hpcaitech / ColossalAI-Benchmark
Performance benchmarking with ColossalAI
☆39Updated 2 years ago
Alternatives and similar repositories for ColossalAI-Benchmark:
Users that are interested in ColossalAI-Benchmark are comparing it to the libraries listed below
- A Python library transfers PyTorch tensors between CPU and NVMe☆115Updated 5 months ago
- Scalable PaLM implementation of PyTorch☆190Updated 2 years ago
- ☆27Updated 3 years ago
- Automated Parallelization System and Infrastructure for Multiple Ecosystems☆78Updated 5 months ago
- Official repository for DistFlashAttn: Distributed Memory-efficient Attention for Long-context LLMs Training☆209Updated 8 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆120Updated 4 months ago
- Chimera: bidirectional pipeline parallelism for efficiently training large-scale models.☆62Updated last month
- ☆72Updated 4 years ago
- Zero Bubble Pipeline Parallelism☆387Updated this week
- ☆82Updated 3 years ago
- Sky Computing: Accelerating Geo-distributed Computing in Federated Learning☆90Updated 2 years ago
- ATC23 AE☆45Updated last year
- ☆79Updated 2 weeks ago
- (NeurIPS 2022) Automatically finding good model-parallel strategies, especially for complex models and clusters.☆38Updated 2 years ago
- ☆31Updated last year
- PyTorch bindings for CUTLASS grouped GEMM.☆88Updated last week
- Examples of training models with hybrid parallelism using ColossalAI☆339Updated 2 years ago
- Odysseus: Playground of LLM Sequence Parallelism☆69Updated 10 months ago
- [USENIX ATC '24] Accelerating the Training of Large Language Models using Efficient Activation Rematerialization and Optimal Hybrid Paral…☆53Updated 9 months ago
- [IJCAI2023] An automated parallel training system that combines the advantages from both data and model parallelism. If you have any inte…☆51Updated last year
- ☆104Updated 8 months ago
- A high-performance distributed deep learning system targeting large-scale and automated distributed training. If you have any interests, …☆111Updated last year
- 📑 Dive into Big Model Training☆111Updated 2 years ago
- ☆42Updated 2 years ago
- Examples for MS-AMP package.☆29Updated last year
- ☆131Updated 2 months ago
- Sequence-level 1F1B schedule for LLMs.☆17Updated 11 months ago
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆248Updated 6 months ago
- PyTorch library for cost-effective, fast and easy serving of MoE models.☆179Updated 2 weeks ago
- ☆93Updated 8 months ago