Results for “ray-serve”
11 skills
orchestra-research ray-data
Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.
10.4k · bundle
qcmuu ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
0 · bundle
qhjqhj00 ray-data
Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.
3 · bundle
tianhao909 ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
1 · bundle
nvidia deepstream-dev
Build video analytics pipelines using NVIDIA DeepStream SDK 9.0 with Python pyservicemaker API, including GStreamer-based video processing, TensorRT inference integration, object detection/tracking, and Kafka/message broker integration.
2.2k · bundle
mariadb-corporation mariadb-rest-service-show
Browse and inspect MariaDB REST Service objects using read-only SHOW REST and SHOW CREATE REST statements to list services, schemas, views, procedures, functions, content sets, auth apps, roles, grants, and dump DDL for reverse-engineering or auditing.
0
nvidia rag-eval
Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.
2.2k · bundle
24601 surrealfs
Provides a persistent, queryable virtual filesystem backed by SurrealDB for AI agents, with a Rust core and a Python agent interface.
34
nous-hermeshub rice
RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]
1
k-dense-ai bioservices
Query 40+ bioinformatics services (UniProt, KEGG, ChEMBL, Reactome) with a unified Python interface for cross-database analysis, identifier mapping, and sequence analysis.
30.2k · bundle
jantoniofc rice
RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]
6