coinstac-dinunet 1.1.9

Distributed Neural Network implementation on COINSTAC.

coinstac-dinunet

Distributed Neural Network implementation on COINSTAC.

pip install coinstac-dinunet

Install supported pytorch & torchvision binaries in your device/docker ecosystem:

torch==1.5.1+cu92
torchvision==0.6.1+cu92

Highlights:

1. Handles multi-network/complex training schemes.
2. Automatic data splitting/k-fold cross validation.
3. Automatic model checkpointing.
4. GPU enabled local sites.
5. Customizable metrics(w/Auto serialization between nodes) to work with any schemes.
6. We can integrate any custom reduction and learning mechanism by extending coinstac_dinunet.distrib.reducer/learner.
7. Realtime profiling each sites by specifying in compspec file(see dinune_fsv example below for details). 
...

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Full working examples

1. FreeSurfer volumes classification.
2. VBM 3D images classification.

General use case:

imports

from coinstac_dinunet import COINNDataset, COINNTrainer, COINNLocal
from coinstac_dinunet.metrics import COINNAverages, Prf1a
from coinstac_dinunet.io import 

1. Define Data Loader

class MyDataset(COINNDataset):
    def __init__(self, **kw):
        super().__init__(**kw)
        self.labels = None

    def load_index(self, id, file):
        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
        ...
        self.indices.append([id, file])

    def __getitem__(self, ix):
        id, file = self.indices[ix]
        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
        label_dir = self.path(id, 'label_dir') # label_dir comes from inputspecs.json
        ...
        # Logic to load, transform single data item.
        ...
        return {'inputs':.., 'labels': ...}

2. Define Trainer

class MyTrainer(COINNTrainer):
    def __init__(self, **kw):
        super().__init__(**kw)

    def _init_nn_model(self):
        self.nn['model'] = MYModel(in_size=self.cache['input_size'], out_size=self.cache['num_class'])

    def iteration(self, batch):
        inputs, labels = batch['inputs'].to(self.device['gpu']).float(), batch['labels'].to(self.device['gpu']).long()

        out = F.log_softmax(self.nn['model'](inputs), 1)
        loss = F.nll_loss(out, labels)
        _, predicted = torch.max(out, 1)
        score = self.new_metrics()
        score.add(predicted, labels)
        val = self.new_averages()
        val.add(loss.item(), len(inputs))
        return {'out': out, 'loss': loss, 'averages': val,
                'metrics': score, 'prediction': predicted}

3. Define remote node in remote.py

from coinstac_dinunet.metrics import Prf1a
from  coinstac_dinunet import COINNRemote
class MyRemote(COINNRemote):
    def _set_monitor_metric(self):
        self.cache['monitor_metric'] = 'f1', 'maximize'

    def _set_log_headers(self):
        self.cache['log_header'] = 'Loss|Accuracy,F1'

    def _new_metrics(self):
        return Prf1a()

4. Define the entry point

from coinstac_dinunet import COINNLocal
from coinstac_dinunet.io import COINPyService


class Server(COINPyService):

    def get_local(self, msg) -> callable:
        pretrain_args = {'epochs': 51, 'batch_size': 16}
        local = COINNLocal(cache=self.cache, input=msg['data']['input'],
                           pretrain_args=None, batch_size=16,
                           state=msg['data']['state'], epochs=21, patience=21, computation_id='fsv_quick')
        return local

    def get_remote(self, msg) -> callable:
        remote = MyRemote(cache=self.cache, input=msg['data']['input'],
                          state=msg['data']['state'])
        return remote

    def get_local_compute_args(self, msg) -> list:
        """
        MyDataHandle and MyLearner are optional
            - MyDataHandle: Can have any custom data loading logic.
            - MyLearner: Can have any custom learning technique 
                when paired with MyReducer argument in get_local_compute_args.
        """
        return [MyTrainer, MyDataset, MyDataHandle, MyLearner]


server = Server(verbose=False)
server.start()

Define custom metrics

• Extend coinstac_dinunet.metrics.COINNMetrics
• Example: coinstac_dinunet.metrics.Prf1a for Precision, Recall, F1, and Accuracy

Default arguments:

• task_name: str = None, Name of the task. [Required]
• mode: str = None, Eg. train/test [Required]
• batch_size: int = 4
• epochs: int = 21
• learning_rate: float = 0.001
• gpus: _List[int] = None, Eg. [0], [1], [0, 1]...
• pin_memory: bool = True, if cuda available
• num_workers: int = 0
• load_limit: int = float('inf'), Limit on dataset to load for debugging purpose.
• pretrained_path: str = None, Path to pretrained weights
• patience: int = 5, patience to end training by monitoring validation scores.
• load_sparse: bool = False, Load each data item in separate loader to reconstruct images from patches, if needed.
• num_folds: int = None, Number of k-folds.
• split_ratio: _List[float] = (0.6, 0.2, 0.2), Exclusive to num_folds.

• Directly passed parameters in coinstac_dinunet.nodes.COINNLocal, args passed through inputspec will override the defaults in the same order.
• Custom data splits can be provided in the path specified by split_dir for each sites in their respective inputspecs file. This is mutually exclusive to both num_folds and split_ratio.

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