deepspeed 0.14.0

DeepSpeed is a Python package developed by Microsoft that provides a deep learning optimization library designed to scale across multiple GPUs and servers. It is capable of training models with billions or even trillions of parameters, achieving excellent system throughput and efficiently scaling to thousands of GPUs.

DeepSpeed is particularly useful for training and inference of large language models, and it falls under the category of Machine Learning Frameworks and Libraries. It is designed to work with PyTorch and offers system innovations such as Zero Redundancy Optimizer (ZeRO), 3D parallelism, and model-parallelism to enable efficient training of large models.

Stars: 32363, Watchers: 32363, Forks: 3810, Open Issues: 1025

The microsoft/DeepSpeed repo was created 4 years ago and the last code push was 14 minutes ago.
The project is extremely popular with a mindblowing 32363 github stars!

How to Install deepspeed

You can install deepspeed using pip

pip install deepspeed

or add it to a project with poetry

poetry add deepspeed

Package Details

Author

DeepSpeed Team

License

Apache Software License 2.0

Homepage

http://deepspeed.ai

PyPi:

https://pypi.org/project/deepspeed/

Documentation:

https://deepspeed.readthedocs.io

GitHub Repo:

https://github.com/microsoft/DeepSpeed

Classifiers

Related Packages

Errors

A list of common deepspeed errors.

Code Examples

Here are some deepspeed code examples and snippets.

GitHub Issues

The deepspeed package has 1025 open issues on GitHub

• [BUG] matmul_ext_update_autotune_table atexit error
• [BUG] Unexpected caculations at backward pass with ZeRO-Infinity SSD offloading
• update ut/doc for glm/codegen
• Multi-node and multi-GPU fine-tuning error: ncclInternalError
• Zero Stage-2 Frozen Layers[BUG]
• [PROBLEM] P2p recv waiting for data will cause other threads under the same process to be unable to perform any operations
• Spread layers more uniformly when using partition_uniform
• Issue with DeepSpeed Inference - Multiple Processes for Model Loading and Memory Allocation
• [BUG] CPU Adam failing
• [BUG] Cannot increase batch size more than 1 with ZeRO-Infinity SSD offloading
• [REQUEST] please provide clear working installation guide
• load linear layer weight with dtype from ckpt
• [QNA] How can i choose adam between fused and cpu?
• Refactor autoTP inference for HE
• [BUG] No runnable example for MoE / PR-MoE GPT inference

See more issues on GitHub