# dir:本地文件写入的路径,(环境变量WANDB_DIR或wandb.init的关键字参数dir) wandb.init(config=all_args,project=your_project_name,entity=your_team_name,notes=socket.gethostname(),name=all_args.experiment_name+"_"+str(all_args.seed),dir=run_dir,group=all_args.scenario_name,job_type="training...
init(config=all_args, project=your_project_name, entity=your_team_name, notes=socket.gethostname(), name=your_experiment_name dir=run_dir, job_type="training", reinit=True) 2. 基本使用 wandb的基础功能就是跟踪训练过程,然后在wandb网站上查看训练数据。wandb通过通用的log()函数,可以展示丰富的...
首先创建在wandb页面中中创建需要可视化的project,然后在代码里面只要指定好team和project,便可以把数据传输到对应的project下: importwandbfrompathlibimportPath# notes:一些文字描述实验发现或备注,也可以在wandb网页的individual experiment panel中添加# dir:本地文件写入的路径,(环境变量WANDB_DIR或wandb.init的关键字参...
wandb.init(dir="<path>",# set the wandb project where this run will be loggedproject="<random string>",name="<random string>",id="<random string>",# track hyperparameters and run metadataconfig=params,anonymous="allow",mode="offline") ...
datetime.now().strftime('%Y-%m-%d%H:%M:%S')wandb.init(project=config.project_name,...
import wandb # Start a W&B Run with wandb.init run = wandb.init(project="my_first_project") # Save model inputs and hyperparameters in a wandb.config object config = run.config config.learning_rate = 0.01 # Model training code here ... # Log metrics over time to visualize performance...
1 安装库 pipinstall wandb 2 创建账户 wandb login 3 初始化 import wandb wandb.init(project="my-...
random # for demo script wandb.login() epochs = 10 lr = 0.01 run = wandb.init( ...
wandb.init(project="my-project") 声明超参数 wandb.config.dropout = 0.2 wandb.config.hidden_layer_size = 128 记录指标 def my_train_loop(): for epoch in range(10): loss = 0 # change as appropriate :) wandb.log({'epoch': epoch, 'loss': loss}) ...
wandb.init() 第三步:模型定义 import torch.nn as nn import torch import numpy as np import torch.nn.functional as F use_cuda = True if torch.cuda.is_available() else False MAX_LENGTH = 512 FILTERS = [2,3,4,5] NUM_LABEL = value_counts.shape[0] ...