.. testsetup:: * from pytorch_lightning.trainer.trainer import Trainer .. _slurm: Computing cluster (SLURM) ========================= Lightning automates the details behind training on a SLURM-powered cluster. .. _multi-node: ---------- Multi-node training ------------------- To train a model using multiple nodes, do the following: 1. Design your :class:`~pytorch_lightning.core.LightningModule`. 2. Enable ddp in the trainer .. code-block:: python # train on 32 GPUs across 4 nodes trainer = Trainer(gpus=8, num_nodes=4, distributed_backend='ddp') 3. It's a good idea to structure your training script like this: .. testcode:: # train.py def main(hparams): model = LightningTemplateModel(hparams) trainer = pl.Trainer( gpus=8, num_nodes=4, distributed_backend='ddp' ) trainer.fit(model) if __name__ == '__main__': root_dir = os.path.dirname(os.path.realpath(__file__)) parent_parser = ArgumentParser(add_help=False) hyperparams = parser.parse_args() # TRAIN main(hyperparams) 4. Create the appropriate SLURM job: .. code-block:: bash # (submit.sh) #!/bin/bash -l # SLURM SUBMIT SCRIPT #SBATCH --nodes=4 #SBATCH --gres=gpu:8 #SBATCH --ntasks-per-node=8 #SBATCH --mem=0 #SBATCH --time=0-02:00:00 # activate conda env source activate $1 # debugging flags (optional) export NCCL_DEBUG=INFO export PYTHONFAULTHANDLER=1 # on your cluster you might need these: # set the network interface # export NCCL_SOCKET_IFNAME=^docker0,lo # might need the latest cuda # module load NCCL/2.4.7-1-cuda.10.0 # run script from above srun python3 train.py 5. If you want auto-resubmit (read below), add this line to the submit.sh script .. code-block:: bash #SBATCH --signal=SIGUSR1@90 6. Submit the SLURM job .. code-block:: bash sbatch submit.sh .. note:: When running in DDP mode, any errors in your code will show up as an NCCL issue. Set the `NCCL_DEBUG=INFO` flag to see the ACTUAL error. Normally now you would need to add a :class:`~torch.utils.data.distributed.DistributedSampler` to your dataset, however Lightning automates this for you. But if you still need to set a sampler set the Trainer flag :paramref:`~pytorch_lightning.Trainer.replace_sampler_ddp` to ``False``. Here's an example of how to add your own sampler (again, not needed with Lightning). .. testcode:: # in your LightningModule def train_dataloader(self): dataset = MyDataset() dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset) dataloader = Dataloader(dataset, sampler=dist_sampler) return dataloader # in your training script trainer = Trainer(replace_sampler_ddp=False) ---------- Wall time auto-resubmit ----------------------- When you use Lightning in a SLURM cluster, it automatically detects when it is about to run into the wall time and does the following: 1. Saves a temporary checkpoint. 2. Requeues the job. 3. When the job starts, it loads the temporary checkpoint. To get this behavior make sure to add the correct signal to your SLURM script .. code-block:: bash # 90 seconds before training ends SBATCH --signal=SIGUSR1@90 ---------- Building SLURM scripts ---------------------- Instead of manually building SLURM scripts, you can use the `SlurmCluster object `_ to do this for you. The SlurmCluster can also run a grid search if you pass in a `HyperOptArgumentParser `_. Here is an example where you run a grid search of 9 combinations of hyperparameters. See also the multi-node examples `here `__. .. code-block:: python # grid search 3 values of learning rate and 3 values of number of layers for your net # this generates 9 experiments (lr=1e-3, layers=16), (lr=1e-3, layers=32), # (lr=1e-3, layers=64), ... (lr=1e-1, layers=64) parser = HyperOptArgumentParser(strategy='grid_search', add_help=False) parser.opt_list('--learning_rate', default=0.001, type=float, options=[1e-3, 1e-2, 1e-1], tunable=True) parser.opt_list('--layers', default=1, type=float, options=[16, 32, 64], tunable=True) hyperparams = parser.parse_args() # Slurm cluster submits 9 jobs, each with a set of hyperparams cluster = SlurmCluster( hyperparam_optimizer=hyperparams, log_path='/some/path/to/save', ) # OPTIONAL FLAGS WHICH MAY BE CLUSTER DEPENDENT # which interface your nodes use for communication cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo') # see output of the NCCL connection process # NCCL is how the nodes talk to each other cluster.add_command('export NCCL_DEBUG=INFO') # setting a master port here is a good idea. cluster.add_command('export MASTER_PORT=%r' % PORT) # ************** DON'T FORGET THIS *************** # MUST load the latest NCCL version cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0']) # configure cluster cluster.per_experiment_nb_nodes = 12 cluster.per_experiment_nb_gpus = 8 cluster.add_slurm_cmd(cmd='ntasks-per-node', value=8, comment='1 task per gpu') # submit a script with 9 combinations of hyper params # (lr=1e-3, layers=16), (lr=1e-3, layers=32), (lr=1e-3, layers=64), ... (lr=1e-1, layers=64) cluster.optimize_parallel_cluster_gpu( main, nb_trials=9, # how many permutations of the grid search to run job_name='name_for_squeue' ) The other option is that you generate scripts on your own via a bash command or use another library. ---------- Self-balancing architecture (COMING SOON) ----------------------------------------- Here Lightning distributes parts of your module across available GPUs to optimize for speed and memory.