Virtual Lab

Setup Environment
- Ensure you have a compatible NVIDIA GPU and that CUDA, cuDNN, and TensorFlow/PyTorch are properly installed.
- Ensure your system supports mixed precision training (e.g., Tensor Cores in NVIDIA Volta, Turing, or Ampere GPUs).

Enable Mixed Precision Training

In TensorFlow (with Keras):
Install TensorFlow:
```
pip install tensorflow
```

Set up mixed precision policy:

import tensorflow as tf
from tensorflow.keras import layers

policy = tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(policy)

Build and compile your model as usual. TensorFlow will automatically use 16-bit precision where applicable.
In PyTorch:
Install PyTorch:
```
pip install torch
```

Use torch.cuda.amp for automatic mixed precision:

import torch
from torch.cuda.amp import autocast, GradScaler

model = MyModel().to(device)
optimizer = torch.optim.Adam(model.parameters())
scaler = GradScaler()

for data, target in train_loader:
    optimizer.zero_grad()

    with autocast():
        output = model(data)
        loss = loss_fn(output, target)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

Implement Quantization Aware Training (QAT)

In TensorFlow:
Install TensorFlow Model Optimization Toolkit:
```
pip install tensorflow-model-optimization
```

Apply QAT:

import tensorflow_model_optimization as tfmot

model = tf.keras.applications.MobileNetV2(weights='imagenet')
quantize_model = tfmot.quantization.keras.quantize_model
model = quantize_model(model)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10)

In PyTorch:

Apply QAT using PyTorch:

import torch
from torch.quantization import quantize_qat, get_default_qat_qconfig

model = MyModel()
model.train()
model.qconfig = get_default_qat_qconfig('fbgemm')
torch.quantization.prepare_qat(model, inplace=True)

# Train the model
for epoch in range(num_epochs):
    # Perform training
    pass

model = torch.quantization.convert(model)

Benchmark and Evaluate
- Compare training speed, memory usage, and model accuracy for mixed precision and QAT models vs full precision models.
- Use benchmarking tools like NVIDIA’s Nsight or PyTorch’s built-in performance utilities to evaluate improvements.
- Document findings and analyze trade-offs in model efficiency vs accuracy.

Procedure