Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs)

Signed-off-by: odidev <[email protected]>

Author: odidev

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_index.md

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+---
+title: Deploy TensorFlow on Google Cloud C4A (Arm-based Axion VMs)
+
+minutes_to_complete: 30
+
+who_is_this_for: This learning path is intended for software developers deploying and optimizing TensorFlow workloads on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by Axion processors. 
+
+learning_objectives:
+  - Provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors)
+  - Install TensorFlow on a SUSE Arm64 (C4A) instance
+  - Verify TensorFlow by running basic computation and model training tests on Arm64  
+  - Benchmark TensorFlow using the Phoronix Test Suite (PTS) to measure inference performance, model execution speed, and system efficiency on Arm64
+
+prerequisites:
+  - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled  
+  - Basic familiarity with [TensorFlow](https://www.tensorflow.org/)
+
+author: Pareena Verma
+
+##### Tags
+skilllevels: Introductory
+subjects: ML
+cloud_service_providers: Google Cloud
+
+armips:
+  - Neoverse
+
+tools_software_languages:
+  - TensorFlow
+  - Python
+  - Phoronix Test Suite (PTS)
+
+operatingsystems:
+  - Linux
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+further_reading:
+  - resource:
+      title: Google Cloud documentation
+      link: https://cloud.google.com/docs
+      type: documentation
+
+  - resource:
+      title: TensorFlow documentation
+      link: https://www.tensorflow.org/learn
+      type: documentation
+  
+  - resource:
+      title: Phoronix Test Suite (PTS) documentation
+      link: https://www.phoronix-test-suite.com/
+      type: documentation   
+
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: "yes"
+---

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/_next-steps.md

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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/background.md

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+---
+title: Getting started with TensorFlow on Google Axion C4A (Arm Neoverse-V2)
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Google Axion C4A Arm instances in Google Cloud
+
+Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as CI/CD pipelines, microservices, media processing, and general-purpose applications.
+
+The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability and performance benefits of the Arm architecture in Google Cloud.
+
+To learn more about Google Axion, refer to the [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu) blog.
+
+## TensorFlow 
+
+[TensorFlow](https://www.tensorflow.org/) is an **open-source machine learning and deep learning framework** developed by **Google**.  It helps developers and researchers **build, train, and deploy AI models** efficiently across **CPUs, GPUs, and TPUs**.
+
+With support for **neural networks**, **natural language processing (NLP)**, and **computer vision**, TensorFlow is widely used for **AI research and production**.  
+Its **flexibility** and **scalability** make it ideal for both **cloud** and **edge environments**.
+
+To learn more, visit the [official TensorFlow website](https://www.tensorflow.org/).

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/baseline.md

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+---
+title: TensorFlow Baseline Testing on Google Axion C4A Arm Virtual Machine
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## TensorFlow Baseline Testing on GCP SUSE VMs
+This section helps you check if TensorFlow is properly installed and working on your **Google Axion C4A Arm64 VM**. You will run small tests to confirm that your CPU can perform TensorFlow operations correctly.
+
+
+### Verify Installation
+This command checks if TensorFlow is installed correctly and prints its version number.
+
+```console
+python -c "import tensorflow as tf; print(tf.__version__)"
+```
+### List Available Devices
+This command shows which hardware devices TensorFlow can use — like CPU or GPU. On most VMs, you’ll see only CPU listed.
+
+```console
+python -c "import tensorflow as tf; print(tf.config.list_physical_devices())"
+```
+
+You should see an output similar to:
+```output
+[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
+```
+
+### Run a Simple Computation
+This test multiplies two large matrices to check that TensorFlow computations work correctly on your CPU and measures how long it takes.
+
+```python
+python -c "import tensorflow as tf; import time; 
+a = tf.random.uniform((1000,1000)); b = tf.random.uniform((1000,1000));
+start = time.time(); c = tf.matmul(a,b); end = time.time(); 
+print('Computation time:', end - start, 'seconds')"
+```
+- This checks **CPU speed** and the correctness of basic operations.
+- Note the **computation time** as your baseline.
+
+You should see an output similar to:
+```output
+Computation time: 0.008263111114501953 seconds
+```
+### Test Neural Network Execution
+Create a new file for testing a simple neural network:
+
+```console
+vi test_nn.py
+```
+This opens a new Python file where you’ll write a short TensorFlow test program.
+Paste the code below into the `test_nn.py` file:
+
+```python
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+import numpy as np
+
+# Dummy data
+x = np.random.rand(1000, 20)
+y = np.random.rand(1000, 1)
+
+# Define the model
+model = Sequential([
+    Dense(64, activation='relu', input_shape=(20,)),
+    Dense(1)
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='mse')
+
+# Train for 1 epoch
+model.fit(x, y, epochs=1, batch_size=32)
+```
+This script creates and trains a simple neural network using random data — just to make sure TensorFlow’s deep learning functions work properly.
+
+**Run the Script**
+
+Execute the script with Python:
+
+```console
+python test_nn.py
+```
+
+**Output**
+
+TensorFlow will print training progress, like:
+```output
+32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1024
+```
+
+This confirms that TensorFlow is working properly on your Arm64 VM.