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| import json
import torch
from torch import nn
from torch.nn import MSELoss
from torch.optim import Adam
from torch.utils.data import TensorDataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import horovod.torch as hvd
from s3_utils import s3_load_pickle, s3_save_model, s3_save_file
import boto3
session = boto3.session.Session()
s3_client = session.client(
service_name='s3',
aws_access_key_id='XXXX',
aws_secret_access_key='XXXX',
endpoint_url='http://10.105.222.7:24850',
)
train_tensor = torch.tensor(s3_load_pickle(s3_client, 'songwei', 'simple_ml/train.pkl'), dtype=torch.float)
test_tensor = torch.tensor(s3_load_pickle(s3_client, 'songwei', 'simple_ml/test.pkl'), dtype=torch.float)
train_dataset = TensorDataset(train_tensor[:, :-1], train_tensor[:, -1:])
test_dataset = TensorDataset(test_tensor[:, :-1], test_tensor[:, -1:])
hvd.init()
if torch.cuda.is_available():
torch.cuda.set_device(hvd.local_rank())
torch.set_num_threads(1)
train_sampler = DistributedSampler(train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_dl = DataLoader(train_dataset, sampler=train_sampler)
test_sampler = DistributedSampler(test_dataset, num_replicas=hvd.size(), rank=hvd.rank())
test_dl = DataLoader(test_dataset, sampler=test_sampler)
in_dim, hidden_dim, out_dim = 2, 4, 1
hp = json.load(open('hp.json', 'r'))
model = nn.Sequential(
nn.Linear(in_dim, hidden_dim),
nn.GELU(),
nn.Dropout(hp["dropout"]),
nn.Linear(hidden_dim, hidden_dim),
nn.GELU(),
nn.Dropout(hp["dropout"]),
nn.Linear(hidden_dim, out_dim)
)
model.cuda()
opt = Adam(model.parameters(), lr=hp["lr"] * hvd.size())
opt = hvd.DistributedOptimizer(opt, named_parameters=model.named_parameters(), op=hvd.Average)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(opt, root_rank=0)
loss_fn = MSELoss()
epoch = 20
model.train()
for e in range(epoch):
train_sampler.set_epoch(e)
avg_loss = []
for x, y in train_dl:
x = x.cuda()
y = y.cuda()
opt.zero_grad()
p = model(x)
loss = loss_fn(p, y)
loss.backward()
opt.step()
avg_loss.append(loss.item())
avg_loss = sum(avg_loss) / len(avg_loss)
print(f'Training. Epoch {e}, MSE loss: {avg_loss}, Worker: {hvd.rank()}')
model.eval()
with torch.no_grad():
avg_loss = []
for x, y in test_dl:
x = x.cuda()
y = y.cuda()
p = model(x)
loss = loss_fn(p, y)
avg_loss.append(loss.item())
avg_loss = sum(avg_loss) / len(avg_loss)
avg_loss = hvd.allreduce(torch.tensor(avg_loss)).item()
print(f'Testing. MSE loss: {avg_loss}, Worker: {hvd.rank()}')
s3_save_model(s3_client, model, 'songwei', 'simple_ml/model_save/1/model.pt')
s3_save_file(s3_client, 'config.pbtxt', 'songwei', 'simple_ml/model_save/1/config.pbtxt')
PYTHON
|
