README.md

Faster Training

Single-machine multi-GPU

ENV

{
    "Python":"3.8.10",
    "torch":"1.8.1",
    "torchvision":"0.9.1",
    "dali": "1.2.0",
    "CUDA":"11.1",
    "cuDNN":8005,
    "GPU":{
        "#0":{
            "name":"Quadro RTX 6000",
            "memory":"23.65GB"
        },
        "#1":{
            "name":"Quadro RTX 6000",
            "memory":"23.65GB"
        }
    },
    "Platform":{
        "system":"Linux",
        "node":"4029GP-TRT",
        "version":"#83~18.04.1-Ubuntu SMP Tue May 11 16:01:00 UTC 2021",
        "machine":"x86_64",
        "processor":"x86_64"
    }
}

Model Running

Batch size: 512, conv layers: 11, epochs: 5

Baseline : 276.980s

		+cudnn_benchmark	+AMP	+cudnn_benchmark +AMP
DP	163.740	104.807	74.948	73.862
DDP	142.497	102.535	67.095	72.998

• DP: torch.nn.DataParallel
• AMP: torch.cuda.amp
• DDP: torch.nn.parallel.DistributedDataParallel
• cudnn_benchmark: torch.backends.cudnn.benchmark = True
• pin_memory=True
• non_blocking=True
• optimizer.zero_grad(set_to_none=True)

Usage

# $1 is the epochs
./running.sh 5

# Or run the commands in the script directly.

Data Loading

Prepare

Drop caches for i/o benchmark test.

sync

# To free pagecache:
echo 1 > /proc/sys/vm/drop_caches
# To free reclaimable slab objects (includes dentries and inodes):
echo 2 > /proc/sys/vm/drop_caches
#To free slab objects and pagecache:
echo 3 > /proc/sys/vm/drop_caches

Data Loading Time

Batch size: 256/2, workers: 8 x 2

		Bottleneck	+DALI/CPU	Bottleneck	+DALI/GPU	Bottleneck
HDD	~25M/s	IO	~40M/s	IO	~40M/s	IO
SSD	~230M/s	CPU	~500M/s	CPU	~600M/s	IO

• SSD
• DALI: The NVIDIA Data Loading Library
• LMDB

Usage

# $1 is the script, $2 is the imagenet dataset path.
./loading.sh loading_faster.py '/datasets/ILSVRC2012/'

# Or run the commands in the script directly.

Downscale ImageNet Dataset (for validating ideas quickly)

The average resolution of ImageNet images is 469x387, but they are usually cropped to 256x256 or 224x224 in your image preprocessing step. So we could speed up reading by downscaling the image size. Especially, the entire dataset can be loaded into memory.

# N: the max size of smaller edge
python resize_imagenet.py --src </path/to/imagenet> --dst </path/to/imagenet/resized> --max-size N

Training with smaller size

As reported in Fixing the train-test resolution discrepancy, you can use smaller image size when training models.