Add the example code of how to accelerate the data sharing with viney…

…ard on fluid platform.

Signed-off-by: Ye Cao <[email protected]>

k8s/examples/vineyard-on-fluid/linear-regression-with-vineyard.py

@@ -0,0 +1,163 @@
+import fluid
+
+from fluid import constants
+from fluid import models
+
+# Use the default kubeconfig file to connect to the Fluid control plane 
+# and create a Fluid client instance
+client_config = fluid.ClientConfig()
+fluid_client = fluid.FluidClient(client_config)
+
+# Create a dataset named "vineyard" in the default namespace
+fluid_client.create_dataset(
+    dataset_name="vineyard",
+    mount_name="dummy-mount-name",
+    mount_point="dummy-mount-point"
+)
+
+# Get the dataset instance of the "vineyard" dataset
+dataset = fluid_client.get_dataset(dataset_name="vineyard")
+
+# Init vineyard runtime configuration and bind the vineyard dataset instance to the runtime.
+# Replicas is 2, and the memory is 30Gi
+dataset.bind_runtime(
+    runtime_type=constants.VINEYARD_RUNTIME_KIND,
+    replicas=2,
+    cache_capacity_GiB=30,
+    cache_medium="MEM",
+    wait=True
+)
+
+# define the script of data preprocessing
+preprocess_data_script = """
+pip3 install numpy pandas pyarrow requests vineyard scikit-learn==1.4.0 joblib==1.3.2
+#!/bin/bash
+set -ex
+
+cat <<EOF > ./preprocess.py
+from sklearn.model_selection import train_test_split
+
+import pandas as pd
+import vineyard
+
+df = pd.read_pickle('/data/df.pkl')
+
+# Preprocess Data
+df = df.drop(df[(df['GrLivArea']>4800)].index)
+X = df.drop('SalePrice', axis=1)  # Features
+y = df['SalePrice']  # Target variable
+
+del df
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+del X, y
+
+vineyard.put(X_train, name="x_train", persist=True)
+vineyard.put(X_test, name="x_test", persist=True)
+vineyard.put(y_train, name="y_train", persist=True)
+vineyard.put(y_test, name="y_test", persist=True)
+
+EOF
+
+python3 ./preprocess.py
+"""
+
+# define the script of model training
+train_data_script = """
+pip3 install numpy pandas pyarrow requests vineyard scikit-learn==1.4.0 joblib==1.3.2
+#!/bin/bash
+set -ex
+
+cat <<EOF > ./train.py
+from sklearn.linear_model import LinearRegression
+
+import joblib
+import pandas as pd
+import vineyard
+
+x_train_data = vineyard.get(name="x_train", fetch=True)
+y_train_data = vineyard.get(name="y_train", fetch=True)
+
+model = LinearRegression()
+model.fit(x_train_data, y_train_data)
+
+joblib.dump(model, '/data/model.pkl')
+
+EOF
+python3 ./train.py
+"""
+
+# define the script of model testing
+test_data_script = """
+pip3 install numpy pandas pyarrow requests vineyard scikit-learn==1.4.0 joblib==1.3.2
+#!/bin/bash
+set -ex
+
+cat <<EOF > ./test.py
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+import vineyard
+import joblib
+import pandas as pd
+
+x_test_data = vineyard.get(name="x_test", fetch=True)
+y_test_data = vineyard.get(name="y_test", fetch=True)
+
+model = joblib.load("/data/model.pkl")
+y_pred = model.predict(x_test_data)
+
+err = mean_squared_error(y_test_data, y_pred)
+
+with open('/data/output.txt', 'a') as f:
+    f.write(str(err))
+
+EOF
+
+python3 ./test.py
+"""
+
+from kubernetes.client import models as k8s_models
+# define the template of the task processor and mount the OSS Volume
+def create_processor(script):
+    return models.Processor(
+        # When enabling fuse affinity scheduling, add the following label
+        # to achieve the best performance of data processing
+        # pod_metadata=models.PodMetadata(
+        #     labels={"fuse.serverful.fluid.io/inject": "true"},
+        # ),
+        script=models.ScriptProcessor(
+            command=["bash"],
+            source=script,
+            image="python",
+            image_tag="3.10",
+            volumes=[k8s_models.V1Volume(
+                name="data",
+                persistent_volume_claim=k8s_models.V1PersistentVolumeClaimVolumeSource(
+                    claim_name="pvc-oss"
+                )
+            )],
+            volume_mounts=[k8s_models.V1VolumeMount(
+                name="data",
+                mount_path="/data"
+            )],
+        )   
+    )
+
+preprocess_processor = create_processor(preprocess_data_script)
+train_processor = create_processor(train_data_script)
+test_processor = create_processor(test_data_script)
+
+# Create a linear regression model task workflow: data preprocessing -> model training -> model testing
+# The following mount path "/var/run" is the default path of the vineyard configuration file
+flow = dataset.process(processor=preprocess_processor, dataset_mountpath="/var/run") \
+              .process(processor=train_processor, dataset_mountpath="/var/run") \
+              .process(processor=test_processor, dataset_mountpath="/var/run")
+
+# Submit the linear regression model task workflow to the Fluid platform and start execution
+run = flow.run(run_id="linear-regression-with-vineyard")
+run.wait()
+
+# Clean up all resources
+dataset.clean_up(wait=True)

k8s/examples/vineyard-on-fluid/prepare-dataset.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pandas as pd
+
+# generate a dataframe with size around 22G
+num_rows = 6000 * 10000
+df = pd.DataFrame({
+    'Id': np.random.randint(1, 100000, num_rows),
+    'MSSubClass': np.random.randint(20, 201, size=num_rows),
+    'LotFrontage': np.random.randint(50, 151, size=num_rows),
+    'LotArea': np.random.randint(5000, 20001, size=num_rows),
+    'OverallQual': np.random.randint(1, 11, size=num_rows),
+    'OverallCond': np.random.randint(1, 11, size=num_rows),
+    'YearBuilt': np.random.randint(1900, 2022, size=num_rows),
+    'YearRemodAdd': np.random.randint(1900, 2022, size=num_rows),
+    'MasVnrArea': np.random.randint(0, 1001, size=num_rows),
+    'BsmtFinSF1': np.random.randint(0, 2001, size=num_rows),
+    'BsmtFinSF2': np.random.randint(0, 1001, size=num_rows),
+    'BsmtUnfSF': np.random.randint(0, 2001, size=num_rows),
+    'TotalBsmtSF': np.random.randint(0, 3001, size=num_rows),
+    '1stFlrSF': np.random.randint(500, 4001, size=num_rows),
+    '2andFlrSF': np.random.randint(0, 2001, size=num_rows),
+    'LowQualFinSF': np.random.randint(0, 201, size=num_rows),
+    'GrLivArea': np.random.randint(600, 5001, size=num_rows),
+    'BsmtFullBath': np.random.randint(0, 4, size=num_rows),
+    'BsmtHalfBath': np.random.randint(0, 3, size=num_rows),
+    'FullBath': np.random.randint(0, 5, size=num_rows),
+    'HalfBath': np.random.randint(0, 3, size=num_rows),
+    'BedroomAbvGr': np.random.randint(0, 11, size=num_rows),
+    'KitchenAbvGr': np.random.randint(0, 4, size=num_rows),
+    'TotRmsAbvGrd': np.random.randint(0, 16, size=num_rows),
+    'Fireplaces': np.random.randint(0, 4, size=num_rows),
+    'GarageYrBlt': np.random.randint(1900, 2022, size=num_rows),
+    'GarageCars': np.random.randint(0, 5, num_rows),
+    'GarageArea': np.random.randint(0, 1001, num_rows),
+    'WoodDeckSF': np.random.randint(0, 501, num_rows),
+    'OpenPorchSF': np.random.randint(0, 301, num_rows),
+    'EnclosedPorch': np.random.randint(0, 201, num_rows),
+    '3SsnPorch': np.random.randint(0, 101, num_rows),
+    'ScreenPorch': np.random.randint(0, 201, num_rows),
+    'PoolArea': np.random.randint(0, 301, num_rows),
+    'MiscVal': np.random.randint(0, 5001, num_rows),
+    'TotalRooms': np.random.randint(2, 11, num_rows),
+    "GarageAge": np.random.randint(1, 31, num_rows),
+    "RemodAge": np.random.randint(1, 31, num_rows),
+    "HouseAge": np.random.randint(1, 31, num_rows),
+    "TotalBath": np.random.randint(1, 5, num_rows),
+    "TotalPorchSF": np.random.randint(1, 1001, num_rows),
+    "TotalSF": np.random.randint(1000, 6001, num_rows),
+    "TotalArea": np.random.randint(1000, 6001, num_rows),
+    'MoSold': np.random.randint(1, 13, num_rows),
+    'YrSold': np.random.randint(2006, 2022, num_rows),
+    'SalePrice': np.random.randint(50000, 800001, num_rows),
+})
+
+import oss2
+import io
+from oss2.credentials import EnvironmentVariableCredentialsProvider
+# Please set your OSS accessKeyID and accessKeySecret as environment variables OSS_ACCESS_KEY_ID and OSS_ACCESS_KEY_SECRET
+auth = oss2.ProviderAuth(EnvironmentVariableCredentialsProvider())
+# Please replace OSS_ENDPOINT and BUCKET_NAME with your OSS Endpoint and Bucket
+bucket = oss2.Bucket(auth, 'OSS_ENDPOINT', 'BUCKET_NAME')
+
+bytes_buffer = io.BytesIO()
+df.to_pickle(bytes_buffer)
+bucket.put_object("df.pkl", bytes_buffer.getvalue())