KV-Cache does not support Object Storage
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 203
- Forks
- 67
- Avg merge
- 20m
- Merged PRs (30d)
- 8
Description
Problem
The KV-Cache workload does currently not support the use of any object storage
Background
All other workloads within MLCommons storage support the use of both file and object storage types, specifically:
- Training
- Checkpointing
- VectorDB (as the backend)
The notable exception is KV-Cache. This support should be enabled IDEALLY in the following way:
- As an optional, 4th tier (Tier-1 = GPU memory, Tier-2 = System memory, Tier-3 = (local) File storage), New: Tier-4 Object Storage
In a future version of KV-Cache, object storage could be used optionally as Tier-3, but this will likely require support for RDMA - GDS access, necessitating the use of GPUs, which is beyond the scope of this effort. Hence, Object Storage for this round should be as an optional 4th tier only.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by tracing the KV-Cache workload and comparing its storage handling with the Training, Checkpointing, and VectorDB workloads mentioned in the issue. The work is done when KV-Cache can optionally use object storage as a fourth tier, while local file storage remains Tier-3.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100