Intro to Google Colab how to use a GPU or TPU for free - Episode 05 скачать в хорошем качестве

Intro to Google Colab how to use a GPU or TPU for free - Episode 05

Thanks for watching, turn on notificatREACTions (🔔) to receive every new video 😍 🔰 MY INSTAGRAM 🔰   / med.max.technology   © Copyright by MED MAX TECHNOLOGY Introduction to Google Colab: Unleashing GPU and TPU Power for Free Google Colab is a cloud-based platform for coding in Python that provides a powerful and collaborative environment. It offers access to GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for free, allowing you to accelerate computations. Here's a concise guide to getting started: 1. Access Google Colab: Go to https://colab.research.google.com/ in your browser. Log in with your Google account. 2. Create a New Notebook: Click "New Notebook" to create a fresh Colab notebook. 3. Enable GPU or TPU: Navigate to "Runtime" "Change runtime type." Choose "GPU" or "TPU" in the "Hardware accelerator" dropdown. 4. Check GPU/TPU Availability: Run this code in a Colab cell to verify: python Copy code import tensorflow as tf print("GPU Available:", tf.config.list_physical_devices('GPU')) print("TPU Available:", tf.config.list_physical_devices('TPU')) 5. Utilize GPU/TPU: With your chosen accelerator, speed up tasks like model training and data analysis. 6. Monitoring Resources: Track GPU/TPU usage in "View" "Table of contents" "Manage sessions." 7. Work with Libraries: Leverage libraries like TensorFlow, PyTorch, and scikit-learn for accelerated computations. 8. Save Your Work: Save your Colab notebook to Google Drive or download it to your local machine. 9. Session Duration: Colab sessions have time limits, so be aware of session duration and potential timeouts. With Google Colab, you can tap into GPU and TPU power without cost, amplifying your coding capabilities and enabling rapid experimentation and learning. © Copyright by MED MAX TECHNOLOGY YT Channel ☞ Do not Reup . Thanks !!! 🔥