A mechine learning pipeline lib.
Project description
ML Solution
This package is used to quickly build a pipline for mechine learning.
Quick Start
Installation
Easy installation with pip:
pip install ml_solution
Code Sample
The code below shows how to use ml_solution to build flexible deep learning model frame quickly:
import torch
from torch import optim
import torch.nn as nn
from dataset import creat_loader
import modeling
from ml_solution.dl_tools import engine, engine_utils, train_utils
from ml_solution import data_utils
from transformers import XLMRobertaTokenizer
train_config = data_utils.json_load('./train_config.json')
dataset_config = data_utils.json_load('./dataset_config.json')
train_loader = creat_loader(dataset_config['train_json_path'],)
valid_loader = creat_loader(dataset_config['valid_json_path'])
dataloaders = {
'train':train_loader,
'valid':valid_loader
}
model = modeling.get_model()
optimizer = optim.Adam(model.parameters(), lr=train_config['lr'])
criterion = train_utils.DictInputWarpper(nn.CrossEntropyLoss(), 'logit', 'label')
metric_grader = engine_utils.ConfusionMetrics(
num_classes=4,
metrics_list=train_config['metrics_list']
)
loss_grader = engine_utils.LossRecorder()
computers = {
'conf_metrics': metric_grader,
'loss': loss_grader
}
grader = engine_utils.Grader(computers)
wandb_init_config = data_utils.json_manipulate_keys(
train_config,
['lr', 'batch_size', "architecture"],
keep=True
)
wandb_init_config['criterion'] = criterion.module.__class__.__name__
wandb_init_config['optimizer'] = optimizer.__class__.__name__
logger = engine_utils.WandbLogger(
config=wandb_init_config, project=train_config['project'])
handler = engine.HandlerSaveModel(
metric_name="ACC",
log_root=train_config['log_root'],
version=logger.version,
ideal_th=5
)
trainer = engine.TorchTrainer(
model, dataloaders, criterion,
optimizer, device=device, mix_pre=train_config['mix_pre']
)
train_pipeline = engine.TrainPipeline(
trainer, grader, logger,
handler=handler
)
train_pipeline.train_epoches(train_config['epoches'])
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