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WeAreDevelopers LIVE • Jun 17, 2020

30 Golden Rules of Deep Learning Performance

Anirudh Koul

Stop buying more compute power to fix GPU starvation. Discover how serializing datasets, shifting augmentation, and using mixed precision can exponentially accelerate your deep learning pipelines.

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#1 about 7 min

Identifying major training inefficiencies in machine learning processes

Waiting for large neural networks to finish training results in wasted engineering cycles and excessive infrastructure costs.

#2 about 3 min

Speeding up model training cycles with transfer learning techniques

Reusing pre-trained convolutional neural weights directly avoids initializing large multi-layer tasks from computational scratch.

#3 about 5 min

Diagnosing training performance bottlenecks with visual profiling tools

Identifying CPU-starved graphic processors is possible using native tensorboard interfaces and terminal device queries.

#4 about 4 min

Optimizing custom dataset preparation to avoid file reading latency

Serializing custom input datasets into chunked file structures naturally prevents I/O choking while reading network files.

#5 about 5 min

Building asynchronous data pipelines with TensorFlow data APIs

Interleaving batch fetching inside background threads ensures the active GPU is constantly being fed data arrays.

#6 about 3 min

Offloading complex image pre-processing and augmentations to GPUs

Porting image manipulation procedures away from main application threads explicitly removes primary CPU processing overheads.

#7 about 5 min

Maximizing GPU utilization via ideal batch sizes and mixed precision

Adopting minimum size variables alongside properly aligned layer multiples enables aggressive tensor core processing yields.

#8 about 2 min

Finding the optimal learning rate automatically to simplify tuning

Running cyclic learning algorithms across isolated experimental trials algorithmically narrows the range of potential validation metrics.

#9 about 2 min

Boosting small loop logic performance by compiling execution graphs

Decorating standard Python callbacks internally compiles backend graphs instead of explicitly interpreting local environment sequences.

#10 about 3 min

Accelerating slow convergence loops using progressive image resizing techniques

Scaling up training complexities gradually through varying image resolutions reduces early step training spans massively.

#11 about 2 min

Leveraging specialized software environments optimized for local hardware specs

Routing default open source dependencies through optimized vendor streams yields instant processor vectorized performance benefits.

#12 about 6 min

Distributing local models across cloud environments and multi-GPU frameworks

Pushing batch workloads to cloud virtual machines drastically offsets expensive single node upfront hardware architectures.

#13 about 3 min

Choosing compact predefined neural network architectures for production environments

Committing to benchmarked open source models prevents memory overflow crashes on severely limited end user devices.

#14 about 1 min

Shrinking compiled models using integer quantization for inference edges

Downgrading compiled float networks to minimal integer values significantly shrinks deployment sizes without major capability drops.

#15 about 2 min

Consolidating software strategies and reviewing deep learning performance resources

Reviewing optimization codes provides developers a comprehensive single sheet integration pipeline for immediate model scale factors.

Matching moments

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Key lessons for large-scale machine learning performance

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1:44 min

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Cost and power economics of hardware acceleration

Ankit Patel Ankit Patel · World Congress 2024

1:49 min

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World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 1

Anatomy of an AI Request: Where Latency and Cost Are Really Born

Dan Fu

VP of Kernels at Together AI

Dan Fu
Open session

World Congress 2026 North America

September 23, 2026 · 10:45–12:45

Stage 10

Agents That Own Their Inference: Building Production AI Agents on Dedicated GPUs

Duan Lightfoot

Sr. AI Engineer, Akamai

Duan Lightfoot
Open session

World Congress 2026 North America

September 25, 2026 · 11:40–12:10

Stage 9

You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 6

Compute for your AI model: GPUs, LPUs, TPUs and beyond..

Kushaagra Goyal

Tech Lead at Rubrik, ex-CTO at Gan.AI, ex-Databricks

Kushaagra Goyal
Open session

World Congress 2026 North America

September 25, 2026 · 15:30–16:00

Stage 7

Trust, But Verify: Continuous GPU Validation at Scale

Kyle Bell

VP of AI @ TensorWave

Kyle Bell
Open session

World Congress 2026 North America

September 24, 2026 · 16:10–16:40

Stage 9

Fast, Cheap, and Accurate: Optimizing LLM Inference with vLLM and Quantization

Legare Kerrison, Cedric Clyburn

Legare Kerrison
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