DeepSpeed
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Explore more → **DeepSpeed**
What is DeepSpeed?
DeepSpeed is a deep learning optimization library for PyTorch designed to improve the scale and speed of training large models. It provides features like ZeRO (Zero Redundancy Optimizer) for memory optimization, allowing models with billions or trillions of parameters to be trained, as well as techniques for efficient data parallelism, model parallelism, and pipeline parallelism. It is commonly used by researchers and engineers to train large language models, recommendation systems, and other computationally intensive AI models.
What other technologies are related to DeepSpeed?
DeepSpeed Competitor Technologies
Megatron
Megatron is a framework for large language model training that offers similar capabilities for distributed training and model parallelism as DeepSpeed. It directly competes in the space of efficient large model training.
FSDP
FSDP (Fully Sharded Data Parallel) is a PyTorch feature that provides similar functionality to DeepSpeed's ZeRO, offering data parallelism with memory efficiency. It is a direct competitor for large model training within the PyTorch ecosystem.
Horovod
Horovod is a distributed training framework that, while broader in scope, competes with DeepSpeed in the area of scaling training across multiple GPUs/nodes. It is an alternative approach to distributed training.
GSPMD
No summary available
GSPMD (Globally Sharded Parameter Model Parallelism) is another approach to model parallelism, similar to what DeepSpeed provides. It offers an alternative for scaling large models.
Megatron-LM
No summary available
Megatron-LM is an end-to-end large language model training framework, directly competing with DeepSpeed in its capabilities for model parallelism and efficient training of massive models.
vLLM
vLLM focuses on high-throughput and efficient inference of large language models. It serves as a competitor in the model serving aspect, providing optimized performance that overlaps with some of DeepSpeed's potential applications.
FasterTransformer
No summary available
FasterTransformer is an NVIDIA library optimized for transformer inference. Its focus on optimized inference presents it as a competitor in certain application scenarios where DeepSpeed might be used for similar purposes.
JAX
JAX is a framework developed by Google, often used for high-performance numerical computing and machine learning research. It competes with DeepSpeed in the area of accelerated computing and large model training, providing an alternative ecosystem.
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DeepSpeed Complementary Technologies
torch.fx
No summary available
torch.fx is a Python-first platform for transforming PyTorch programs. It can be used to analyze and modify models before training or inference with DeepSpeed, making it a complementary tool for model optimization.
XLA
XLA (Accelerated Linear Algebra) is a compiler for optimizing linear algebra computations. While it can be used with TensorFlow, it also has integrations with PyTorch and Jax, allowing DeepSpeed to potentially benefit from XLA's optimizations through compatible frameworks.
Triton
Triton is a programming language designed to write efficient GPU kernels. It can be used to develop custom operations that can be integrated into models trained or deployed with DeepSpeed, making it a complementary technology for performance optimization.
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Which job functions commonly mention DeepSpeed?
MLOps Engineer
0.5% of all MLOps Engineer jobs mention DeepSpeed
View 38
jobs on Sumble
AI Engineer
0.5% of all AI Engineer jobs mention DeepSpeed
View 384
jobs on Sumble
Machine Learning
0.4% of all Machine Learning jobs mention DeepSpeed
View 462
jobs on Sumble
Research Scientist
0.2% of all Research Scientist jobs mention DeepSpeed
View 71
jobs on Sumble
Data, Analytics & Machine Learning
0.1% of all Data, Analytics & Machine Learning jobs mention DeepSpeed
View 889
jobs on Sumble
Which organizations are mentioning DeepSpeed?
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