This directory contains NVFLARE job templates.
Each job template contains the following informations
- client-side configuration: config_fed_client.conf
- server-side configuration: config_fed_server.conf
- job meta info: meta.conf
- (Optional) data exchange configuration: config_exchange.conf. This is only used with the new FLARE ML to FL transition Client API
- information card: info.md for display purpose
- information config: used by program
Refer to the Job CLI Documentation for details on how to use the Job Templates with the Job CLI.
Configurations are written in HOCON (human optimized object Notation). As a variant of JSON, .conf can also use json format. The pyhocon format allows for comments, and you can remove many of the double quotes as well as replace ":" with "=" to make the configurations look cleaner. You can find details in pyhocon: HOCON Parser for python.
View all the available job templates with the following command:
nvflare job list_templates
| Example | Controller-Type | Execution API Type | Description |
|---|---|---|---|
| cyclic_cc_pt | client | client_api | client-controlled cyclic workflow with PyTorch ClientAPI trainer |
| cyclic_pt | server | client_api | server-controlled cyclic workflow with PyTorch ClientAPI trainer |
| psi_csv | server | Executor | private-set intersection for csv data |
| sag_cross_np | server | client executor | scatter & gather and cross-site validation using numpy |
| sag_cse_pt | server | client_api | scatter & gather workflow and cross-site evaluation with PyTorch |
| sag_gnn | server | client_api | scatter & gather workflow for gnn learning |
| sag_nemo | server | client_api | Scatter and Gather Workflow for NeMo |
| sag_np | server | client_api | scatter & gather workflow using numpy |
| sag_np_cell_pipe | server | client_api | scatter & gather workflow using numpy |
| sag_np_metrics | server | client_api | scatter & gather workflow using numpy |
| sag_pt | server | client_api | scatter & gather workflow using pytorch |
| sag_pt_deploy_map | server | client_api | SAG workflow with pytorch, deploy_map, site-specific configs |
| sag_pt_executor | server | Executor | scatter & gather workflow and cross-site evaluation with PyTorch |
| sag_pt_he | server | client_api | scatter & gather workflow using pytorch and homomorphic encryption |
| sag_pt_mlflow | server | client_api | scatter & gather workflow using pytorch with MLflow tracking |
| sag_pt_model_learner | server | ModelLearner | scatter & gather workflow and cross-site evaluation with PyTorch |
| sag_tf | server | client_api | scatter & gather workflow using TensorFlow |
| sklearn_kmeans | server | client_api | scikit-learn KMeans model |
| sklearn_linear | server | client_api | scikit-learn linear model |
| sklearn_svm | server | client_api | scikit-learn SVM model |
| stats_df | server | stats executor | FedStats: tabular data with pandas |
| stats_image | server | stats executor | FedStats: image intensity histogram |
| swarm_cse_pt | client | client_api | Swarm Learning with Cross-Site Evaluation with PyTorch |
| swarm_cse_pt_model_learner | client | ModelLearner | Swarm Learning with Cross-Site Evaluation with PyTorch ModelLearner |
| vertical_xgb | server | Executor | vertical federated xgboost |
| xgboost_tree | server | client_api | xgboost horizontal tree-based collaboration model |