Add instructions for AMD-SEV tests (#46)

* Use .venv for AMD-SEV tests Signed-off-by: Darko Draskovic <darko.draskovic@gmail.com> * Add instructions for VM Signed-off-by: Darko Draskovic <darko.draskovic@gmail.com> --------- Signed-off-by: Darko Draskovic <darko.draskovic@gmail.com>
2026-06-23 04:10:25 +00:00 · 2023-09-21 15:38:56 +02:00
parent 5171882634
commit 4f05805600
4 changed files with 223 additions and 6 deletions
@@ -42,6 +42,7 @@ type agentService struct {
 }

 const socketPath = "unix_socket"
+const pyRuntime = "python3"

 var _ Service = (*agentService)(nil)

@@ -118,7 +119,7 @@ func run(algoContent []byte, dataContent []byte) ([]byte, error) {
 	// Construct the Python script content with CSV data as a command-line argument
 	script := string(algoContent)
 	data := string(dataContent)
-	cmd := exec.Command("python3", "-c", script, data, socketPath)
+	cmd := exec.Command(pyRuntime, "-c", script, data, socketPath)

 	if err := cmd.Start(); err != nil {
 		return nil, fmt.Errorf("error starting Python script: %v", err)
@@ -2,16 +2,30 @@

 ## CLI

-Open a console and start `agent`
+Throughout the tests, we assume that our current working directory is the root of the `agent` repository, both on the host machine and in the VM.
+
+### Python requirements
+
+Do this both on the host machine and in the VM.

 ```sh
-AGENT_LOG_LEVEL=info go run cmd/agent/main.go
+apt update
+apt install python3-pip
+pip3 install pandas sklearn scikit-learn
 ```

-Open another console and run
+### Agent-CLI interaction
+
+In the VM, open a console and start `agent`:

 ```sh
-export AGENT_GRPC_URL=localhost:7002
+AGENT_LOG_LEVEL=info AGENT_GRPC_URL=10.0.2.15:7002 go run cmd/agent/main.go
+```
+
+Open console on the host, and run
+
+```sh
+export AGENT_GRPC_URL=localhost:7020

 # Run the CLI program with algorithm input
 go run cmd/cli/main.go algo test/manual/algo/lin_reg.py
@@ -53,4 +67,4 @@ Iris-versicolor      0.923     0.889     0.906        27
       accuracy                          0.933        75
      macro avg      0.939     0.938     0.938        75
   weighted avg      0.934     0.933     0.933        75
-```
+```
@@ -0,0 +1,51 @@
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+import joblib
+
+import sys
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
+csv_file_path = sys.argv[1]
+model_filename = sys.argv[2]
+
+# Load the CSV file into a Pandas DataFrame
+iris = pd.read_csv(csv_file_path)
+
+log_reg = joblib.load(model_filename)
+
+# Now you have the Iris dataset loaded into the iris_df DataFrame
+print(iris.head())  # Display the first few rows of the DataFrame
+
+# Droping the Species since we only need the measurements
+X = iris.drop(['Species'], axis=1)
+
+# converting into numpy array and assigning petal length and petal width
+X = X.to_numpy()[:, (3,4)]
+y = iris['Species']
+
+# Splitting into train and test
+X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5, random_state=42)
+
+training_prediction = log_reg.predict(X_train)
+test_prediction = log_reg.predict(X_test)
+
+print("Precision, Recall, Confusion matrix, in training\n")
+
+# Precision Recall scores
+print(metrics.classification_report(y_train, training_prediction, digits=3))
+
+# Confusion matrix
+print(metrics.confusion_matrix(y_train, training_prediction))
+
+print("Precision, Recall, Confusion matrix, in testing\n")
+
+# Precision Recall scores
+print(metrics.classification_report(y_test, test_prediction, digits=3))
+
+# Confusion matrix
+print(metrics.confusion_matrix(y_test, test_prediction))
@@ -0,0 +1,151 @@
+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica