revert local change that made its way upstream (showing graphs by it instead of epoch)
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@ -1882,7 +1882,7 @@ def update_training_dataplot(x_min=None, x_max=None, y_min=None, y_max=None, con
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losses = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['loss']),
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x_lim=x_lim, y_lim=y_lim,
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x="it", y="value", # x="epoch",
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x="epoch", y="value", # x="it",
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title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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@ -1890,7 +1890,7 @@ def update_training_dataplot(x_min=None, x_max=None, y_min=None, y_max=None, con
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lrs = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['lr']),
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x_lim=x_lim,
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x="it", y="value", # x="epoch",
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x="epoch", y="value", # x="it",
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title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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@ -1898,7 +1898,7 @@ def update_training_dataplot(x_min=None, x_max=None, y_min=None, y_max=None, con
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grad_norms = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['grad_norm']),
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x_lim=x_lim,
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x="it", y="value", # x="epoch",
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x="epoch", y="value", # x="it",
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title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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