This section describes how to launch and connect toTensorFlow production & development Kit
in a Google Compute environment using the available Cloud Launcher
- In your browser, log in to the Google Compute Engine Console
- In the left navigation panel, select Cloud
Launcher. If it is not visible in the left panel, search for Cloud
Launcher in the search box.
- On the Cloud Launcher page, search for tensorflow-prod-dev-kit (Techlatest.net)
and select the tensorflow-prod-dev-kit (Techlatest.net) offering. The following page
- Click Launch on Compute Engine.
- Select a zone where you want to launch the VM(such as us-east1-)
- Optionally change the number of cores and amount of
memory. (This defaults to 2 vCPUs and 7.5 GB ram.)
- Optionally change the boot disk type and size. (This defaults to "Standard Persistent Disk" and 10 GB respectively)
- Optionally change the network name and subnetwork names.
Be sure that whichever network you specify has ports 22 (for ssh), 3389 (for RDP) and 80 (for HTTP) exposed.
- Click Deploy when you are done.
TensorFlow Production & Development Kit will begin deploying.
- A summary page displays when the compute engine is
successfully deployed. Click on the Instance link to to go to the instance page .
- On the instance page, click on the "SSH" button, select "Open in browser window".
- This will open SSH window in a browser.
- Run below command to set the password for "ubuntu" user
- Now the password for ubuntu user is set, you can connect to the VM's desktop environment from any local windows machine using RDP protocol.
- To connect using RDP, first note the external IP of the VM from VM details page as highlighted below
- Then From you local windows machine, goto "start" menu , in the search box type and select "Remote desktop connection"
- In the "Remote Desktop connection" wizard, copy the external ip and click connect
- This will connect you to the VM's desktop environment. Provide "ubuntu" as the userid and the password set in step 8 to authenticate. Click OK
- Now you are connected to the out of box TensorFlow Production and Development environment.
- You can use the remote desktop you connected in above step for using the VM, however, more convenient and better method is to use the Jupyter/Ipython notebook which comes with the VM .
The Notebook is available on the same public IP you used for remote desktop and accessible via any browser. Just open the browser and type the public IP address (in this case http:// 220.127.116.11 ) and you will get below screen for login . Use “ubuntu” as username and the password you set in step 8 to login.
Note : Make sure you use “http” and not “https” in the url
- To know how to use the developer kit, please refer to the video tutorial series available on TensorFlow Production and Development kit Support.