What are the two methods for getting started with Deep Learning VM Images?
Two methods for getting started with Deep Learning VM Images on Google Cloud Platform are using the Google Cloud Console and using the gcloud command-line tool. These methods provide users with different ways to deploy and manage Deep Learning VM Images based on their preferences and familiarity with the tools.
1. Google Cloud Console:
The Google Cloud Console is a web-based interface that allows users to interact with and manage their Google Cloud resources. To get started with Deep Learning VM Images using the console, follow these steps:
a. Open the Google Cloud Console in a web browser and log in to your Google Cloud account.
b. Select the project in which you want to create the Deep Learning VM instance.
c. In the navigation menu, click on "Compute Engine" and then "VM instances".
d. Click on the "Create" button to create a new VM instance.
e. In the "Boot disk" section, click on the "Change" button and select "Deep Learning VM" from the "OS images" tab.
f. Choose the desired Deep Learning VM Image from the available options, such as TensorFlow, PyTorch, or JupyterLab.
g. Configure the remaining settings for the VM instance, such as machine type, region, and disk size.
h. Click on the "Create" button to create the Deep Learning VM instance.
2. gcloud command-line tool:
The gcloud command-line tool is a powerful and flexible tool for managing Google Cloud resources. To get started with Deep Learning VM Images using the gcloud tool, follow these steps:
a. Open a terminal or command prompt on your local machine.
b. Install and set up the gcloud command-line tool if you haven't already done so.
c. Authenticate the gcloud tool with your Google Cloud account by running the command "gcloud auth login".
d. Set the project in which you want to create the Deep Learning VM instance by running the command "gcloud config set project PROJECT_ID".
e. Run the command "gcloud compute instances create INSTANCE_NAME –image-family IMAGE_FAMILY –image-project IMAGE_PROJECT" to create the Deep Learning VM instance. Replace INSTANCE_NAME with the desired name for the instance, IMAGE_FAMILY with the Deep Learning VM Image family (e.g., "tf-latest-cpu"), and IMAGE_PROJECT with the project ID of the image (e.g., "deeplearning-platform-release").
These two methods provide users with flexibility in deploying and managing Deep Learning VM Images. The Google Cloud Console offers a graphical interface for users who prefer a visual approach, while the gcloud command-line tool provides a command-line interface for users who prefer automation and scripting. By leveraging these methods, users can quickly and efficiently get started with Deep Learning VM Images on Google Cloud Platform.
Other recent questions and answers regarding Advancing in Machine Learning:
- When a kernel is forked with data and the original is private, can the forked one be public and if so is not a privacy breach?
- What are the limitations in working with large datasets in machine learning?
- Can machine learning do some dialogic assitance?
- What is the TensorFlow playground?
- Does eager mode prevent the distributed computing functionality of TensorFlow?
- Can Google cloud solutions be used to decouple computing from storage for a more efficient training of the ML model with big data?
- Does the Google Cloud Machine Learning Engine (CMLE) offer automatic resource acquisition and configuration and handle resource shutdown after the training of the model is finished?
- Is it possible to train machine learning models on arbitrarily large data sets with no hiccups?
- When using CMLE, does creating a version require specifying a source of an exported model?
- Can CMLE read from Google Cloud storage data and use a specified trained model for inference?
View more questions and answers in Advancing in Machine Learning