Restore default CI configuration for VAE and Siamese examples using accelerator API (#1342)

* Restore default CI configuration for VAE and Siamese examples using Accelerator API

* Update Siamese Readme for consistency with accelerator argument

* Update siamese_network/README.md

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

* Update siamese_network/README.md

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

* Update siamese_network/main.py

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

* Update vae/main.py

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

* Improve Readme files for clearer descriptions

* Update Readme file structure to enhance organization

---------

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>

Author: eromomon

siamese_network/README.md

@@ -8,11 +8,27 @@ This implementation varies from FaceNet as we use the `ResNet-18` model from
 [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) as our feature extractor.
 In addition, we aren't using `TripletLoss` as the MNIST dataset is simple, so `BCELoss` can do the trick.
 
+### Usage
+
+Install the required dependencies:
 ```bash
 pip install -r requirements.txt
+```
+
+To run the example, execute:
+```bahs
 python main.py
 # CUDA_VISIBLE_DEVICES=2 python main.py  # to specify GPU id to ex. 2
 ```
+
+If a hardware accelerator device is detected, the example will execute on the accelerator; otherwise, it will run on the CPU.
+
+To force execution on the CPU, use `--no-accel` command line argument:
+
+```bash
+python main.py --no-accel
+```
+
 Optionally, you can add the following arguments to customize your execution.
 
 ```bash
@@ -21,17 +37,9 @@ Optionally, you can add the following arguments to customize your execution.
 --epochs                number of epochs to train (default: 14)
 --lr                    learning rate (default: 1.0)
 --gamma                 learning rate step gamma (default: 0.7)
---accel                 use accelerator
+--no-accel              disables accelerator
 --dry-run               quickly check a single pass
 --seed                  random seed (default: 1)
 --log-interval          how many batches to wait before logging training status
 --save-model            Saving the current Model
 ```
-
-To execute in an GPU, add the --accel argument to the command. For example:
-
-```bash
-python main.py --accel
-```
-
-This command will execute the example on the detected GPU.

siamese_network/main.py

@@ -247,8 +247,8 @@ def main():
                         help='learning rate (default: 1.0)')
     parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                         help='Learning rate step gamma (default: 0.7)')
-    parser.add_argument('--accel', action='store_true', 
-                    help='use accelerator')
+    parser.add_argument('--no-accel', action='store_true', 
+                    help='disables accelerator')
     parser.add_argument('--dry-run', action='store_true', default=False,
                         help='quickly check a single pass')
     parser.add_argument('--seed', type=int, default=1, metavar='S',
@@ -258,16 +258,13 @@ def main():
     parser.add_argument('--save-model', action='store_true', default=False,
                         help='For Saving the current Model')
     args = parser.parse_args()
+    
+    use_accel = not args.no_accel and torch.accelerator.is_available()
 
     torch.manual_seed(args.seed)
 
-    if args.accel and not torch.accelerator.is_available():
-        print("ERROR: accelerator is not available, try running on CPU")
-        sys.exit(1)
-    if not args.accel and torch.accelerator.is_available():
-        print("WARNING: accelerator is available, run with --accel to enable it")
 
-    if args.accel:
+    if use_accel:
         device = torch.accelerator.current_accelerator()
     else:
         device = torch.device("cpu")
@@ -276,12 +273,12 @@ def main():
 
     train_kwargs = {'batch_size': args.batch_size}
     test_kwargs = {'batch_size': args.test_batch_size}
-    if device=="cuda":
-        cuda_kwargs = {'num_workers': 1,
+    if use_accel:
+        accel_kwargs = {'num_workers': 1,
                        'pin_memory': True,
                        'shuffle': True}
-        train_kwargs.update(cuda_kwargs)
-        test_kwargs.update(cuda_kwargs)
+        train_kwargs.update(accel_kwargs)
+        test_kwargs.update(accel_kwargs)
 
     train_dataset = APP_MATCHER('../data', train=True, download=True)
     test_dataset = APP_MATCHER('../data', train=False)

vae/README.md

@@ -3,17 +3,32 @@
 This is an improved implementation of the paper [Auto-Encoding Variational Bayes](http://arxiv.org/abs/1312.6114) by Kingma and Welling.
 It uses ReLUs and the adam optimizer, instead of sigmoids and adagrad. These changes make the network converge much faster.
 
+### Usage
+Install the required dependencies:
 ```bash
 pip install -r requirements.txt
+```
+
+To run the example, execute:
+```bash
 python main.py
 ```
 
+If a hardware accelerator device is detected, the example will execute on the accelerator; otherwise, it will run on the CPU.
+
+To force execution on the CPU, use `--no-accel` command line argument:
+
+```bash
+python main.py --no-accel
+```
+
 The main.py script accepts the following optional arguments:
 
 ```bash
 --batch-size            input batch size for training (default: 128)
 --epochs                number of epochs to train (default: 10)
---accel                 use accelerator
+--no-accel              disables accelerator
 --seed                  random seed (default: 1)
 --log-interval	        how many batches to wait before logging training status
-```
+```
+

vae/main.py

@@ -13,31 +13,27 @@
                     help='input batch size for training (default: 128)')
 parser.add_argument('--epochs', type=int, default=10, metavar='N',
                     help='number of epochs to train (default: 10)')
-parser.add_argument('--accel', action='store_true', 
-                    help='use accelerator')
+parser.add_argument('--no-accel', action='store_true', 
+                    help='disables accelerator')
 parser.add_argument('--seed', type=int, default=1, metavar='S',
                     help='random seed (default: 1)')
 parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                     help='how many batches to wait before logging training status')
 args = parser.parse_args()
 
+use_accel = not args.no_accel and torch.accelerator.is_available()
 
 torch.manual_seed(args.seed)
 
-if args.accel and not torch.accelerator.is_available():
-    print("ERROR: accelerator is not available, try running on CPU")
-    sys.exit(1)
-if not args.accel and torch.accelerator.is_available():
-         print("WARNING: accelerator is available, run with --accel to enable it")
 
-if args.accel:
+if use_accel:
     device = torch.accelerator.current_accelerator()
 else:
     device = torch.device("cpu")
 
 print(f"Using device: {device}")
 
-kwargs = {'num_workers': 1, 'pin_memory': True} if device=="cuda" else {}
+kwargs = {'num_workers': 1, 'pin_memory': True} if use_accel else {}
 train_loader = torch.utils.data.DataLoader(
     datasets.MNIST('../data', train=True, download=True,
                    transform=transforms.ToTensor()),