Quick start
Quick Start¶
- install
fate_client
with extra packagefate
python -m pip install -U pip && python -m pip install fate_client[fate,fate_flow]==2.1.1
mkdir fate_workspace
fate_flow init --ip 127.0.0.1 --port 9380 --home $(pwd)/fate_workspace
pipeline init --ip 127.0.0.1 --port 9380
fate_flow start
fate_flow status # make sure fate_flow service is started
- download example data
wget https://raw.githubusercontent.com/wiki/FederatedAI/FATE/example/data/breast_hetero_guest.csv && \
wget https://raw.githubusercontent.com/wiki/FederatedAI/FATE/example/data/breast_hetero_host.csv
- transform example data to dataframe using in fate
import os from fate_client.pipeline import FateFlowPipeline base_path = os.path.abspath(os.path.join(__file__, os.path.pardir)) guest_data_path = os.path.join(base_path, "breast_hetero_guest.csv") host_data_path = os.path.join(base_path, "breast_hetero_host.csv") data_pipeline = FateFlowPipeline().set_parties(local="0") guest_meta = { "delimiter": ",", "dtype": "float64", "label_type": "int64","label_name": "y", "match_id_name": "id" } host_meta = { "delimiter": ",", "input_format": "dense", "match_id_name": "id" } data_pipeline.transform_local_file_to_dataframe(file=guest_data_path, namespace="experiment", name="breast_hetero_guest", meta=guest_meta, head=True, extend_sid=True) data_pipeline.transform_local_file_to_dataframe(file=host_data_path, namespace="experiment", name="breast_hetero_host", meta=host_meta, head=True, extend_sid=True)
- run training example and save pipeline
from fate_client.pipeline.components.fate import (
HeteroSecureBoost,
Reader,
PSI,
Evaluation
)
from fate_client.pipeline import FateFlowPipeline
# create pipeline for training
pipeline = FateFlowPipeline().set_parties(guest="9999", host="10000")
# create reader task_desc
reader_0 = Reader("reader_0")
reader_0.guest.task_parameters(namespace="experiment", name="breast_hetero_guest")
reader_0.hosts[0].task_parameters(namespace="experiment", name="breast_hetero_host")
# create psi component_desc
psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
# create hetero secure_boost component_desc
hetero_secureboost_0 = HeteroSecureBoost(
"hetero_secureboost_0", num_trees=1, max_depth=5,
train_data=psi_0.outputs["output_data"],
validate_data=psi_0.outputs["output_data"]
)
# create evaluation component_desc
evaluation_0 = Evaluation(
'evaluation_0', runtime_parties=dict(guest="9999"), metrics=["auc"], input_datas=[hetero_secureboost_0.outputs["train_output_data"]]
)
# add training task
pipeline.add_tasks([reader_0, psi_0, hetero_secureboost_0, evaluation_0])
# compile and train
pipeline.compile()
pipeline.fit()
# print metric and model info
print (pipeline.get_task_info("hetero_secureboost_0").get_output_model())
print (pipeline.get_task_info("evaluation_0").get_output_metric())
# save pipeline for later usage
pipeline.dump_model("./pipeline.pkl")
-
reload trained pipeline and do prediction
from fate_client.pipeline import FateFlowPipeline from fate_client.pipeline.components.fate import Reader # create pipeline for predicting predict_pipeline = FateFlowPipeline() # reload trained pipeline pipeline = FateFlowPipeline.load_model("./pipeline.pkl") # deploy task for inference pipeline.deploy([pipeline.psi_0, pipeline.hetero_secureboost_0]) # add input to deployed_pipeline deployed_pipeline = pipeline.get_deployed_pipeline() reader_1 = Reader("reader_1") reader_1.guest.task_parameters(namespace="experiment", name="breast_hetero_guest") reader_1.hosts[0].task_parameters(namespace="experiment", name="breast_hetero_host") deployed_pipeline.psi_0.input_data = reader_1.outputs["output_data"] # add task to predict pipeline predict_pipeline.add_tasks([reader_1, deployed_pipeline]) # compile and predict predict_pipeline.compile() predict_pipeline.predict()
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More tutorials More pipeline api guides can be found in this link