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Add log_freq param to WandbCallback #3401

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Add log_freq param to WandbCallback #3401

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18 changes: 12 additions & 6 deletions nbs/70_callback.wandb.ipynb
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Expand Up @@ -100,14 +100,14 @@
" # Record if watch has been called previously (even in another instance)\n",
" _wandb_watch_called = False\n",
"\n",
" def __init__(self, log=\"gradients\", log_preds=True, log_model=True, log_dataset=False, dataset_name=None, valid_dl=None, n_preds=36, seed=12345, reorder=True):\n",
" def __init__(self, log=\"gradients\", log_preds=True, log_model=True, log_dataset=False, dataset_name=None, valid_dl=None, n_preds=36, seed=12345, reorder=True, log_freq=1000):\n",
" # Check if wandb.init has been called\n",
" if wandb.run is None:\n",
" raise ValueError('You must call wandb.init() before WandbCallback()')\n",
" # W&B log step\n",
" self._wandb_step = wandb.run.step - 1 # -1 except if the run has previously logged data (incremented at each batch)\n",
" self._wandb_epoch = 0 if not(wandb.run.step) else math.ceil(wandb.run.summary['epoch']) # continue to next epoch\n",
" store_attr('log,log_preds,log_model,log_dataset,dataset_name,valid_dl,n_preds,seed,reorder')\n",
" store_attr('log,log_preds,log_model,log_dataset,dataset_name,valid_dl,n_preds,seed,reorder,log_freq')\n",
"\n",
" def before_fit(self):\n",
" \"Call watch method to log model topology, gradients & weights\"\n",
Expand All @@ -125,7 +125,7 @@
" if not WandbCallback._wandb_watch_called:\n",
" WandbCallback._wandb_watch_called = True\n",
" # Logs model topology and optionally gradients and weights\n",
" wandb.watch(self.learn.model, log=self.log)\n",
" wandb.watch(self.learn.model, log=self.log, log_freq=self.log_freq)\n",
"\n",
" # log dataset\n",
" assert isinstance(self.log_dataset, (str, Path, bool)), 'log_dataset must be a path or a boolean'\n",
Expand Down Expand Up @@ -873,15 +873,21 @@
}
],
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