docs(fate): add quick start

Signed-off-by: weiwee <wbwmat@gmail.com>

doc/2.0/Quick Start.md

@@ -0,0 +1,102 @@
+## Quick Start
+
+1. install `fate_client` with extra package `fate`  
+
+```sh
+python -m pip install -U pip && python -m pip install fate_client[fate]==2.0.0a0
+```
+
+2.  download example data
+
+```sh
+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
+```
+
+3. run example with fate_client
+
+```python
+import os
+
+from fate_client.pipeline import StandalonePipeline
+from fate_client.pipeline.components.fate import (
+    Evaluation,
+    FeatureScale,
+    HeteroLR,
+    Intersection,
+    Reader,
+)
+
+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")
+
+# create pipeline
+pipeline = StandalonePipeline().set_roles(guest="9999", host="10000", arbiter="10001")
+
+# create reader component
+reader_0 = Reader(name="reader_0")
+reader_0.guest.component_param(
+    path=f"file://${guest_data_path}",
+    format="csv",
+    id_name="id",
+    delimiter=",",
+    label_name="y",
+    label_type="float32",
+    dtype="float32",
+)
+reader_0.hosts[0].component_param(
+    path=f"file://${host_data_path}",
+    format="csv",
+    id_name="id",
+    delimiter=",",
+    label_name=None,
+    dtype="float32",
+)
+
+# create intersection component
+intersection_0 = Intersection(name="intersection_0", method="raw", input_data=reader_0.outputs["output_data"])
+intersection_1 = Intersection(name="intersection_1", method="raw", input_data=reader_0.outputs["output_data"])
+
+# create feature scale component
+feature_scale_0 = FeatureScale(
+    name="feature_scale_0", method="standard", train_data=intersection_0.outputs["output_data"]
+)
+feature_scale_1 = FeatureScale(
+    name="feature_scale_1",
+    test_data=intersection_1.outputs["output_data"],
+    input_model=feature_scale_0.outputs["output_model"],
+)
+
+# create lr component
+lr_0 = HeteroLR(
+    name="lr_0",
+    train_data=feature_scale_0.outputs["train_output_data"],
+    validate_data=feature_scale_1.outputs["test_output_data"],
+    max_iter=100,
+    learning_rate=0.03,
+    batch_size=-1,
+)
+
+# create evaluation component
+evaluation_0 = Evaluation(name="evaluation_0", runtime_roles="guest", input_data=lr_0.outputs["train_output_data"])
+
+# add components
+pipeline.add_task(reader_0)
+pipeline.add_task(feature_scale_0)
+pipeline.add_task(feature_scale_1)
+pipeline.add_task(intersection_0)
+pipeline.add_task(intersection_1)
+pipeline.add_task(lr_0)
+pipeline.add_task(evaluation_0)
+
+# train
+pipeline.compile()
+print(pipeline.get_dag())
+pipeline.fit()
+print(pipeline.get_task_info("feature_scale_0").get_output_model())
+print(pipeline.get_task_info("lr_0").get_output_model())
+print(pipeline.get_task_info("lr_0").get_output_data())
+print(pipeline.get_task_info("evaluation_0").get_output_metrics())
+print(pipeline.deploy([intersection_0, feature_scale_0, lr_0]))
+```