Here’s a step-by-step guide to get started with Google Cloud Platform (GCP) for data scientists:
Welcome to a beginner’s guide tailored for data scientists and anyone interested in harnessing the power of Google Cloud Platform’s cloud storage (buckets) and GPU resources.
- Getting Started with Google Cloud Platform:
- Sign up for a GCP account using your Gmail credentials or create a new Gmail account if you don’t have one already. Navigate to the GCP website and click on “TRY IT FREE” to begin the signup process. Complete the required information and agree to the terms and conditions to receive $300 in free credits.
- Accessing the GCP Console:
- After successful signup, you’ll be directed to the GCP console. This serves as your central hub for managing all GCP-related activities.
- Creating a Project:
- Every action on GCP is performed within the context of a project. Create a new project from the console to organize your resources effectively.
- Requesting GPU Quota:
- Deep learning tasks often require GPU resources. To access GPUs on GCP, navigate to the Quotas page under Compute Engine in the Navigation Menu. Specify the required GPU quota and submit your request. Be prepared to wait for processing, and in some cases, pay a nominal fee.
- Setting Up Cloud Storage (Buckets):
- Cloud Storage buckets are fundamental for storing data in GCP. Create a new bucket from the GCP Console under Storage > Browser. Customize the settings to suit your needs.
- Installing the gsutil Tool:
- gsutil is a Python-based command-line tool that facilitates interaction with Cloud Storage. Download and install the Cloud SDK from the GCP website. Follow the installation instructions, ensuring that you update your system’s PATH variable for easy access to gsutil commands.
- Transferring Files with gsutil:
- Use gsutil commands to transfer files between your local machine and GCP buckets. Copy files from your local machine to a bucket or vice versa using commands like
gsutil cp.
- Use gsutil commands to transfer files between your local machine and GCP buckets. Copy files from your local machine to a bucket or vice versa using commands like
- Exploring Further:
- Delve into the gsutil documentation for more advanced commands and features to enhance your cloud storage experience.
By following these steps, you’ll be well on your way to leveraging GCP’s cloud storage and GPU resources for your data science projects. Remember to monitor your resource usage to avoid exceeding the free trial limits and incurring unexpected charges.