Results for “cluster-scaling”
12 skills
eliferjunior Ray
Framework for scaling Python applications from a laptop to a cluster. Includes Ray Core for distributed computing, Ray Serve for model serving, Ray Tune for hyperparameter optimization, and Ray Data for distributed data processing.
0
tianhao909 Ray Train
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
1 · bundle
More results
github Qdrant Scaling Query Volume
Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.
36.2k
pymodel Sub Skill
Discover and reorganize the skill inventory into hierarchical sub-skill bundles. Use when the user asks to review, group, or consolidate skills into a parent bundle.
14
softnanolab Handoff
Package an in-progress task on this cluster so a Claude agent on another cluster (MMM Young, CX3, HX1, Isambard) can pick it up, or resume a task handed off to this cluster. Use when the user says hand off / migrate / continue this on <cluster>, or asks you to pick up an existing handoff.
1 · bundle
k-dense-ai Dask
Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.
30.2k · bundle
jiachen-t-wang Sa 1b Segment Anything 1 Billion Masks Dataset Arxiv Sa1b 20
SA-1B: Segment Anything 1 Billion Masks Dataset
6
muratcankoylan Context Optimization
Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.
16.9k · bundle
dokhacgiakhoa Nosql Expert
Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.
505 · bundle
pymodel Consolidate
Apply an approved sub-skill grouping by moving user-specified skills into a parent bundle, with timestamped backups of every modified directory.
14
qcmuu Ray Train
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
0 · bundle
orchestra-research Ray Train
Scales machine learning training from single GPU to multi-node clusters with minimal code changes. Supports PyTorch, TensorFlow, and HuggingFace with built-in hyperparameter tuning, fault tolerance, and elastic scaling.
10.4k · bundle