QipengZhou / Efficient-PyTorch

My best practice of training large dataset using PyTorch.

Efficient-PyTorch

My best practice of training large dataset using PyTorch.

Speed overview

By following the tips, we can reach achieve ~730 images/second with PyTorch when training ResNet-50 on ImageNet. According to benchmark reported on Tensorflow and MXNet, the performance is still competitive.

Epoch: [0][430/5005]    Time 0.409 (0.405)      Data 626.6 (728.0)      Loss 6.8381 (6.9754)    Error@1 100.000 (99.850) Error@5 99.609 (99.259)
Epoch: [0][440/5005]    Time 0.364 (0.404)      Data 704.2 (727.9)      Loss 6.8506 (6.9725)    Error@1 100.000 (99.851) Error@5 99.609 (99.258)
Epoch: [0][450/5005]    Time 0.350 (0.403)      Data 730.7 (727.3)      Loss 6.8846 (6.9700)    Error@1 100.000 (99.847) Error@5 99.609 (99.258)
Epoch: [0][460/5005]    Time 0.357 (0.402)      Data 716.8 (727.4)      Loss 6.9129 (6.9680)    Error@1 100.000 (99.849) Error@5 99.609 (99.256)
Epoch: [0][470/5005]    Time 0.346 (0.401)      Data 740.8 (727.4)      Loss 6.8574 (6.9657)    Error@1 100.000 (99.850) Error@5 98.828 (99.249)
Epoch: [0][480/5005]    Time 0.425 (0.400)      Data 601.8 (727.3)      Loss 6.8467 (6.9632)    Error@1 100.000 (99.849) Error@5 99.609 (99.239)
Epoch: [0][490/5005]    Time 0.358 (0.399)      Data 715.2 (727.2)      Loss 6.8319 (6.9607)    Error@1 100.000 (99.848) Error@5 99.609 (99.232)
Epoch: [0][500/5005]    Time 0.347 (0.399)      Data 737.4 (726.9)      Loss 6.8426 (6.9583)    Error@1 99.609 (99.843)  Error@5 98.047 (99.220)
Epoch: [0][510/5005]    Time 0.346 (0.398)      Data 740.5 (726.7)      Loss 6.8245 (6.9561)    Error@1 100.000 (99.839) Error@5 99.609 (99.211)
Epoch: [0][520/5005]    Time 0.350 (0.452)      Data 730.7 (724.0)      Loss 6.8270 (6.9538)    Error@1 99.609 (99.834)  Error@5 97.656 (99.193)
Epoch: [0][530/5005]    Time 0.340 (0.450)      Data 752.9 (724.4)      Loss 6.8149 (6.9516)    Error@1 100.000 (99.832) Error@5 98.047 (99.183)

Key Points of Efficiency

Now most frameworks adapt CUDNN as their backends. Without special optimization, the inference time is similiar across frameworks. To optimize training time, we focus on other points such as

Data Loader

The default combination datasets.ImageFolder + data.DataLoader is not enough for large scale classification. According to my experience, even I upgrade to Samsung 960 Pro (read 3.5 GB/s, write 2.0 GB/s), whole training pipeline still suffers at disk I/O.

The reason causing is the slow reading of discountiuous small chunks. To optimize, we need to dump small JPEG images into a large binary file. TensorFlow has its own TFRecord and MXNet uses recordIO. Beside these two, there are other options like hdf5, pth, n5, lmdb etc. Here I choose lmdb because

1. TFRecord is a private protocal which is hard to hack into. RecordIO's documentation is confusing and do not provide a clean python API.
2. hdf5 pth n5, though with a straightforward json-like API, require to put the whole file into memory. This is not practicle when you play with large dataset like imagenet.

Data Parallel

The default data parallel of PyTorch, powerd by nn.DataParallel, is in-efficienct! Fisrt, because the GIL of Python, multi-threading do not fully utilize all cores torch/nn/parallel/parallel_apply.py#47. Second, the collective scheme of DataParallel is to gather all results on cuda:0. It leads to imbalance workload and sometimes OOM especially you are running segmentation models.

nn.DistributedDataParllel provides a more elegant solution: Instead of launching call from different threads, it starts with multiple processes (no GIL) and assigns a balanced workload for all GPUs.

(on-going) detailed scripts and experiment numbers.