def train(config, checkpoint=False):
for i in range(10):
tune.report(test=i)
checkpoint_dir = tune.make_checkpoint_dir(step=10)
checkpoint_path = os.path.join(checkpoint_dir, "hello")
with open(checkpoint_path, "w") as f:
f.write("hello")
tune.save_checkpoint(checkpoint_path)
def train(config, checkpoint=None):
for step in range(10):
if step % 3 == 0:
checkpoint_dir = tune.make_checkpoint_dir(step=step)
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"step": step}))
tune.save_checkpoint(path)
tune.report(test=step)
def train(config, checkpoint=False):
itr = 0
if checkpoint:
with open(checkpoint, "r") as f:
itr = int(f.read()) + 1
for i in range(itr, config["max_iter"]):
checkpoint_dir = tune.make_checkpoint_dir(step=i)
checkpoint_path = os.path.join(checkpoint_dir, "goodbye")
with open(checkpoint_path, "w") as f:
f.write(str(i))
tune.save_checkpoint(checkpoint_path)
tune.report(test=i, training_iteration=i)
def train(config, checkpoint=None):
restored = bool(checkpoint)
itr = 0
if checkpoint:
with open(checkpoint, "r") as f:
itr = int(f.read()) + 1
for i in range(itr, 10):
if i == 5 and not restored:
raise Exception("try to fail me")
checkpoint_dir = tune.make_checkpoint_dir()
checkpoint_path = os.path.join(checkpoint_dir, "goodbye")
with open(checkpoint_path, "w") as f:
f.write(str(i))
tune.save_checkpoint(checkpoint_path)
tune.report(test=i, training_iteration=i)
def train(config, checkpoint=None):
step = 0
if checkpoint:
with open(checkpoint) as f:
step = json.loads(f.read())["timestep"]
for timestep in range(step, 100):
v = np.tanh(float(timestep) / config.get("width", 1))
v *= config.get("height", 1)
if timestep % 3 == 0:
checkpoint_dir = tune.make_checkpoint_dir(step=timestep)
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"timestep": timestep}))
tune.save_checkpoint(path)
# Here we use `episode_reward_mean`, but you can also report other
# objectives such as loss or accuracy.
tune.report(episode_reward_mean=v)
def on_validation_end(self, trainer, pl_module):
path = tune.make_checkpoint_dir(trainer.global_step)
trainer.save_checkpoint(os.path.join(path, "checkpoint"))
tune.save_checkpoint(path)
def pbt_function(config, checkpoint=None):
"""Toy PBT problem for benchmarking adaptive learning rate.
The goal is to optimize this trainable's accuracy. The accuracy increases
fastest at the optimal lr, which is a function of the current accuracy.
The optimal lr schedule for this problem is the triangle wave as follows.
Note that many lr schedules for real models also follow this shape:
best lr
^
| /\
| / \
| / \
| / \
------------> accuracy
In this problem, using PBT with a population of 2-4 is sufficient to
roughly approximate this lr schedule. Higher population sizes will yield
faster convergence. Training will not converge without PBT.
"""
lr = config["lr"]
accuracy = 0.0 # end = 1000
start = 0
if checkpoint:
with open(checkpoint) as f:
state = json.loads(f.read())
accuracy = state["acc"]
start = state["step"]
midpoint = 100 # lr starts decreasing after acc > midpoint
q_tolerance = 3 # penalize exceeding lr by more than this multiple
noise_level = 2 # add gaussian noise to the acc increase
# triangle wave:
# - start at 0.001 @ t=0,
# - peak at 0.01 @ t=midpoint,
# - end at 0.001 @ t=midpoint * 2,
for step in range(start, 100):
if accuracy < midpoint:
optimal_lr = 0.01 * accuracy / midpoint
else:
optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
optimal_lr = min(0.01, max(0.001, optimal_lr))
# compute accuracy increase
q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
if q_err < q_tolerance:
accuracy += (1.0 / q_err) * random.random()
elif lr > optimal_lr:
accuracy -= (q_err - q_tolerance) * random.random()
accuracy += noise_level * np.random.normal()
accuracy = max(0, accuracy)
if step % 3 == 0:
checkpoint_dir = tune.make_checkpoint_dir(step=step)
path = os.path.join(checkpoint_dir, "checkpoint")
with open(path, "w") as f:
f.write(json.dumps({"acc": accuracy, "step": start}))
tune.save_checkpoint(path)
tune.report(
mean_accuracy=accuracy,
cur_lr=lr,
optimal_lr=optimal_lr, # for debugging
q_err=q_err, # for debugging
done=accuracy > midpoint * 2)
def train_cifar(config, checkpoint=None, data_dir=None):
net = Net(config["l1"], config["l2"])
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
if torch.cuda.device_count() > 1:
net = nn.DataParallel(net)
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)
if checkpoint:
print("loading checkpoint {}".format(checkpoint))
model_state, optimizer_state = torch.load(checkpoint)
net.load_state_dict(model_state)
optimizer.load_state_dict(optimizer_state)
trainset, testset = load_data(data_dir)
test_abs = int(len(trainset) * 0.8)
train_subset, val_subset = random_split(
trainset, [test_abs, len(trainset) - test_abs])
trainloader = torch.utils.data.DataLoader(
train_subset,
batch_size=int(config["batch_size"]),
shuffle=True,
num_workers=8)
valloader = torch.utils.data.DataLoader(
val_subset,
batch_size=int(config["batch_size"]),
shuffle=True,
num_workers=8)
for epoch in range(10): # loop over the dataset multiple times
running_loss = 0.0
epoch_steps = 0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
epoch_steps += 1
if i % 2000 == 1999: # print every 2000 mini-batches
print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1,
running_loss / epoch_steps))
running_loss = 0.0
# Validation loss
val_loss = 0.0
val_steps = 0
total = 0
correct = 0
for i, data in enumerate(valloader, 0):
with torch.no_grad():
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss = criterion(outputs, labels)
val_loss += loss.cpu().numpy()
val_steps += 1
checkpoint_dir = tune.make_checkpoint_dir(epoch)
path = os.path.join(checkpoint_dir, "checkpoint")
torch.save((net.state_dict(), optimizer.state_dict()), path)
tune.save_checkpoint(path)
tune.report(loss=(val_loss / val_steps), accuracy=correct / total)
print("Finished Training")
def __exit__(self, type, value, traceback):
if torch.distributed.get_rank() == 0 and not self.disable:
tune.save_checkpoint(self.file)