wandb使用教程（持续更新ing...）

诸神缄默不语-个人CSDN博文目录

wandb官网：Weights & Biases – Developer tools for ML

最近更新时间：2023.2.2
最早更新时间：2022.5.17

账号注册和登录应该很简单，我就不说了。

1. 系统整体设置

在https://wandb.ai/settings中可以设置什么时候以邮件或slack（我没下，所以我全用邮件了）提醒：

其中run finished在使用jupyter notebook环境时不会起效，以防每个cell运行结束后都提醒一次。如果用jupyter notebook的话，需要用wandb.alert()来触发提醒。

此外还可以关联GitHub以迅速提交benchmark runs（这个我还没试过，所以还没有关联）：

储存空间可以进行管理，免费的有100个G，点击“manage storage”可以直接删不需要的文件：

2. 学术

2.1 免费学术小组

这个我还没试过，意思应该是学校或科研机构之类的可以免费建小组。如果有需要可以使用：https://wandb.ai/site/academic

2.2 在论文中引用wandb的方式

来自https://wandb.ai/site/academic：

@misc{wandb,
title = {Experiment Tracking with Weights and Biases},
year = {2020},
note = {Software available from wandb.com},
url={https://www.wandb.com/},
author = {Biewald, Lukas},
}

2.3 wandb白皮书

https://www.dropbox.com/s/0ipub9ewwkml8jf/Experiment%20Tracking%20with%20Weights%20%26%20Biases.pdf?dl=1

3. wandb文档

Weights & Biases - Documentation

wandb支持多平台、多框架。

3.1 wandb quickstart

Quickstart - Documentation
Jupyter Notebook代码实践：Intro_to_Weights_&_Biases.ipynb - Colaboratory

3.1.1 set up

命令行：

pip install wandb
wandb login

（如果代码中有用到wandb，其实会自动要求登录，但是如果用nohup挂起脚本的话就无法实现，所以还是建议提前登录好）
API key的位置在：https://wandb.ai/authorize
（值得注意的是，这个配置应该是全局的，虽然我在一个虚拟环境下登录了我的账号，但是在别的虚拟环境下重新安装wandb还是可以直接通用。我估计是因为缓存在了本地）
用wandb login --relogin强制重新登录。

用jupyter notebook则为：

!pip install wandb
wandb.login()

3.1.2 开启新项目，跟踪指标、超参，添加报警信息

感觉就是init新项目，然后指标用log（在下面例子里可以看到，可以分组（如分为train/val）），超参用config，需要报警的内容就添加alert，结果添加到summary，最后finish就行，比较好用。

init文档：Launch Experiments with wandb.init - Documentation
对dashboard整体布局（run page）的介绍：Run Page - Documentation
数据可视化/跟踪指标：Data Visualization - Documentation
跟踪超参：Configure Experiments with wandb.config - Documentation
警告部分文档：Send Alerts with wandb.alert - Documentation

在测试代码时如临时不想与wandb同步，需设置环境变量，使wandb模式变成离线： WANDB_MODE=offline（具体做法是在运行Python代码的命令行中，在python前面加上这句命令）

文档中给出的核心代码：

import wandb
wandb.init(project="my-awesome-project")  #其他入参：name（见下例），config（见下）wandb.log({'accuracy': train_acc, 'loss': train_loss})wandb.config.dropout = 0.2wandb.alert(title="Low accuracy", text=f"Accuracy {acc} is below the acceptable threshold {thresh}"
)

在jupyter notebook上，用一个假示例来模拟：

import random# Launch 5 simulated experiments
total_runs = 5
for run in range(total_runs):# 🐝 1️⃣ Start a new run to track this scriptwandb.init(# Set the project where this run will be loggedproject="wandbexample1", # We pass a run name (otherwise it’ll be randomly assigned, like sunshine-lollypop-10)name=f"experiment_{run}", # Track hyperparameters and run metadataconfig={"learning_rate": 0.02,"architecture": "CNN","dataset": "CIFAR-100","epochs": 10,})# This simple block simulates a training loop logging metricsepochs = 10offset = random.random() / 5for epoch in range(2, epochs):acc = 1 - 2 ** -epoch - random.random() / epoch - offsetloss = 2 ** -epoch + random.random() / epoch + offset# 🐝 2️⃣ Log metrics from your script to W&Bwandb.log({"acc": acc, "loss": loss})# Mark the run as finishedwandb.finish()

在输出中会给出wandb项目链接，在浏览器中打开即可。
记录的内容：

输出的内容：

可以看出运行数据保存在了本地的wandb文件夹，额外的信息都没有上传到wandb上。

3.1.3 上一节的PyTorch例子

jupyter notebook上：MNIST分类器

#@title
import wandb
import math
import random
import torch, torchvision
import torch.nn as nn
import torchvision.transforms as T
from tqdm.notebook import tqdmdevice = "cuda:0" if torch.cuda.is_available() else "cpu"def get_dataloader(is_train, batch_size, slice=5):"Get a training dataloader"full_dataset = torchvision.datasets.MNIST(root=".", train=is_train, transform=T.ToTensor(), download=True)sub_dataset = torch.utils.data.Subset(full_dataset, indices=range(0, len(full_dataset), slice))loader = torch.utils.data.DataLoader(dataset=sub_dataset, batch_size=batch_size, shuffle=True if is_train else False, pin_memory=True, num_workers=2)return loaderdef get_model(dropout):"A simple model"model = nn.Sequential(nn.Flatten(),nn.Linear(28*28, 256),nn.BatchNorm1d(256),nn.ReLU(),nn.Dropout(dropout),nn.Linear(256,10)).to(device)return modeldef validate_model(model, valid_dl, loss_func, log_images=False, batch_idx=0):"Compute performance of the model on the validation dataset and log a wandb.Table"model.eval()val_loss = 0.with torch.inference_mode():correct = 0for i, (images, labels) in tqdm(enumerate(valid_dl), leave=False):images, labels = images.to(device), labels.to(device)# Forward pass ➡outputs = model(images)val_loss += loss_func(outputs, labels)*labels.size(0)# Compute accuracy and accumulate_, predicted = torch.max(outputs.data, 1)correct += (predicted == labels).sum().item()# Log one batch of images to the dashboard, always same batch_idx.if i==batch_idx and log_images:log_image_table(images, predicted, labels, outputs.softmax(dim=1))return val_loss / len(valid_dl.dataset), correct / len(valid_dl.dataset)def log_image_table(images, predicted, labels, probs):"Log a wandb.Table with (img, pred, target, scores)"# 🐝 Create a wandb Table to log images, labels and predictions totable = wandb.Table(columns=["image", "pred", "target"]+[f"score_{i}" for i in range(10)])for img, pred, targ, prob in zip(images.to("cpu"), predicted.to("cpu"), labels.to("cpu"), probs.to("cpu")):table.add_data(wandb.Image(img[0].numpy()*255), pred, targ, *prob.numpy())wandb.log({"predictions_table":table}, commit=False)

训练：

# Launch 5 experiments, trying different dropout rates
for i in range(5):# 🐝 initialise a wandb runwandb.init(project="wandbexample1",name="pytorch_example"+str(i),config={"epochs": 10,"batch_size": 128,"lr": 1e-3,"dropout": random.uniform(0.01, 0.80),})# Copy your config config = wandb.config# Get the datatrain_dl = get_dataloader(is_train=True, batch_size=config.batch_size)valid_dl = get_dataloader(is_train=False, batch_size=2*config.batch_size)n_steps_per_epoch = math.ceil(len(train_dl.dataset) / config.batch_size)# A simple MLP modelmodel = get_model(config.dropout)# Make the loss and optimizerloss_func = nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=config.lr)# Trainingexample_ct = 0step_ct = 0for epoch in tqdm(range(config.epochs)):model.train()for step, (images, labels) in enumerate(tqdm(train_dl, leave=False)):images, labels = images.to(device), labels.to(device)outputs = model(images)train_loss = loss_func(outputs, labels)optimizer.zero_grad()train_loss.backward()optimizer.step()example_ct += len(images)metrics = {"train/train_loss": train_loss, "train/epoch": (step + 1 + (n_steps_per_epoch * epoch)) / n_steps_per_epoch, "train/example_ct": example_ct}if step + 1 < n_steps_per_epoch:# 🐝 Log train metrics to wandb wandb.log(metrics)step_ct += 1val_loss, accuracy = validate_model(model, valid_dl, loss_func, log_images=(epoch==(config.epochs-1)))# 🐝 Log train and validation metrics to wandbval_metrics = {"val/val_loss": val_loss, "val/val_accuracy": accuracy}wandb.log({**metrics, **val_metrics})print(f"Train Loss: {train_loss:.3f}, Valid Loss: {val_loss:3f}, Accuracy: {accuracy:.2f}")# If you had a test set, this is how you could log it as a Summary metricwandb.summary['test_accuracy'] = 0.8# 🐝 Close your wandb run wandb.finish()

wandb网页首页：

点进一个run内：

输出：

3.1.4 jupyter notebook wandb警告示例

# Start a wandb run
wandb.init(project="wandbexample1")# Simulating a model training loop
acc_threshold = 0.3
for training_step in range(1000):# Generate a random number for accuracyaccuracy = round(random.random() + random.random(), 3)print(f'Accuracy is: {accuracy}, {acc_threshold}')# 🐝 Log accuracy to wandbwandb.log({"Accuracy": accuracy})# 🔔 If the accuracy is below the threshold, fire a W&B Alert and stop the runif accuracy <= acc_threshold:# 🐝 Send the wandb Alertwandb.alert(title='Low Accuracy',text=f'Accuracy {accuracy} at step {training_step} is below the acceptable theshold, {acc_threshold}',)print('Alert triggered')break# Mark the run as finished (useful in Jupyter notebooks)
wandb.finish()

acc变成0.155，小于阈值0.3，所以报了警告。因为我没有slack所以是给发到邮件上：

项目的dashboard：

3.2 调参

Hyperparameter Tuning - Documentation
Organizing_Hyperparameter_Sweeps_in_PyTorch_with_W&B.ipynb

3.3 合作报告

Collaborative Reports - Documentation

3.4 跟踪pipeline的数据和模型版本

Data + Model Versioning - Documentation

3.5 数据可视化/跟踪指标日志

Data Visualization - Documentation

3.6 自动化深度学习平台的配置文件

Environment Variables - Documentation

3.7 本地化的解决方案

这个我暂时用不到，但是也列在这里以便备用：
Private Hosting - Documentation

3.8 示例

Examples - Documentation
示例的dashboard：wandb_example Workspace – Weights & Biases

3.9 Interagations

Integrations - Documentation

3.10 输入输出储存到wandb上的数据

Import & Export Data - Documentation

3.11 函数文档

3.11.1 log()

wandb.log - Documentation

1. step（需要是0,1,2,…这样）
   wandb.log({'loss': 0.1, 'epoch': 1, 'batch': 3})
wandb.log({'loss': 0.2}, step=step)
2. commit

4. 常见问题

4.1 wandb.errors.UsageError: Error communicating with wandb process

参考https://docs.wandb.ai/library/init#init-start-error：在wandb.init()中添加入参settings=wandb.Settings(start_method="fork")即可解决问题。

4.2 本地储存空间不够→删除本地缓存

在 wandb.log() 处报错。

报错信息：

Thread SenderThread:
OSError: [Errno 28] No space left on deviceDuring handling of the above exception, another exception occurred:Traceback (most recent call last):File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/internal_util.py", line 51, in runself._run()File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/internal_util.py", line 102, in _runself._process(record)File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/internal.py", line 310, in _processself._sm.send(record)File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/sender.py", line 304, in sendsend_handler(record)File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/sender.py", line 931, in send_summaryself._update_summary()File "env_path/lib/python3.8/site-packages/wandb/sdk/internal/sender.py", line 944, in _update_summaryf.write(json_summary)
OSError: [Errno 28] No space left on device
wandb: ERROR Internal wandb error: file data was not synced
Traceback (most recent call last):File "c49.py", line 293, in <module>wandb.log({'valid_law_acc':law_accuracy,'valid_charge_acc':charge_accuracy,File "env_path/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 289, in wrapperreturn func(self, *args, **kwargs)File "env_path/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 255, in wrapperreturn func(self, *args, **kwargs)File "env_path/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 1591, in logself._log(data=data, step=step, commit=commit)File "env_path/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 1375, in _logself._partial_history_callback(data, step, commit)File "env_path/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 1259, in _partial_history_callbackself._backend.interface.publish_partial_history(File "env_path/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 553, in publish_partial_historyself._publish_partial_history(partial_history)File "env_path/lib/python3.8/site-packages/wandb/sdk/interface/interface_shared.py", line 67, in _publish_partial_historyself._publish(rec)File "env_path/lib/python3.8/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publishself._sock_client.send_record_publish(record)File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 150, in send_record_publishself.send_server_request(server_req)File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 84, in send_server_requestself._send_message(msg)File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 81, in _send_messageself._sendall_with_error_handle(header + data)File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 61, in _sendall_with_error_handlesent = self._sock.send(data[total_sent:])
BrokenPipeError: [Errno 32] Broken pipe
Error in atexit._run_exitfuncs:
Traceback (most recent call last):File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 81, in _send_messageself._sendall_with_error_handle(header + data)File "env_path/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 61, in _sendall_with_error_handlesent = self._sock.send(data[total_sent:])
BrokenPipeError: [Errno 32] Broken pipe

解决方案：wandb sync --clean
（参考https://docs.wandb.ai/ref/cli/wandb-sync）