def to_air_checkpoint(self) -> Optional[Checkpoint]:
from ray.tune.trainable.util import TrainableUtil
checkpoint_data = self.dir_or_data
if not checkpoint_data:
return None
if isinstance(checkpoint_data, ray.ObjectRef):
checkpoint_data = ray.get(checkpoint_data)
if isinstance(checkpoint_data, str):
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_data)
checkpoint = Checkpoint.from_directory(checkpoint_dir)
elif isinstance(checkpoint_data, bytes):
with tempfile.TemporaryDirectory() as tmpdir:
TrainableUtil.create_from_pickle(checkpoint_data, tmpdir)
# Double wrap in checkpoint so we hold the data in memory and
# can remove the temp directory
checkpoint = Checkpoint.from_dict(
Checkpoint.from_directory(tmpdir).to_dict())
elif isinstance(checkpoint_data, dict):
checkpoint = Checkpoint.from_dict(checkpoint_data)
else:
raise RuntimeError(
f"Unknown checkpoint data type: {type(checkpoint_data)}")
return checkpoint
def train_func(config):
itr = 0
ckpt = session.get_checkpoint()
if ckpt is not None:
ckpt = ckpt.to_dict()
itr = ckpt["iter"] + 1
for i in range(itr, config["max_iter"]):
session.report(
dict(test=i, training_iteration=i),
checkpoint=Checkpoint.from_dict(dict(iter=i)),
)
def train_func():
ckpt = session.get_checkpoint()
restored = bool(ckpt) # Does a previous checkpoint exist?
itr = 0
if ckpt:
ckpt = ckpt.to_dict()
itr = ckpt["iter"] + 1
for i in range(itr, 4):
if i == 2 and not restored:
raise Exception("try to fail me")
session.report(
dict(test=i, training_iteration=i),
checkpoint=Checkpoint.from_dict(dict(iter=i)),
)
def test_resume_from_checkpoint():
with pytest.raises(ValueError):
DummyTrainer(resume_from_checkpoint="invalid")
with pytest.raises(ValueError):
DummyTrainer(resume_from_checkpoint=False)
with pytest.raises(ValueError):
DummyTrainer(resume_from_checkpoint=True)
with pytest.raises(ValueError):
DummyTrainer(resume_from_checkpoint={})
# Succeed
DummyTrainer(resume_from_checkpoint=None)
# Succeed
DummyTrainer(resume_from_checkpoint=Checkpoint.from_dict({"empty": ""}))
def commit(self, path: Optional[Path] = None) -> None:
"""Commit checkpoint to disk, if needed.
Args:
path: Path to commit checkpoint to.
"""
if self.storage_mode == CheckpointStorage.MEMORY:
# Do not persist memory checkpoints
return
if not path:
# If no path is given, skip
return
if not isinstance(self.dir_or_data, dict):
# Only persist dictionaries
return
checkpoint = Checkpoint.from_dict(self.dir_or_data)
self.dir_or_data = checkpoint.to_directory(str(path))
def to_air_checkpoint(self) -> Optional[Checkpoint]:
from ray.tune.trainable.util import TrainableUtil
checkpoint_data = self.dir_or_data
if not checkpoint_data:
return None
if isinstance(checkpoint_data, ray.ObjectRef):
checkpoint_data = ray.get(checkpoint_data)
if isinstance(checkpoint_data, str):
try:
checkpoint_dir = TrainableUtil.find_checkpoint_dir(
checkpoint_data)
except FileNotFoundError:
if log_once("checkpoint_not_available"):
logger.error(
f"The requested checkpoint is not available on this node, "
f"most likely because you are using Ray client or disabled "
f"checkpoint synchronization. To avoid this, enable checkpoint "
f"synchronization to cloud storage by specifying a "
f"`SyncConfig`. The checkpoint may be available on a different "
f"node - please check this location on worker nodes: "
f"{checkpoint_data}")
return None
checkpoint = Checkpoint.from_directory(checkpoint_dir)
elif isinstance(checkpoint_data, bytes):
checkpoint = Checkpoint.from_bytes(checkpoint_data)
elif isinstance(checkpoint_data, dict):
checkpoint = Checkpoint.from_dict(checkpoint_data)
else:
raise RuntimeError(
f"Unknown checkpoint data type: {type(checkpoint_data)}")
return checkpoint
def function_trainable_dict(config):
session.report({"metric": 2},
checkpoint=Checkpoint.from_dict({"checkpoint_data": 3}))
def train_func():
for i in range(9):
session.report(dict(test=i))
session.report(
dict(test=i + 1), checkpoint=Checkpoint.from_dict(dict(hello="world"))
)
def train_func(config):
use_gpu = config["use_gpu"]
num_epochs = config["num_epochs"]
batch_size = config["batch_size"]
num_layers = config["num_layers"]
num_hidden = config["num_hidden"]
dropout_every = config["dropout_every"]
dropout_prob = config["dropout_prob"]
num_features = config["num_features"]
print("Defining model, loss, and optimizer...")
# Setup device.
device = torch.device(f"cuda:{session.get_local_rank()}"
if use_gpu and torch.cuda.is_available() else "cpu")
print(f"Device: {device}")
# Setup data.
train_dataset_pipeline = session.get_dataset_shard("train")
train_dataset_epoch_iterator = train_dataset_pipeline.iter_epochs()
test_dataset = session.get_dataset_shard("test")
test_torch_dataset = test_dataset.to_torch(label_column="label",
batch_size=batch_size,
drop_last=True)
net = Net(
n_layers=num_layers,
n_features=num_features,
num_hidden=num_hidden,
dropout_every=dropout_every,
drop_prob=dropout_prob,
).to(device)
print(net.parameters)
net = train.torch.prepare_model(net)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(net.parameters(), weight_decay=0.0001)
print("Starting training...")
for epoch in range(num_epochs):
train_dataset = next(train_dataset_epoch_iterator)
train_torch_dataset = train_dataset.to_torch(label_column="label",
batch_size=batch_size)
train_running_loss, train_num_correct, train_num_total = train_epoch(
train_torch_dataset, net, device, criterion, optimizer)
train_acc = train_num_correct / train_num_total
print(f"epoch [{epoch + 1}]: training accuracy: "
f"{train_num_correct} / {train_num_total} = {train_acc:.4f}")
test_running_loss, test_num_correct, test_num_total = test_epoch(
test_torch_dataset, net, device, criterion)
test_acc = test_num_correct / test_num_total
print(f"epoch [{epoch + 1}]: testing accuracy: "
f"{test_num_correct} / {test_num_total} = {test_acc:.4f}")
# Checkpoint model.
module = net.module if isinstance(net,
DistributedDataParallel) else net
checkpoint = Checkpoint.from_dict(dict(model=module.state_dict()))
# Record and log stats.
print(f"session report on {session.get_world_rank()}")
session.report(
dict(
train_acc=train_acc,
train_loss=train_running_loss,
test_acc=test_acc,
test_loss=test_running_loss,
),
checkpoint=checkpoint,
)