mmdetection/configs/_base_/default_runtime.py

checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]

dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]

# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
Fix typos (#2768) 5 years ago			`checkpoint_config = dict(interval=1)`
			`# yapf:disable`
			`log_config = dict(`
			`interval=50,`
			`hooks=[`
			`dict(type='TextLoggerHook'),`
			`# dict(type='TensorboardLoggerHook')`
			`])`
			`# yapf:enable`
add CompatibleCheckHook (#4508) * add CompatibleCheckHook * check val * add docstr * move hook to default runtime * add unitest * fix a typo * fix unitest * update docs * add check for CLASSES is None * fix doc 4 years ago			`custom_hooks = [dict(type='NumClassCheckHook')]`

Fix typos (#2768) 5 years ago			`dist_params = dict(backend='nccl')`
			`log_level = 'INFO'`
			`load_from = None`
			`resume_from = None`
			`workflow = [('train', 1)]`
[Enhance] Continue to speed up training. (#6974) * [Enhance] Speed up training time. * set in cfg 3 years ago
			`# disable opencv multithreading to avoid system being overloaded`
			`opencv_num_threads = 0`
			# set multi-process start method as `fork` to speed up the training
			`mp_start_method = 'fork'`
[Feature] Support automatically scaling LR according to GPU number and samples per GPU (#7482) * Add default_gpu_number flag in config : default_runtime.py * Support automatically scaling LR according to GPU number * Improve code comments * Improve code comments * Imporve variable naming * Improve log message for scale lr, add print samples_per_gpu * Modify formula for scale lr * using function to encapsulate scale LR * Add special flag for GPU and samples_per_gpu on config and will be used when scaling LR * Add distributed flag in function scale_lr * Docs add Learning rate automatically scale and Add disable_auto_scale_lr flag * Update doc * Use len(cfg.gpu_ids) to get gpu number when distributed mode * Update doc about Learning rate automatically scale * Using default batch size instead of GPU x Sample per GPU, Add flag in each config which not equal default batch size * Using default_batch_size or mmdet_official_special_batch_size to scale the LR * Add argument --auto-scale-lr to enable auto scale lr. * Update doc about learning rate automatically scale * Use default_batch_size in each config file instead of mmdet_official_special_batch_size * make if branch better * Not set default batch size in dataset config * Default batch size set to 16, Fix some code lint * undo some change * Fix lint problem * Doc add the function of default_batch_size and enable_auto_scale_lr * remove `f` where is no variable. * Updata doc about the default_batch_size setting * Update explanation about default_batch_size * Update explanation about default_batch_size * Add default_batch_size in config files which default batch size is not 16. Update Doc * Update Doc * fix lint : double quoted strings * Improve the naming of some variables * Updata doc about the Learning rate automatically scale * According to configs/cityscapes, set default_batch_size = 8 in config files * Fix some comments according to the review * Fix some doc according to the review * Fix some doc according to the review * Not using assert in the function autoscale_lr * Imporve variable naming : enable_auto_scale_lr to auto_scale_lr * Using world_size to get gpu number when is distributed. * function autoscale_lr rename to auto_scale_lr for unifying * Fix lint problem * Fix lint problem * Add warning message when can not find default_batch_size * Add warning message when can not find default_batch_size * Improve the doc accroding to the review * Add `default_initial_lr` for auto scal lr. Using auto_scale_lr_config dict to contain all setting of auto scale lr * Improve coding style * Improve Doc * Improve doc * Remove a previous `default_batch_size` * Fix doc bug * Add warming message when cfg.optimizer.lr != default_initial_lr * Always use `cfg.optimizer.lr` to calculate the new LR * Fix lint problem * warning message version change to 2.24 * Fix logger info line * Set None when can't find value * Fix num_gpu when using distributed * Improve doc * Add more detail for `optimizer.lr` and `auto_scale_lr_config.auto_scale_lr` * Fix lint problem * Using new config dict `auto_scale_lr` * auto_scale_lr logic using `auto_scale_lr` * Improve doc about new auto_scale_lr * Fix logger string * Improve logger info * Fix lint problem * delete some blank lines * Imporve coding * Add the `auto_scale_lr` to those config files which `sample_per_gpu` is not `2` and number of GPU is not specifying in README at the same time * Add the `auto_scale_lr` to those config files which `sample_per_gpu` is not `2` and number of GPU is not specifying in README at the same time 3 years ago
			`# Default setting for scaling LR automatically`
			# - `enable` means enable scaling LR automatically
			`# or not by default.`
			# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
			`auto_scale_lr = dict(enable=False, base_batch_size=16)`