With the continuous deepening of cloud migration and digital transformation and upgrading of enterprises, the important role of cloud computing in the field of artificial intelligence has become increasingly prominent. Many artificial intelligence model training requires high-performance computing. The cloud E computing power platform independently developed by CLOUDAM Cloud can provide solutions and computing power services for enterprises and individual users with computing power needs. We use an artificial intelligence case to introduce in detail Cloudam cloud, how the cloud E computing power platform helps users quickly complete model training.
1. High-performance computing platform on the cloud helps artificial intelligence
An artificial intelligence company is engaged in the research and development of voice equipment related technologies. After the A round of financing, the demand for computing power has increased with the rapid expansion of the scale, so the company is eager for a flexible and flexible HPC solution to meet the training of speech recognition related models. The calculation and training of artificial intelligence often consume a lot of computer time and memory. Users need a solution that can use a large number of GPUs, support multi-card tasks, and at the same time support AI common frameworks, such as Notebook, Pytorch, Tensorflow, Kaldi, etc. .
In response to this problem, cloud E computing power platform SaaS access, users can directly use Notebook, Pytorch, etc. through the browser to trigger artificial intelligence training tasks, the operation is very simple. At the same time, Cloud E automatically uploads the desensitized training data at night through the script, which will automatically trigger the training process. Fully automatic upload can make full use of bandwidth and help users upload files quickly and efficiently. In addition, Cloudam Cloud signs data security and confidentiality agreements with customers, and the Cloud E platform will also strictly guarantee the security and privacy of user input data and calculation results, giving users perfect security.
This program has achieved remarkable results. This deployment realizes model training that supports multi-team and multi-task parallelism. A single model uses up to 40 Nvidia V100 GPUs, which shortens the training cycle by more than 5 times compared with the local user, and makes artificial intelligence training and research more efficient. At the same time, it allows customers to put many research-oriented training tasks into the cloud, and quickly verify the results through large-scale parallel computing, which greatly improves the innovation speed of customers and perfectly assists enterprise development and innovation.
Artificial intelligence data analysis and prediction often require a large amount of high-performance computing, and large-scale high-performance computing requires a large amount of computer time. The cloud E computing power platform provides a one-stop solution for high-performance computing for artificial intelligence. It uses idle resources instead of resources according to the amount, and integrates multiple cloud resources into a unified exclusive computing resource pool. For the existing heterogeneous cloud Resources are rationally managed and allocated.
Cloud Cloudam optimizes the computing power of existing resources through unified integration and management of resources. Cloud E adopts automatic data uploading, making full use of bandwidth, allowing users to quickly upload and download massive amounts of data, improving transmission efficiency. At the same time, the automated deployment of clusters can eliminate the need for all machines to be turned on at the same time. In addition to the full load of cloud resources during the task operation period, only some machines need to be turned on during the data processing and data upload stage, and no machines need to be turned on during other preparation times.
It is worth mentioning that after the task is completed, the results will be downloaded in time and resources will be automatically released to prevent waste of resources. Cloud E can automatically monitor the number of tasks submitted by users and resource requirements, dynamically open and manage the required computing resources, and effectively reduce costs while improving efficiency. In addition, users can also set the upper and lower limits of the automated scheduling cluster according to their own needs. If, in actual operation, there is a temporary shortage of resources in a certain availability zone, Cloud E will try to open resources from other regions, or select instances with similar configurations to supplement.
In this case, we can see that the Cloud E computing power platform really effectively solves the problem of insufficient computing power and complex resource management, and provides a unified solution for enterprises with high computing power needs.