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Found 11 papers, 1 papers with code
DLSpec: A Deep Learning Task Exchange Specification
Deep Learning (DL) innovations are being introduced at a rapid pace.
MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
The Design and Implementation of a Scalable DL Benchmarking Platform
MLModelScope defines abstractions for frameworks and supports board range of DL models and evaluation scenarios.
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)
We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e. g. within $95\%$ accuracy and up to $4. 4\times$ benchmarking time speedup on Amazon EC2 c5. xlarge).
Benanza: Automatic $μ$Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs
An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities.
MLModelScope: A Distributed Platform for ML Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs
Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack).
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML.
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines.
Distributed, Parallel, and Cluster Computing
Frustrated with Replicating Claims of a Shared Model? A Solution
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them.
SCOPE: C3SR Systems Characterization and Benchmarking Framework
This report presents the design of the Scope infrastructure for extensible and portable benchmarking.
Performance
