What Alibaba Cloud servers can do in-depth learning? How to purchase Alibaba Cloud deep learning server?

Can do Alibaba Cloud servers for in-depth learning Including: lightweight GPU computing instance type family vgn5i, lightweight GPU computing instance type family vgn6i, GPU computing instance type family gn5, GPU computing instance type family gn5i, GPU computing instance type family gn7, GPU computing instance type family gn6e, GPU computing instance type family gn6v, FPGA computing instance type family f1, FPGA computing instance type family f3 NPU computing instance type family ebman1, GPU computing elastic bare metal server instance type family ebmgn6e, GPU computing elastic bare metal server instance type family ebmgn6v, GPU computing elastic bare metal server instance type family ebmgn7 and other models.

1. Some models are not available for sale, such as GPU computing instance type family gn7. For example, the lightweight GPU computing instance type family vgn6i can be purchased in AliCloud product page purchase To.

2. Alibaba Cloud deep learning server purchase steps

Get it first AliCloud thousand yuan voucher , click Go AliCloud product page Click the "Buy Now" button to enter the custom configuration page, as shown in the following figure.

• Region: Beijing, Shanghai, Guangzhou, etc;
• Architecture: heterogeneous computing GPU/FPGA/NPU;
• Classification: GPU computing type.
  

At this time, the following server list shows the corresponding specific specifications, including instance type family, number of CPU cores, memory, GPU/FPGA, processor main frequency/Rui frequency, intranet bandwidth, number of packets sent and received, etc.

Select a suitable model, enter the number of instances purchased, images, storage, discount, bandwidth, security group, etc. The longer the purchase time is, the more favorable it will be. Check the use of vouchers on the payment page, and make sure there is no problem before you can pay.

The following teaching practice environment and model experiment environment for in-depth learning can be used.