Workflow executor example workflow API #1102
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| #!/bin/bash | ||
| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| set -e | ||
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| WORKPATH=$(dirname "$PWD") | ||
| vllm_port=${vllm_port} | ||
| [[ -z "$vllm_port" ]] && vllm_port=8084 | ||
| export WORKDIR=$WORKPATH/../../ | ||
| echo "WORKDIR=${WORKDIR}" | ||
| export SDK_BASE_URL=$1 | ||
| echo "SDK_BASE_URL=$1" | ||
| export SERVING_TOKEN=${SERVING_TOKEN} | ||
| export HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} | ||
| export llm_engine=vllm | ||
| export ip_address=$(hostname -I | awk '{print $1}') | ||
| export llm_endpoint_url=http://${ip_address}:${vllm_port} | ||
| export model=mistralai/Mistral-7B-Instruct-v0.3 | ||
| export recursion_limit=25 | ||
| export temperature=0 | ||
| export max_new_tokens=1000 | ||
| export TOOLSET_PATH=$WORKDIR/GenAIExamples/WorkflowExecAgent/tools/ | ||
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| function start_agent() { | ||
| echo "Starting Agent services" | ||
| cd $WORKDIR/GenAIExamples/WorkflowExecAgent/docker_compose/intel/cpu/xeon | ||
| WORKDIR=$WORKPATH/docker_image_build/ docker compose -f compose_vllm.yaml up -d | ||
| echo "Waiting agent service ready" | ||
| sleep 5s | ||
| } | ||
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| function main() { | ||
| echo "==================== Start agent service ====================" | ||
| start_agent | ||
| echo "==================== Agent service started ====================" | ||
| } | ||
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| main |
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| #!/bin/bash | ||
| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| set -e | ||
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| wf_api_port=${wf_api_port} | ||
| [[ -z "$wf_api_port" ]] && wf_api_port=5005 | ||
| WORKPATH=$(dirname "$PWD") | ||
| LOG_PATH="$WORKPATH/tests/example_workflow" | ||
| export WORKDIR=$WORKPATH/../../ | ||
| echo "WORKDIR=${WORKDIR}" | ||
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| function start_example_workflow_api() { | ||
| echo "Starting example workflow API" | ||
| cd $WORKDIR/GenAIExamples/WorkflowExecAgent/tests/example_workflow | ||
| docker build -f Dockerfile.example_workflow_api -t example-workflow-service . | ||
| docker run -d -p ${wf_api_port}:${wf_api_port} --rm --network=host --name example-workflow-service -it example-workflow-service | ||
| echo "Waiting example workflow API ready" | ||
| until [[ "$n" -ge 100 ]] || [[ $ready == true ]]; do | ||
| docker logs example-workflow-service &> ${LOG_PATH}/example-workflow-service.log | ||
| n=$((n+1)) | ||
| if grep -q "Uvicorn running on" ${LOG_PATH}/example-workflow-service.log; then | ||
| break | ||
| fi | ||
| if grep -q "No such container" ${LOG_PATH}/example-workflow-service.log; then | ||
| echo "container example-workflow-service not found" | ||
| exit 1 | ||
| fi | ||
| sleep 5s | ||
| done | ||
| } | ||
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| function main() { | ||
| echo "==================== Start example workflow API ====================" | ||
| start_example_workflow_api | ||
| echo "==================== Example workflow API started ====================" | ||
| } | ||
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| main |
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| #!/bin/bash | ||
| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| set -e | ||
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| WORKPATH=$(dirname "$PWD") | ||
| export WORKDIR=$WORKPATH/../../ | ||
| echo "WORKDIR=${WORKDIR}" | ||
| export ip_address=$(hostname -I | awk '{print $1}') | ||
| query=$1 | ||
| validate_result=$2 | ||
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| function validate() { | ||
| local CONTENT="$1" | ||
| local EXPECTED_RESULT="$2" | ||
| local SERVICE_NAME="$3" | ||
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| if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then | ||
| echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT" | ||
| echo "[TEST INFO]: Workflow Executor agent service PASSED" | ||
| else | ||
| echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT" | ||
| echo "[TEST INFO]: Workflow Executor agent service FAILED" | ||
| fi | ||
| } | ||
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| function validate_agent_service() { | ||
| echo "----------------Test agent ----------------" | ||
| local CONTENT=$(curl http://${ip_address}:9091/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{ | ||
| "messages": "'"${query}"'" | ||
| }') | ||
| validate "$CONTENT" "$validate_result" "workflowexec-agent-endpoint" | ||
| docker logs workflowexec-agent-endpoint | ||
| } | ||
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| function main() { | ||
| echo "==================== Validate agent service ====================" | ||
| validate_agent_service | ||
| echo "==================== Agent service validated ====================" | ||
| } | ||
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| main |
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| FROM ubuntu:22.04 | ||
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| RUN apt-get -qq update | ||
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| WORKDIR /home/ubuntu | ||
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| COPY launch_workflow_service.sh requirements.txt main.py workflow.py ./ | ||
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| RUN chmod +x ./launch_workflow_service.sh | ||
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| CMD ["./launch_workflow_service.sh"] | ||
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| #!/bin/bash | ||
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| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| apt-get -qq -y install --no-install-recommends unzip curl ca-certificates | ||
| apt-get -qq -y install --no-install-recommends python3 python3-pip | ||
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| curl -L -o ./archive.zip https://www.kaggle.com/api/v1/datasets/download/blastchar/telco-customer-churn | ||
| unzip ./archive.zip -d ./ | ||
| rm ./archive.zip | ||
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| pip install virtualenv && \ | ||
| virtualenv venv && \ | ||
| source venv/bin/activate && \ | ||
| pip install -r requirements.txt | ||
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| uvicorn main:app --reload --port=5005 --host=0.0.0.0 |
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| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import logging | ||
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| from fastapi import APIRouter, FastAPI | ||
| from workflow import run_workflow | ||
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| logger = logging.getLogger(__name__) | ||
| logger.setLevel(logging.DEBUG) | ||
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| app = FastAPI() | ||
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| router = APIRouter(prefix="/serving", tags=["Workflow Serving"]) | ||
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| app.results = {} | ||
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| @router.post("/servable_workflows/{wf_id}/start", summary="Start Workflow") | ||
| async def start_workflow(wf_id: int, params: dict): | ||
| try: | ||
| app.results = run_workflow(params["params"]) | ||
| wf_key = "example_key" | ||
| return {"msg": "ok", "wf_key": wf_key} | ||
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| except Exception as e: | ||
| logging.error(e, exc_info=True) | ||
| return {"msg": "error occurred"} | ||
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| @router.get("/serving_workflows/{wf_key}/status", summary="Get Workflow Status") | ||
| async def get_status(wf_key: str): | ||
| try: | ||
| if app.results: | ||
| status = "finished" | ||
| else: | ||
| status = "failed" | ||
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| return {"workflow_status": status} | ||
| except Exception as e: | ||
| logging.error(e) | ||
| return {"msg": "error occurred"} | ||
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| @router.get("/serving_workflows/{wf_key}/results", summary="Get Workflow Results") | ||
| async def get_results(wf_key: str): | ||
| try: | ||
| if app.results: | ||
| return app.results | ||
| else: | ||
| return {"msg": "There is an issue while getting results !!"} | ||
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| except Exception as e: | ||
| logging.error(e) | ||
| return {"msg": "There is an issue while getting results !!"} | ||
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| app.include_router(router) |
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| fastapi | ||
| numpy | ||
| pandas | ||
| requests==2.28.1 | ||
| scikit-learn | ||
| uvicorn | ||
| xgboost | ||
|
There was a problem hiding this comment. please check with Suyue if There was a problem hiding this comment. Hi @lvliang-intel, Suyue has confirmed xgboost is in the BOM list |
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| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import json | ||
| import warnings | ||
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| warnings.filterwarnings("ignore") | ||
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| import pandas as pd | ||
| from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier | ||
| from sklearn.linear_model import LogisticRegression | ||
| from sklearn.metrics import accuracy_score | ||
| from sklearn.model_selection import GridSearchCV, train_test_split | ||
| from sklearn.naive_bayes import GaussianNB | ||
| from sklearn.neighbors import KNeighborsClassifier | ||
| from sklearn.pipeline import Pipeline | ||
| from sklearn.preprocessing import LabelEncoder, MinMaxScaler | ||
| from sklearn.svm import SVC | ||
| from sklearn.tree import DecisionTreeClassifier | ||
| from xgboost import XGBClassifier | ||
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| def churn_prediction(params): | ||
| df = pd.read_csv("./WA_Fn-UseC_-Telco-Customer-Churn.csv") | ||
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| # Data Cleaning | ||
| df = df.drop(["customerID"], axis=1) | ||
| select_cols = ["gender", "tenure", "MonthlyCharges", "TotalCharges", "Churn"] | ||
| df = df[select_cols] | ||
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| df["TotalCharges"] = pd.to_numeric(df.TotalCharges, errors="coerce") | ||
| df.drop(labels=df[df["tenure"] == 0].index, axis=0, inplace=True) | ||
| df.fillna(df["TotalCharges"].mean()) | ||
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| # Data Preprocessing | ||
| encoders = {} | ||
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| def object_to_int(dataframe_series): | ||
| if dataframe_series.dtype == "object": | ||
| encoders[dataframe_series.name] = LabelEncoder().fit(dataframe_series) | ||
| dataframe_series = encoders[dataframe_series.name].transform(dataframe_series) | ||
| return dataframe_series | ||
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| df = df.apply(lambda x: object_to_int(x)) | ||
| X = df.drop(columns=["Churn"]) | ||
| y = df["Churn"].values | ||
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=40, stratify=y) | ||
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| model_scores = [] | ||
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| models = [ | ||
| ( | ||
| "Random Forest", | ||
| RandomForestClassifier(random_state=42), | ||
| {"model__n_estimators": [50, 100, 200], "model__max_depth": [None, 10, 20]}, | ||
| ), # Add hyperparameters for Random Forest | ||
| ( | ||
| "Gradient Boosting", | ||
| GradientBoostingClassifier(random_state=42), | ||
| {"model__n_estimators": [50, 100, 200], "model__learning_rate": [0.05, 0.1, 0.5]}, | ||
| ), # Add hyperparameters for Gradient Boosting | ||
| ( | ||
| "Support Vector Machine", | ||
| SVC(random_state=42, class_weight="balanced"), | ||
| {"model__C": [0.1, 1, 10], "model__gamma": ["scale", "auto"]}, | ||
| ), # Add hyperparameters for SVM | ||
| ( | ||
| "Logistic Regression", | ||
| LogisticRegression(random_state=42, class_weight="balanced"), | ||
| {"model__C": [0.1, 1, 10], "model__penalty": ["l1", "l2"]}, | ||
| ), # Add hyperparameters for Logistic Regression | ||
| ( | ||
| "K-Nearest Neighbors", | ||
| KNeighborsClassifier(), | ||
| {"model__n_neighbors": [3, 5, 7], "model__weights": ["uniform", "distance"]}, | ||
| ), # Add hyperparameters for KNN | ||
| ( | ||
| "Decision Tree", | ||
| DecisionTreeClassifier(random_state=42), | ||
| {"model__max_depth": [None, 10, 20], "model__min_samples_split": [2, 5, 10]}, | ||
| ), # Add hyperparameters for Decision Tree | ||
| ( | ||
| "Ada Boost", | ||
| AdaBoostClassifier(random_state=42), | ||
| {"model__n_estimators": [50, 100, 200], "model__learning_rate": [0.05, 0.1, 0.5]}, | ||
| ), # Add hyperparameters for Ada Boost | ||
| ( | ||
| "XG Boost", | ||
| XGBClassifier(random_state=42), | ||
| {"model__n_estimators": [50, 100, 200], "model__learning_rate": [0.05, 0.1, 0.5]}, | ||
| ), # Add hyperparameters for XG Boost | ||
| ("Naive Bayes", GaussianNB(), {}), # No hyperparameters for Naive Bayes | ||
| ] | ||
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| best_model = None | ||
| best_accuracy = 0.0 | ||
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| for name, model, param_grid in models: | ||
| # Create a pipeline for each model | ||
| pipeline = Pipeline([("scaler", MinMaxScaler()), ("model", model)]) # Feature Scaling | ||
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| # Hyperparameter tuning using GridSearchCV | ||
| if param_grid: | ||
| grid_search = GridSearchCV(pipeline, param_grid, cv=2) | ||
| grid_search.fit(X_train, y_train) | ||
| pipeline = grid_search.best_estimator_ | ||
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| # Fit the pipeline on the training data | ||
| pipeline.fit(X_train, y_train) | ||
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| # Make predictions on the test data | ||
| y_pred = pipeline.predict(X_test) | ||
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| # Calculate accuracy score | ||
| accuracy = accuracy_score(y_test, y_pred) | ||
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| # Append model name and accuracy to the list | ||
| model_scores.append({"Model": name, "Accuracy": accuracy}) | ||
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| # Convert the list to a DataFrame | ||
| scores_df = pd.DataFrame(model_scores) | ||
| print("Model:", name) | ||
| print("Test Accuracy:", round(accuracy, 3), "%\n") | ||
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| # Check if the current model has the best accuracy | ||
| if accuracy > best_accuracy: | ||
| best_accuracy = accuracy | ||
| best_model = pipeline | ||
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| # Retrieve the overall best model | ||
| print("Best Model:") | ||
| print("Test Accuracy:", best_accuracy) | ||
| print("Model Pipeline:", best_model, "with accuracy", round(best_accuracy, 2), "%\n") | ||
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| # Process and Predict input values from user | ||
| def transform_params(name, value): | ||
| return encoders[name].transform(value)[0] | ||
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| predict_input = {} | ||
| print("Predicting with user provided params:", params) | ||
| for key, value in params.items(): | ||
| if key in encoders.keys(): | ||
| predict_input[key] = transform_params(key, [value]) | ||
| else: | ||
| predict_input[key] = value | ||
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| predict_input = pd.DataFrame([predict_input]) | ||
| result = best_model.predict(predict_input) | ||
| params["prediction"] = encoders["Churn"].inverse_transform(result)[0] | ||
| result = json.dumps(params) | ||
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| return result | ||
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| def run_workflow(params): | ||
| return churn_prediction(params) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| #!/bin/bash | ||
| # Copyright (C) 2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| wf_api_port=${wf_api_port} | ||
| [[ -z "$wf_api_port" ]] && wf_api_port=5005 && export wf_api_port=5005 | ||
| api_server_url=http://$(hostname -I | awk '{print $1}'):${wf_api_port}/ | ||
| workflow_id=10071 | ||
| query="I have a data with gender Female, tenure 55, MonthlyCharges 103.7, TotalCharges 1840.75. Predict if this entry will churn. My workflow id is ${workflow_id}." | ||
| validate_result="the prediction is No" | ||
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| function stop_agent_and_api_server() { | ||
| echo "Stopping Agent services" | ||
| docker rm --force $(docker ps -a -q --filter="name=workflowexec-agent-endpoint") | ||
| docker rm --force $(docker ps -a -q --filter="name=example-workflow-service") | ||
| } | ||
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| function stop_vllm_docker() { | ||
| cid=$(docker ps -aq --filter "name=test-comps-vllm-service") | ||
| echo "Stopping the docker containers "${cid} | ||
| if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi | ||
| echo "Docker containers stopped successfully" | ||
| } | ||
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| echo "=================== #1 Building docker images ====================" | ||
| bash 1_build_images.sh | ||
| echo "=================== #1 Building docker images completed ====================" | ||
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| echo "=================== #2 Start vllm service ====================" | ||
| bash 2_start_vllm_service.sh | ||
| echo "=================== #2 Start vllm service completed ====================" | ||
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| echo "=================== #3 Start agent service ====================" | ||
| bash 3_launch_agent_service.sh $api_server_url | ||
| echo "=================== #3 Agent service started ====================" | ||
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| echo "=================== #4 Start example workflow API ====================" | ||
| bash 3_launch_example_wf_api.sh | ||
| echo "=================== #4 Example workflow API started ====================" | ||
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| echo "=================== #5 Start validate agent ====================" | ||
| bash 4_validate_agent.sh "$query" "$validate_result" | ||
| echo "=================== #5 Validate agent completed ====================" | ||
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| echo "=================== #4 Stop all services ====================" | ||
| stop_agent_and_api_server | ||
| stop_vllm_docker | ||
| echo "=================== #4 All services stopped ====================" | ||
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| echo "ALL DONE!" |
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