CIFAR-10 Tutorial
If you haven’t already, we advise you to first read through the Getting Started guide before stepping through this tutorial.
In this tutorial we will be adding DeepSpeed to CIFAR-10 model, which is small image classification model.
First we will go over how to run original CIFAR-10. Then we will proceed step-by-step in enabling this model to run with DeepSpeed.
Running Original CIFAR-10
Original model code from CIFAR-10 Tutorial, We’ve copied this repo under DeepSpeedExamples/cifar/ and made it available as a submodule. To download, execute:
git submodule update --init --recursive
To install requirements for CIFAR-10:
cd DeepSpeedExamples/cifar
pip install -r requirements.txt
Run python cifar10_tutorial.py, it downloads the training data set at first run.
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
170500096it [00:02, 61124868.24it/s]
Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified
cat frog frog frog
[1, 2000] loss: 2.170
[1, 4000] loss: 1.879
[1, 6000] loss: 1.690
[1, 8000] loss: 1.591
[1, 10000] loss: 1.545
[1, 12000] loss: 1.467
[2, 2000] loss: 1.377
[2, 4000] loss: 1.374
[2, 6000] loss: 1.363
[2, 8000] loss: 1.322
[2, 10000] loss: 1.295
[2, 12000] loss: 1.287
Finished Training
GroundTruth: cat ship ship plane
Predicted: cat ship plane plane
Accuracy of the network on the 10000 test images: 53 %
Accuracy of plane : 69 %
Accuracy of car : 59 %
Accuracy of bird : 56 %
Accuracy of cat : 36 %
Accuracy of deer : 37 %
Accuracy of dog : 26 %
Accuracy of frog : 70 %
Accuracy of horse : 61 %
Accuracy of ship : 51 %
Accuracy of truck : 63 %
cuda:0
Enabling DeepSpeed
Argument Parsing
The first step to apply DeepSpeed is adding DeepSpeed arguments to CIFAR-10 model, using deepspeed.add_config_arguments() function as below.
import argparse
import deepspeed
def add_argument():
parser=argparse.ArgumentParser(description='CIFAR')
#data
# cuda
parser.add_argument('--with_cuda', default=False, action='store_true',
help='use CPU in case there\'s no GPU support')
parser.add_argument('--use_ema', default=False, action='store_true',
help='whether use exponential moving average')
# train
parser.add_argument('-b', '--batch_size', default=32, type=int,
help='mini-batch size (default: 32)')
parser.add_argument('-e', '--epochs', default=30, type=int,
help='number of total epochs (default: 30)')
parser.add_argument('--local_rank', type=int, default=-1,
help='local rank passed from distributed launcher')
# Include DeepSpeed configuration arguments
parser = deepspeed.add_config_arguments(parser)
args=parser.parse_args()
return args
Initialization
We create model_engine, optimizer and trainloader with the help of deepspeed.initialize, which is defined as following:
def initialize(args,
model,
optimizer=None,
model_params=None,
training_data=None,
lr_scheduler=None,
mpu=None,
dist_init_required=True,
collate_fn=None):
Here we initialize DeepSpeed with CIFAR-10 model (net), args, parameters and trainset:
parameters = filter(lambda p: p.requires_grad, net.parameters())
args=add_argument()
# Initialize DeepSpeed to use the following features
# 1) Distributed model
# 2) Distributed data loader
# 3) DeepSpeed optimizer
model_engine, optimizer, trainloader, _ = deepspeed.initialize(args=args, model=net, model_parameters=parameters, training_data=trainset)
After initializing DeepSpeed, the original device and optimizer are removed:
#from deepspeed.accelerator import get_accelerator
#device = torch.device(get_accelerator().device_name(0) if get_accelerator().is_available() else "cpu")
#net.to(device)
#optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
Training API
The model returned by deepspeed.initialize is the DeepSpeed Model Engine that we will use to train the model using the forward, backward and step API.
for i, data in enumerate(trainloader):
# get the inputs; data is a list of [inputs, labels]
inputs = data[0].to(model_engine.device)
labels = data[1].to(model_engine.device)
outputs = model_engine(inputs)
loss = criterion(outputs, labels)
model_engine.backward(loss)
model_engine.step()
Zeroing the gradients is handled automatically by DeepSpeed after the weights have been updated using a mini-batch.
Configuration
The next step to use DeepSpeed is to create a configuration JSON file (ds_config.json). This file provides DeepSpeed specific parameters defined by the user, e.g., batch size, optimizer, scheduler and other parameters.
{
"train_batch_size": 4,
"steps_per_print": 2000,
"optimizer": {
"type": "Adam",
"params": {
"lr": 0.001,
"betas": [
0.8,
0.999
],
"eps": 1e-8,
"weight_decay": 3e-7
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 0.001,
"warmup_num_steps": 1000
}
},
"wall_clock_breakdown": false
}
Run CIFAR-10 Model with DeepSpeed Enabled
To start training CIFAR-10 model with DeepSpeed applied, execute the following command, it will use all detected GPUs by default.
deepspeed cifar10_deepspeed.py --deepspeed_config ds_config.json
DeepSpeed usually prints more training details for user to monitor, including training settings, performance statistics and loss trends.
deepspeed.pt cifar10_deepspeed.py --deepspeed_config ds_config.json
Warning: Permanently added '[192.168.0.22]:42227' (ECDSA) to the list of known hosts.
cmd=['pdsh', '-w', 'worker-0', 'export NCCL_VERSION=2.4.2; ', 'cd /data/users/deepscale/test/ds_v2/examples/cifar;', '/usr/bin/python', '-u', '-m', 'deepspeed.pt.deepspeed_launch', '--world_info=eyJ3b3JrZXItMCI6IFswXX0=', '--node_rank=%n', '--master_addr=192.168.0.22', '--master_port=29500', 'cifar10_deepspeed.py', '--deepspeed', '--deepspeed_config', 'ds_config.json']
worker-0: Warning: Permanently added '[192.168.0.22]:42227' (ECDSA) to the list of known hosts.
worker-0: 0 NCCL_VERSION 2.4.2
worker-0: WORLD INFO DICT: {'worker-0': [0]}
worker-0: nnodes=1, num_local_procs=1, node_rank=0
worker-0: global_rank_mapping=defaultdict(<class 'list'>, {'worker-0': [0]})
worker-0: dist_world_size=1
worker-0: Setting CUDA_VISIBLE_DEVICES=0
worker-0: Files already downloaded and verified
worker-0: Files already downloaded and verified
worker-0: bird car horse ship
worker-0: DeepSpeed info: version=2.1, git-hash=fa937e7, git-branch=master
worker-0: [INFO 2020-02-06 19:53:49] Set device to local rank 0 within node.
worker-0: 1 1
worker-0: [INFO 2020-02-06 19:53:56] Using DeepSpeed Optimizer param name adam as basic optimizer
worker-0: [INFO 2020-02-06 19:53:56] DeepSpeed Basic Optimizer = FusedAdam (
worker-0: Parameter Group 0
worker-0: betas: [0.8, 0.999]
worker-0: bias_correction: True
worker-0: eps: 1e-08
worker-0: lr: 0.001
worker-0: max_grad_norm: 0.0
worker-0: weight_decay: 3e-07
worker-0: )
worker-0: [INFO 2020-02-06 19:53:56] DeepSpeed using configured LR scheduler = WarmupLR
worker-0: [INFO 2020-02-06 19:53:56] DeepSpeed LR Scheduler = <deepspeed.pt.deepspeed_lr_schedules.WarmupLR object at 0x7f64c4c09c18>
worker-0: [INFO 2020-02-06 19:53:56] rank:0 step=0, skipped=0, lr=[0.001], mom=[[0.8, 0.999]]
worker-0: DeepSpeedLight configuration:
worker-0: allgather_size ............... 500000000
worker-0: allreduce_always_fp32 ........ False
worker-0: disable_allgather ............ False
worker-0: dump_state ................... False
worker-0: dynamic_loss_scale_args ...... None
worker-0: fp16_enabled ................. False
worker-0: global_rank .................. 0
worker-0: gradient_accumulation_steps .. 1
worker-0: gradient_clipping ............ 0.0
worker-0: initial_dynamic_scale ........ 4294967296
worker-0: loss_scale ................... 0
worker-0: optimizer_name ............... adam
worker-0: optimizer_params ............. {'lr': 0.001, 'betas': [0.8, 0.999], 'eps': 1e-08, 'weight_decay': 3e-07}
worker-0: prescale_gradients ........... False
worker-0: scheduler_name ............... WarmupLR
worker-0: scheduler_params ............. {'warmup_min_lr': 0, 'warmup_max_lr': 0.001, 'warmup_num_steps': 1000}
worker-0: sparse_gradients_enabled ..... False
worker-0: steps_per_print .............. 2000
worker-0: tensorboard_enabled .......... False
worker-0: tensorboard_job_name ......... DeepSpeedJobName
worker-0: tensorboard_output_path ......
worker-0: train_batch_size ............. 4
worker-0: train_micro_batch_size_per_gpu 4
worker-0: wall_clock_breakdown ......... False
worker-0: world_size ................... 1
worker-0: zero_enabled ................. False
worker-0: json = {
worker-0: "optimizer":{
worker-0: "params":{
worker-0: "betas":[
worker-0: 0.8,
worker-0: 0.999
worker-0: ],
worker-0: "eps":1e-08,
worker-0: "lr":0.001,
worker-0: "weight_decay":3e-07
worker-0: },
worker-0: "type":"Adam"
worker-0: },
worker-0: "scheduler":{
worker-0: "params":{
worker-0: "warmup_max_lr":0.001,
worker-0: "warmup_min_lr":0,
worker-0: "warmup_num_steps":1000
worker-0: },
worker-0: "type":"WarmupLR"
worker-0: },
worker-0: "steps_per_print":2000,
worker-0: "train_batch_size":4,
worker-0: "wall_clock_breakdown":false
worker-0: }
worker-0: [INFO 2020-02-06 19:53:56] 0/50, SamplesPerSec=1292.6411179579866
worker-0: [INFO 2020-02-06 19:53:56] 0/100, SamplesPerSec=1303.6726433398537
worker-0: [INFO 2020-02-06 19:53:56] 0/150, SamplesPerSec=1304.4251022567403
......
worker-0: [2, 12000] loss: 1.247
worker-0: [INFO 2020-02-06 20:35:23] 0/24550, SamplesPerSec=1284.4954513975558
worker-0: [INFO 2020-02-06 20:35:23] 0/24600, SamplesPerSec=1284.384033658866
worker-0: [INFO 2020-02-06 20:35:23] 0/24650, SamplesPerSec=1284.4433482972925
worker-0: [INFO 2020-02-06 20:35:23] 0/24700, SamplesPerSec=1284.4664449792422
worker-0: [INFO 2020-02-06 20:35:23] 0/24750, SamplesPerSec=1284.4950124403447
worker-0: [INFO 2020-02-06 20:35:23] 0/24800, SamplesPerSec=1284.4756105952233
worker-0: [INFO 2020-02-06 20:35:24] 0/24850, SamplesPerSec=1284.5251526215386
worker-0: [INFO 2020-02-06 20:35:24] 0/24900, SamplesPerSec=1284.531217073863
worker-0: [INFO 2020-02-06 20:35:24] 0/24950, SamplesPerSec=1284.5125323220368
worker-0: [INFO 2020-02-06 20:35:24] 0/25000, SamplesPerSec=1284.5698818883018
worker-0: Finished Training
worker-0: GroundTruth: cat ship ship plane
worker-0: Predicted: cat car car plane
worker-0: Accuracy of the network on the 10000 test images: 57 %
worker-0: Accuracy of plane : 61 %
worker-0: Accuracy of car : 74 %
worker-0: Accuracy of bird : 49 %
worker-0: Accuracy of cat : 36 %
worker-0: Accuracy of deer : 44 %
worker-0: Accuracy of dog : 52 %
worker-0: Accuracy of frog : 67 %
worker-0: Accuracy of horse : 58 %
worker-0: Accuracy of ship : 70 %
worker-0: Accuracy of truck : 59 %