coinstac-dinunet 0.2.3

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.
...

---

Pipeline for reducing gradients across sites.

General use case:

1. Define Data Loader

2. Define Local Node

3. Define Remote Node

4. Define Trainer

5. Define custom metrics

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.

---