Computation Backends & Keras 3

Understanding the ecosystem of deep learning frameworks and how Keras 3 abstracts hardware acceleration through backend engines.

The Stack: From Python to Silicon

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Section 1.18 - Core Computation Engines

TensorFlow (Google)

• Developer: Google Brain Team (2015)
• Key Trait: Static computation graphs (define-and-run)
  
• Strengths:
  • Production-grade deployment (TF Serving, TFLite)
    
  • Tight TPU integration
    
• Weakness: Less flexible for research prototyping

PyTorch (Meta)

• Developer: Facebook AI Research (2016)
  
• Key Trait: Dynamic computation graphs (define-by-run)
  
• Strengths:
  • Pythonic debugging experience
    
  • Dominant in academic research
    
• Weakness: Historically weaker mobile/edge support

JAX (Google)

• Developer: Google Research (2018)
  
• Key Trait: Functional programming + composable transforms
  
• Strengths:
  • Automatic vectorization (vmap)
    
  • Native support for higher-order gradients
    
• Weakness: Steeper learning curve

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Section 1.19 - Keras 3: Unified Abstraction Layer

Key Innovation

Keras 3 acts as a backend-agnostic interface:

# Same code runs on TensorFlow, PyTorch, or JAX
import os
os.environ["KERAS_BACKEND"] = "jax"  # Environment variable needs to be set prior to importing keras, default is tensorflow

from keras import layers

model = keras.Sequential([
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])

model.compile() 

Architecture

┌──────────────────────────┐
│ Keras API (Python)       │ ← You code here
├──────────────────────────┤
│ Backend Adapter          │ ← Converts Keras ops to backend primitives
├───────┬────────┬─────────┤
│ TF    │ PyTorch│ JAX     │ ← Backend engines
├───────┴────────┴─────────┤
│ XLA/CUDA/C++/ROCm        │ ← Hardware-specific optimization
└──────────────────────────┘

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Section 1.20 - The Performance Layer

Under the Hood

All frameworks ultimately delegate computation to:

1. Optimized C/C++ Kernels:
   • BLAS (e.g., Intel MKL, OpenBLAS) for linear algebra
     
   • Custom ops for neural networks (e.g., convolution)
     
2. GPU Acceleration:
   • CUDA (NVIDIA) / ROCm (AMD) for parallel computation
     
   • Kernel fusion via XLA (TensorFlow/JAX) or TorchScript (PyTorch)

Example Stack Trace

keras.layers.Dense(..)  # Python
↓
keras.backend.matmul()  # Backend-agnostic op
↓
tf.linalg.matmul()      # TensorFlow implementation
↓
Eigen::Tensor contraction  # C++/CUDA kernel

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Section 1.21 - Why Abstraction Matters

1. Portability: Same model code runs on CPU/GPU/TPU
   
2. Vendor Independence: Avoid lock-in to any ecosystem
   
3. Performance: Leverage decades of HPC optimization

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