This flexibility was very important to Prodrive, as it enables the engineers to better manage their computational resources and workload.
您甚至可以在周末关闭所有的计算资源来降低成本,然后在周一早上重新启动。
One could even shut down all the compute resources over the weekend to reduce costs, and then start them up again on Monday morning.
请注意,创建只读副本涉及更多的计算资源,因此需要花费更多的钱,这可能是一些人的重要约束。
Note that creating read replicas involves more computing resources and thus costs more money, which could be a significant constraint for some.
与传统的计算机视觉相比,在深度学习中学到的模型需要更大的内存、更大的磁盘存储空间和更多的计算资源。
Compared to traditional computer vision, the learned models in deep learning require orders of magnitude more memory, much more disk storage, and more computational resources.
For example, a small, independent research organization could leverage unlimited compute resources to support grant cycles and complete necessary work without infrastructure investment.
被称为矿工的一些货币网络成员将自己的计算资源分配给确认其他用户交易的任务。
Some members of the currency network, known as miners, allocate their own computing resources toward the task of confirming the transactions of other users.
虽然这个框架提供了大量的抽象访问群集的计算资源,但用户还是想要更多。
Although this framework provides numerous abstractions for accessing a cluster's computational resources, users still want more.
数据中心管理者面临着相同的挑战:业务需要更多的计算资源,但同时也希望降低能耗,冷却和其他设施费用。
Data center managers face a consistent challenge: the business demands more compute resources, but also wants to reduce power consumption, cooling and other facilities costs.
同样,每个用户都有望保持存储在他或她的费用计算资源的所有信息的保密性。
Similarly, each user is expected to maintain the confidentiality of all information stored on computing resources in his or her charge.
这样的系统需要一种货币来支付运行应用程序或程序所需的计算资源。
The system needs currency to pay for computational resources which are required to run an application or a program.
部分原因在于廉价数据和廉价的计算资源,它们符合深度学习的自然优势。
This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning.
如果业务增长超出预期,公司并不总是能够利用机会,因为他们的计算资源不足。
If business growth exceeds expectations, companies are not always able to capitalize on the opportunities because of their insufficient computing resources.
现在的机器学习技术通常依赖于大量的训练数据、巨大的计算资源、耗费时间的试验与误差方法论。
Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology.
训练大型AI模型以消耗大量能量所需的计算资源,这会产生碳排放问题.
The compute resources required to train large AI models to consume huge amounts of energy, which creates a carbon emission problem.
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