diff --git a/demo-notebooks/guided-demos/1_cluster_job_client.ipynb b/demo-notebooks/guided-demos/1_cluster_job_client.ipynb
index e46dc62a9..787bd315d 100644
--- a/demo-notebooks/guided-demos/1_cluster_job_client.ipynb
+++ b/demo-notebooks/guided-demos/1_cluster_job_client.ipynb
@@ -50,7 +50,8 @@
     "    max_cpus=1,\n",
     "    min_memory=4,\n",
     "    max_memory=4,\n",
-    "    num_gpus=0,\n",
+    "    num_gpus=1,\n",
+    "    head_gpus=1,\n",
     "    image=\"quay.io/project-codeflare/ray:latest-py39-cu118\",\n",
     "    write_to_file=False, # When enabled Ray Cluster yaml files are written to /HOME/.codeflare/resources \n",
     "    # local_queue=\"local-queue-name\" # Specify the local queue manually\n",
@@ -114,7 +115,7 @@
    "source": [
     "# Submit an example mnist job using the Job Submission Client\n",
     "submission_id = client.submit_job(\n",
-    "    entrypoint=\"python mnist.py\",\n",
+    "    entrypoint=\"python mnist_fashion.py\",\n",
     "    runtime_env={\"working_dir\": \"./\",\"pip\": \"requirements.txt\"},\n",
     ")\n",
     "print(submission_id)"
diff --git a/demo-notebooks/guided-demos/mnist_fashion.py b/demo-notebooks/guided-demos/mnist_fashion.py
new file mode 100644
index 000000000..2b75a8653
--- /dev/null
+++ b/demo-notebooks/guided-demos/mnist_fashion.py
@@ -0,0 +1,84 @@
+import torch
+import torch.nn as nn
+import ray
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+from ray.train.torch import TorchTrainer
+from ray.train import ScalingConfig
+
+
+def get_dataset():
+    return datasets.FashionMNIST(
+        root="/tmp/data",
+        train=True,
+        download=True,
+        transform=ToTensor(),
+    )
+
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28 * 28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, 10),
+        )
+
+    def forward(self, inputs):
+        inputs = self.flatten(inputs)
+        logits = self.linear_relu_stack(inputs)
+        return logits
+
+
+def get_dataset():
+    return datasets.FashionMNIST(
+        root="/tmp/data",
+        train=True,
+        download=True,
+        transform=ToTensor(),
+    )
+
+
+def train_func_distributed():
+    num_epochs = 3
+    batch_size = 64
+
+    dataset = get_dataset()
+    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+    dataloader = ray.train.torch.prepare_data_loader(dataloader)
+
+    model = NeuralNetwork()
+    model = ray.train.torch.prepare_model(model)
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+    for epoch in range(num_epochs):
+        if ray.train.get_context().get_world_size() > 1:
+            dataloader.sampler.set_epoch(epoch)
+
+        for inputs, labels in dataloader:
+            optimizer.zero_grad()
+            pred = model(inputs)
+            loss = criterion(pred, labels)
+            loss.backward()
+            optimizer.step()
+        print(f"epoch: {epoch}, loss: {loss.item()}")
+
+
+# For GPU Training, set `use_gpu` to True.
+use_gpu = True
+
+trainer = TorchTrainer(
+    train_func_distributed,
+    scaling_config=ScalingConfig(
+        num_workers=3, use_gpu=use_gpu
+    ),  # num_workers = number of worker nodes with the ray head node included
+)
+
+results = trainer.fit()
diff --git a/demo-notebooks/guided-demos/notebook-ex-outputs/1_cluster_job_client.ipynb b/demo-notebooks/guided-demos/notebook-ex-outputs/1_cluster_job_client.ipynb
index e46dc62a9..787bd315d 100644
--- a/demo-notebooks/guided-demos/notebook-ex-outputs/1_cluster_job_client.ipynb
+++ b/demo-notebooks/guided-demos/notebook-ex-outputs/1_cluster_job_client.ipynb
@@ -50,7 +50,8 @@
     "    max_cpus=1,\n",
     "    min_memory=4,\n",
     "    max_memory=4,\n",
-    "    num_gpus=0,\n",
+    "    num_gpus=1,\n",
+    "    head_gpus=1,\n",
     "    image=\"quay.io/project-codeflare/ray:latest-py39-cu118\",\n",
     "    write_to_file=False, # When enabled Ray Cluster yaml files are written to /HOME/.codeflare/resources \n",
     "    # local_queue=\"local-queue-name\" # Specify the local queue manually\n",
@@ -114,7 +115,7 @@
    "source": [
     "# Submit an example mnist job using the Job Submission Client\n",
     "submission_id = client.submit_job(\n",
-    "    entrypoint=\"python mnist.py\",\n",
+    "    entrypoint=\"python mnist_fashion.py\",\n",
     "    runtime_env={\"working_dir\": \"./\",\"pip\": \"requirements.txt\"},\n",
     ")\n",
     "print(submission_id)"
diff --git a/demo-notebooks/guided-demos/preview_nbs/1_cluster_job_client.ipynb b/demo-notebooks/guided-demos/preview_nbs/1_cluster_job_client.ipynb
index b20f920bd..db11cada0 100644
--- a/demo-notebooks/guided-demos/preview_nbs/1_cluster_job_client.ipynb
+++ b/demo-notebooks/guided-demos/preview_nbs/1_cluster_job_client.ipynb
@@ -50,7 +50,8 @@
     "    max_cpus=1,\n",
     "    min_memory=4,\n",
     "    max_memory=4,\n",
-    "    num_gpus=0,\n",
+    "    num_gpus=1,\n",
+    "    head_gpus=1,\n",
     "    image=\"quay.io/project-codeflare/ray:latest-py39-cu118\",\n",
     "    write_to_file=False, # When enabled Ray Cluster yaml files are written to /HOME/.codeflare/resources\n",
     "    # local_queue=\"local-queue-name\" # Specify the local queue manually\n",
@@ -114,7 +115,7 @@
    "source": [
     "# Submit an example mnist job using the Job Submission Client\n",
     "submission_id = client.submit_job(\n",
-    "    entrypoint=\"python mnist.py\",\n",
+    "    entrypoint=\"python mnist_fashion.py\",\n",
     "    runtime_env={\"working_dir\": \"./\",\"pip\": \"requirements.txt\"},\n",
     ")\n",
     "print(submission_id)"