torchkeras 4.0.4

pytorch❤️keras

Pytorch❤️Keras

English | 简体中文

The torchkeras library is a simple tool for training neural network in pytorch jusk in a keras style. 😋😋

1, Introduction

With torchkeras, You need not to write your training loop with many lines of code, all you need to do is just

like these two steps as below:

(i) create your network and wrap it and the loss_fn together with torchkeras.KerasModel like this: model = torchkeras.KerasModel(net,loss_fn=nn.BCEWithLogitsLoss()).

(ii) fit your model with the training data and validate data.

The main code of use torchkeras is like below.

import torch 
import torchkeras

model = torchkeras.KerasModel(net,
                              loss_fn = nn.BCEWithLogitsLoss(),
                              optimizer= torch.optim.Adam(net.parameters(),lr = 0.001),
                              metrics_dict = {"acc":torchmetrics.Accuracy(task='binary')}
                             )
dfhistory=model.fit(train_data=dl_train, 
                    val_data=dl_val, 
                    epochs=20, 
                    patience=3, 
                    ckpt_path='checkpoint',
                    monitor="val_acc",
                    mode="max",
                    plot=True
                   )

Besides，You can use torchkeras.VLog to get the dynamic training visualization any where as you like ~

import time
import math,random
from torchkeras import VLog

epochs = 10
batchs = 30

#0, init vlog
vlog = VLog(epochs, monitor_metric='val_loss', monitor_mode='min') 

#1, log_start 
vlog.log_start() 

for epoch in range(epochs):
    
    #train
    for step in range(batchs):
        
        #2, log_step (for training step)
        vlog.log_step({'train_loss':100-2.5*epoch+math.sin(2*step/batchs)}) 
        time.sleep(0.05)
        
    #eval    
    for step in range(20):
        
        #3, log_step (for eval step)
        vlog.log_step({'val_loss':100-2*epoch+math.sin(2*step/batchs)},training=False)
        time.sleep(0.05)
        
    #4, log_epoch
    vlog.log_epoch({'val_loss':100 - 2*epoch+2*random.random()-1,
                    'train_loss':100-2.5*epoch+2*random.random()-1})  

# 5, log_end
vlog.log_end()

This project seems somehow powerful, but the source code is very simple.

Actually, only about 200 lines of Python code.

If you want to understand or modify some details of this project, feel free to read and change the source code!!!

2, Features

The main features supported by torchkeras are listed below.

Versions when these features are introduced and the libraries which they used or inspired from are given.

features	supported from version	used or inspired by library
✅ training progress bar	3.0.0	use tqdm,inspired by keras
✅ training metrics	3.0.0	inspired by pytorch_lightning
✅ notebook visualization in traning	3.8.0	inspired by fastai
✅ early stopping	3.0.0	inspired by keras
✅ gpu training	3.0.0	use accelerate
✅ multi-gpus training(ddp)	3.6.0	use accelerate
✅ fp16/bf16 training	3.6.0	use accelerate
✅ tensorboard callback	3.7.0	use tensorboard
✅ wandb callback	3.7.0	use wandb
✅ VLog	3.9.5	use matplotlib

3, Basic Examples

You can follow these full examples to get started with torchkeras.

example	read notebook code	run example in kaggle
①kerasmodel basic 🔥🔥	torchkeras.KerasModel example
②kerasmodel wandb 🔥🔥🔥	torchkeras.KerasModel with wandb demo
③kerasmodel tunning 🔥🔥🔥	torchkeras.KerasModel with wandb sweep demo
④kerasmodel tensorboard	torchkeras.KerasModel with tensorboard example
⑤kerasmodel ddp/tpu	torchkeras.KerasModel ddp tpu examples
⑥ VLog for lightgbm/ultralytics/transformers🔥🔥🔥	VLog example

4, Advanced Examples

In some using cases, because of the differences of the model input types, you need to rewrite the StepRunner of KerasModel. Here are some examples.

example	model library	notebook
RL
ReinforcementLearning——Q-Learning🔥🔥	-	Q-learning
ReinforcementLearning——DQN	-	DQN
Tabular
BinaryClassification——LightGBM	-	LightGBM
MultiClassification——FTTransformer🔥🔥🔥🔥🔥	-	FTTransformer
BinaryClassification——FM	-	FM
BinaryClassification——DeepFM	-	DeepFM
BinaryClassification——DeepCross	-	DeepCross
CV
ImageClassification——Resnet	-	Resnet
ImageSegmentation——UNet	-	UNet
ObjectDetection——SSD	-	SSD
OCR——CRNN 🔥🔥	-	CRNN-CTC
ImageClassification——SwinTransformer	timm	Swin
ObjectDetection——FasterRCNN	torchvision	FasterRCNN
ImageSegmentation——DeepLabV3++	segmentation_models_pytorch	Deeplabv3++
InstanceSegmentation——MaskRCNN	detectron2	MaskRCNN
ObjectDetection——YOLOv8 🔥🔥🔥	ultralytics	YOLOv8
InstanceSegmentation——YOLOv8 🔥🔥🔥	ultralytics	YOLOv8
NLP
Seq2Seq——Transformer🔥🔥	-	Transformer
TextGeneration——Llama🔥	-	Llama
TextClassification——BERT	transformers	BERT
TokenClassification——BERT	transformers	BERT_NER
FinetuneLLM——ChatGLM2_LoRA 🔥🔥🔥	transformers,peft	ChatGLM2_LoRA
FinetuneLLM——ChatGLM2_AdaLoRA 🔥	transformers,peft	ChatGLM2_AdaLoRA
FinetuneLLM——ChatGLM2_QLoRA🔥	transformers	ChatGLM2_QLoRA_Kaggle
FinetuneLLM——BaiChuan13B_QLoRA🔥	transformers	BaiChuan13B_QLoRA
FinetuneLLM——BaiChuan13B_NER 🔥🔥🔥	transformers	BaiChuan13B_NER
FinetuneLLM——BaiChuan13B_MultiRounds 🔥	transformers	BaiChuan13B_MultiRounds
FinetuneLLM——Qwen7B_MultiRounds 🔥🔥🔥	transformers	Qwen7B_MultiRounds
FinetuneLLM——BaiChuan2_13B 🔥	transformers	BaiChuan2_13B

If you want to understand or modify some details of this project, feel free to read and change the source code!!!

Any other questions, you can contact the author form the wechat official account below:

算法美食屋