From 68e3aa7c5e8948e5957596de79492ba58a609277 Mon Sep 17 00:00:00 2001
From: Joseph Chu <122298491+cfjchu@users.noreply.github.com>
Date: Thu, 10 Oct 2024 15:13:47 -0700
Subject: [PATCH] #13693: Fixes to tech_report/Programming Mesh of Devices
 (#13714)

---
 .../Programming Mesh of Devices with TT-NN.md               | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tech_reports/Programming Mesh of Devices/Programming Mesh of Devices with TT-NN.md b/tech_reports/Programming Mesh of Devices/Programming Mesh of Devices with TT-NN.md
index 0ae6064b4fa..e507bbccf8f 100644
--- a/tech_reports/Programming Mesh of Devices/Programming Mesh of Devices with TT-NN.md	
+++ b/tech_reports/Programming Mesh of Devices/Programming Mesh of Devices with TT-NN.md	
@@ -289,7 +289,7 @@ Let's see an example of how to use the Ring All-Gather operation:
 ```py
 import ttnn
 
-mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2, 4))
+mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2, 4), mesh_type=ttnn.MeshType.Ring)
 
 # Construct test tensor of data; 8 chunks of 32x32
 torch_tensor = torch.rand((1,1,32,128), dtype=torch.bfloat16)
@@ -318,7 +318,7 @@ This time, we'll issue the CCL Line All-Gather operation along the cluster y-axi
 ```py
 import ttnn
 
-mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2, 4))
+mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2, 4), mesh_type=ttnn.MeshType.Ring)
 
 # Construct test tensor of data; 8 chunks of 32x32
 torch_tensor = torch.rand((1,1,32,128), dtype=torch.bfloat16)
@@ -516,7 +516,7 @@ torch_hidden_states = (torch.rand(batch_size, 1, sequence_length, config.hidden_
 torch_output = model.forward(torch_hidden_states)
 
 # Device Initialization
-mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2,4))
+mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(2,4), mesh_type=ttnn.MeshType.Ring)
 
 # Initialize input activations on all devices in the mesh
 # Alternatively, we can shard the input activations on the height dimension and