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SCREENSHOTS
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DeepSpeed
DESCRIPTION
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can:
1. Train/Inference dense or sparse models with billions or trillions of parameters
2. Achieve excellent system throughput and efficiently scale to thousands of GPUs
3. Train/Inference on resource constrained GPU systems
4. Achieve unprecedented low latency and high throughput for inference
5. Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs
DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar.
Features
- DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar
- To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar.
- The DeepSpeed library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/deepspeed.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
