a command: use recommendation_spark; I wanted to see tables so I typed this: show tables; I verifed that ratings.csv was loaded into ratings table: MySQL [recommendation_spark]> show tables; +--------------------------------+ | Tables_in_recommendation_spark | +--------------------------------+ | Accommodation | | Rating | | Recommendation | +--------------------------------+ 3 rows in set (0.04 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> select count(*) from Rating; +----------+ | count(*) | +----------+ | 1186 | +----------+ 1 row in set (0.03 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> select * from Rating limit 4; +--------+--------+--------+ | userId | accoId | rating | +--------+--------+--------+ | 10 | 1 | 1 | | 13 | 1 | 1 | | 18 | 1 | 2 | | 12 | 10 | 3 | +--------+--------+--------+ 4 rows in set (0.04 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> I entered a SQL command: MySQL [recommendation_spark]> MySQL [recommendation_spark]> select * from Accommodation where type = 'castle' and price < 1500; +----+--------------------------+--------------+-------+-------+--------+--------+ | id | title | location | price | rooms | rating | type | +----+--------------------------+--------------+-------+-------+--------+--------+ | 14 | Colossal Peaceful Palace | Melbourne | 1200 | 21 | 1.5 | castle | | 15 | Vast Private Fort | London | 1300 | 18 | 2.6 | castle | | 26 | Enormous Peaceful Palace | Paris | 1300 | 18 | 1.1 | castle | | 31 | Colossal Private Castle | Buenos Aires | 1400 | 15 | 3.3 | castle | | 45 | Vast Quiet Chateau | Tokyo | 1100 | 19 | 2.3 | castle | +----+--------------------------+--------------+-------+-------+--------+--------+ 5 rows in set (0.04 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> That ended my interaction with the CPB100-node I call: "Setup rentals data in Cloud SQL". [top] Dataproc Next, in the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#0 In the Cloud-Shell tab I verifed that repo: "training-data-analyst" was still available. I verified that my MySQL instance and tables were still available. In the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#1 I studied the page it served me. I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#2 I studied the page it served me. In the GCP-tab I noted the region of my Cloud SQL instance: "us-central1". I clicked the upper-left-hamburger-link and from there clicked: "Dataproc". GCP served me a page with blue-button: "Enable API". I clicked it. GCP served me a page with blue-button: "Create cluster". I saw this: I clicked it. GCP served me a form to configure the cluster. I ensured the cluster was in same region as my Cloud SQL instance: "us-central1". I changed machine type of both master and worker nodes to: "2vCPUs 7.5 GB memory" I specified the cluster have two nodes (the minimum). I saw this: I clicked the blue-button: "Create". GCP served a page indicating it was busy building the cluster. I saw this: Eventually, it finished. I saw this: In Cloud-Shell-tab I issued shell commands: cd ~/training-data-analyst/CPB100/lab3b bash authorize_dataproc.sh cluster-d037 us-central1 2 It served me an error: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ bash authorize_dataproc.sh cluster-d037 us-central1 2 Machines to authorize: cluster-d037-m cluster-d037-w-0 cluster-d037-w-1 in us-central1 ... finding their IP addresses ERROR: (gcloud.compute.instances.describe) Could not fetch resource: - Invalid value for field 'zone': 'us-central1'. Unknown zone. IP address of cluster-d037-m is /32 ERROR: (gcloud.compute.instances.describe) Could not fetch resource: - Invalid value for field 'zone': 'us-central1'. Unknown zone. IP address of cluster-d037-w-0 is /32 ERROR: (gcloud.compute.instances.describe) Could not fetch resource: - Invalid value for field 'zone': 'us-central1'. Unknown zone. IP address of cluster-d037-w-1 is /32 Authorizing [/32,/32,/32] to access cloudsql=rentals ERROR: (gcloud.sql.instances.patch) argument --authorized-networks: Bad value [/32]: Must be specified in CIDR notation, also known as 'slash' notation (e.g. 192.168.100.0/24). Usage: gcloud sql instances patch INSTANCE [optional flags] optional flags may be --activation-policy | --assign-ip | --async | --authorized-gae-apps | --authorized-networks | --no-backup | --backup-start-time | --clear-authorized-networks | --clear-database-flags | --clear-gae-apps | --cpu | --database-flags | --diff | --enable-bin-log | --enable-database-replication | --follow-gae-app | --gce-zone | --help | --maintenance-release-channel | --maintenance-window-any | --maintenance-window-day | --maintenance-window-hour | --memory | --pricing-plan | --replication | --require-ssl | --storage-auto-increase | --storage-size | --tier For detailed information on this command and its flags, run: gcloud sql instances patch --help cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ In the GCP-tab, I studied the page at this URL: https://console.cloud.google.com/dataproc/clusters In Cloud-Shell-tab I tried this shell command: bash authorize_dataproc.sh cluster-d037 us-central1-f 2 I saw this: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ bash authorize_dataproc.sh cluster-d037 us-central1-f 2 Machines to authorize: cluster-d037-m cluster-d037-w-0 cluster-d037-w-1 in us-central1-f ... finding their IP addresses IP address of cluster-d037-m is 35.224.123.22/32 IP address of cluster-d037-w-0 is 35.192.238.24/32 IP address of cluster-d037-w-1 is 35.225.251.161/32 Authorizing [35.224.123.22/32,35.192.238.24/32,35.225.251.161/32] to access cloudsql=rentals When adding a new IP address to authorized networks, make sure to also include any IP addresses that have already been authorized. Otherwise, they will be overwritten and de-authorized. Do you want to continue (Y/n)? y The following message will be used for the patch API method. {"project": "cpb100-195004", "name": "rentals", "settings": {"ipConfiguration": {"authorizedNetworks": [{"value": "35.224.123.22/32"}, {"value": "35.192.238.24/32"}, {"value": "35.225.251.161/32"}]}}} Patching Cloud SQL instance...done. Updated [https://www.googleapis.com/sql/v1beta4/projects/cpb100-195004/instances/rentals]. cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ In the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#3 I studied that page. I ran a shell command to open an editor in Cloud-Shell-tab: nano sparkml/train_and_apply.py I typed in the IP-address of the Cloud-SQL-Instance: 104.197.219.252 I saw this: I entered a shell command: gsutil cp sparkml/tr*.py gs://cs101feb2018/ In GCP-tab, I visited this URL: https://console.cloud.google.com/dataproc/clusters/ I clicked the "Jobs" icon on the left. It served me a page with blue-button: "Submit job". I clicked "Submit job". GCP served me a job-submission form for Hadoop. I changed the job-type to: "PySpark" I indicated the location of the Python script to be this: gs://cs101feb2018/train_and_apply.py I saw this: Near the bottom, I clicked on the blue-button: "Submit" GCP accepted the job and then gave it a status of: "Running". I saw this: After two minutes, GCP changed the status to "Succeeded". In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#4 I studied that page. In GCP-tab, I got the IP address of MySQL from this URL: https://console.cloud.google.com/sql/instances In Cloud-Shell-tab, I verified that I could connect to MySQL. mysql --host=104.197.219.252 --user=root --password It failed because GCP lost the authorization for Cloud-Shell. I got authorization again with these shell commands: cd ~/training-data-analyst/CPB100/lab3a bash authorize_cloudshell.sh In Cloud-Shell-tab, I verified that I could connect to MySQL. mysql --host=104.197.219.252 --user=root --password I issued a command: use recommendation_spark; I issued a SQL commands: select count(*) from Recommendation; select r.userid, r.accoid, r.prediction, a.title, a.location, a.price, a.rooms, a.rating, a.type from Recommendation as r, Accommodation as a where r.accoid = a.id and r.userid = 10; I saw this: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab3b$ mysql --host=35.192.114.78 --user=root --password Enter password: Welcome to the MariaDB monitor. Commands end with ; or \g. Your MySQL connection id is 1121 Server version: 5.7.14-google-log (Google) Copyright (c) 2000, 2017, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MySQL [(none)]> MySQL [(none)]> MySQL [(none)]> MySQL [(none)]> use recommendation_spark; Reading table information for completion of table and column names You can turn off this feature to get a quicker startup with -A Database changed MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> select count(*) from Recommendation; +----------+ | count(*) | +----------+ | 125 | +----------+ 1 row in set (0.03 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> select r.userid, -> r.accoid, -> r.prediction, -> a.title, -> a.location, -> a.price, -> a.rooms, -> a.rating, -> a.type -> from Recommendation as r, -> Accommodation as a -> where r.accoid = a.id and -> r.userid = 10; +--------+--------+------------+-------------------------+---------------+-------+-------+--------+--------+ | userid | accoid | prediction | title | location | price | rooms | rating | type | +--------+--------+------------+-------------------------+---------------+-------+-------+--------+--------+ | 10 | 40 | 1.5812576 | Colossal Private Castle | Seattle | 2900 | 24 | 1.5 | castle | | 10 | 35 | 1.5657468 | Colossal Quiet Chateau | NYC | 2300 | 14 | 4.6 | castle | | 10 | 45 | 1.5110599 | Vast Quiet Chateau | Tokyo | 1100 | 19 | 2.3 | castle | | 10 | 74 | 1.4872638 | Giant Calm Fort | Melbourne | 2400 | 12 | 2.3 | castle | | 10 | 46 | 1.4497858 | Colossal Private Castle | San Francisco | 1900 | 15 | 3.7 | castle | +--------+--------+------------+-------------------------+---------------+-------+-------+--------+--------+ 5 rows in set (0.04 sec) MySQL [recommendation_spark]> MySQL [recommendation_spark]> MySQL [recommendation_spark]> In the above output I see predictions. So, I see that as solid evidence that I was able to finish the Dataproc lab. In the codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-dataproc/#5 I studied that page. In the GCP-tab, I visited this URL: https://console.cloud.google.com/sql/instances I selected my instance and asked GCP to delete it to prevent more charges against my account. I visited this URL: https://console.cloud.google.com/dataproc I selected my instance and asked GCP to delete it to prevent more charges against my account. That ended my interaction with the CPB100-node I call: "Dataproc". [top] Datalab Next, in codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-datalab I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-datalab/#0 I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-datalab/#1 In the GCP-tab, I visited the URL below: https://console.cloud.google.com In the Cloud-Shell-tab I entered a command: gcloud compute zones list I decided I want to use: "us-central1-f" I entered a shell command: datalab create mydatalabvm --zone us-central1-f GCP issued an error: cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ datalab create mydatalabvm --zone us-central1-f Creating the network datalab-network Creating the firewall rule datalab-network-allow-ssh Creating the disk mydatalabvm-pd Creating the repository datalab-notebooks ERROR: (gcloud.source.repos.create) ResponseError: status=[PERMISSION_DENIED], code=[403], message=[User [cs101@gmail.com] does not have permission to access project [cpb100-195004] (or it may not exist): The caller does not have permission]. details: - Cloud Source Repositories API is not enabled. Please enable the API on the Google Cloud console. enable at: https://console.cloud.google.com/apis/library/sourcerepo.googleapis.com/?project=cpb100-195004 Failed to find or create the repository datalab-notebooks. Ask a project owner to create it for you. cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ In the GCP-tab, I visited the URL below: https://console.cloud.google.com/apis/library/sourcerepo.googleapis.com The above page served a blue-button: "ENABLE". I clicked the button. In Cloud-Shell-tab I entered a shell command: datalab create mydatalabvm --zone us-central1-f GCP offered a better response: cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ datalab create mydatalabvm --zone us-central1-f Creating the repository datalab-notebooks Creating the instance mydatalabvm Created [https://www.googleapis.com/compute/v1/projects/cpb100-195004/zones/us-central1-f/instances/mydatalabvm]. Connecting to mydatalabvm. This will create an SSH tunnel and may prompt you to create an rsa key pair. To manage these keys, see https://cloud.google.com/compute/docs/instances/adding-removing-ssh-keys Waiting for Datalab to be reachable at http://localhost:8081/ This tool needs to create the directory [/home/cs101/.ssh] before being able to generate SSH keys. Do you want to continue (Y/n)? y Generating public/private rsa key pair. Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in /home/cs101/.ssh/google_compute_engine. Your public key has been saved in /home/cs101/.ssh/google_compute_engine.pub. The key fingerprint is: SHA256:/Nyvn1cGO66I19S66NaH92mwa1q1bUpTzPUH0W0DE6E cs101@cs-6000-devshell-vm-041ee3e0-98cc-4471-b54e-de69c3f602a9 The key's randomart image is: +---[RSA 2048]----+ | ==..| | . .o+| | E ..o| | . .+o| | S . +*| | o ...=.*| | o+.==++| | .oo*=+*o| | .++.*OB+.| +----[SHA256]-----+ Updating project ssh metadata.../Updated [https://www.googleapis.com/compute/v1/projects/cpb100-195004]. Updating project ssh metadata...done. Waiting for SSH key to propagate. The connection to Datalab is now open and will remain until this command is killed. Click on the *Web Preview* (square button at top-right), select *Change port > Port 8081*, and start using Datalab. I clicked the web preview square-button at top-right. The button looks like this: I opted to change the port: I changed the port to 8081 and clicked through. GCP loaded a page from this URL: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab/notebooks# I made sure that page was in tab 6, the tab after the Cloud-Shell-tab. I call tab 6 the DataLab-tab. I captured an image of the page: In the GCP-tab, I visited this URL: https://console.cloud.google.com/compute/instances The page told me that GCP had just started an VM instance named "mydatalabvm". I saw this: In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-datalab/#2 I studied the page. I ran an experiment to verify the information there: In GCP-tab, I stopped VM instance named "mydatalabvm" In DataLab-tab, I verified that I could not access: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab# In Cloud-Shell tab I ran a command: datalab connect mydatalabvm GCP responded to that by restarting the VM instance named "mydatalabvm". I wait a minute for the VM to boot. In DataLab-tab, I verified that I could access: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab# It worked! That ended my interaction with the CPB100-node I call: "Datalab". [top] Bigquery-Dataset In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset/#0 I clicked the next node in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset/#1 I studied that page. I clicked the next node in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset/#2 I studied that page. In DataLab-tab, I verified this URL was active: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab# In codelabs-tab, I clicked the next node in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-bigquery-dataset/#3 I studied that page. In DataLab-tab, I visited this URL: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab# I clicked the +notebook button. The server responded with a page which reminds me of a Jupyter Notebook UI: In Github-tab, I visited this URL: demandforecast.ipynb I copied some Python syntax from demandforecast.ipynb into the field in the new notebook UI: So, I was bouncing between the Github-tab and DataLab-tab. In the DataLab-tab, I asked the UI to run it. It ran fine. I continued the process of copy-paste-run on each section of code I found in demandforecast.ipynb Each section ran just as expected. Next, in GCP-tab, I visited this URL: https://console.cloud.google.com/compute/instances I stopped mydatalabvm to avoid adding costs to my account. That ended my interaction with the CPB100-node I call: "Bigquery-Dataset". [top] TensorFlow In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-tensorflow I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-tensorflow/#0 I studied the next page: https://codelabs.developers.google.com/codelabs/cpb100-tensorflow/#1 I studied the next page: https://codelabs.developers.google.com/codelabs/cpb100-tensorflow/#2 In GCP-tab, I visited the console-URL: https://console.cloud.google.com In Cloud-Shell-tab, I issued a shell command: datalab connect mydatalabvm GCP offered a good response: cs101@cpb100-195004:~$ cs101@cpb100-195004:~$ datalab connect mydatalabvm Starting instance(s) mydatalabvm...done. Updated [https://www.googleapis.com/compute/v1/projects/cpb100-195004/zones/us-central1-f/instances/mydatalabvm]. Connecting to mydatalabvm. This will create an SSH tunnel and may prompt you to create an rsa key pair. To manage these keys, see https://cloud.google.com/compute/docs/instances/adding-removing-ssh-keys Waiting for Datalab to be reachable at http://localhost:8081/ The connection to Datalab is now open and will remain until this command is killed. Click on the *Web Preview* (square button at top-right), select *Change port > Port 8081*, and start using Datalab. I clicked the Web-Preview button in upper right and changed the port to 8081. GCP loaded this URL into my browser: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab/notebooks I move that page so it occupied DataLab-tab [ tab 6 ]. I clicked the +notebook button in upper-left. GCP served a page with name: "Untitled Notebook1". The page offered an input field for notebook commands. The author of this notebook server assumes that I know how to operate a Python Jupyter notebook. A GCP-datalab notebook offers behavior similar to a Jupyter notebook. In Github-tab, I visited the URL listed below which offers syntax for a Jupyter notebook: demandforecast.ipynb I scrolled to this h2-element [about 2/3 to page-end]: "Machine Learning with Tensorflow": I studied four paragraphs below the h2-element. I studied the Python syntax below the paragraphs: import tensorflow as tf shuffled = data2.sample(frac=1, random_state=13) # It would be a good idea, if we had more data, to treat the days as categorical variables # with the small amount of data, we have though, the model tends to overfit #predictors = shuffled.iloc[:,2:5] #for day in xrange(1,8): # matching = shuffled['dayofweek'] == day # key = 'day_' + str(day) # predictors[key] = pd.Series(matching, index=predictors.index, dtype=float) predictors = shuffled.iloc[:,1:5] predictors[:5] I returned to the top of demandforecast.ipynb I copied syntax from demandforecast.ipynb into "Untitled Notebook1", in DataLab-tab. I did the copying bit-by-bit; I was patient. I wanted to study the output at a slow pace. Eventually I worked all the way through the syntax in: demandforecast.ipynb Next, in GCP-tab, I visited this URL: https://console.cloud.google.com/compute/instances I stopped mydatalabvm to avoid adding costs to my account. That ended my interaction with the CPB100-node I call: "TensorFlow". [top] Translate-API In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-translate-api I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-translate-api/#0 I studied the next page: https://codelabs.developers.google.com/codelabs/cpb100-translate-api/#1 I studied the next page: https://codelabs.developers.google.com/codelabs/cpb100-translate-api/#2 In GCP-tab, I visited this URL: https://console.cloud.google.com/apis I saw a blue-button: "ENABLE APIS AND SERVICES". I clicked it. GCP served me a large list of APIs. I enabled: "Google Cloud Vision API". GCP served me this page: https://console.cloud.google.com/apis/api/vision.googleapis.com/overview I clicked the credentials-key-icon on the left. It served me a blue-button: "Create credentials". I clicked it. At that point the UI mis-matched the instructions I was following in cpb100. So I was confused. I visited this URL: https://console.cloud.google.com/apis/credentials/wizard From select-option control I picked value: "Google Cloud Vision API". Next I picked radio-button: "Yes I am using one or both". I clicked blue-button: "What credentials do I need?" GCP served me a message: "You don't need to create new credentials" So, I clicked blue-button: "Done". I followed the same process to enable Translate API, Speech API, and Natural Language APIs. In codelabs-tab, I studied the next codelabs-page: https://codelabs.developers.google.com/codelabs/cpb100-translate-api/#3 In GCP-tab, I visited my console URL: https://console.cloud.google.com In Cloud-Shell-tab, I entered a shell command: datalab connect mydatalabvm GCP offered good news: Welcome to Cloud Shell! Type "help" to get started. cs101@cpb100-195004:~$ datalab connect mydatalabvm Starting instance(s) mydatalabvm...done. Updated [https://www.googleapis.com/compute/v1/projects/cpb100-195004/zones/us-central1-f/instances/mydatalabvm]. Connecting to mydatalabvm. This will create an SSH tunnel and may prompt you to create an rsa key pair. To manage these keys, see https://cloud.google.com/compute/docs/instances/adding-removing-ssh-keys Waiting for Datalab to be reachable at http://localhost:8081/ The connection to Datalab is now open and will remain until this command is killed. Click on the *Web Preview* (square button at top-right), select *Change port > Port 8081*, and start using Datalab. I use the Web-Preview link in the upper-right of Cloud-Shell to load Datalab into a new tab in my browser. The Datalab URL served to me looked like this: https://8081-dot-3516970-dot-devshell.appspot.com/tree/datalab/notebooks# I moved that page so it was tab 6, the DataLab-tab. In codelabs-tab, I studied the next codelabs-page: https://codelabs.developers.google.com/codelabs/cpb100-translate-api/#4 I visited the GCP-tab and loaded a URL: https://console.cloud.google.com/apis I clicked credentials-icon on left. I clicked blue-button: "Create credentials". I clicked API-key. GCP gave me this key: AIzaSyBhcruU8RW0PeOllj-yxkfLnef3YRHwCt In Github-tab, I loaded a URL from github.com: mlapis.ipynb I studied comments in that page. I returned to my Datalab-tab. I clicked +Notebook It served me a new notebook with a field for syntax. I typed in this syntax: APIKEY="AIzaSyBhcruU8RW0PeOllj-yxkfLnef3YRHwCt" I entered Shift-Enter which activated the syntax and then opened a new syntax-field. Next, one field at a time, I carefully copy-pasted syntax from Github-tab to my Datalab-tab. I saw responses from GCP which matched information in the Github-tab. So, I am confident I completed the lab with no error. In my GCP-tab I loaded this URL: https://console.cloud.google.com/compute/instances I stopped the instance named: "mydatalabvm". Next, I loaded this URL: https://console.cloud.google.com/apis/credentials I used the trash-can-icon to remove the API key I no longer needed. That ended my interaction with the CPB100-node I call: "Translate-API". [top] Serverless-Ingest In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest/#0 I clicked the next node in the sequence: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest/#1 In Cloud-Shell-tab, I issued a command: git clone https://github.com/GoogleCloudPlatform/training-data-analyst Cloud-Shell served me an error: fatal: destination path 'training-data-analyst' already exists and is not an empty directory. I see that as a good error. It means that I had already git-cloned training-data-analyst. I responded with a shell command: cd ~/training-data-analyst/CPB100/lab2b/scheduled I examined a Python script in that folder: pg ingestapp.py In my Github-tab I studied ingestapp.py by visiting the URL listed below: https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/CPB100/lab2b/scheduled/ingestapp.py The script is a simple Python Flask App. This app has one interesting route: '/ingest' Flask uses that route to act as a proxy for the URL listed below: http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_week.csv Flask transforms the CSV-data into an image of earthquake activity. This happens during a Flask response to a request of the '/ingest' route. Near the end of the response, Flask uploads the image to GCP-Cloud-Storage. The author of the script separated ingest_last_week() into sections: verify that this is a cron job request create png upload to cloud storage change permissions: make_public() After I finished my study of that script, I returned to the codelabs-tab. In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest/#2 I studied the above page. In Cloud-Shell-tab I ran a command: cat Dockerfile I saw this: cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ cat Dockerfile FROM ubuntu:latest MAINTAINER Rajdeep Dua "dua_rajdeep@yahoo.com" RUN apt-get update -y RUN apt-get install -y python-pip python-dev build-essential python-mpltoolkits.basemap python-numpy python-matplotlib COPY . /app WORKDIR /app RUN pip install -r requirements.txt ENTRYPOINT ["python"] CMD ["ingestapp.py"] cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ I saw that this Dockerfile declares that I want an Ubuntu host. After the host is available, root should run shell commands: apt-get update -y apt-get install -y python-pip python-dev build-essential python-mpltoolkits.basemap python-numpy python-matplotlib Next, Docker should copy "." into /app of the host. Then, Docker should make /app the Current Working Directory. Next, Docker should run a shell command: pip install -r requirements.txt Then, Docker should run python script: ingestapp.py I assume that after Docker runs ingestapp.py, then a Flask app will be running on the host. In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest/#3 I studied the above page. In Cloud-Shell-tab, I used the nano editor to enhance cron.yaml: cron: - description : ingest earthquake data url : /ingest schedule: every 2 hours target: default I wanted to use the bucket I had created in the Cloud-Storage lab. That bucket is named: "cs101feb2018". In Cloud-Shell-tab, I used the nano editor to enhance app.yaml: runtime: custom env: flex entrypoint: gunicorn -b :$PORT ingestapp:app service: default #[START env] env_variables: CLOUD_STORAGE_BUCKET: cs101feb2018 #[END env] handlers: - url: /ingest script: ingestapp.app - url: /.* script: ingestapp.app In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-serverless-ingest/#4 I studied the above page. In Cloud-Shell-tab, I ran a shell command: gcloud app deploy --quiet app.yaml cron.yaml I saw this: snip ... Stored in directory: /root/.cache/pip/wheels/fc/a8/66/24d655233c757e178d45dea2de22a04c6d92766abfb741129a Running setup.py bdist_wheel for MarkupSafe: started Running setup.py bdist_wheel for MarkupSafe: finished with status 'done' Stored in directory: /root/.cache/pip/wheels/88/a7/30/e39a54a87bcbe25308fa3ca64e8ddc75d9b3e5afa21ee32d57 Running setup.py bdist_wheel for googleapis-common-protos: started Running setup.py bdist_wheel for googleapis-common-protos: finished with status 'done' Stored in directory: /root/.cache/pip/wheels/93/e0/cb/b06866f012310b96fba79c37f199aaf973a2e97a42ca7ef696 Successfully built itsdangerous MarkupSafe googleapis-common-protos Installing collected packages: MarkupSafe, Jinja2, Werkzeug, click, itsdangerous, Flask, gunicorn, setuptools, protobuf, googleapis-common-protos, futures, rsa, cachetools, pyasn1-modules, google-auth, certifi, chardet, idna, urllib3, requests, google-api-core, google-cloud-core, google-cloud-storage Found existing installation: setuptools 20.7.0 Not uninstalling setuptools at /usr/lib/python2.7/dist-packages, outside environment /usr Found existing installation: idna 2.0 Not uninstalling idna at /usr/lib/python2.7/dist-packages, outside environment /usr Successfully installed Flask-0.11.1 Jinja2-2.10 MarkupSafe-1.0 Werkzeug-0.14.1 cachetools-2.0.1 certifi-2018.1.18 chardet-3.0.4 click-6.7 futures-3.2.0 google-api-core-0.1.4 google-auth-1.4.1 google-cloud-core-0.28.0 google-cloud-storage-0.21.0 googleapis-common-protos-1.5.3 gunicorn-19.6.0 idna-2.6 itsdangerous-0.24 protobuf-3.5.1 pyasn1-modules-0.2.1 requests-2.18.4 rsa-3.4.2 setuptools-38.5.1 urllib3-1.22 You are using pip version 8.1.1, however version 9.0.1 is available. You should consider upgrading via the 'pip install --upgrade pip' command. ---> 6fed58c358cf Removing intermediate container fa0f0b0f8404 Step 8/9 : ENTRYPOINT python ---> Running in acfefa5495d2 ---> dbd5b9d59bfe Removing intermediate container acfefa5495d2 Step 9/9 : CMD ingestapp.py ---> Running in d2b66192cfa2 ---> 1074a82f102e Removing intermediate container d2b66192cfa2 Successfully built 1074a82f102e Successfully tagged us.gcr.io/cpb100-195004/appengine/default.20180219t183619:latest PUSH Pushing us.gcr.io/cpb100-195004/appengine/default.20180219t183619:latest The push refers to a repository [us.gcr.io/cpb100-195004/appengine/default.20180219t183619] a062c5a0f6af: Preparing 64501b77450c: Preparing b5a37c3f1cb8: Preparing f469dd62d29b: Preparing 6f4ce6b88849: Preparing 92914665e7f6: Preparing c98ef191df4b: Preparing 9c7183e0ea88: Preparing ff986b10a018: Preparing 92914665e7f6: Waiting c98ef191df4b: Waiting 9c7183e0ea88: Waiting ff986b10a018: Waiting 6f4ce6b88849: Layer already exists 92914665e7f6: Layer already exists c98ef191df4b: Layer already exists 9c7183e0ea88: Layer already exists ff986b10a018: Layer already exists 64501b77450c: Pushed f469dd62d29b: Pushed a062c5a0f6af: Pushed b5a37c3f1cb8: Pushed latest: digest: sha256:5f25cb6cad3e00e27fa7caff79e71b3e22c2123932becf081a92bd5896df3470 size: 2202 DONE ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- ------------------------------------------------------- Updating service [default] (this may take several minutes)...failed. ERROR: (gcloud.app.deploy) Error Response: [9] Application startup error: /usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment. warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.') Traceback (most recent call last): File "ingestapp.py", line 22, in <module> import google.cloud.storage as gcs File "/usr/local/lib/python2.7/dist-packages/google/cloud/storage/__init__.py", line 39, in <module> from google.cloud.storage.batch import Batch File "/usr/local/lib/python2.7/dist-packages/google/cloud/storage/batch.py", line 29, in <module> from google.cloud.exceptions import make_exception ImportError: cannot import name make_exception cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ cs101@cpb100-195004:~/training-data-analyst/CPB100/lab2b/scheduled$ The above error made little sense to me. I decided to abandon this effort. Perhaps in the future I will encounter App-Engine training content which works better. That ended my interaction with the CPB100-node I call: "Serverless-Ingest". [top] Distributed-Landsat In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat I studied the page it served me: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat/#0 I studied the next page it served me: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat/#1 In the GCP-tab I visited this URL: https://console.cloud.google.com/apis I clicked the blue-button: "ENABLE APIS AND SERVICES". I searched for: "Google Dataflow API". I found it; I enabled it. In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat/#2 I studied that page. In tab-7 I visited the URL below: how-to-do-distributed-processing-of-landsat-data-in-python In Github-tab, I visited the URL below: dfndvi.py In the above page I studied the run() method. Below, I list sections of that method which I see: parse arguments create beam.Pipeline object Read the index file and find all scenes that cover this area for each month and spacecraft-coverage-pattern (given by the path and row), find clearest scene write out info about scene compute ndvi on scene In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat/#3 I studied that page. In Cloud-Shell-tab, I ran commands: cd ~/training-data-analyst/blogs/landsat/ cat run_oncloud.sh I ran another shell command which listed both my project and bucket I want to use: ./run_oncloud.sh cpb100-195004 cs101feb2018 The command failed: cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ ./run_oncloud.sh cpb100-195004 cs101feb2018 CommandException: 1 files/objects could not be removed. Traceback (most recent call last): File "./dfndvi.py", line 16, in <module> import apache_beam as beam ImportError: No module named apache_beam cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ The above error was caused by a missing Python module and/or package. I ran two shell scripts I found in the folder: sudo bash install_ee.sh sudo bash install_packages.sh I ran another shell command which listed both my project and bucket I want to use: ./run_oncloud.sh cpb100-195004 cs101feb2018 I saw this: cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ ./run_oncloud.sh cpb100-195004 cs101feb2018 CommandException: 1 files/objects could not be removed. No handlers could be found for logger "oauth2client.contrib.multistore_file" /usr/local/lib/python2.7/dist-packages/apache_beam/io/gcp/gcsio.py:122: DeprecationWarning: object() takes no parameters super(GcsIO, cls).__new__(cls, storage_client)) /usr/local/lib/python2.7/dist-packages/apache_beam/coders/typecoders.py:134: UserWarning: Using fallback coder for typehint: Any. warnings.warn('Using fallback coder for typehint: %r.' % typehint) /usr/local/lib/python2.7/dist-packages/apache_beam/coders/typecoders.py:134: UserWarning: Using fallback coder for typehint: <class __main__.SceneInfo at 0x7f9de6c73a10>. warnings.warn('Using fallback coder for typehint: %r.' % typehint) /usr/local/lib/python2.7/dist-packages/apache_beam/coders/typecoders.py:134: UserWarning: Using fallback coder for typehint: <type 'NoneType'>. warnings.warn('Using fallback coder for typehint: %r.' % typehint) running sdist running egg_info creating landsatmonthly.egg-info writing requirements to landsatmonthly.egg-info/requires.txt writing landsatmonthly.egg-info/PKG-INFO writing top-level names to landsatmonthly.egg-info/top_level.txt writing dependency_links to landsatmonthly.egg-info/dependency_links.txt writing manifest file 'landsatmonthly.egg-info/SOURCES.txt' reading manifest file 'landsatmonthly.egg-info/SOURCES.txt' writing manifest file 'landsatmonthly.egg-info/SOURCES.txt' warning: sdist: standard file not found: should have one of README, README.rst, README.txt, README.md running check warning: check: missing required meta-data: url warning: check: missing meta-data: either (author and author_email) or (maintainer and maintainer_email) must be supplied creating landsatmonthly-0.0.1 creating landsatmonthly-0.0.1/landsatmonthly.egg-info copying files to landsatmonthly-0.0.1... copying dfndvi.py -> landsatmonthly-0.0.1 copying ndvi.py -> landsatmonthly-0.0.1 copying setup.py -> landsatmonthly-0.0.1 copying landsatmonthly.egg-info/PKG-INFO -> landsatmonthly-0.0.1/landsatmonthly.egg-info copying landsatmonthly.egg-info/SOURCES.txt -> landsatmonthly-0.0.1/landsatmonthly.egg-info copying landsatmonthly.egg-info/dependency_links.txt -> landsatmonthly-0.0.1/landsatmonthly.egg-info copying landsatmonthly.egg-info/requires.txt -> landsatmonthly-0.0.1/landsatmonthly.egg-info copying landsatmonthly.egg-info/top_level.txt -> landsatmonthly-0.0.1/landsatmonthly.egg-info Writing landsatmonthly-0.0.1/setup.cfg Creating tar archive removing 'landsatmonthly-0.0.1' (and everything under it) DEPRECATION: pip install --download has been deprecated and will be removed in the future. Pip now has a download command that should be used instead. Collecting google-cloud-dataflow==2.2.0 Downloading google-cloud-dataflow-2.2.0.tar.gz Saved /tmp/tmp6uRpmJ/google-cloud-dataflow-2.2.0.tar.gz Successfully downloaded google-cloud-dataflow cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ cs101@cpb100-195004:~/training-data-analyst/blogs/landsat$ In codelabs-tab, I clicked the next link in the series: https://codelabs.developers.google.com/codelabs/cpb100-distributed-landsat/#4 I studied that page. In GCP-tab, I visited the URL below: http://console.cloud.google.com/dataflow I saw this: I saw that as good evidence that my DataFlow job was running. Eventually, it finished: In GCP tab I visited URL: http://console.cloud.google.com/storage I searched about and found much output from DataFlow: That ended my interaction with the CPB100-node I call: "Distributed-Landsat" Also that ended my interaction with the CPB100. [top]

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https://cs101.herokuapp.com/lessons/lesson002

Lesson 002 [ More Google GCP ] I have taught this lesson several times now. Most of this lesson is done with a browser. I offer tips for success. Use Google-Chrome browser. Dedicate a new browser window to this lesson. If you need a browser to check news or e-mail, do it in another window. Get a new window with: File -> New Window Why? Because you want to avoid working in a window with many tabs. On a piece of paper, write down a useful tab-organization: Tab 1: cs101.us Tab 2: codelabs Tab 3: github Tab 4: GCP 1 Tab 5: GCP Cloud-Shell Tab 6: DataLab Tab 7: GCP 3 I started this lesson by creating a new Chrome-window. I created 7 tabs in that window. In tab 1 I loaded this URL: http://cs101.us In tab 2 I loaded this URL: https://codelabs.developers.google.com/cp100 In tab 3 I loaded this URL: https://github.com/GoogleCloudPlatform In tab 4 I loaded this URL: https://console.cloud.google.com In tabs 5,6,7 I had done nothing (yet). After I created the 7 tabs, my Chrome-window looked like this: I clicked on tab 2, the codelabs-tab. I continued this lesson by studying the URL listed below: https://codelabs.developers.google.com/cp100 I saw this: On that page I assume the correct ordering of links is listed below: https://codelabs.developers.google.com/codelabs/cp100-free-trial https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher https://codelabs.developers.google.com/codelabs/cp100-app-engine https://codelabs.developers.google.com/codelabs/cp100-cloud-storage https://codelabs.developers.google.com/codelabs/cp100-compute-engine https://codelabs.developers.google.com/codelabs/cp100-container-engine https://codelabs.developers.google.com/codelabs/cp100-big-query The above links are discussed below: free-trial cloud-launcher app-engine cloud-storage compute-engine container-engine big-query Free-Trial Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-free-trial I saw a simple path through 4 pages. The third page had a noteworthy URL I used to get $300 of cloud-credit from Google: I loaded the URL into tab 3, the GCP-tab: https://console.developers.google.com/freetrial I logged in using my gmail account. It worked well; I gave it my credit-card info. It gave me $300 of cloud-credit from Google. I hope that you can also get that credit. Next, I visited the URL listed below: https://console.cloud.google.com/projectcreate I used that page to create a project called cp100. After I created cp100, GCP displayed a "CP100" select-option-link at the top of every GCP page in my GCP-tab: That ended my interaction with the CP100-node I call: "Free-Trial". [top] Cloud-Launcher Next, in the codelabs-tab, I clicked this link:https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher I studied this page: https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher/#1 I studied this page: https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher/#2 In the GCP-tab of my browser, I confirmed that I had project CP100 loaded. I visited this URL: https://console.cloud.google.com/ I saw this: I clicked the "upper-left-hamburger-link": I clicked "APIs & Services": I saw this: Near the top I clicked blue-button: "ENABLE APIS AND SERVICES": I searched for "Compute Enginge". I found it: I clicked the blue-button: "ENABLE". After two minutes, GCP signaled success. I returned to the codelabs-tab. I studied the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher/#3 I returned to the GCP-tab. I clicked the "upper-left-hamburger-link". I clicked "Cloud-Launcher": I found LAMP by Bitnami: I clicked blue-button: "LAUNCH ON COMPUTE ENGINE". After a short delay, GCP served me a configuration page: I assumed that the default configuration was fine for my training. I clicked the blue-button: "Deploy". GCP served status during deployment: In the codelabs-tab, I studied the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-launcher/#4 In the GCP-tab I clicked the link site address near the top. A LAMP server responded: I closed that tab. In the GCP-tab I clicked the SSH link: After 30 seconds, GCP served me an SSH window: In that window I issued shell commands: cd /opt/bitnami sudo cp docs/phpinfo.php apache2/htdocs exit In the GCP-tab I clicked the link site address near the top. The LAMP server appeared in a new tab with this URL: http://146.148.39.70/ I enhanced the URL: http://146.148.39.70/phpinfo.php The server responded with configuration information: The above page convinced me that I successfully completed this exercise. In the GCP-tab I clicked the blue-trashcan-button "DELETE" near the top: GCP deleted that server for me. That ended my interaction with the CP100-node I call: "Cloud-Launcher". [top] App-Engine Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-app-engine I saw this: I studied that page. I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#1 I studied the page. I bookmarked useful links in that page: https://cloud.google.com/docs https://cloud.google.com/appengine/docs https://cloud.google.com/datastore https://cloud.google.com/appengine/docs/python/tools/webapp2 https://cloud.google.com/sql/docs/introduction https://cloud.google.com/source-repositories/docs https://cloud.google.com/shell/docs I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#2 I studied the page. In the GCP-tab of my browser, I loaded a URL: https://console.cloud.google.com/ I clicked the upper-left-hamburger-link. From there I picked "Source Repositories": GCP served me a page. I clicked blue-button "CREATE REPOSITORY": GCP asked me to name my new repository. I called it: "default": GCP served me a page: At the upper-right of that page I clicked a small icon to activate: "Cloud-Shell": I moved the Cloud-Shell window into its own tab (tab 5). I changed the text to be black on white: In that Cloud-Shell-tab, I entered commands: mkdir cp100 cd cp100 gcloud source repos clone default cd default ls -la git pull https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf ls -la git push origin master I saw this: Welcome to Cloud Shell! Type "help" to get started. bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ mkdir cp100 bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ cd cp100 bikle101@cp100-196102:~/cp100$ bikle101@cp100-196102:~/cp100$ bikle101@cp100-196102:~/cp100$ gcloud source repos clone default Cloning into '/home/bikle101/cp100/default'... default warning: You appear to have cloned an empty repository. Project [cp100-196102] repository [default] was cloned to [/home/bikle101/cp100/default]. bikle101@cp100-196102:~/cp100$ bikle101@cp100-196102:~/cp100$ bikle101@cp100-196102:~/cp100$ cd default bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ ls -la total 12 drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 . drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 .. drwxr-xr-x 7 bikle101 bikle101 4096 Feb 23 15:22 .git bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ git pull https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf remote: Counting objects: 814, done. remote: Total 814 (delta 0), reused 0 (delta 0), pack-reused 814 Receiving objects: 100% (814/814), 180.14 KiB | 0 bytes/s, done. Resolving deltas: 100% (533/533), done. From https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf * branch HEAD -> FETCH_HEAD bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ ls -la total 52 drwxr-xr-x 7 bikle101 bikle101 4096 Feb 23 15:22 . drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 .. drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 app-engine drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 cloud-storage drwxr-xr-x 4 bikle101 bikle101 4096 Feb 23 15:22 compute-engine drwxr-xr-x 3 bikle101 bikle101 4096 Feb 23 15:22 container-engine -rw-r--r-- 1 bikle101 bikle101 1642 Feb 23 15:22 CONTRIBUTING.md drwxr-xr-x 8 bikle101 bikle101 4096 Feb 23 15:22 .git -rw-r--r-- 1 bikle101 bikle101 409 Feb 23 15:22 .gitignore -rw-r--r-- 1 bikle101 bikle101 11341 Feb 23 15:22 LICENSE -rw-r--r-- 1 bikle101 bikle101 1015 Feb 23 15:22 README.md bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ git push origin master Counting objects: 814, done. Compressing objects: 100% (265/265), done. Writing objects: 100% (814/814), 180.11 KiB | 0 bytes/s, done. Total 814 (delta 533), reused 814 (delta 533) remote: Resolving deltas: 100% (533/533) To https://source.developers.google.com/p/cp100-196102/r/default * [new branch] master -> master bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ bikle101@cp100-196102:~/cp100/default$ In the github-tab of my browser I loaded the URL I saw in the above output: https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf I saw this: I studied the URLs listed below: https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf/tree/master/app-engine https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf/tree/master/cloud-storage https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf/tree/master/compute-engine https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf/tree/master/container-engine https://github.com/GoogleCloudPlatformTraining/cp100-bookshelf/blob/master/README.md I want to learn Docker. This file looked interesting: container-engine/Dockerfile In the codelabs-tab, I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#3 I studied the page. In the GCP-tab I loaded this URL: https://console.cloud.google.com/code I studied the page and recognized it as a git repo. I saw this: I clicked the folder: "app-engine". I clicked the file: "app.yaml". I saw that it was a simple yaml file. I clicked the folder: "app-engine". I clicked the file: "main.py". I saw that it was a simple Python file. I clicked the folder: "app-engine". I clicked the file: "config.py". I saw that it was a simple Python file. I clicked the folder: "app-engine". I clicked the folder: "bookshelf". I studied files in that folder. In the codelabs-tab, I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#4 I studied the page. In Cloud-Shell tab I entered commands: cd ~/cp100/default/app-engine cat requirements.txt pip install -r requirements.txt -t lib cat appengine_config.py gcloud app deploy gcloud app browse exit I saw this: Welcome to Cloud Shell! Type "help" to get started. bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ cd ~/cp100/default/app-engine bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ cat requirements.txt Flask==0.11.1 gunicorn==19.6.0 bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ pip install -r requirements.txt -t lib Collecting Flask==0.11.1 (from -r requirements.txt (line 1)) Downloading Flask-0.11.1-py2.py3-none-any.whl (80kB) 100% |████████████████████████████████| 81kB 2.5MB/s Collecting gunicorn==19.6.0 (from -r requirements.txt (line 2)) Downloading gunicorn-19.6.0-py2.py3-none-any.whl (114kB) 100% |████████████████████████████████| 122kB 3.2MB/s Collecting itsdangerous>=0.21 (from Flask==0.11.1->-r requirements.txt (line 1)) Downloading itsdangerous-0.24.tar.gz (46kB) 100% |████████████████████████████████| 51kB 3.9MB/s Collecting click>=2.0 (from Flask==0.11.1->-r requirements.txt (line 1)) Downloading click-6.7-py2.py3-none-any.whl (71kB) 100% |████████████████████████████████| 71kB 2.9MB/s Collecting Werkzeug>=0.7 (from Flask==0.11.1->-r requirements.txt (line 1)) Downloading Werkzeug-0.14.1-py2.py3-none-any.whl (322kB) 100% |████████████████████████████████| 327kB 2.5MB/s Collecting Jinja2>=2.4 (from Flask==0.11.1->-r requirements.txt (line 1)) Downloading Jinja2-2.10-py2.py3-none-any.whl (126kB) 100% |████████████████████████████████| 133kB 4.5MB/s Collecting MarkupSafe>=0.23 (from Jinja2>=2.4->Flask==0.11.1->-r requirements.txt (line 1)) Downloading MarkupSafe-1.0.tar.gz Building wheels for collected packages: itsdangerous, MarkupSafe Running setup.py bdist_wheel for itsdangerous ... done Stored in directory: /home/bikle101/.cache/pip/wheels/fc/a8/66/24d655233c757e178d45dea2de22a04c6d92766abfb741129a Running setup.py bdist_wheel for MarkupSafe ... done Stored in directory: /home/bikle101/.cache/pip/wheels/88/a7/30/e39a54a87bcbe25308fa3ca64e8ddc75d9b3e5afa21ee32d57 Successfully built itsdangerous MarkupSafe Installing collected packages: itsdangerous, click, Werkzeug, MarkupSafe, Jinja2, Flask, gunicorn Successfully installed Flask-0.11.1 Jinja2-2.10 MarkupSafe-1.0 Werkzeug-0.14.1 click-6.7 gunicorn-19.6.0 itsdangerous-0.24 bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ cat appengine_config.py """ `appengine_config.py` is automatically loaded when Google App Engine starts a new instance of your application. This runs before any WSGI applications specified in app.yaml are loaded. """ from google.appengine.ext import vendor import tempfile tempfile.SpooledTemporaryFile = tempfile.TemporaryFile # Third-party libraries are stored in "lib", vendoring will make # sure that they are importable by the application. vendor.add('lib') bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ gcloud app deploy You are creating an app for project [cp100-196102]. WARNING: Creating an App Engine application for a project is irreversible and the region cannot be changed. More information about regions is at <https://cloud.google.com/appengine/docs/locations>. Please choose the region where you want your App Engine application located: [1] europe-west2 (supports standard and flexible) [2] us-east1 (supports standard and flexible) [3] us-east4 (supports standard and flexible) [4] asia-northeast1 (supports standard and flexible) [5] asia-south1 (supports standard and flexible) [6] australia-southeast1 (supports standard and flexible) [7] southamerica-east1 (supports standard and flexible) [8] northamerica-northeast1 (supports standard and flexible) [9] us-central (supports standard and flexible) [10] europe-west3 (supports standard and flexible) [11] europe-west (supports standard and flexible) [12] cancel Please enter your numeric choice: 9 Creating App Engine application in project [cp100-196102] and region [us-central]....done. Services to deploy: descriptor: [/home/bikle101/cp100/default/app-engine/app.yaml] source: [/home/bikle101/cp100/default/app-engine] target project: [cp100-196102] target service: [default] target version: [20180223t170733] target url: [https://cp100-196102.appspot.com] Do you want to continue (Y/n)? Beginning deployment of service [default]... /google/google-cloud-sdk/lib/googlecloudsdk/core/util/files.py:622: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal for chunk in iter(lambda: fp.read(4096), ''): Some files were skipped. Pass `--verbosity=info` to see which ones. You may also view the gcloud log file, found at [/tmp/tmp.jSPbOyfTpW/logs/2018.02.23/17.06.34.652615.log]. ╔════════════════════════════════════════════════════════════╗ ╠═ Uploading 226 files to Google Cloud Storage ═╣ ╚════════════════════════════════════════════════════════════╝ File upload done. Updating service [default]...done. Waiting for operation [apps/cp100-196102/operations/127ce446-a28d-4ab7-b55d-0a3b002ca388] to complete...done. Updating service [default]...done. Deployed service [default] to [https://cp100-196102.appspot.com] You can stream logs from the command line by running: $ gcloud app logs tail -s default To view your application in the web browser run: $ gcloud app browse bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ gcloud app browse Did not detect your browser. Go to this link to view your app: https://cp100-196102.appspot.com bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ bikle101@cp100-196102:~/cp100/default/app-engine$ In tab-6 I loaded this URL: https://cp100-196102.appspot.com I saw this: In the codelabs-tab, I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#5 I studied the page. I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#6 I studied the page. In tab-6 I asked for browser-refresh with ctrl-r. After that I saw this: I clicked green-button: "Add book". I saw this: I entered information about a book. I clicked green-button: "Save". I saw this: In the GCP-tab I visited a URL: https://console.cloud.google.com/datastore I saw this: In the GCP-tab I visited a URL: https://console.cloud.google.com/code I clicked app-engine folder. I clicked bookshelf folder. I clicked model_datastore.py I saw this: In the codelabs-tab, I studied this page: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#6 I carefully read: "Step 5". In my mind I joined that section with model_datastore.py (in GCP-tab). Now I understand Cloud Datastore better. If I ever need to add a No-SQL datastore to a python-web-app I will return to this lesson. In the codelabs-tab, I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-app-engine/#7 I studied the page. That ended my interaction with the CP100-node I call: "App-Engine". [top] Cloud-Storage Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage I saw this: I studied that page. I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage/#1 I studied that page. I bookmarked three links in that page: https://cloud.google.com/storage/docs/ python/googlecloudstorageclient/setting-up-cloud-storage https://cloud.google.com/apis/docs/cloud-client-libraries I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage/#2 I studied that page. I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage/#3 I studied that page to understand the shell commands below: In the GCP-tab I visited a URL: https://console.cloud.google.com In the top-right, I clicked the Cloud-Shell icon to start a new Cloud-Shell. I moved the new Cloud-Shell window to tab 5. In Cloud-Shell-tab, I entered commands: echo $DEVSHELL_PROJECT_ID gcloud compute zones list|grep central gsutil mb -l us-central1 gs://$DEVSHELL_PROJECT_ID I saw this: In Cloud-Shell-tab, I entered another command: gsutil defacl ch -u AllUsers:R gs://$DEVSHELL_PROJECT_ID GCP indicated success. In the GCP-tab I visited a URL: https://console.cloud.google.com/code I clicked folder: "cloud-storage". I clicked "config.py". I studied lines 47 through 49. I clicked folder: "cloud-storage". I clicked folder: "bookshelf". I clicked "storage.py". I studied lines 47 through 69. I clicked folder: "bookshelf". I clicked folder: "templates". I clicked "list.html". I studied lines 31 through 35. I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage/#4 I studied that page. In Cloud-Shell-tab I entered command: cd ~/cp100/default/cloud-storage I used sed-command to edit a file: sed -i s/your-bucket-name/$DEVSHELL_PROJECT_ID/ config.py grep BUCKET config.py I entered more commands: cat requirements.txt pip install -r requirements.txt -t lib gcloud app deploy I saw this: I clicked the next link in the sequence: https://codelabs.developers.google.com/codelabs/cp100-cloud-storage/#5 I studied that page. I studied the output from gcloud app deploy (displayed above). It gave me the URL of my app on appspot: target url: [https://cp100-196102.appspot.com] I visited https://cp100-196102.appspot.com I saw this: I clicked geen-button: "Add book". I added a book and pressed the green-button: "Save": I saw this: In GCP-tab I loaded URL: https://console.cloud.google.com/storage I found the image for the book: That ended my interaction with the CP100-node I call: "Cloud-Storage". [top] Compute-Engine Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-compute-engine I saw this: I studied that page. I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#1 I bookmarked links in that page: https://cloud.google.com/compute/docs/ https://cloud.google.com/python/tutorials/bookshelf-on-compute-engine#multiple_instances https://cloud.google.com/compute/docs/startupscript https://cloud.google.com/solutions/google-compute-engine-management-puppet-chef-salt-ansible https://cloud.google.com/deployment-manager/overview I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#2 In the GCP-tab, I visited URL: https://console.cloud.google.com/code I clicked folder: "compute-engine". I clicked folder: "startup-scripts". I clicked file: "startup-script.sh" I studied lines: 20, 45, 52. In the codelabs-tab, I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#3 In GCP-tab I visited URL: https://console.cloud.google.com I used the top-right Cloud-Shell icon to start a new Cloud-Shell. I moved Cloud-Shell to tab-5. In Cloud-Shell, I issued commands: cd ~/cp100/default/compute-engine gcloud compute zones list | grep central gcloud config set compute/zone us-central1-f GCP indicated success. In Cloud-Shell, I entered a long command: gcloud compute instances create bookshelf \ --image-family=debian-8 \ --image-project=debian-cloud \ --machine-type=g1-small \ --scopes userinfo-email,cloud-platform \ --metadata-from-file startup-script=startup-scripts/startup-script.sh \ --tags http-server In Cloud-Shell, I entered another long command: gcloud compute firewall-rules create default-allow-http-8080 \ --allow tcp:8080 \ --source-ranges 0.0.0.0/0 \ --target-tags http-server \ --description "Allow port 8080 access to http-server" I saw this: bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ gcloud compute instances create bookshelf \ > --image-family=debian-8 \ > --image-project=debian-cloud \ > --machine-type=g1-small \ > --scopes userinfo-email,cloud-platform \ > --metadata-from-file startup-script=startup-scripts/startup-script.sh \ > --tags http-server Created [https://www.googleapis.com/compute/v1/projects/cp100-196102/zones/us-central1-f/instances/bookshelf]. NAME ZONE MACHINE_TYPE PREEMPTIBLE INTERNAL_IP EXTERNAL_IP STATUS bookshelf us-central1-f g1-small 10.128.0.2 35.224.163.97 RUNNING bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ gcloud compute firewall-rules create default-allow-http-8080 \ > --allow tcp:8080 \ > --source-ranges 0.0.0.0/0 \ > --target-tags http-server \ > --description "Allow port 8080 access to http-server" Creating firewall...\ Created [https://www.googleapis.com/compute/v1/projects/cp100-196102/global/firewalls/default-allow-http-8080]. Creating firewall...done. NAME NETWORK DIRECTION PRIORITY ALLOW DENY default-allow-http-8080 default INGRESS 1000 tcp:8080 bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ bikle101@cp100-196102:~/cp100/default/compute-engine$ In GCP-tab I visited URL: https://console.cloud.google.com/compute I saw this: I made note of the IP address: 35.224.163.97 I visited URL: http://35.224.163.97:8080 I saw this: In the codelabs-tab, I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#4 I visited the Bookshelf-tab. I clicked green-button: "Add book". I filled out the form and clicked green-button: "Save". I saw this: In the codelabs-tab, I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#5 I bookmarked links in that page: https://cloud.google.com/appengine/docs/python/ndb/ https://cloud.google.com/sdk/cloud-client-libraries https://cloud.google.com/appengine/docs/python/googlecloudstorageclient/using-cloud-storage https://cloud.google.com/docs/choosing-a-compute-option#comparing_options In the codelabs-tab, I studied the next page: https://codelabs.developers.google.com/codelabs/cp100-compute-engine/#6 In the GCP-tab I visited this URL: https://console.cloud.google.com/compute I deleted the instance: "bookshelf". In the GCP-tab I visited this URL: https://console.cloud.google.com/networking I saw this: I clicked link "default" near top-left. I clicked link "Firewall rules": I deleted the rule for 8080. GCP indicated success. That ended my interaction with the CP100-node I call: "Compute-Engine". [top] Container-Engine Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-container-engine I saw this: I studied that page. I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#1 I bookmarked links in that page: https://cloud.google.com/container-engine/docs/ https://kubernetes.io/ https://kubernetes.io/docs/user-guide/kubectl-overview/ https://kubernetes.io/docs/admin/node/ https://cloud.google.com/compute/docs/instances/ https://kubernetes.io/docs/user-guide/pods/ https://kubernetes.io/docs/user-guide/replication-controller/ https://kubernetes.io/docs/user-guide/services/ https://cloud.google.com/compute/docs/load-balancing/network/ https://cloud.google.com/container-registry/docs/ I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#2 I studied that page. In GCP-tab I visited this URL: https://console.cloud.google.com/apis Near the top I clicked blue-button: "ENABLE APIS AND SERVICES": I searched for "Kubernetes Engine API". I found it; I clicked on the square: I was ready to click the blue-button: "ENABLE". But, GCP indicated that it was already enabled: In the Cloud-Shell-tab I clicked "Reconnect": I entered shell commands: gcloud compute zones list | grep central gcloud config set compute/zone us-central1-f gcloud config list gcloud container clusters create bookshelf \ --scopes "https://www.googleapis.com/auth/userinfo.email","cloud-platform" \ --num-nodes 2 I saw this: bikle101@cp100-196102:~$ gcloud compute zones list | grep central us-central1-c us-central1 UP us-central1-a us-central1 UP us-central1-f us-central1 UP us-central1-b us-central1 UP bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ gcloud config set compute/zone us-central1-f Updated property [compute/zone]. bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ gcloud config list [component_manager] disable_update_check = True [compute] gce_metadata_read_timeout_sec = 5 zone = us-central1-f [core] account = danbikle611@gmail.com check_gce_metadata = False disable_usage_reporting = False project = cp100-196102 [metrics] environment = devshell Your active configuration is: [cloudshell-11149] bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ gcloud container clusters create bookshelf \ > --scopes "https://www.googleapis.com/auth/userinfo.email","cloud-platform" \ > --num-nodes 2 WARNING: The behavior of --scopes will change in a future gcloud release: service-control and service-management scopes will no longer be added to what is specified in --scopes. To use these scopes, add them explicitly to --scopes. To use the new behavior, set container/new_scopes_behavior property (gcloud config set container/new_scopes_behavior true). WARNING: Starting in Kubernetes v1.10, new clusters will no longer get compute-rw and storage-ro scopes added to what is specified in --scopes (though the latter will remain included in the default --scopes). To use these scopes, add them explicitly to --scopes. To use the new behavior, set container/new_scopes_behavior property (gcloud config set container/new_scopes_behavior true). Creating cluster bookshelf.../ Creating cluster bookshelf...done. Created [https://container.googleapis.com/v1/projects/cp100-196102/zones/us-central1-f/clusters/bookshelf]. To inspect the contents of your cluster, go to: https://console.cloud.google.com/kubernetes/workload_/gcloud/us-central1-f/bookshelf?project=cp100-196102 kubeconfig entry generated for bookshelf. NAME LOCATION MASTER_VERSION MASTER_IP MACHINE_TYPE NODE_VERSION NUM_NODES STATUS bookshelf us-central1-f 1.8.7-gke.1 35.226.76.228 n1-standard-1 1.8.7-gke.1 2 RUNNING bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ bikle101@cp100-196102:~$ Next, in the GCP-tab, I entered this URL: https://console.cloud.google.com/code I clicked folder: "container-engine". I clicked folder: "bookshelf". I clicked file: "storage.py". I studied line 18. I studied lines 24,25,26. I studied lines 49 through 70. I returned to folder: "container-engine". I clicked file: "bookshelf-frontend.yaml". I studied lines 24 through 52. I studied lines 63 through 77. In the codelabs-tab I clicked through to the next URL: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#3 I studied that page. In the Cloud-Shell-tab I entered commands: cd ~/cp100/default/container-engine sed -i s/your-project-id/$DEVSHELL_PROJECT_ID/ config.py sed -i s/your-project-id/$DEVSHELL_PROJECT_ID/ bookshelf-frontend.yaml gcloud config set container/cluster bookshelf docker build -t gcr.io/$DEVSHELL_PROJECT_ID/bookshelf . gcloud docker -- push gcr.io/$DEVSHELL_PROJECT_ID/bookshelf gcloud container clusters get-credentials bookshelf kubectl create -f bookshelf-frontend.yaml kubectl get pods kubectl get services bookshelf-frontend Those commands made Cloud-Shell very busy. I captured output from the end: bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ gcloud docker -- push gcr.io/$DEVSHELL_PROJECT_ID/bookshelf The push refers to a repository [gcr.io/cp100-196102/bookshelf] 4cdee793aceb: Pushed 11405187a2ec: Pushed fddb4ab72d72: Pushed c27b0a837362: Pushed 54eed10b560f: Pushed a3f6a30be11b: Pushed d75d22fe5420: Pushed 65d9aa2a4c52: Pushed d263c7228acb: Mounted from google_appengine/base 21df82f90a72: Mounted from google_appengine/base latest: digest: sha256:3b5032e6ca1209ec7f00788d4ed13165ba0989fd93f5c46c8486b83cc866b820 size: 2415 bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ gcloud container clusters get-credentials bookshelf Fetching cluster endpoint and auth data. kubeconfig entry generated for bookshelf. bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ kubectl create -f bookshelf-frontend.yaml replicationcontroller "bookshelf-frontend" created service "bookshelf-frontend" created bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ kubectl get pods NAME READY STATUS RESTARTS AGE bookshelf-frontend-cd8dc 1/1 Running 0 2m bookshelf-frontend-prbrp 1/1 Running 0 2m bookshelf-frontend-vbn4x 1/1 Running 0 2m bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ kubectl get services bookshelf-frontend NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE bookshelf-frontend LoadBalancer 10.11.240.183 35.226.210.138 80:31348/TCP 2m bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ bikle101@cp100-196102:~/cp100/default/container-engine$ I made note of the IP address from the above output: 35.226.210.138 In tab-6 I entered this URL: http://35.226.210.138 I saw this: In codelabs-tab I clicked to the next page: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#4 I studied that page. In the bookshelf-tab (should be tab-6), I clicked the green-button: "Add book". I filled out the form: I clicked green-button "Save"; It responded: In codelabs-tab I clicked to the next page: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#5 I studied that page. In codelabs-tab I clicked to the next page: https://codelabs.developers.google.com/codelabs/cp100-container-engine/#6 I studied that page. In GCP-tab, I visited URL: https://console.cloud.google.com/kubernetes I clicked the icon: "bookshelf". I clicked the trash-icon on the left; I wanted to delete the cluster: In GCP-tab, I clicked the hamburger-link. I picked "Container Registry". I saw this: I clicked "bookshelf": I checked it; that activated blue-button: "DELETE". I clicked: "DELETE": In GCP-tab I visited URL: https://console.cloud.google.com/networking/firewalls I saw this: I deleted the two firewall-rules for bookshelf: In GCP-tab I visited URL: https://console.cloud.google.com/net-services/loadbalancing I saw this: I clicked the trashcan-icon to delete that service: I visited the VM URL to ensure I had no VMs eating my GCP budget: https://console.cloud.google.com/compute GCP showed that none were running. That ended my interaction with the CP100-node I call: "Container-Engine". [top] Big Query Next, in the codelabs-tab, I clicked this link: https://codelabs.developers.google.com/codelabs/cp100-big-query I saw this: I studied that page; I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-big-query#1 I bookmarked links from that page: https://developers.google.com/bigquery/docs/data%23aclsandrights https://code.google.com/apis/console/ https://developers.google.com/bigquery/docs/reference/v2/projects https://developers.google.com/bigquery/docs/reference/v2/datasets https://developers.google.com/bigquery/docs/reference/v2/tables https://cloud.google.com/bigquery/federated-data-sources https://cloud.google.com/bigquery/what-is-bigquery#jobs https://cloud.google.com/bigquery/streaming-data-into-bigquery https://cloud.google.com/storage/ https://cloud.google.com/datastore/ https://developers.google.com/bigquery/docs/reference/v2/jobs I collected an image from that page: I memorized the above image as this phrase: Project has many Datasets has many Tables has many Rows with Columns I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-big-query#2 I studied that page. In the GCP-tab I visited the URL to set my project to CP100: https://console.cloud.google.com After I set the project to "cp100", I clicked the top-left-hamburger link. From there I clicked BigQuery near the bottom. It opened a new tab with this URL: https://bigquery.cloud.google.com I saw this: I clicked the select-option to the right of cp100: I asked for a new Dataset named: "cp100": I clicked blue-button: "OK". Next, I clicked the tiny-blue-button: "+". GCP offered me a form to specify Source-Data: I filled it out: At the bottom I clicked blue-button: "Create Table". I saw this: I inspected the new table: I returned to the codelabs-tab. I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-big-query#3 I studied that page. I clicked through to the next page: https://codelabs.developers.google.com/codelabs/cp100-big-query#4 I studied that page. In the BigQuery-tab I clicked link: "Compose Query" BigQuery offered a TextArea-element for me to enter syntax: I entered syntax: SELECT name, COUNT FROM cp100.namedata WHERE gender = 'F' ORDER BY COUNT DESC LIMIT 5 I clicked red-button: "Run Query". BigQuery responded quickly: I returned to my Cloud-Shell. I asked it to "Reconnect". I entered command: bq query \ "SELECT name,count FROM cp100.namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" I saw this: Welcome to Cloud Shell! Type "help" to get started. bikle101@cpb100-195004:~$ bq query \ > "SELECT name,count FROM cp100.namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" Welcome to BigQuery! This script will walk you through the process of initializing your .bigqueryrc configuration file. First, we need to set up your credentials if they do not already exist. Credential creation complete. Now we will select a default project. List of projects: # projectId friendlyName --- --------------- -------------- 1 cp100-196102 CP100 2 cpb100-195004 cpb100 3 proj10-194905 proj10 Found multiple projects. Please enter a selection for which should be the default, or leave blank to not set a default. Enter a selection (1 - 3): I entered: "1". That caused BigQuery to fail. I tried other ideas but they all failed: Welcome to Cloud Shell! Type "help" to get started. bikle101@cpb100-195004:~$ bq query \ > "SELECT name,count FROM cp100.namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" Welcome to BigQuery! This script will walk you through the process of initializing your .bigqueryrc configuration file. First, we need to set up your credentials if they do not already exist. Credential creation complete. Now we will select a default project. List of projects: # projectId friendlyName --- --------------- -------------- 1 cp100-196102 CP100 2 cpb100-195004 cpb100 3 proj10-194905 proj10 Found multiple projects. Please enter a selection for which should be the default, or leave blank to not set a default. Enter a selection (1 - 3): 1 BigQuery configuration complete! Type "bq" to get started. BigQuery error in query operation: Error processing job 'cpb100-195004:bqjob_r17dd4d1fe039067b_00000161cfa318f3_1': Not found: Table cpb100-195004:cp100.namedata bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bq query "SELECT name,count FROM cp100.namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" BigQuery error in query operation: Error processing job 'cpb100-195004:bqjob_r387c27926d45a9f4_00000161cfa40090_1': Not found: Table cpb100-195004:cp100.namedata bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bq query "SELECT name,count FROM CP100.namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" BigQuery error in query operation: Error processing job 'cpb100-195004:bqjob_r21ee3ea3975e4639_00000161cfa46411_1': Not found: Table cpb100-195004:CP100.namedata bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bq query "SELECT name,count FROM namedata WHERE gender = 'M' ORDER BY count ASC LIMIT 5" BigQuery error in query operation: Error processing job 'cpb100-195004:bqjob_r1ed371a2ee77f33f_00000161cfa556af_1': Table name "namedata" cannot be resolved: dataset name is missing. bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ bikle101@cpb100-195004:~$ I decided to avoid BigQuery. To clean-up, I clicked on my BigQuery tab. I found the link to "Delete Dataset"; I clicked it: BigQuery indicated successful delete. That ended my interaction with the CP100-node I call: "Big Query". Also that ends lesson002 of cs101.us [top]

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Lesson 003 [ Intro Data Science ] Topics: Anaconda Jupyter Python Data Science Questions/Answers I did this lesson and collected notes during my efforts. Anaconda Anaconda runs on Mac, Windows, and Linux. I started by downloading Anaconda 5.1.0 for my laptop: Mac: https://drive.google.com/open?id=1XasIhnctEdHKkWFt8aK_-2goVgo_KV8_ Windows: https://drive.google.com/open?id=1QqaRx4UvpneVMzLZShIzeamE0rPvrj8C Linux: https://drive.google.com/open?id=1WULX7krUKayH0l4BVIb3dCr0Z3i-gQHR If I want an Anaconda version other than 5.1.0, it should be available here: https://repo.continuum.io/archive/ After I downloaded it, I started the install by clicking on the download. During the install, I answered yes to all the defaults. After I installed Anaconda, I saw this: dan@p95:~ $ ls -la total 300 drwxr-xr-x 45 dan dan 4096 Feb 26 17:08 ./ drwxr-xr-x 5 root root 4096 Feb 20 18:12 ../ drwxrwxr-x 22 dan dan 4096 Feb 26 17:10 anaconda3/ -rw-r--r-- 1 dan dan 3643 Jan 11 18:02 .bashrc I verified installation with shell commands: dan@p95:~ $ dan@p95:~ $ ls -la anaconda3/ total 200 drwxrwxr-x 22 dan dan 4096 Feb 26 17:10 . drwxr-xr-x 45 dan dan 4096 Feb 26 17:08 .. drwxrwxr-x 2 dan dan 12288 Feb 26 17:08 bin drwxrwxr-x 2 dan dan 4096 Feb 26 17:03 compiler_compat drwxrwxr-x 2 dan dan 20480 Feb 26 17:10 conda-meta drwxrwxr-x 3 dan dan 4096 Feb 26 17:05 doc drwxrwxr-x 2 dan dan 4096 Feb 26 17:06 envs drwxrwxr-x 7 dan dan 4096 Feb 26 17:06 etc drwxrwxr-x 31 dan dan 12288 Feb 26 17:06 include drwxrwxr-x 22 dan dan 36864 Feb 26 17:06 lib drwxrwxr-x 4 dan dan 4096 Feb 26 17:05 libexec -rw-rw-r-- 1 dan dan 5629 Feb 13 03:25 LICENSE.txt drwxrwxr-x 3 dan dan 4096 Feb 26 17:03 man drwxrwxr-x 97 dan dan 4096 Feb 26 17:05 mkspecs drwxrwxr-x 2 dan dan 4096 Feb 26 17:05 phrasebooks drwxrwxr-x 247 dan dan 36864 Feb 26 17:10 pkgs drwxrwxr-x 16 dan dan 4096 Feb 26 17:05 plugins drwxrwxr-x 14 dan dan 4096 Feb 26 17:05 qml drwxrwxr-x 29 dan dan 4096 Feb 26 17:06 share drwxrwxr-x 4 dan dan 4096 Feb 26 17:04 ssl drwxrwxr-x 2 dan dan 12288 Feb 26 17:05 translations drwxrwxr-x 3 dan dan 4096 Feb 26 17:05 var -rw-rw-r-- 1 dan dan 0 Feb 26 17:07 vscode_inst.py.log drwxrwxr-x 3 dan dan 4096 Feb 26 17:03 x86_64-conda_cos6-linux-gnu dan@p95:~ $ dan@p95:~ $ dan@p95:~ $ dan@p95:~ $ which python /home/dan/anaconda3/bin/python dan@p95:~ $ dan@p95:~ $ dan@p95:~ $ dan@p95:~ $ python Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) [GCC 7.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> >>> >>> Jupyter Jupyter is an interesting application. You can learn a bit about Jupyter at the URL listed below: http://jupyter.org/ I see Jupyter as a mashup of a Python interpreter and Web technology. I can use Jupyter to expose some Python syntax to a group of people who are running browsers who then want to see that syntax run in their browswers. I bumped into Jupyter in two types of situations. The first type is what I call the presentation-training-scenario. If I present a small amount of Python based content to a room full of Python programmers, Jupyter can be a good fit for that task. The second type is what I call the development-scenario. In this situation I need to write some code to deliver a feature in a production app. Is the production app running in Jupyter? Probably not. So to me it's obvious that Jupyter makes a poor development environment. So I remember two points: Jupyter is good for training. Jupyter is poor for development. To start Jupyter I typed a simple shell command: jupyter notebook The above command should work on both Mac and Linux. On windows you should find the right icon and then click on it. On windows if you have trouble, I offer a tip: https://www.google.com/search?q=On+windows+how+to+start+Jupyter On my laptop after I typed: "jupyter notebook", I saw this: On the right, I clicked the new-link and I saw this: I typed in some Python syntax: print('hello jupyter.') I pressed the enter-key. That just made the field larger. I entered shift-enter. Python came to life and ran that Python syntax: Now you know enough Jupyter to learn some Python. [top] Python Here is some Python: print("Hello, Python!") Try to copy-paste it into Jupyter. Then in Jupyter, use shift-enter to run it. I did that. First, I saw this: Then, I pasted: Then, I pressed shift-enter: If you can do that in your laptop, you are on the right path. Time to move along. I show you More python below. Try to slowly and carefully run pieces of it in Jupyter: # coding: utf-8 """ py4ds1.py This is a simple script used for training people new to Python. """ # ## Python for Data Science # ### Learning Center V0.9 # ### Date: 20Jan2018 print("Hello, Python!") # ### --- Exercise 1 # ### Print the following string "Learning Python for Data Science" counter = 100 # An integer assignment miles = 1000.0 # A floating point name = "John" # A string print(counter) print(miles) print(name) a = b = c = 1 print(a) print(b) print(c) # ### --- Exrecise 2 # ### Create variables with your first_name and last_name # ### print first_name and last_name first_name = "Learning" last_name = "Center" print(first_name) print(last_name) print(first_name," is ",last_name) str = 'Hello World!' print(str) # Prints complete string print(str[0]) # Prints first character of the string print(str[2:5]) # Prints characters starting from 3rd to 5th print(str[2:]) # Prints string starting from 3rd character print(str * 2) # Prints string two times print(str + " " + "TEST") # Prints concatenated string # ### --- Exercise 3 # ### print first two characters of first_name and last two characters of last_name first_name = "Learning" last_name = "Center" print(first_name[0:2]) print(last_name[-2:]) ########################################################### ## Python Lists ########################################################### list = [ 'abcd', 786 , 2.23, 'john', 70.2 ] print(list) # Prints complete list print(list[0]) # Prints first element of the list print(list[1:3]) # Prints elements starting from 2nd till 3rd ########################################################### ## Python Tuples ########################################################### tuple = ('abcd', 786 , 2.23, 'john', 70.2 ) print(tuple) # Prints complete list print(tuple[0]) # Prints first element of the list print(tuple[1:3]) # Prints elements starting from 2nd till 3rd ## Decisions in Python var = 100 if var == 2000: print("1 - Got a true expression value") print(var) elif var == 150: print("2 - Got a true expression value") print(var) elif var == 100: print("3 - Got a true expression value") print(var) else: print("4 - Got a false expression value") print(var) ## For and while Loops ## while loop count = 9 while (count > 0): print('The count is:', count) count = count - 1 ## for loop for letter in 'Python': # First Example print('Current Letter :', letter) fruits = ['banana', 'apple', 'mango'] for fruit in fruits: # Second Example print('Current fruit :', fruit) ## Regular expressions import re line = "Cats are smarter than dogs" ## Matching using re matchObj = re.match(r'(.*) are (.*)',line,re.M|re.I) print(matchObj.group(1)) print(matchObj.group(2)) ## search and replace phone = "2004-959-559 # This is Phone Number" # make 2004 to be 4002 num = re.sub(r'2004',"4002", phone) print("Phone Num : ", num) ## Functions in Python def add_numbers(a,b): c = a + b return c result = add_numbers(2,3) print(result) for x in range(10): print(x) ## Pass list as function argument def f(x,l=[]): for i in range(x): l.append(i*i) print(l) f(2) f(3,[3,2,1]) ## Appending and extending Python lists list1 = [1,11,111] list2 = [2,22,222] list1.append(1111) print("list1-a = %s" % list1) list1.extend(list2) print("list1-b = %s" % list1) ## Integer and floating division def div1(x,y): print("%s/%s = %s" % (x, y, x/y)) def div2(x,y): print("%s//%s = %s" % (x, y, x//y)) # integer division div1(5,2) div1(5.,2) div2(5,2) div2(5.,2.) # Printing list length list = ['a', 'b', 'c', 'd', 'e'] print(len(list)) # Splitting strings ss = "This is Sunnyvale This is Cupertino" words = ss.split() print(words) d = {}.fromkeys(words,0) for w in words: d[w] += 1 print(d) [top] Data Science To do this exercise I shutdown Jupyter. Then I downloaded two CSV files into my Downloads folder on my laptop: boston_housing_data.csv family.csv I wanted Jupyter to see those two files. So, I used two shell commands to start Jupyter from the Downloads folder: cd ~/Downloads jupyter notebook Here is some Python which introduces you to Data Science. I ran the Python in my Jupyter Notebook and it worked well: # coding: utf-8 """ py4ds2.py This is a simple script used for training people new to Python. """ ## Reading csv files def read_file(filename): line_count = 0 file_open = open(filename,"r") data_array = [] for line in iter(file_open): line_no_newline = line.rstrip() line_split = line_no_newline.split(",") fn = line_split[0] work = line_split[2] concat = [fn,work] data_array.append(concat) return data_array array1 = read_file("family.csv") print(array1) # Regression example in Machine Learning get_ipython().run_line_magic('matplotlib', 'inline') import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt import sklearn from sklearn.datasets import load_boston boston = load_boston() print(boston.feature_names) """ Dataset description 00 - CRIM per capita crime rate by town 01 - ZN proportion of residential land zoned for lots over 25,000 sq.ft. 02 - INDUS proportion of non-retail business acres per town 03 - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) 04 - NOX nitric oxides concentration (parts per 10 million) 05 - RM average number of rooms per dwelling 06 - AGE proportion of owner-occupied units built prior to 1940 07 - DIS weighted distances to five Boston employment centres 08 - RAD index of accessibility to radial highways 09 - TAX full-value property-tax rate per $10,000 10 - PTRATIO pupil-teacher ratio by town 11 - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 12 - LSTAT % lower status of the population 13 - MEDV (Target) Median value of owner-occupied homes in $1000's 13 X Variables; 1 Y variable Data Points = 506 """ bos = pd.DataFrame(boston.data) bos.describe() bos.head() boston.target[:5] bos['PRICE'] = boston.target from sklearn.linear_model import LinearRegression X = bos.drop('PRICE',axis=1) X[0:5] bos.PRICE[0:5] lm = LinearRegression() lm.fit(X,bos.PRICE) print(lm.intercept_) print(lm.coef_) print ("Number of coefficients is ",len(lm.coef_)) lm.predict(X)[0:5] plt.scatter(bos.PRICE,lm.predict(X)) plt.xlabel("Actual Prices") plt.ylabel("Predicted Prices") mseFull = np.mean((bos.PRICE - lm.predict(X)) ** 2) print(mseFull) X[0:2] ## first two data rows print(boston.target[0:2]) row_1 = [0.00632,18.0,2.31,0.0,0.538,6.575,65.2,4.0900,1.0,296.0,15.3,396.90,4.98] row_2 = [0.02731,0.0,7.07,0.0,0.469,6.421,78.9,4.9671,2.0,242.0,17.8,396.90,9.14] tests = [row_1,row_2] lm.predict(tests) I saw this: I saw this: I saw this: Questions/Answers What is Data Science? Why do you want to learn Data Science? What is the best way to learn Data Science? What is Anaconda? Why use Anaconda? What are alternatives to Anaconda? How to install Anaconda? How to operate Anaconda? What is Jupyter? Why use Jupyter? What are alternatives to Jupyter? How to install Jupyter? How to operate Jupyter? What is Python? Why use Python? What are alternatives to Python? How to install Python? How to operate Python? What are the main Python data types? How to print part of a String? How to concatenate two Strings? What is a Python List? How is a List like a String? How is a List different than a String? What is a Python Tuple? How is a Tuple like a List? How is a Tuple different than a List? How to write if-then-else logic in Python? How to write a while-loop in Python? How to write a for-loop in Python? How is a while-loop like a for-loop? How is a while-loop different than a for-loop? What is a Regular Expression? Why use Regular Expressions? In Python how to operate Regular Expressions? In a Regular Expression, what is a group? In a Regular Expression, how to create a group? In Python what is a Function? In Python why use Functions? In Python how to create a Function? In a Function, what does the return statement do? In a Function how to declare a default value for an input parameter? In Python what does the range() function do? In Python how to concatenate two lists? When I call the print() function, if I see a %s parameter, what does that mean? In Python what is Integer division? When I call split() to split a String, what do I get? When I call split(",") to split a String, what do I get? How do I operate Jupyter so it sees local CSV files? What is a local CSV file? In Python what does the open() function do? In Python what does the iter() function do? In Python what does the rstrip() function do? In Python where do I usually see the append() function? In Python, what does the import command do? What is NumPy? Why use NumPy? What is Pandas? Why use Pandas? What is SciPy? Why use SciPy? What is matplotlib? Why use matplotlib? What is sklearn? Why use sklearn? In Machine Learning, what is a "Feature"? The Boston-Housing-example uses the word: "Target"; what is "Target"? In Machine Learning, what is "X"? In Machine Learning, what is "Y"? In Machine Learning, what is Linear Regression? In Linear Regression what is "Intercept"? In Linear Regression what is "Coefficient"? In Linear Regression what is acronym: "MSE"? In sklearn-LinearRegression, how do I get a prediction? When I get a prediction, what does it look like? In Data Science, what is a "Scatter Plot"? In Data Science, why use a "Scatter Plot"? In Python how to create a "Scatter Plot"? [top]

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Lesson 004 [ GCP Dataflow ] GCP Dataflow is described in this URL: https://cloud.google.com/dataflow/ If you want a free GCP account to do this lesson, you should be able to get one: https://console.developers.google.com/freetrial Google will want you to login using gmail. Also they will want your credit-card information. So, you should do that now. Or, if you want, just keep reading. To do this lesson, I needed only a browser. I used a Chrome browser. To start, I logged into gmail. Next I loaded a URL: https://console.cloud.google.com/projectcreate I saw this: I created a project: Notice in the above image that I did not use the suggested project-name. Instead I called my new project: "dfnow". Dataflow depends on a bucket: I created a bucket called "dfnow": I checked that it was available in the browser: Next, I loaded a URL: https://console.cloud.google.com/dataflow I saw this: I clicked blue-button: "CREATE JOB FROM TEMPLATE". GCP served me a form to fill out: I wanted to test if Dataflow would work for me. So, I supplied minimal information to the form. I clicked blue-button: "Run job". GCP served me an error: I looked for help: I returned to URL: https://console.cloud.google.com/dataflow I saw this: I clicked blue-button: "Try Dataflow": It offered me help with my permission problem. I clicked through: I clicked blue-button: "Enable APIs": It needed two minutes: It finished. I clicked blue-button, "CREATE JOB FROM TEMPLATE": GCP served me a form to fill out: I wanted to test if Dataflow would work for me. So, I supplied minimal information to the form. I clicked blue-button: "Run job". Instead of serving an error, GCP launched a Job: I saw information about the job as it progressed: After 4 min 28 sec, it finished: I visited a URL to search for the job-output: https://console.cloud.google.com/storage I found it and clicked on a file in the dfnow-bucket: I saw output from the job: After I studied the files, I deleted them from the bucket: At that point I was convinced that I had the permissions I need to submit Dataflow-jobs. [top] Python DataFlow I started this effort by activating Cloud-Shell: I clicked button on the right to give Cloud-Shell its own tab: I like to have my Cloud-Shell tab to the right of my GCP-tab: I visited a URL and studied it: https://cloud.google.com/dataflow/docs/quickstarts/quickstart-python I should have started there. The above page offers information on how to avoid the API-permission-error I encountered. In Cloud-Shell-tab I issued commands: virtualenv venv10 . venv10/bin/activate pip install google-cloud-dataflow That quickly finished. Then, I prepared two shell variables for a large Python command: env|grep PROJECT export BUCKET=gs://dfnow I launched a Dataflow-job using Python: python -m apache_beam.examples.wordcount \ --project $DEVSHELL_PROJECT_ID \ --runner DataflowRunner \ --staging_location $BUCKET/staging \ --temp_location $BUCKET/temp \ --output $BUCKET/results/output Much text flew by in the tab; I captured some: Eventually it finished: In the buckets-Browser, I looked for output from the job: I found output from the job: After I saw that output, I returned to the buckets-Browser and removed files from that job. That concludes this lesson on GCP-Dataflow. [top]

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