1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
import torch
import ignite
import ignite.distributed as idist
from ignite.handlers import DiskSaver
def initialize(config):
device = idist.device()
model = config.model.to(device)
optimizer = config.optimizer
# Adapt model to dist config
model = idist.auto_model(model)
optimizer = idist.auto_optim(optimizer)
criterion = config.criterion.to(device)
return model, optimizer, criterion
def log_basic_info(logger, config):
logger.info(f"- PyTorch version: {torch.__version__}")
logger.info(f"- Ignite version: {ignite.__version__}")
if torch.cuda.is_available():
# explicitly import cudnn as
# torch.backends.cudnn can not be pickled with hvd spawning procs
from torch.backends import cudnn
logger.info(f"- GPU Device: {torch.cuda.get_device_name(idist.get_local_rank())}")
logger.info(f"- CUDA version: {torch.version.cuda}")
logger.info(f"- CUDNN version: {cudnn.version()}")
logger.info("\n")
logger.info("Configuration:")
for key, value in config.items():
logger.info(f"\t{key}: {value}")
logger.info("\n")
if idist.get_world_size() > 1:
logger.info("\nDistributed setting:")
logger.info(f"\tbackend: {idist.backend()}")
logger.info(f"\tworld size: {idist.get_world_size()}")
logger.info("\n")
def log_metrics(logger, epoch, elapsed, tag, metrics):
metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()])
logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}")
def get_save_handler(output_path, with_clearml):
if with_clearml:
from ignite.handlers.clearml_logger import ClearMLSaver
return ClearMLSaver(dirname=output_path)
return DiskSaver(output_path)
|
