PyTorch Distributed Data Parallel
E1312495
UNEXPLORED
PyTorch Distributed Data Parallel is a PyTorch feature that enables efficient, synchronized training of neural networks across multiple GPUs and machines by replicating models and aggregating gradients in parallel.
All labels observed (1)
| Label | Occurrences |
|---|---|
| PyTorch Distributed Data Parallel canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T18205451 — 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: PyTorch Distributed Data Parallel Context triple: [Hugging Face Accelerate, supportsBackend, PyTorch Distributed Data Parallel]
-
A.
SageMaker Distributed Data Parallel
SageMaker Distributed Data Parallel is a high-performance training library in Amazon SageMaker that accelerates deep learning model training across multiple GPUs and instances by efficiently distributing data and gradients.
-
B.
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.
-
C.
SageMaker Model Parallelism
SageMaker Model Parallelism is an Amazon SageMaker capability that automatically partitions large deep learning models across multiple GPUs or instances to enable training models that don’t fit on a single device.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
E.
DeepSpeed
DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
- 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: PyTorch Distributed Data Parallel Target entity description: PyTorch Distributed Data Parallel is a PyTorch feature that enables efficient, synchronized training of neural networks across multiple GPUs and machines by replicating models and aggregating gradients in parallel.
-
A.
SageMaker Distributed Data Parallel
SageMaker Distributed Data Parallel is a high-performance training library in Amazon SageMaker that accelerates deep learning model training across multiple GPUs and instances by efficiently distributing data and gradients.
-
B.
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.
-
C.
SageMaker Model Parallelism
SageMaker Model Parallelism is an Amazon SageMaker capability that automatically partitions large deep learning models across multiple GPUs or instances to enable training models that don’t fit on a single device.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
E.
DeepSpeed
DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
- F. None of above. chosen
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.