II.
LibraryProcess overview
Reference · livelib-process:data-science-ml--distributed-training
specializations/data-science-ml/distributed-training overview
Distributed Training Orchestration - Design and execute distributed training strategies for large-scale ML models with resource allocation, parallelization strategy, fault tolerance, and performance optimization across multiple nodes/GPUs.
Attributes
displayName
specializations/data-science-ml/distributed-training
description
Distributed Training Orchestration - Design and execute distributed training strategies for large-scale ML models
with resource allocation, parallelization strategy, fault tolerance, and performance optimization across multiple nodes/GPUs.
libraryPath
library/specializations/data-science-ml/distributed-training.js
specialization
data-science-ml
references
- - PyTorch Distributed Training: https://pytorch.org/tutorials/beginner/dist_overview.html
- - TensorFlow Distributed Strategies: https://www.tensorflow.org/guide/distributed_training
- - Horovod Framework: https://horovod.readthedocs.io/
- - DeepSpeed: https://www.deepspeed.ai/
- - Ray Train: https://docs.ray.io/en/latest/train/train.html
- - Model Parallelism Patterns: https://arxiv.org/abs/1909.08053
example
const result = await orchestrate('specializations/data-science-ml/distributed-training', {
projectName: 'Large Language Model Training',
modelArchitecture: 'Transformer with 7B parameters',
datasetSize: '500GB text corpus',
trainingObjective: 'Pre-train language model from scratch',
availableResources: { gpus: 32, nodes: 4, memory: '2TB', storage: '10TB' }
});
usesAgents
- general-purpose
Outgoing edges
lib_applies_to_domain1
- domain:data-science·DomainData Science
lib_belongs_to_specialization1
- specialization:data-science-ml·Specialization
lib_implements_workflow1
- workflow:data-pipeline-deployment·WorkflowData Pipeline Deployment
Incoming edges
None.