Using TensorFlow Enterprise with Deep Learning VM

This page describes how to get started using TensorFlow Enterprise with a Deep Learning VM Images instance.

In this example, you create a TensorFlow Enterprise Deep Learning VM instance, connect to the instance using SSH, open a JupyterLab notebook, and run a classification tutorial on using neural networks with Keras.

Before you begin

  1. Google Cloud 계정에 로그인합니다. Google Cloud를 처음 사용하는 경우 계정을 만들고 Google 제품의 실제 성능을 평가해 보세요. 신규 고객에게는 워크로드를 실행, 테스트, 배포하는 데 사용할 수 있는 $300의 무료 크레딧이 제공됩니다.
  2. Google Cloud Console의 프로젝트 선택기 페이지에서 Google Cloud 프로젝트를 선택하거나 만듭니다.

    프로젝트 선택기로 이동

  3. Cloud 프로젝트에 결제가 사용 설정되어 있는지 확인합니다. 프로젝트에 결제가 사용 설정되어 있는지 확인하는 방법을 알아보세요.

Create a Deep Learning VM instance

To create a TensorFlow Enterprise Deep Learning VM instance, complete these steps:

  1. Go to the Deep Learning VM Cloud Marketplace page in the Google Cloud Console.

    Go to the Deep Learning VM Cloud Marketplace page

  2. Click Launch on Compute Engine. If you see a project selection window, choose the project in which to create the instance. If this is the first time you've launched Compute Engine, you must wait for the initial API configuration process to complete.

  3. On the New Deep Learning VM deployment page, enter a Deployment name. This will be the root of your virtual machine name. Compute Engine appends -vm to this name when creating your instance.

  4. Under Number of GPUs, select None. You won't need them to complete the instructions in this guide.

  5. Under Framework, select TensorFlow Enterprise 2.3 (CUDA 11.0).

  6. For this example, you can leave the remaining settings as they are.

  7. Click Deploy.

You've just created your first instance of a Deep Learning VM. After the instance is created, the Deployment Manager opens. This is where you can manage your Deep Learning VM instance and other deployments.

Connect with SSH, open a notebook, and run a classification tutorial

Complete these steps to set up an SSH connection to your Deep Learning VM instance, open a JupyterLab notebook, and run a tutorial on using neural networks with Keras:

  1. To complete these steps, you can use either Cloud Shell or any environment where the Cloud SDK can be installed. Cloud Shell and Cloud SDK are command line tools that you can use to interface with your instance.

    • If you want to use Cloud Shell, in Google Cloud, in the upper-right corner, click the Activate Cloud Shell button.

      Google Cloud Platform console

    • If you want to use Cloud SDK, download and install Cloud SDK on your local machine.

  2. In Cloud Shell or in a local terminal window, use the following command to create an SSH connection to your instance. Replace my-project-id, my-zone, and my-instance-name with the relevant information.

    gcloud compute ssh --project my-project-id --zone my-zone \
      my-instance-name -- -L 8080:localhost:8080
    
  3. In your local browser, visit http://localhost:8080 to access a JupyterLab notebook that is included in your instance by default.

  4. In the notebook, on the left, double-click tutorials to open the folder, and navigate to and open tutorials/tf2_course/01_neural_nets_with_keras.ipynb.

  5. Click the run button to run cells of the tutorial.

What's next