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Petastorm: Data Access for Deep Learning Training Challenges of Training on Large Datasets
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Classroom Contents
Horovod - Distributed Deep Learning for Reliable MLOps
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- 1 Intro
- 2 Early Adoption of Horovod
- 3 Deep Learning Refresher
- 4 Distributed Deep Learning
- 5 Early Distributed Training - Parameter Servers
- 6 Parameter Servers - Tradeoffs
- 7 Horovod Technique: Allreduce
- 8 Benchmarking
- 9 Deep Learning in Research
- 10 Deep Learning in Production
- 11 Feature Store
- 12 Model Training
- 13 Preprocessing
- 14 Spark ML Pipelines
- 15 Petastorm: Data Access for Deep Learning Training Challenges of Training on Large Datasets
- 16 Spark 3.0: Resource Aware Scheduling
- 17 What if my Spark cluster doesn't have GPUs? Horovod Lambda - Run data processing on CPUs with Spark
- 18 Online Prediction
- 19 Neuropod: Out-of-Process Execution
- 20 Workflow Authoring Can we ideate, define, evaluate and deploy a Deep Learning model all within a single script?
- 21 Feature Engineering
- 22 Model Construction
- 23 Model Deployment
- 24 Elastic Horovod: Control Flow
