Thuê máy chủ GPU train model ngon rẻ bổ trên ThueGPU.vn - Mì AI
TLDRIn this informative vlog, the speaker addresses the challenges faced by students and enthusiasts in the field of artificial intelligence (AI) due to the lack of access to powerful GPUs. Highlighting the limitations of popular platforms like Google Colab, such as restricted session times, limited VRAM, and storage constraints, the vlog introduces a solution: a rental service for cloud GPUs. This service offers flexible hourly rates, avoids the need for credit card payments with QR code transactions, and features servers based in Vietnam for faster data transfers. The speaker demonstrates how to register, manage accounts, and efficiently use the service to run AI models, emphasizing its affordability and convenience for students and professionals alike.
Takeaways
- 🌐 The video introduces a service for renting GPU servers, which is beneficial for those without access to a GPU and needing computational power for AI models.
- 💡 The service, named 'thugpu.com', is based in Vietnam, offering fast data upload and download speeds, which is ideal for large data handling.
- 💰 Users can pay for the service via bank transfer or QR Code, which is convenient for students and those without credit cards.
- 🔧 The service provides various configurations of GPU servers, with options suitable for different needs and budgets.
- 🛠️ The video demonstrates the ease of creating a virtual GPU server and the process of transferring data to the server for model training.
- 📊 The cost of the service is based on usage time, with no extra charges for setup or idle time, ensuring users only pay for the actual time the GPU is utilized.
- 🗂️ The platform allows for easy management of accounts, with a clear separation between account balance and cloud balance for tracking expenses.
- 🔄 The video highlights the convenience of data transfer using SFTP for Linux users and built-in FTP servers for Windows users.
- 📈 The service addresses common issues faced with other platforms, such as limited time usage, small GPU memory, and slow data transfer speeds.
- 🔧 The video provides a practical guide on how to set up and use the GPU server for AI model training, including the installation of necessary libraries and running of code.
- 🎓 The introduction of this service is particularly useful for students and researchers who require GPU resources but may not have the financial means to invest in a personal setup.
Q & A
What is the main issue discussed in the video?
-The main issue discussed in the video is the difficulty and limitations faced by students and researchers in running AI models due to the lack of GPU resources, time limitations on platforms like Colab, and storage constraints.
What are the limitations of using Colab for running AI models?
-The limitations of using Colab include limited time (10 to 12 hours), small GPU VRAM (only 12 GB), and storage issues where data is deleted after 12 hours and the slow speed of data transfer from Google Drive.
What is the solution proposed in the video to overcome the limitations of Colab?
-The solution proposed in the video is to rent a GPU server from a service provider, specifically mentioning a service called Thugpu.com, which offers cloud GPU services within Vietnam, providing better data transfer speeds and flexible payment options.
How does the Thugpu.com service differ from international cloud GPU providers?
-Thugpu.com differs from international providers by offering services within Vietnam, leading to faster data upload and download speeds. It also allows payment via bank transfer and QR Code, which is more convenient for local students and businesses.
What are the benefits of using a local GPU server rental service like Thugpu.com?
-The benefits include faster data transfer speeds due to local infrastructure, convenient payment methods like QR Code and bank transfer, and potentially lower costs and better support for local businesses and students.
What are the system requirements and configurations offered by Thugpu.com?
-Thugpu.com offers configurations with a CPU of 20 cores, 48 GB of RAM, and a GPU with 24 GB of VRAM. The storage capacity is 200 GB, and the operating system can be either Ubuntu or Windows.
How does the payment system work on Thugpu.com?
-The payment system works by first depositing money into your account. Then, you can transfer a portion of your account balance to your cloud account, which is separate from your regular account balance. The service charges based on the actual usage of the GPU server.
How to check the GPU configuration of the rented server?
-You can check the GPU configuration by using the 'nvidia-smi' command in the terminal, which will display the model and memory of the GPU, ensuring it matches the requirements you rented.
What is the process for uploading data to the rented GPU server?
-For Linux users, you can use the 'scp' or 'sftp' commands to transfer files. For Windows users, an FTP client like FileZilla can be used, or you can directly copy and paste files if using Remote Desktop.
How does the video demonstrate the effectiveness of the rented GPU server?
-The video demonstrates the effectiveness by showing the speed at which the server can download data and run AI models. It compares this to the limitations of Colab and emphasizes the ease and speed of data transfer and model training on the rented server.
What is the estimated monthly cost for using the GPU server from Thugpu.com?
-The estimated monthly cost for using the GPU server with the mentioned configuration is 5.7 million Vietnamese Dong.
Outlines
🚀 Introduction to GPU Server Rental Services
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 issues. The speaker then presents a solution by introducing a local GPU server rental service, highlighting its benefits like high data upload/download speeds, convenient payment methods, and the availability of invoices for businesses.
💻 Setting Up and Configuring Your GPU Server
This section walks through the process of setting up a rented GPU server. It covers the registration and login process, the dashboard overview, and the steps to create a virtual GPU server. The speaker emphasizes the importance of managing your cloud budget and the server's configuration, including CPU, RAM, and GPU specifications. It also mentions the operating system options and the ease of accessing the server using familiar tools.
🔧 Testing and Utilizing the GPU Server
The speaker demonstrates how to test the GPU server by installing necessary packages and checking the GPU's performance. They show the process of downloading data and running a model, highlighting the speed and efficiency of the server compared to Colab. The paragraph also explains how to monitor GPU usage and the cost implications of running the server for extended periods.
📂 Data Transfer and Model Deployment on the GPU Server
This part focuses on transferring data to the GPU server and deploying machine learning models. The speaker discusses the use of SFTP for Linux users and FTP clients or direct copy-paste methods for Windows users. They emphasize the convenience and speed of data transfer within the same network. The paragraph concludes with an encouragement for users to explore the rental service, especially those without the means to invest in a local PC with a GPU.
Mindmap
Keywords
💡GPU server
💡Machine learning
💡Cloud computing
💡Collaboratory (Colab)
💡Storage capacity
💡Data upload/download speed
💡Credit card
💡QR Code
💡Virtual machine
💡Operating system (OS)
💡TensorFlow
💡SSH (Secure Shell)
Highlights
Introduction to a service for renting GPU servers to overcome limitations of free platforms like Colab.
Challenges faced by students using Colab, such as time limitations and limited GPU memory.
The issue of storage capacity on Colab and the inconvenience of data deletion after 12 hours.
The slow data transfer speed when using Google Drive in conjunction with Colab.
Introduction of the service 'Thu gpu.com', a GPU rental service based in Vietnam with fast upload/download speeds.
Convenient payment options for students, including bank transfer and QR code payments.
The ability to issue invoices for businesses, which is a plus for those requiring official documentation.
Configuration details of the GPU server, including 24 GB of GPU memory and 48 GB of RAM.
The ease of creating and managing virtual GPU servers through the service's dashboard.
The option to choose between different operating systems, such as Ubuntu and Windows.
The provision of a static IP address for running services on the rented server.
The quick setup and deployment of the server, with no additional charges for the operating system setup.
The ease of verifying the GPU specifications and memory upon server setup completion.
The demonstration of installing necessary libraries and frameworks on the server, showcasing the server's capabilities.
Instructions on how to upload data to the server using SFTP for Linux users and FTP or copy-paste for Windows users.
The practical application of the server in training AI models, as shown by the presenter's demonstration.
The overall benefits of renting a GPU server for AI model training, especially for those without the resources to invest in a local PC.
The recommendation for students to learn and use Linux commands for better server management and deployment.