wandb是\"我爱你，大baby\"首字母的缩写。
顾名思义，她是炼丹师的大宝贝，是炼丹师最爱的炼丹伴侣。
公众号算法美食屋后台回复关键词：wandb，获取本教程 notebook源码 和 B站视频演示。
just kidding, 开个玩笑！
wandb全称weights&bias，是一款类似TensorBoard
的机器学习可视化分析工具。
相比TensorBoard，wandb具有如下主要优势：
日志上传云端永久存储，便于分享不怕丢失。
可以存管代码,数据集和模型的版本，随时复现。(wandb.Artifact)
可以使用交互式表格进行case分析(wandb.Table)
可以自动化模型调参。(wandb.sweep)
官方文档：https://docs.wandb.ai/
总体来说，wandb目前的核心功能有以下4个：
1，实验跟踪：experiment tracking （wandb.log）
2，版本管理：version management (wandb.log_artifact, wandb.save)
3，case分析：case visualization (wandb.Table, wandb.Image)
4，超参调优：model optimization (wandb.sweep)
本文我们主要介绍 前3个能力，超参调优的介绍在下一篇文章。
〇，注册wandb
使用wandb可视化模型训练过程需要在 https://wandb.ai/ 注册账户，
并在个人settings页面获取 API keys。
#import os
#os.environ[\"WANDB_API_KEY\"] = \"xxxx\"
import wandb
wandb.login()
一，实验跟踪
wandb 提供了类似 TensorBoard的实验跟踪能力，主要包括：
模型配置超参数的记录
模型训练过程中loss，metric等各种指标的记录和可视化
图像的可视化(wandb.Image)
其他各种Media(wandb.Vedio, wandb.Audio, wandb.Html, 3D点云等)
import os,PIL
import numpy as np
from torch.utils.data import DataLoader, Dataset
import torch
from torch import nn
import torchvision
from torchvision import transforms
import datetime
import wandb
from argparse import Namespace
device = torch.device(\'cuda\' if torch.cuda.is_available() else \'cpu\')
config = Namespace(
project_name = \'wandb_demo\',
batch_size = 512,
hidden_layer_width = 64,
dropout_p = 0.1,
lr = 1e-4,
optim_type = \'Adam\',
epochs = 15,
ckpt_path = \'checkpoint.pt\'
)
def create_dataloaders(config):
transform = transforms.Compose([transforms.ToTensor()])
ds_train = torchvision.datasets.MNIST(root=\"./mnist/\",train=True,download=True,transform=transform)
ds_val = torchvision.datasets.MNIST(root=\"./mnist/\",train=False,download=True,transform=transform)
ds_train_sub = torch.utils.data.Subset(ds_train, indices=range(0, len(ds_train), 5))
dl_train = torch.utils.data.DataLoader(ds_train_sub, batch_size=config.batch_size, shuffle=True,
num_workers=2,drop_last=True)
dl_val = torch.utils.data.DataLoader(ds_val, batch_size=config.batch_size, shuffle=False,
num_workers=2,drop_last=True)
return dl_train,dl_val
def create_net(config):
net = nn.Sequential()
net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=config.hidden_layer_width,kernel_size = 3))
net.add_module(\"pool1\",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module(\"conv2\",nn.Conv2d(in_channels=config.hidden_layer_width,
out_channels=config.hidden_layer_width,kernel_size = 5))
net.add_module(\"pool2\",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module(\"dropout\",nn.Dropout2d(p = config.dropout_p))
net.add_module(\"adaptive_pool\",nn.AdaptiveMaxPool2d((1,1)))
net.add_module(\"flatten\",nn.Flatten())
net.add_module(\"linear1\",nn.Linear(config.hidden_layer_width,config.hidden_layer_width))
net.add_module(\"relu\",nn.ReLU())
net.add_module(\"linear2\",nn.Linear(config.hidden_layer_width,10))
net.to(device)
return net
def train_epoch(model,dl_train,optimizer):
model.train()
for step, batch in enumerate(dl_train):
features,labels = batch
features,labels = features.to(device),labels.to(device)
preds = model(features)
loss = nn.CrossEntropyLoss()(preds,labels)
loss.backward()
optimizer.step()
optimizer.zero_grad()
return model
def eval_epoch(model,dl_val):
model.eval()
accurate = 0
num_elems = 0
for batch in dl_val:
features,labels = batch
features,labels = features.to(device),labels.to(device)
with torch.no_grad():
preds = model(features)
predictions = preds.argmax(dim=-1)
accurate_preds = (predictions==labels)
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
val_acc = accurate.item() / num_elems
return val_acc
def train(config = config):
dl_train, dl_val = create_dataloaders(config)
model = create_net(config);
optimizer = torch.optim.__dict__[config.optim_type](params=model.parameters(), lr=config.lr)
#======================================================================
nowtime = datetime.datetime.now().strftime(\'%Y-%m-%d %H:%M:%S\')
wandb.init(project=config.project_name, config = config.__dict__, name = nowtime, save_code=True)
model.run_id = wandb.run.id
#======================================================================
model.best_metric = -1.0
for epoch in range(1,config.epochs+1):
model = train_epoch(model,dl_train,optimizer)
val_acc = eval_epoch(model,dl_val)
if val_acc>model.best_metric:
model.best_metric = val_acc
torch.save(model.state_dict(),config.ckpt_path)
nowtime = datetime.datetime.now().strftime(\'%Y-%m-%d %H:%M:%S\')
print(f\"epoch【{epoch}】@{nowtime} --> val_acc= {100 * val_acc:.2f}%\")
#======================================================================
wandb.log({\'epoch\':epoch, \'val_acc\': val_acc, \'best_val_acc\':model.best_metric})
#======================================================================
#======================================================================
wandb.finish()
#======================================================================
return model
model = train(config) ##3,2,1 点火