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Wttch's train helper

Project description

wttch 的 AI 训练工具包

一、消息通知

1.1 钉钉 webhook 通知

钉钉 webhook 机器人API

from wttch.train.notification import DingtalkNotification

# 钉钉机器人 webhook 的链接
webhook_url = ''
# 消息签名的密钥
secret = ''

notification = DingtalkNotification(webhook_url, secret)

# 发送文本通知
notification.send_text("")
# 发送markdown
notification.send_markdown('')

1.2 企业微信 webhook 通知

企业微信机器人API

from wttch.train.notification import WechatNotification

# 企业微信机器人 webhook 的链接
webhook_url = ''
notification = WechatNotification(webhook_url)

# 发送文本通知
notification.send_text("")
# 发送markdown
notification.send_markdown('')

二、训练工具包

2.1 缓存工具

不需要修改太多代码就可以帮助你缓存数据到指定的缓存文件去。

(1). 添加 cache_wrapper; (2). 正常调用你的函数。

from wttch.train.utils import cache_wrapper

# 缓存的文件名字前缀,函数的参数会被添加到该名字后面
prefix = 'dataset'
# 缓存的文件夹位置
save_path = './dataset_cache'


@cache_wrapper(prefix=prefix, save_path=save_path)
def you_load_dataset_function():
    return {'a': 1, 'b': 2}


you_load_dataset_function()

2.2 计时器

from wttch.train.utils import StopWatch

stopwatch = StopWatch()
stopwatch.start("job 1")
# 费时操作
stopwatch.stop()
stopwatch.start("job 2")
# 费时操作
stopwatch.stop()

# 格式化打印
stopwatch.display()

三、torch 工具包

3.1 方便设备获取

(1). 将使用的设备写入 thread local; (2). 从 thread local 中获取设备数据; (3). 训练。

from wttch.train.torch.utils import try_gpu, get_device_local, set_device_local

# 尝试获取 gpu 并写入 thread local
set_device_local(try_gpu(device_no=0))

# 从 thread local 读取设备
device = get_device_local()

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