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Wandb config added to docs #1853

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Wandb config added to docs #1853

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1d023d4
Wandb config added to docs
Nourollah Aug 29, 2022
4e06afb
[Docs] Hooks Updated in Config File
Nourollah Aug 29, 2022
758d6c3
[Docs] Details added for WandB
Nourollah Aug 29, 2022
29f74fc
[Docs] lint test passed
Nourollah Sep 2, 2022
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10 changes: 7 additions & 3 deletions docs/tutorials/1_config.md
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Expand Up @@ -646,9 +646,13 @@ We incorporate modular design into our config system, which is convenient to con
interval=1, save_best='[email protected]') # Interval to perform evaluation and the key for saving best checkpoint
log_config = dict( # Config to register logger hook
interval=20, # Interval to print the log
hooks=[ # Hooks to be implemented during training
dict(type='TextLoggerHook'), # The logger used to record the training process
])
hooks=[ # Hooks to be implemented during training
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='WandbLoggerHook', by_epoch=False,
init_kwargs={'entity': entity, 'project': project, 'config': cfg_dict}), # The Wandb logger is also supported, It requires `wandb` to be installed.
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How to define project and cfg_dict? Thanks

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It's simple. project is the project name in WandB.ai, and the entity is the user entity/name in WandB.ai. I'll make a few changes to provide better docs.

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also, cfg_dict is a dictionary containing parameters we need to log into WandB.

# ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation.
])

# runtime settings
dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set
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10 changes: 6 additions & 4 deletions docs_zh_CN/tutorials/1_config.md
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Expand Up @@ -415,10 +415,12 @@ MMAction2 将模块化设计整合到配置文件系统中，以便执行各类
save_best='top_k_accuracy') # 设置 `top_k_accuracy` 作为指示器，用于存储最好的模型权重文件
log_config = dict( # 注册日志钩子的设置
interval=20, # 打印日志间隔
hooks=[ # 训练期间执行的钩子
dict(type='TextLoggerHook'), # 记录训练过程信息的日志
# dict(type='TensorboardLoggerHook'), # 同时支持 Tensorboard 日志
])
hooks=[ # 训练期间执行的钩子
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='WandbLoggerHook', by_epoch=False,
init_kwargs={'entity': entity, 'project': project, 'config': cfg_dict}), # 同样支持 Wandb 日志
])

# 运行设置
dist_params = dict(backend='nccl') # 建立分布式训练的设置，其中端口号也可以设置
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