17985120. Deep Learning Scheduler Toolkit simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 Deep Learning Scheduler Toolkit
Deep Learning Scheduler Toolkit
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Amar Phanishayee of Seattle WA (US)
Saurabh Agarwal of Madison WI (US)
Deep Learning Scheduler Toolkit - A simplified explanation of the abstract
This abstract first appeared for US patent application 17985120 titled 'Deep Learning Scheduler Toolkit
Simplified Explanation
The description pertains to a deep learning cluster scheduler modular toolkit, allowing users to compose multiple DL scheduler abstraction modules to create a deep learning scheduler.
- The patent application involves creating a deep learning cluster scheduler modular toolkit.
- The toolkit includes multiple DL scheduler abstraction modules.
- Users can interact with and compose these modules to build a customized deep learning scheduler.
Potential Applications
The technology can be applied in various industries such as healthcare, finance, and autonomous vehicles for optimizing deep learning tasks.
Problems Solved
1. Streamlining the process of managing deep learning clusters. 2. Enhancing the efficiency of deep learning tasks by allowing user customization.
Benefits
1. Improved performance and resource utilization in deep learning tasks. 2. Flexibility and adaptability in designing deep learning schedulers.
Potential Commercial Applications
Optimizing deep learning processes in cloud computing services for businesses.
Possible Prior Art
While there are existing deep learning cluster management tools, the specific approach of modular DL scheduler abstraction modules for user composition may be novel.
Unanswered Questions
How does this technology compare to existing deep learning cluster management tools?
The article does not provide a direct comparison with other tools in the market. It would be beneficial to understand the unique features and advantages of this modular toolkit over existing solutions.
What level of technical expertise is required to effectively utilize this deep learning cluster scheduler modular toolkit?
The article does not address the technical proficiency needed to operate the toolkit. It would be helpful to know if specialized knowledge is required for users to leverage the full potential of this technology.
Original Abstract Submitted
The description relates to deep learning cluster scheduler modular toolkits. One example can include generating a deep learning cluster scheduler modular toolkit that includes multiple DL scheduler abstraction modules and interactions between the multiple DL scheduler abstraction modules and allows user composition of the multiple DL scheduler abstraction modules to realize a deep learning scheduler.
