Optimizing LSTM Training for Text Generation on CPU

Text generation using deep learning has gained traction due to its wide array of applications: from writing poems to auto-generating code. But while working with this, many encounter a significant hurdle - prolonged training times, especially if one lacks GPU resources. So, how do we optimize the process for CPU?

Understanding the Problem

In our example, we are trying to train a character-level LSTM model for text generation using the book "2600-0.txt". However, each epoch takes too long. Given that we are training on a CPU, we'll need to make some modifications to ensure the process runs more efficiently.

Effective Optimizations for CPU

1. Efficient Data Handling with tf.data API:

   • The tf.data API lets you handle large amounts of data efficiently. With this, while the model is being trained on a batch, the next batch is being prepared.

   • Create a dataset from your raw text and convert this into sequences. You can then shuffle and batch this dataset.

char_dataset = tf.data.Dataset.from_tensor_slices(raw_int)
sequences = char_dataset.batch(seqLength+1, drop_remainder=True)
dataset = sequences.map(split_input_target).shuffle(10000).batch(128, drop_remainder=True)

1. Simplify Model Complexity:

   • Instead of multiple layers, consider using a single LSTM layer initially.

   • Consider reducing the sequence length to feed in shorter sequences of text.

2. Sparse Representation over One-hot Encoding:

   • Using integer representations of characters with an Embedding layer is a more memory-efficient way than one-hot encoding.

model = Sequential([
    Embedding(nVocabs, 256, input_length=seqLength),
    LSTM(hiddenUnits, return_sequences=True),
    Dense(nVocabs, activation='softmax')
])

1. Hyperparameter Tuning and Gradient Clipping:

   • Using Gradient Clipping can prevent exploding gradients, a common issue in RNNs.

   • Adjusting learning rates, batch sizes, etc., even minutely can make a huge difference in training times.

optimizer = Adam(lr=0.002, clipnorm=1.0)

1. Use Callbacks:

   • ModelCheckpoint allows us to save the best models.

   • EarlyStopping prevents overfitting by stopping the training once the model stops improving.

Other Considerations

• Distributed Training: Using distributed training frameworks can let you train your model across multiple CPUs.

• Cloud Solutions: If local resources are not sufficient, consider cloud platforms offering computational power.

• Profiling: Use TensorFlow Profiler or similar tools to identify bottlenecks in your code. This helps in understanding which parts of your code need optimization.

Conclusion

Optimizing model training on a CPU is challenging, but with the right strategies, it's not an insurmountable hurdle. By optimizing data handling, simplifying the model, and tuning hyperparameters, you can achieve faster training times, even without a GPU. As you delve deeper into deep learning, always remember to iterate, experiment, and optimize!