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<label class="md-nav__title" for="__toc">Table of contents</label>
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<ul class="md-nav__list" data-md-scrollfix>
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||||
|
||||
<li class="md-nav__item">
|
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<a href="#template-model-definition" title="Template model definition" class="md-nav__link">
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Template model definition
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</a>
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||||
|
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</li>
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<li class="md-nav__item">
|
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<a href="#trainer-example" title="Trainer Example" class="md-nav__link">
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Trainer Example
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<li class="md-nav__item">
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<a href="#cpu-hyperparameter-search" title="CPU hyperparameter search" class="md-nav__link">
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CPU hyperparameter search
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</a>
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|
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</li>
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<li class="md-nav__item">
|
||||
<a href="#hyperparameter-search-on-a-single-or-multiple-gpus" title="Hyperparameter search on a single or multiple GPUs" class="md-nav__link">
|
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Hyperparameter search on a single or multiple GPUs
|
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</a>
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|
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</li>
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<li class="md-nav__item">
|
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<a href="#hyperparameter-search-on-a-slurm-hpc-cluster" title="Hyperparameter search on a SLURM HPC cluster" class="md-nav__link">
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Hyperparameter search on a SLURM HPC cluster
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</a>
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</ul>
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</li>
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</div>
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</div>
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<div class="md-content">
|
||||
<article class="md-content__inner md-typeset">
|
||||
|
||||
|
||||
<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/examples/Examples.md" title="Edit this page" class="md-icon md-content__icon"></a>
|
||||
|
||||
|
||||
<h1>Examples</h1>
|
||||
|
||||
<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>
|
||||
|
||||
<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__':
|
||||
|
||||
# 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):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# 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'
|
||||
)
|
||||
|
||||
# set the hparams for the experiment
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# build model
|
||||
model = MyLightningModule(hparams)
|
||||
|
||||
# callbacks
|
||||
early_stop = EarlyStopping(
|
||||
monitor=hparams.early_stop_metric,
|
||||
patience=hparams.early_stop_patience,
|
||||
verbose=True,
|
||||
mode=hparams.early_stop_mode
|
||||
)
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
# configure trainer
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
cluster=cluster,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
)
|
||||
|
||||
# train model
|
||||
trainer.fit(model)
|
||||
</code></pre>
|
||||
|
||||
<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>
|
||||
|
||||
<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>
|
||||
|
||||
<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>
|
||||
|
||||
<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
|
||||
)
|
||||
|
||||
# email for cluster coms
|
||||
cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
|
||||
|
||||
# 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
|
||||
|
||||
# any modules for code to run in env
|
||||
cluster.add_command('source activate pytorch_lightning')
|
||||
|
||||
# name of exp
|
||||
job_display_name = hyperparams.tt_name.split('_')[0]
|
||||
job_display_name = job_display_name[0:3]
|
||||
|
||||
# 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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
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