Troubleshooting

This guide covers common issues you may encounter when using nanoGPT and how to resolve them.

PyTorch 2.0 compile issues

The most common issue is related to PyTorch 2.0’s torch.compile() feature.

Compile not available

From README.md:224: “Note that by default this repo uses PyTorch 2.0 (i.e. torch.compile). This is fairly new and experimental, and not yet available on all platforms (e.g. Windows).”

Problem: Training fails with compile-related errors Solution: Disable compile mode:

python train.py --compile=False

This will slow down the code but ensures it runs on all platforms.

Platform compatibility

Affected platforms:

Windows (limited support)
Older Linux distributions
Some cloud environments

Recommendation: Use PyTorch nightly builds for best compatibility:

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118

Select the appropriate PyTorch version at https://pytorch.org/get-started/locally/

Flash Attention warnings

WARNING: using slow attention

Problem: You see this warning during training:

WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0

Impact: Training will be slower but still functional Solution: Upgrade to PyTorch 2.0 or later:

pip install --upgrade torch

From model.py:44-47, Flash Attention is automatically enabled when available:

self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.flash:
    print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")

Out of memory errors

CUDA out of memory

Problem: Training crashes with CUDA out of memory error Solutions (try in order):

1. Reduce batch size

Decrease the batch size to use less GPU memory:

python train.py --batch_size=8  # default is 12

Or even smaller:

python train.py --batch_size=4

2. Reduce block size (context length)

Memory usage scales quadratically with sequence length:

python train.py --block_size=512  # default is 1024

Or smaller:

python train.py --block_size=256

3. Reduce model size

Train a smaller model:

python train.py --n_layer=6 --n_head=6 --n_embd=384

From README.md:166, available GPT-2 model sizes:

gpt2 (124M) - default
gpt2-medium (350M)
gpt2-large (774M)
gpt2-xl (1558M)

4. Use gradient accumulation

Simulate larger batch sizes without using more memory:

python train.py --batch_size=4 --gradient_accumulation_steps=3

This gives an effective batch size of 4 × 3 = 12.

5. Use float16 instead of bfloat16

On GPUs without bfloat16 support, this is automatic. To force float16:

python train.py --dtype=float16

CPU/MPS training issues

Training on CPU

Problem: Need to train on CPU-only system Solution: Adjust settings for CPU training from README.md:82-88:

python train.py config/train_shakespeare_char.py \
    --device=cpu \
    --compile=False \
    --eval_iters=20 \
    --log_interval=1 \
    --block_size=64 \
    --batch_size=12 \
    --n_layer=4 \
    --n_head=4 \
    --n_embd=128 \
    --max_iters=2000 \
    --lr_decay_iters=2000 \
    --dropout=0.0

You must set both --device=cpu AND --compile=False for CPU training.

Apple Silicon (M1/M2/M3) Macs

Problem: Training on Apple Silicon Solution: Use the MPS (Metal Performance Shaders) backend from README.md:105:

python train.py --device=mps

MPS can provide 2-3x speedup compared to CPU on Apple Silicon Macs. See Issue 28 for more details.

Multi-node training issues

Slow multi-node training

Problem: Multi-node training is extremely slow Cause: Missing or slow network interconnect Solution: From README.md:132: Benchmark your interconnect:

iperf3 -s  # on one node
iperf3 -c <node_ip>  # on another node

If you don’t have Infiniband, disable IB:

NCCL_IB_DISABLE=1 torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 \
    --master_addr=123.456.123.456 --master_port=1234 train.py

Without Infiniband, multi-node training will work but most likely crawl.

Data loading issues

Data files not found

Problem: Error about missing train.bin or val.bin Solution: Prepare the dataset first: For Shakespeare:

python data/shakespeare_char/prepare.py

For OpenWebText:

python data/openwebtext/prepare.py

Memory leak with data loading

Problem: Memory usage grows over time Solution: The code already handles this from train.py:117-122:

def get_batch(split):
    # We recreate np.memmap every batch to avoid a memory leak, as per
    # https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122
    if split == 'train':
        data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')

If you modified this code, ensure you recreate the memmap each batch.

Checkpointing issues

Checkpoint loading errors

Problem: Error when resuming from checkpoint with _orig_mod. prefix Solution: This is handled automatically in train.py:174-177:

unwanted_prefix = '_orig_mod.'
for k,v in list(state_dict.items()):
    if k.startswith(unwanted_prefix):
        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)

Incompatible checkpoint

Problem: Can’t resume training from checkpoint Cause: Model architecture mismatch Check: The checkpoint enforces matching architecture from train.py:166-167:

for k in ['n_layer', 'n_head', 'n_embd', 'block_size', 'bias', 'vocab_size']:
    model_args[k] = checkpoint_model_args[k]

Solution: Ensure you’re using the same model configuration, or start training from scratch.

Sampling/Inference issues

Sampling fails or produces bad output

Problem: sample.py produces errors or nonsensical text Solutions:

Wrong output directory

Ensure you’re pointing to the correct checkpoint:

python sample.py --out_dir=out-shakespeare-char

Insufficient training

The model may not be trained enough. Check training loss:

Shakespeare char-level: aim for loss < 1.5
Shakespeare with GPT-2: aim for loss < 1.0

Temperature too high/low

Adjust sampling temperature (default 1.0):

# More conservative
python sample.py --temperature=0.8

# More creative
python sample.py --temperature=1.2

Performance issues

Training is slower than expected

Checklist:

Compile enabled?

python train.py --compile=True  # should be default

Using appropriate dtype?

# For A100/H100
python train.py --dtype=bfloat16

# For other GPUs
python train.py --dtype=float16

Flash Attention available? Check for the warning about slow attention and upgrade PyTorch if needed.
GPU utilization Monitor with:
nvidia-smi -l 1
GPU utilization should be >80%.

Low MFU (Model FLOPs Utilization)

Problem: MFU is less than 40% Potential causes and solutions:

Small batch size

Increase batch size to improve GPU utilization:

python train.py --batch_size=16  # if memory allows

Data loading bottleneck

Ensure data is on fast storage (NVMe SSD, not network drive).The code already uses async data loading from train.py:302-303:

# immediately async prefetch next batch while model is doing the forward pass
X, Y = get_batch('train')

Compile not enabled

Ensure --compile=True (default).

Loss not decreasing

Loss is NaN

Problem: Training loss becomes NaN Common causes:

Learning rate too high

python train.py --learning_rate=3e-4  # reduce from default 6e-4

Mixed precision instability

python train.py --dtype=float32  # more stable but slower

Gradient clipping disabled Ensure gradient clipping is enabled (default grad_clip=1.0)

Loss not improving

Problem: Loss plateaus too high Solutions:

Insufficient training
- Check you’re running enough iterations
- For GPT-2 124M: aim for 600k iterations
Learning rate decay
- Ensure --decay_lr=True (default)
- Check lr_decay_iters matches max_iters
Data issues
- Verify dataset prepared correctly
- Check data files aren’t corrupted

Logging and monitoring

Weights & Biases (wandb) issues

Problem: wandb logging not working Solution: Enable wandb logging:

python train.py --wandb_log=True --wandb_project=my_project --wandb_run_name=my_run

Ensure wandb is installed and configured:

pip install wandb
wandb login

Getting help

If you’re still experiencing issues:

Check the GitHub issues: Many common problems are already documented
- nanoGPT Issues
Watch the educational video: From README.md:226-227
- Zero To Hero series
- GPT video
Join the Discord: From README.md:228-230
- #nanoGPT channel on Discord
- https://discord.gg/3zy8kqD9Cp

When asking for help, include:

Your exact command
Full error message
PyTorch version (python -c "import torch; print(torch.__version__)")
GPU model (if applicable)
OS and platform details

Getting Started

Training

Inference

Configuration

Advanced

Troubleshooting

PyTorch 2.0 compile issues

Compile not available

Platform compatibility

Flash Attention warnings

WARNING: using slow attention

Out of memory errors

CUDA out of memory

CPU/MPS training issues

Training on CPU

Apple Silicon (M1/M2/M3) Macs

Multi-node training issues

Slow multi-node training

Data loading issues

Data files not found

Memory leak with data loading

Checkpointing issues

Checkpoint loading errors

Incompatible checkpoint

Sampling/Inference issues

Sampling fails or produces bad output

Performance issues

Training is slower than expected

Low MFU (Model FLOPs Utilization)

Loss not decreasing

Loss is NaN

Loss not improving

Logging and monitoring

Weights & Biases (wandb) issues

Getting help

​PyTorch 2.0 compile issues

​Compile not available

​Platform compatibility

​Flash Attention warnings

​WARNING: using slow attention

​Out of memory errors

​CUDA out of memory

​CPU/MPS training issues

​Training on CPU

​Apple Silicon (M1/M2/M3) Macs

​Multi-node training issues

​Slow multi-node training

​Data loading issues

​Data files not found

​Memory leak with data loading

​Checkpointing issues

​Checkpoint loading errors

​Incompatible checkpoint

​Sampling/Inference issues

​Sampling fails or produces bad output

​Performance issues

​Training is slower than expected

​Low MFU (Model FLOPs Utilization)

​Loss not decreasing

​Loss is NaN

​Loss not improving

​Logging and monitoring

​Weights & Biases (wandb) issues

​Getting help

PyTorch 2.0 compile issues

Compile not available

Platform compatibility

Flash Attention warnings

WARNING: using slow attention

Out of memory errors

CUDA out of memory

CPU/MPS training issues

Training on CPU

Apple Silicon (M1/M2/M3) Macs

Multi-node training issues

Slow multi-node training

Data loading issues

Data files not found

Memory leak with data loading

Checkpointing issues

Checkpoint loading errors

Incompatible checkpoint

Sampling/Inference issues

Sampling fails or produces bad output

Performance issues

Training is slower than expected

Low MFU (Model FLOPs Utilization)

Loss not decreasing

Loss is NaN

Loss not improving

Logging and monitoring

Weights & Biases (wandb) issues

Getting help