Thuê máy chủ GPU train model ngon rẻ bổ trên ThueGPU.vn - Mì AI

The paragraph introduces the audience to the concept of renting GPU servers for machine learning tasks. It discusses the limitations faced by students using free platforms like Colab, such as time restrictions, limited GPU memory, and storage capacity. The speaker then presents a solution by introducing a local GPU server rental service, highlighting its benefits such as high data upload/download speeds, convenient payment methods, and the availability of invoices for businesses. The service is praised for its support of the local internet infrastructure and its user-friendly payment system, which is particularly beneficial for students.

This paragraph walks through the process of setting up and configuring the rented GPU server. It explains how to log in to the account, navigate the dashboard, and top up the account balance. The speaker then demonstrates how to create a virtual GPU server, detailing the allocation of funds from the account to the server rental. The paragraph also covers the selection of server specifications, such as CPU, RAM, and GPU sizes, and the choice of operating systems like Ubuntu or Windows. The importance of monitoring the server's usage to avoid overcharging is emphasized, and the process of creating and naming the virtual machine is outlined.

The speaker tests the performance of the newly created GPU server by installing necessary software and running machine learning models. They describe the ease of installing Python libraries and the pre-installed support for CUDA and GPU drivers. The paragraph details the process of checking the GPU's performance using commands and software like Nvidia smmi and TensorFlow. The speaker also demonstrates the speed of data transfer and model training on the server, emphasizing the significant improvement in speed and efficiency compared to using Colab. The paragraph concludes with a reminder that the service only charges for the time the virtual machine is running, ensuring cost-effective usage.

This paragraph focuses on the data transfer process and model training on the GPU server. The speaker explains how to use SFTP for Linux users and FTP clients like FileZilla for Windows users to transfer data to the server. They also discuss the convenience of direct file copying and pasting during a Remote Desktop session for Windows users. The paragraph highlights the ease of reading data from the uploaded folders and running machine learning models on the server. The speaker guides the audience through the process of training a model and emphasizes the rapid data processing and model training capabilities of the GPU server, showcasing its potential for efficient machine learning tasks.