wassname/pytorch-lightning
1
0
Fork 0
mirror of https://github.com/wassname/pytorch-lightning.git synced 2026-09-21 13:20:08 +08:00
Code Issues Packages Projects Releases Wiki Activity

Deployed eaad3c7 with MkDocs version: 1.0.4

Browse Source
This commit is contained in:
William Falcon
2019-06-28 17:34:16 -05:00
parent 37e2372acd
commit 418c0057f8
5 changed files with 157 additions and 219 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Examples/index.html
+145 -207
View File
@@ -61,6 +61,19 @@
<li class="toctree-l2"><a href="#template-model-definition">Template model definition</a></li>
<li class="toctree-l2"><a href="#trainer-example">Trainer Example</a></li>
<ul>
<li><a class="toctree-l3" href="#cpu-hyperparameter-search">CPU hyperparameter search</a></li>
<li><a class="toctree-l3" href="#hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on a single or multiple GPUs</a></li>
<li><a class="toctree-l3" href="#hyperparameter-search-on-a-slurm-hpc-cluster">Hyperparameter search on a SLURM HPC cluster</a></li>
</ul>
</ul>
</li>
@@ -157,234 +170,159 @@
<div role="main">
<div class="section">
<h4 id="template-model-definition">Template model definition</h4>
<p>In 99% of cases you want to just copy this template to start a new lightningModule and change the core of what your model is actually trying to do.</p>
<pre><code class="python">import os
from collections import OrderedDict
import torch.nn as nn
from torchvision.datasets import MNIST
import torchvision.transforms as transforms
import torch
import torch.nn.functional as F
from test_tube import HyperOptArgumentParser
from torch import optim
<h3 id="template-model-definition">Template model definition</h3>
<p>In 99% of cases you want to just copy <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py">this template</a> to start a new lightningModule and change the core of what your model is actually trying to do.</p>
<pre><code class="bash"># get a copy of the module template
wget https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py
</code></pre>
from pytorch_lightning.root_module.root_module import LightningModule
<hr />
<h3 id="trainer-example">Trainer Example</h3>
<p><strong> __main__ function</strong> </p>
<p>Normally, we want to let the __main__ function start the training.
Inside the main we parse training arguments with whatever hyperparameters we want. Your LightningModule will have a
chance to add hyperparameters. </p>
<pre><code class="python">from test_tube import HyperOptArgumentParser
if __name__ == '__main__':
class LightningTemplateModel(LightningModule):
# use default args given by lightning
root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
add_default_args(parent_parser, root_dir)
# allow model to overwrite or extend args
parser = ExampleModel.add_model_specific_args(parent_parser)
hyperparams = parser.parse_args()
# train model
main(hyperparams)
</code></pre>
<p><strong>Main Function</strong> </p>
<p>The main function is your entry into the program. This is where you init your model, checkpoint directory, and launch the training.
The main function should have 3 arguments: <br />
- hparams: a configuration of hyperparameters. <br />
- slurm_manager: Slurm cluster manager object (can be None)
- dict: for you to return any values you want (useful in meta-learning, otherwise set to _) </p>
<pre><code>def main(hparams, cluster, results_dict):
&quot;&quot;&quot;
Sample model to show how to define a template
Main training routine specific for this project
:param hparams:
:return:
&quot;&quot;&quot;
# init experiment
log_dir = os.path.dirname(os.path.realpath(__file__))
exp = Experiment(
name='test_tube_exp',
debug=True,
save_dir=log_dir,
version=0,
autosave=False,
description='test demo'
)
def __init__(self, hparams):
&quot;&quot;&quot;
Pass in parsed HyperOptArgumentParser to the model
:param hparams:
&quot;&quot;&quot;
# init superclass
super(LightningTemplateModel, self).__init__(hparams)
# set the hparams for the experiment
exp.argparse(hparams)
exp.save()
self.batch_size = hparams.batch_size
# build model
model = MyLightningModule(hparams)
# build model
self.__build_model()
# callbacks
early_stop = EarlyStopping(
monitor=hparams.early_stop_metric,
patience=hparams.early_stop_patience,
verbose=True,
mode=hparams.early_stop_mode
)
# ---------------------
# MODEL SETUP
# ---------------------
def __build_model(self):
&quot;&quot;&quot;
Layout model
:return:
&quot;&quot;&quot;
self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
checkpoint = ModelCheckpoint(
filepath=model_save_path,
save_function=None,
save_best_only=True,
verbose=True,
monitor=hparams.model_save_monitor_value,
mode=hparams.model_save_monitor_mode
)
self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
# configure trainer
trainer = Trainer(
experiment=exp,
cluster=cluster,
checkpoint_callback=checkpoint,
early_stop_callback=early_stop,
)
# ---------------------
# TRAINING
# ---------------------
def forward(self, x):
&quot;&quot;&quot;
No special modification required for lightning, define as you normally would
:param x:
:return:
&quot;&quot;&quot;
# train model
trainer.fit(model)
</code></pre>
x = self.c_d1(x)
x = torch.tanh(x)
x = self.c_d1_bn(x)
x = self.c_d1_drop(x)
<p>The <strong>main</strong> function will start training on your <strong>main</strong> function. If you use the HyperParameterOptimizer
in hyper parameter optimization mode, this main function will get one set of hyperparameters. If you use it as a simple
argument parser you get the default arguments in the argument parser.</p>
<p>So, calling main(hyperparams) runs the model with the default argparse arguments. </p>
<pre><code class="python">main(hyperparams)
</code></pre>
x = self.c_d2(x)
logits = F.log_softmax(x, dim=1)
<hr />
<h4 id="cpu-hyperparameter-search">CPU hyperparameter search</h4>
<pre><code class="python"># run a grid search over 20 hyperparameter combinations.
hyperparams.optimize_parallel_cpu(
main_local,
nb_trials=20,
nb_workers=1
)
</code></pre>
return logits
<hr />
<h4 id="hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on a single or multiple GPUs</h4>
<pre><code class="python"># run a grid search over 20 hyperparameter combinations.
hyperparams.optimize_parallel_gpu(
main_local,
nb_trials=20,
nb_workers=1,
gpus=[0,1,2,3]
)
</code></pre>
def loss(self, labels, logits):
nll = F.nll_loss(logits, labels)
return nll
<hr />
<h4 id="hyperparameter-search-on-a-slurm-hpc-cluster">Hyperparameter search on a SLURM HPC cluster</h4>
<pre><code class="python">def optimize_on_cluster(hyperparams):
# enable cluster training
cluster = SlurmCluster(
hyperparam_optimizer=hyperparams,
log_path=hyperparams.tt_save_path,
test_tube_exp_name=hyperparams.tt_name
)
def training_step(self, data_batch, batch_i):
&quot;&quot;&quot;
Lightning calls this inside the training loop
:param data_batch:
:return:
&quot;&quot;&quot;
# forward pass
x, y = data_batch
x = x.view(x.size(0), -1)
y_hat = self.forward(x)
# email for cluster coms
cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
# calculate loss
loss_val = self.loss(y, y_hat)
# configure cluster
cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
cluster.job_time = '48:00:00'
cluster.gpu_type = '1080ti'
cluster.memory_mb_per_node = 48000
output = OrderedDict({
'loss': loss_val,
'tqdm_metrics': {}
})
return output
# any modules for code to run in env
cluster.add_command('source activate pytorch_lightning')
def validation_step(self, data_batch, batch_i):
&quot;&quot;&quot;
Lightning calls this inside the validation loop
:param data_batch:
:return:
&quot;&quot;&quot;
x, y = data_batch
x = x.view(x.size(0), -1)
y_hat = self.forward(x)
# name of exp
job_display_name = hyperparams.tt_name.split('_')[0]
job_display_name = job_display_name[0:3]
loss_val = self.loss(y, y_hat)
# acc
labels_hat = torch.argmax(y_hat, dim=1)
val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
output = OrderedDict({
'val_loss': loss_val,
'val_acc': torch.tensor(val_acc),
})
return output
def validation_end(self, outputs):
&quot;&quot;&quot;
Called at the end of validation to aggregate outputs
:param outputs: list of individual outputs of each validation step
:return:
&quot;&quot;&quot;
val_loss_mean = 0
val_acc_mean = 0
for output in outputs:
val_loss_mean += output['val_loss']
val_acc_mean += output['val_acc']
val_loss_mean /= len(outputs)
val_acc_mean /= len(outputs)
tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
return tqdm_dic
def update_tng_log_metrics(self, logs):
return logs
# ---------------------
# MODEL SAVING
# ---------------------
def get_save_dict(self):
checkpoint = {'state_dict': self.state_dict()}
return checkpoint
def load_model_specific(self, checkpoint):
self.load_state_dict(checkpoint['state_dict'])
pass
# ---------------------
# TRAINING SETUP
# ---------------------
def configure_optimizers(self):
&quot;&quot;&quot;
return whatever optimizers we want here
:return: list of optimizers
&quot;&quot;&quot;
optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
return [optimizer]
def __dataloader(self, train):
# init data generators
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
shuffle=True
)
return loader
@property
def tng_dataloader(self):
if self._tng_dataloader is None:
try:
self._tng_dataloader = self.__dataloader(train=True)
except Exception as e:
print(e)
raise e
return self._tng_dataloader
@property
def val_dataloader(self):
if self._val_dataloader is None:
try:
self._val_dataloader = self.__dataloader(train=False)
except Exception as e:
print(e)
raise e
return self._val_dataloader
@property
def test_dataloader(self):
if self._test_dataloader is None:
try:
self._test_dataloader = self.__dataloader(train=False)
except Exception as e:
print(e)
raise e
return self._test_dataloader
@staticmethod
def add_model_specific_args(parent_parser, root_dir):
&quot;&quot;&quot;
Parameters you define here will be available to your model through self.hparams
:param parent_parser:
:param root_dir:
:return:
&quot;&quot;&quot;
parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
# param overwrites
# parser.set_defaults(gradient_clip=5.0)
# network params
parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
parser.add_argument('--in_features', default=28*28)
parser.add_argument('--out_features', default=10)
parser.add_argument('--hidden_dim', default=50000) # use 500 for CPU, 50000 for GPU to see speed difference
# data
parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
# training params (opt)
parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
tunable=False)
parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
return parser
# run hopt
print('submitting jobs...')
cluster.optimize_parallel_cluster_gpu(
main,
nb_trials=hyperparams.nb_hopt_trials,
job_name=job_display_name
)
# run cluster hyperparameter search
optimize_on_cluster(hyperparams)
</code></pre>
</div>