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https://github.com/wassname/pytorch-lightning.git
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testing slurm ddp
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@@ -209,6 +209,8 @@ class LightningTemplateModel(LightningModule):
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# parser.set_defaults(gradient_clip=5.0)
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# network params
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parser.add_argument('--nb_gpu_nodes', type=int, default=2)
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parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
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parser.add_argument('--in_features', default=28*28)
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parser.add_argument('--out_features', default=10)
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@@ -96,7 +96,7 @@ def main(hparams, cluster, results_dict):
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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gpus=hparams.gpus,
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nb_gpu_nodes=1
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nb_gpu_nodes=hyperparams.nb_gpu_nodes
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)
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# train model
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@@ -130,12 +130,15 @@ def optimize_on_cluster(hyperparams):
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# configure cluster
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cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
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cluster.job_time = '48:00:00'
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cluster.gpu_type = '1080ti'
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cluster.memory_mb_per_node = 48000
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cluster.per_experiment_nb_nodes = hyperparams.nb_gpu_nodes
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cluster.job_time = '2:00:00'
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cluster.gpu_type = 'volta'
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cluster.memory_mb_per_node = 0
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# any modules for code to run in env
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cluster.add_command('source activate pytorch_lightning')
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cluster.add_command('source activate lightning')
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cluster.add_slurm_cmd(cmd='constraint', value='volta32gb', comment='use 32gb gpus')
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cluster.add_slurm_cmd(cmd='partition', value=hyperparams.gpu_partition, comment='use 32gb gpus')
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# name of exp
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job_display_name = hyperparams.tt_name.split('_')[0]
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@@ -159,6 +162,7 @@ if __name__ == '__main__':
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# use default args
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
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parent_parser.add_argument('-gpu_partition', type=str)
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# allow model to overwrite or extend args
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TRAINING_MODEL = AVAILABLE_MODELS[model_name]
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