Skip to content
CMS Machine Learning Documentation
Performance
Initializing search
cms-ml/documentation
CMS Machine Learning Documentation
cms-ml/documentation
Home
Innovation
Innovation
ML Journal Club
ML Hackathons
Resources
Resources
Cloud Resources
Dataset Resources
FPGA Resource
GPU Resources
GPU Resources
lxplus-gpu
CERN HTCondor
SWAN
ml.cern.ch
Guides
Guides
Software environments
Software environments
LCG environments
Using containers
Optimization
Optimization
Model optimization
Feature importance
Data augmentation
General Advice
General Advice
Introduction
Before training
Before training
Domains
Features
Inputs
Model
Metrics & Losses
During training
During training
Overfitting
Cross-validation
Optimisation problems
After training
Inference
Inference
Direct inference
Direct inference
TensorFlow 2
PyTorch
PyTorch Geometric
ONNX
XGBoost
hls4ml
conifer
Inference as a service
Inference as a service
Sonic/Triton
TFaaS
Non-standard workflows
Non-standard workflows
Standalone framework
SWAN + AWS
Integration checklist
Performance
Successful integrations
Successful integrations
ParticleNet
Training
Training
Decorrelation
Training as a Service
Training as a Service
MLaaS4HEP
Autoencoders
Performance of inference tools
¶
Last update:
January 8, 2024
