aws / aws/amazon-sagemaker-feedback
[Wish] A different way to see and run notebook instances
- Dominant language
- No language data
- Stars
- 10
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Description
### Product Version
- [ ] Amazon SageMaker Studio Classic
- [x] Amazon SageMaker Studio
- [ ] It is not related to SageMaker Studio
### Product Category
JupyterLab
### Description
1. The columns are not useful for example, the last run notebook time says 4 days ago which is not really helpful. However if I have information like the GPU memory, no of CPU cores, RAM, NVMe, networking speed in the form of smart drop down I can filter based on that.
2. For every instance type in a SageMaker, keep a notebook ready with a default ephemeral storage that can be changed. Show top 5 frequently used instance type, rest can be paginated based in the smart drop down filters mentioned in 1. Now user comes and says Run, to get a notebook running. For example, I want to try out a model real quick, I select the instance based on the GPU spec I am looking for and hit the run button.
### Other Details
_No response_
Contributor guide
Research direction
No repository files, tests, or implementation entry points are named. Start by reviewing the SageMaker Studio JupyterLab notebook-instance workflow and the requested filtering, prepared instances, storage, pagination, and run behavior. Done would require an agreed product design and implementation scope before coding can begin.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, jupyter
- Domain
- cloud, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100