nanoGPT CPU Training Configuration Explained | Generated by AI
Command Breakdown
The full command runs the train.py script from the nanoGPT repository using Python 3.11, loading defaults from the Shakespeare character-level dataset configuration, and overriding specific hyperparameters for a smaller, CPU-based training run. Below, I’ll explain each component and parameter step by step.
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python3.11: Specifies the Python interpreter version (3.11) to execute the script. nanoGPT requires Python 3.8+; this ensures compatibility with recent features.
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train.py: The main training script in nanoGPT. It handles data loading, model initialization, the training loop (forward/backward passes, optimization), evaluation, logging, and checkpointing.
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config/train_shakespeare_char.py: A configuration file that sets dataset-specific defaults (e.g.,
dataset = 'shakespeare_char',vocab_size = 65, initial learning rate, etc.). It defines the task: training on character-level text from Shakespeare’s works. All subsequent--flags override values from this config.
Override Parameters
These are command-line flags passed to train.py via argparse, allowing customization without editing files. They control hardware, training behavior, model architecture, and regularization.
| Parameter | Value | Explanation |
|---|---|---|
--device |
cpu |
Specifies the compute device: 'cpu' runs everything on the host CPU (slower but no GPU needed). Defaults to 'cuda' if a GPU is available. Useful for testing or low-resource setups. |
--compile |
False |
Enables/disables PyTorch’s torch.compile() optimization on the model (introduced in PyTorch 2.0 for faster execution via graph compilation). Set to False to avoid compatibility issues (e.g., on older hardware or non-CUDA devices). Defaults to True. |
--eval_iters |
20 |
Number of forward passes (iterations) to run during evaluation to estimate validation loss. Higher values give more accurate estimates but take longer. Defaults to 200; here it’s reduced for quicker checks. |
--log_interval |
1 |
Frequency (in iterations) at which to print training loss to the console. Set to 1 for verbose output every step; defaults to 10 for less noise. |
--block_size |
64 |
Maximum context length (sequence length) the model can process in one go. Affects memory use and how much history the model “remembers.” Defaults to 256 in the config; 64 is smaller for faster training on limited hardware. |
--batch_size |
12 |
Number of sequences processed in parallel per training step (batch size). Larger batches use more memory but can speed up training via better GPU/CPU utilization. Defaults to 64; 12 is scaled down for CPU. |
--n_layer |
4 |
Number of transformer decoder layers (depth of the network). More layers increase capacity but risk overfitting and require more compute. Defaults to 6; 4 makes a tinier model. |
--n_head |
4 |
Number of multi-head attention heads per layer. Controls parallelism in attention computation; must divide evenly into n_embd. Defaults to 6; 4 reduces complexity. |
--n_embd |
128 |
Dimension of the model’s embeddings and hidden states (model width). Larger values increase expressiveness but memory/compute needs. Defaults to 384; 128 creates a very small model (~0.5M parameters). |
--max_iters |
2000 |
Total number of training iterations (steps). Each iteration processes one batch. Training stops after this many unless early-stopped. Defaults to 5000; 2000 shortens the run for experimentation. |
--lr_decay_iters |
2000 |
Number of iterations over which the learning rate (LR) decays linearly to zero (cosine or linear decay schedule). Matches max_iters here for full decay by end. Defaults to 5000; ties decay to total steps. |
--dropout |
0.0 |
Dropout probability (fraction of units randomly zeroed during training) for regularization. 0.0 disables it entirely (no regularization), useful for small datasets/models. Defaults to 0.2. |
This setup trains a tiny GPT-2-like model (4 layers, 128-dim) on Shakespeare text for 2000 steps on CPU, with frequent logging and no dropout—ideal for quick prototyping or debugging.