Restructure Python examples

.github/workflows/conda-package.yml

@@ -522,7 +522,7 @@ jobs:
     runs-on: ${{ matrix.runner }}
     strategy:
       matrix:
-        python: ['3.11']
+        python: ['3.14']
         experimental: [false]
         runner: [ubuntu-latest]
     continue-on-error: ${{ matrix.experimental }}
@@ -682,7 +682,7 @@ jobs:
           conda activate "${{ env.EXAMPLES_ENV_NAME }}"
           while IFS= read -r script; do
             echo "Executing ${script}"
-            python "${script}" || exit 1
+            python "${script}" --run all || exit 1
           done < <(find . \( -not -name "_*" -and -name "*.py" \))
 
   cleanup_packages:

examples/python/subdevices.py

@@ -43,7 +43,11 @@ def subdivide_root_cpu_device():
         "cpu_d is "
         + ("a root device." if is_root_device(cpu_d) else "not a root device.")
     )
-    sub_devs = cpu_d.create_sub_devices(partition=4)
+    try:
+        sub_devs = cpu_d.create_sub_devices(partition=4)
+    except dpctl.SyclSubDeviceCreationError:
+        print("Device partitioning was not successful.")
+        return
     print("Sub-device #EU: ", [d.max_compute_units for d in sub_devs])
     print("Sub-device is_root: ", [is_root_device(d) for d in sub_devs])
     print(
@@ -70,7 +74,7 @@ def subdivide_by_affinity(affinity="numa"):
             f"{len(sub_devs)} sub-devices were created with respective #EUs "
             f"being {[d.max_compute_units for d in sub_devs]}"
         )
-    except Exception:
+    except dpctl.SyclSubDeviceCreationError:
         print("Device partitioning by affinity was not successful.")
 
 
@@ -82,7 +86,11 @@ def create_subdevice_queue():
     """
     cpu_d = dpctl.SyclDevice("cpu")
     cpu_count = cpu_d.max_compute_units
-    sub_devs = cpu_d.create_sub_devices(partition=cpu_count // 2)
+    try:
+        sub_devs = cpu_d.create_sub_devices(partition=cpu_count // 2)
+    except dpctl.SyclSubDeviceCreationError:
+        print("Device partitioning was not successful.")
+        return
     multidevice_ctx = dpctl.SyclContext(sub_devs)
     # create a SyclQueue for each sub-device, using common
     # multi-device context

examples/python/usm_operations.py

@@ -0,0 +1,140 @@
+#                      Data Parallel Control (dpctl)
+#
+# Copyright 2020-2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Demonstrates SYCL USM memory usage in Python using dpctl.memory.
+Includes allocation, host access, and host-device copying.
+"""
+
+import numpy as np
+
+import dpctl.memory as dpmem
+
+
+def usm_allocation():
+    """
+    Example demonstrating ways to allocate USM using dpctl.memory.
+    """
+    # allocate USM-shared byte-buffer
+    ms = dpmem.MemoryUSMShared(16)
+    print(f"USM-shared buffer allocated with size: {len(ms)}")
+
+    # allocate USM-device byte-buffer
+    md = dpmem.MemoryUSMDevice(16)
+    print(f"USM-device buffer allocated with size: {len(md)}")
+
+    # allocate USM-host byte-buffer
+    mh = dpmem.MemoryUSMHost(16)
+    print(f"USM-host buffer allocated with size: {len(mh)}")
+
+    # specify alignment
+    # TODO: add alignment check
+    mda = dpmem.MemoryUSMDevice(128, alignment=16)
+    print(f"16-byte aligned USM-device buffer allocated with size: {len(mda)}")
+
+    # allocate using given queue,
+    # i.e. on the device and bound to the context stored in the queue
+    mdq = dpmem.MemoryUSMDevice(256, queue=mda.sycl_queue)
+    print(
+        "USM-device buffers share the same queue: "
+        f"{mdq.sycl_queue == mda.sycl_queue}"
+    )
+
+    # information about device associate with USM buffer
+    print("Allocation performed on device:")
+    mda.sycl_queue.print_device_info()
+
+
+def usm_host_access():
+    """
+    Example demonstrating that shared and host USM allocations are
+    host-accessible and thus accessible from Python via buffer protocol.
+    """
+    # USM-shared and USM-host pointers are host-accessible,
+    # meaning they are accessible from Python, therefore
+    # they implement Python buffer protocol
+
+    # allocate 1K of USM-shared buffer
+    ms = dpmem.MemoryUSMShared(1024)
+
+    # create memoryview into USM-shared buffer
+    msv = memoryview(ms)
+
+    # populate buffer from host one byte at a time
+    for i in range(len(ms)):
+        ir = i % 256
+        msv[i] = ir**2 % 256
+
+    mh = dpmem.MemoryUSMHost(64)
+    mhv = memoryview(mh)
+
+    # copy content of block of USM-shared buffer to
+    # USM-host buffer
+    mhv[:] = msv[78 : 78 + len(mh)]
+
+    print("Byte-values of the USM-host buffer")
+    print(list(mhv))
+
+    # USM-device buffer is not host accessible
+    md = dpmem.MemoryUSMDevice(16)
+    try:
+        memoryview(md)
+    except Exception as e:
+        print("")
+        print(
+            "An expected exception was raised during attempted construction of "
+            "memoryview from USM-device memory object."
+        )
+        print(f"\t{e}")
+
+
+def usm_host_device_copy():
+    """
+    Example demonstrating copying operations using dpctl.memory.
+    """
+    ms = dpmem.MemoryUSMShared(32)
+    md = dpmem.MemoryUSMDevice(32)
+
+    host_buf = np.random.randint(0, 42, dtype=np.uint8, size=32)
+
+    # copy host byte-like object to USM-device buffer
+    md.copy_from_host(host_buf)
+
+    # copy USM-device buffer to USM-shared buffer in parallel using
+    # sycl::queue::memcpy.
+    ms.copy_from_device(md)
+
+    # build numpy array reusing host-accessible USM-shared memory
+    X = np.ndarray((len(ms),), buffer=ms, dtype=np.uint8)
+
+    # Display Python object NumPy ndarray is viewing into
+    print("numpy.ndarray.base: ", X.base)
+    print("")
+
+    # Print content of the view
+    print("View..........: ", X)
+
+    # Print content of the original host buffer
+    print("host_buf......: ", host_buf)
+
+    # use copy_to_host to retrieve memory of USM-device memory
+    print("copy_to_host(): ", md.copy_to_host())
+
+
+if __name__ == "__main__":
+    import _runner as runner
+
+    runner.run_examples("Memory examples for dpctl.", globals())