daochenzha / autoshardLinks
[KDD 2022] AutoShard: Automated Embedding Table Sharding for Recommender Systems
☆22Updated 2 years ago
Alternatives and similar repositories for autoshard
Users that are interested in autoshard are comparing it to the libraries listed below
Sorting:
- Set of datasets for the deep learning recommendation model (DLRM).☆47Updated 2 years ago
- [MLSys 2023] Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models☆16Updated 2 years ago
- [NeurIPS 2022] DreamShard: Generalizable Embedding Table Placement for Recommender Systems☆29Updated 2 years ago
- Distributed Deep Graph Learning Framework for Dynamic Graphs☆14Updated last year
- ☆21Updated 3 years ago
- Accelerating Recommender model training by leveraging popular choices -- VLDB 2022☆30Updated 10 months ago
- Largest realworld open-source graph dataset - Worked done under IBM-Illinois Discovery Accelerator Institute and Amazon Research Awards a…☆83Updated last month
- ICLR 2021☆48Updated 4 years ago
- [MLSys 2022] "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node …☆56Updated last year
- [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhang…☆66Updated last year
- Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'☆13Updated 2 years ago
- A scalable graph learning toolkit for extremely large graph datasets. (WWW'22, 🏆 Best Student Paper Award)☆154Updated last year
- Official implementation of our VQ-GNN paper (NeurIPS2021)☆38Updated 3 years ago
- ☆13Updated last year
- Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture (accepted by PVLDB)☆44Updated 2 years ago
- [NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architec…☆134Updated 3 years ago
- [ NeurIPS '22 ] Data distillation for recommender systems. Shows equivalent performance with 2-3 orders less data.☆23Updated 2 years ago
- A GPU-accelerated graph learning library for PyTorch, facilitating the scaling of GNN training and inference.☆138Updated 3 months ago
- [ WSDM '22 ] On Sampling Collaborative Filtering Datasets☆20Updated 3 years ago
- ☆70Updated 4 years ago
- An efficient PyTorch implementation of the evaluation metrics in recommender systems.☆27Updated 2 years ago
- ☆210Updated last year
- PyTorch Library for Low-Latency, High-Throughput Graph Learning on GPUs.☆302Updated last year
- Enabling pure data parallel training of DLRM via caching and prefetching☆17Updated 3 years ago
- Graphiler is a compiler stack built on top of DGL and TorchScript which compiles GNNs defined using user-defined functions (UDFs) into ef…☆60Updated 2 years ago
- This is an authors' implementation of the NIPS 2022 dataset and Benchmark Track Paper "A Comprehensive Study on Large Scale Graph Trainin…☆65Updated 2 years ago
- The official SALIENT system described in the paper "Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and P…☆40Updated 2 years ago
- [ICLR 2022] "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication" by Cheng Wan, Y…☆33Updated 2 years ago
- Time-based Sequence Model for Personalization and Recommendation Systems☆49Updated 3 years ago
- ☆47Updated 3 months ago