#1036: Added pytorch and numba integration test compatible with CPU only VM

flavors/test-Exasol-8-cuda-ml/flavor_base/testconfig

@@ -1,2 +1,2 @@
 generic_language_tests=python3
-test_folders=python3/all pandas/all pandas/pandas2
+test_folders=python3/all pandas/all pandas/pandas2 python3-cuda-flavor

test_container/tests/test/python3-cuda-flavor/numba.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+
+from exasol_python_test_framework import udf
+
+
+class NumbaTest(udf.TestCase):
+    def setUp(self):
+        self.query('create schema numbabasic', ignore_errors=True)
+
+    def test_import_keras(self):
+        self.query(udf.fixindent('''
+                CREATE OR REPLACE PYTHON3 SCALAR SCRIPT
+                test_numba(epochs INTEGER)
+                RETURNS VARCHAR(10000) AS
+
+                import math
+                from numba import vectorize, cuda
+                import numpy as np
+                import os
+
+                @vectorize(['float32(float32, float32, float32)',
+                            'float64(float64, float64, float64)',],
+                             #target='cuda'
+                            )
+                def cu_discriminant(a, b, c):
+                    return math.sqrt(b ** 2 - 4 * a * c)
+
+                def run(ctx):
+                    N = ctx.epochs
+                    dtype = np.float32
+
+                    # prepare the input
+                    A = np.array(np.random.sample(N), dtype=dtype)
+                    B = np.array(np.random.sample(N) + 10, dtype=dtype)
+                    C = np.array(np.random.sample(N), dtype=dtype)
+
+                    D = cu_discriminant(A, B, C)
+
+                    return str(D)
+                /
+                '''))
+
+        row = self.query("SELECT numbabasic.test_numba(10000);")[0]
+
+
+if __name__ == '__main__':
+    udf.main()

test_container/tests/test/python3-cuda-flavor/pytorch.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+import shutil
+import tarfile
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+import requests
+from exasol_python_test_framework import udf
+from exasol_python_test_framework.exatest.utils import obj_from_json_file
+from requests.auth import HTTPBasicAuth
+
+
+class PytorchTest(udf.TestCase):
+    def setUp(self):
+        self.query('create schema pytorchbasic', ignore_errors=True)
+
+    def test_pytorch(self):
+        self.query(udf.fixindent('''
+                CREATE OR REPLACE PYTHON3 SCALAR SCRIPT
+                test_pytorch(epochs INTEGER)
+                RETURNS VARCHAR(10000) AS
+
+                import torch
+                import torch.nn as nn
+                import torch.optim as optim
+                import numpy as np
+
+                def run(ctx):
+                    # Generate random data
+                    np.random.seed(42)
+                    x = np.random.rand(100, 1).astype(np.float32)  # Random x values
+                    y = 10 * x  # Corresponding y values
+
+                    # Convert numpy arrays to torch tensors
+                    x_train = torch.from_numpy(x)
+                    y_train = torch.from_numpy(y)
+
+                    # Define a simple linear regression model
+                    class LinearModel(nn.Module):
+                        def __init__(self):
+                            super(LinearModel, self).__init__()
+                            self.linear = nn.Linear(1, 1)  # Input and output both have size 1
+
+                        def forward(self, x):
+                            return self.linear(x)
+
+                    # Initialize the model, loss function, and optimizer
+                    model = LinearModel()
+                    criterion = nn.MSELoss()
+                    optimizer = optim.SGD(model.parameters(), lr=0.01)
+
+                    # Training loop
+                    epochs = ctx.epochs
+                    for epoch in range(epochs):
+                        model
+                    # Check accuracy
+                    model.eval()
+                    with torch.no_grad():
+                        y_pred = model(x_train)
+                        mse = criterion(y_pred, y_train)
+                        return f'Mean Squared Error: {mse.item():.4f}'
+                /
+                '''))
+
+        row = self.query(f"SELECT pytorchbasic.test_pytorch(1000);")[0]
+        self.assertIn('Mean Squared Error', row[0])
+
+
+if __name__ == '__main__':
+    udf.main()