I'm trying to save a plot with wandb.log. Their docs say to do:
wandb.log({"chart": plt})
but this fails for me.
I get two errors, 1st error (when I do NOT do plt.show() before trying to do wand.log):
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydevd_bundle/pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 256, in wrapper
return func(self, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 222, in wrapper
return func(self, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1548, in log
self._log(data=data, step=step, commit=commit)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1339, in _log
self._partial_history_callback(data, step, commit)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1228, in _partial_history_callback
self._backend.interface.publish_partial_history(
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/interface/interface.py", line 541, in publish_partial_history
data = history_dict_to_json(run, data, step=user_step, ignore_copy_err=True)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/utils.py", line 54, in history_dict_to_json
payload[key] = val_to_json(
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/utils.py", line 82, in val_to_json
val = Plotly.make_plot_media(val)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/plotly.py", line 48, in make_plot_media
val = util.matplotlib_to_plotly(val)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/util.py", line 560, in matplotlib_to_plotly
return tools.mpl_to_plotly(obj)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/tools.py", line 112, in mpl_to_plotly
matplotlylib.Exporter(renderer).run(fig)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 53, in run
self.crawl_fig(fig)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 124, in crawl_fig
self.crawl_ax(ax)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 146, in crawl_ax
self.draw_collection(ax, collection)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 289, in draw_collection
offset_order = offset_dict[collection.get_offset_position()]
AttributeError: 'LineCollection' object has no attribute 'get_offset_position'
I get two errors, 2nd error (when I DO plt.show() before trying to do wand.log):
Traceback (most recent call last):
File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydevd_bundle/pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 256, in wrapper
return func(self, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 222, in wrapper
return func(self, *args, **kwargs)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1548, in log
self._log(data=data, step=step, commit=commit)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1339, in _log
self._partial_history_callback(data, step, commit)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1228, in _partial_history_callback
self._backend.interface.publish_partial_history(
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/interface/interface.py", line 541, in publish_partial_history
data = history_dict_to_json(run, data, step=user_step, ignore_copy_err=True)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/utils.py", line 54, in history_dict_to_json
payload[key] = val_to_json(
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/utils.py", line 82, in val_to_json
val = Plotly.make_plot_media(val)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/sdk/data_types/plotly.py", line 48, in make_plot_media
val = util.matplotlib_to_plotly(val)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/wandb/util.py", line 560, in matplotlib_to_plotly
return tools.mpl_to_plotly(obj)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/tools.py", line 112, in mpl_to_plotly
matplotlylib.Exporter(renderer).run(fig)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 53, in run
self.crawl_fig(fig)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/exporter.py", line 122, in crawl_fig
with self.renderer.draw_figure(fig=fig, props=utils.get_figure_properties(fig)):
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mplexporter/renderers/base.py", line 45, in draw_figure
self.open_figure(fig=fig, props=props)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/renderer.py", line 90, in open_figure
self.mpl_x_bounds, self.mpl_y_bounds = mpltools.get_axes_bounds(fig)
File "/Users/brandomiranda/opt/anaconda3/envs/meta_learning/lib/python3.9/site-packages/plotly/matplotlylib/mpltools.py", line 265, in get_axes_bounds
x_min, y_min, x_max, y_max = min(x_min), min(y_min), max(x_max), max(y_max)
ValueError: min() arg is an empty sequence
Note that their trivial example DOES work:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel("some interesting numbers")
wandb.log({"chart": plt})
for me.
cross posted: https://community.wandb.ai/t/how-does-one-save-a-plot-in-wandb-with-wandb-log/2373
Use:
wandb.log({"plot": wandb.Image(fig)})
– Rylan Schaeffer
Full working example:
import numpy as np
import matplotlib.pyplot as plt
import wandb
def generate_data() -> tuple[np.ndarray, np.ndarray]:
# Initialize constants and data arrays
C = np.array(
[[7e9, 2e12], [13e9, 2e12], [34e9, 2e12], [70e9, 2e12]]
) # [m, Din] = [4, 2]
m = C.shape[0] # 4
K = 1 # Number of columns for L_target
# Create target loss values and add noise
L_target = np.array([[1.85], [1.66], [1.55], [1.5]])
L_target = np.repeat(L_target, K, axis=1) # [m, K]
assert m == L_target.shape[0], f"{m=} {L_target.shape[0]=}"
noise = np.random.normal(loc=0.0, scale=0.01, size=(m, K))
L_target += noise # [m, K]
return C, L_target
def main():
# Initialize Weights & Biases
wandb.init(project='your_project_name')
# Generate data
C, L_target = generate_data()
# Flatten the arrays for plotting
compute_values = C[:, 0] # Taking the first column for compute values
loss_values = L_target.flatten() # Flatten the loss values to 1D array
# Create the plot
fig, ax = plt.subplots()
ax.plot(compute_values, loss_values, linestyle='-', marker='x', label='Random Scaling Law')
ax.set_xscale("log")
ax.set_yscale("linear")
ax.set_xlabel("Log Compute (log C), C=6ND")
ax.set_ylabel("Loss")
ax.set_title("Scaling Law Prediction (Log-Log Plot)")
ax.grid(True)
ax.legend()
# Log the plot as an image to wandb
wandb.log({"plot": wandb.Image(fig)})
# Display the plot
plt.show()
if __name__ == "__main__":
main()