DeepSpeed
E760434
DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DeepSpeed canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8823604 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DeepSpeed Context triple: [NCCL, usedBy, DeepSpeed]
-
A.
Hugging Face Accelerate
Hugging Face Accelerate is a lightweight library that simplifies running and scaling PyTorch and Transformers models across CPUs, GPUs, and distributed hardware with minimal code changes.
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B.
NVIDIA TensorRT
NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
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C.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
-
D.
LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
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E.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DeepSpeed Target entity description: DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
-
A.
Hugging Face Accelerate
Hugging Face Accelerate is a lightweight library that simplifies running and scaling PyTorch and Transformers models across CPUs, GPUs, and distributed hardware with minimal code changes.
-
B.
NVIDIA TensorRT
NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
-
C.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
-
D.
LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
-
E.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning optimization library
ⓘ
open-source software project ⓘ |
| category |
distributed computing library
ⓘ
machine learning software ⓘ |
| developer |
Microsoft
ⓘ
Microsoft DeepSpeed team NERFINISHED ⓘ |
| enables |
scaling to thousands of GPUs
ⓘ
training models that do not fit in single-GPU memory ⓘ |
| feature |
3D parallelism
ⓘ
BF16 training ⓘ DeepSpeed-Inference NERFINISHED ⓘ DeepSpeed-MoE NERFINISHED ⓘ FP16 training ⓘ Mixture-of-Experts training support ⓘ ZeRO optimizer NERFINISHED ⓘ ZeRO-Infinity NERFINISHED ⓘ ZeRO-Offload NERFINISHED ⓘ activation checkpointing ⓘ activation partitioning ⓘ checkpointing utilities ⓘ communication optimization ⓘ data parallelism ⓘ gradient checkpointing ⓘ mixed precision training ⓘ model parallelism ⓘ optimizer state partitioning ⓘ parameter partitioning ⓘ pipeline parallelism ⓘ sparse attention ⓘ tensor parallelism ⓘ throughput benchmarking tools ⓘ |
| license | MIT License ⓘ |
| optimizedFor | NVIDIA GPUs NERFINISHED ⓘ |
| programmingLanguage |
C++
ⓘ
Python ⓘ |
| repository | https://github.com/microsoft/DeepSpeed ⓘ |
| specializesIn |
GPU acceleration
ⓘ
distributed training ⓘ large-scale model training ⓘ memory optimization ⓘ training throughput optimization ⓘ |
| supports |
model parallel training of billion-parameter models
ⓘ
offloading to CPU memory ⓘ offloading to NVMe storage ⓘ |
| supportsFramework | PyTorch NERFINISHED ⓘ |
| useCase |
multi-GPU training
ⓘ
multi-node training ⓘ training transformer models ⓘ training very large language models ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: DeepSpeed Description of subject: DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.