wandb.log
Logs a dictonary of data to the current run's history.
log(
data: Dict[str, Any],
step: Optional[int] = None,
commit: Optional[bool] = None,
sync: Optional[bool] = None
) -> None
Use
wandb.log to log data from runs, such as scalars, images, video, histograms, plots, and tables.The most basic usage is
wandb.log({"train-loss": 0.5, "accuracy": 0.9}). This will save the loss and accuracy to the run's history and update the summary values for these metrics.Visualize logged data in the workspace at wandb.ai, or locally on a self-hosted instance of the W&B app, or export data to visualize and explore locally, e.g. in Jupyter notebooks, with our API.
In the UI, summary values show up in the run table to compare single values across runs. Summary values can also be set directly with
wandb.run.summary["key"] = value.Logged values don't have to be scalars. Logging any wandb object is supported. For example
wandb.log({"example": wandb.Image("myimage.jpg")}) will log an example image which will be displayed nicely in the W&B UI. See the reference documentation for all of the different supported types or check out our guides to logging for examples, from 3D molecular structures and segmentation masks to PR curves and histograms. wandb.Tables can be used to logged structured data. See our guide to logging tables for details.Logging nested metrics is encouraged and is supported in the W&B UI. If you log with a nested dictionary like
wandb.log({"train": {"acc": 0.9}, "val": {"acc": 0.8}}), the metrics will be organized into train and val sections in the W&B UI.wandb keeps track of a global step, which by default increments with each call to
wandb.log, so logging related metrics together is encouraged. If it's inconvenient to log related metrics together calling wandb.log({"train-loss": 0.5}, commit=False) and then wandb.log({"accuracy": 0.9}) is equivalent to calling wandb.log({"train-loss": 0.5, "accuracy": 0.9}).wandb.log is not intended to be called more than a few times per second. If you want to log more frequently than that it's better to aggregate the data on the client side or you may get degraded performance.Arguments | Text |
|---|---|
data | (dict, optional) A dict of serializable python objects i.e str, ints, floats, Tensors, dicts, or any of the wandb.data_types. |
commit | (boolean, optional) Save the metrics dict to the wandb server and increment the step. If false wandb.log just updates the current metrics dict with the data argument and metrics won't be saved until wandb.log is called with commit=True. |
step | (integer, optional) The global step in processing. This persists any non-committed earlier steps but defaults to not committing the specified step. |
sync | (boolean, True) This argument is deprecated and currently doesn't change the behaviour of wandb.log. |
import wandb
wandb.init()
wandb.log({"accuracy": 0.9, "epoch": 5})
import wandb
wandb.init()
wandb.log({"loss": 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
wandb.log({"accuracy": 0.8})
import numpy as np
import wandb
# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
wandb.init()
wandb.log({"gradients": wandb.Histogram(gradients)})
import numpy as np
import wandb
wandb.init()
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
image = wandb.Image(pixels, caption=f"random field {i}")
examples.append(image)
wandb.log({"examples": examples})
import numpy as np
from PIL import Image as PILImage
import wandb
wandb.init()
examples = []
for i in range(3):
pixels = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
pil_image = PILImage.fromarray(pixels, mode="RGB")
image = wandb.Image(pil_image, caption=f"random field {i}")
examples.append(image)
wandb.log({"examples": examples})
import numpy as np
import wandb
wandb.init()
# axes are (time, channel, height, width)
frames = np.random.randint(low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8)
wandb.log({"video": wandb.Video(frames, fps=4)})
from matplotlib import pyplot as plt
import numpy as np
import wandb
wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y) # plot y = x^2
wandb.log({"chart": fig})
wandb.log({"pr": wandb.plots.precision_recall(y_test, y_probas, labels)})
wandb.log(
{
"generated_samples": [
wandb.Object3D(open("sample.obj")),
wandb.Object3D(open("sample.gltf")),
wandb.Object3D(open("sample.glb")),
]
}
)
Raises | Text |
|---|---|
wandb.Error | if called before wandb.init |
ValueError | if invalid data is passed |
Last modified 3mo ago