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Log Data with wandb.log
Keep track of metrics, videos, custom plots, and more
Call
wandb.log(dict) to log a dictionary of metrics, media, or custom objects to a step. Each time you log, we increment the step by default, so you can see how your models and data evolve over time.Example Usage
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wandb.log({"loss": 0.314, "epoch": 5,
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"inputs": wandb.Image(inputs),
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"logits": wandb.Histogram(ouputs),
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"captions": wandb.Html(captions)})
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Common Workflows
- 1.Compare the best accuracy: To compare the best value of a metric across runs, set the summary value for that metric. By default, summary is set to the last value you logged for each key. This is useful in the table in the UI, where you can sort and filter runs based on their summary metrics — so you could compare runs in a table or bar chart based on their best accuracy, instead of final accuracy. For example, you could set summary like so:
wandb.run.summary["best_accuracy"] = best_accuracy - 2.Multiple metrics on one chart: Log multiple metrics in the same call to
wandb.log, like this:wandb.log({"acc'": 0.9, "loss": 0.1})and they will both be available to plot against in the UI - 3.Custom x-axis: Add a custom x-axis to the same log call to visualize your metrics against a different axis in the W&B dashboard. For example:
wandb.log({'acc': 0.9, 'epoch': 3, 'batch': 117}). To set the default x-axis for a given metric use Run.define_metric()​ - 4.Log rich media and charts:
wandb.logsupports the logging of a wide variety of data types, from media like images and videos to tables and charts.
In-Depth Guides
For in-depth information on how to log everything from histograms to 3d molecules, check out the guides below.
Reference Documentation
For precise details about the signatures and behavior of logging functions, review the reference docs, generated from the
wandb Python library.Stepwise and Incremental Logging
Information logged to Weights & Biases with
wandb.log is tracked over time, forming the "history" of a run. By default, each call to wandb.log is a new step and all of our charts and panels use the history step as the x-axis.If you want to plot your metrics against different x-axes, you can log those values like you would any other metric, like
wandb.log({'loss': 0.1, 'epoch': 1, 'batch': 3}). In the UI you can switch between x-axes in the chart settings.If you want to log to a single history step from lots of different places in your code you can pass a step index to
wandb.log() as follows:1
wandb.log({'loss': 0.2}, step=step)
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As long as you keep passing the same value for
step, W&B will collect the keys and values from each call in one unified dictionary. As soon you call wandb.log() with a different value for step than the previous one, W&B will write all the collected keys and values to the history, and start collection over again. Note that this means you should only use this with consecutive values for step: 0, 1, 2, .... This feature doesn't let you write to absolutely any history step that you'd like, only the "current" one and the "next" one.You can also set
commit=False in wandb.log to accumulate metrics, just be sure to eventually call wandb.log with commit=True (the default) to persist the metrics.1
wandb.log({'loss': 0.2}, commit=False)
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# Somewhere else when I'm ready to report this step:
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wandb.log({'accuracy': 0.8})
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Summary Metrics
In addition to values that change over time during training, it's often important to track a single value that summarizes a model or a preprocessing step, stored in the run's
summary dictionary. For values that are logged with wandb.log, we automatically set summary to the last value added. You can also add metrics or media to the summary directly or overwrite the default values. If a summary metric is modified, the previous value is lost.1
wandb.init(config=args)
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​
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best_accuracy = 0
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for epoch in range(1, args.epochs + 1):
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test_loss, test_accuracy = test()
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if (test_accuracy > best_accuracy):
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wandb.run.summary["best_accuracy"] = test_accuracy
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best_accuracy = test_accuracy
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You may want to store evaluation metrics in a runs summary after training has completed. Summary can handle numpy arrays, PyTorch tensors or TensorFlow tensors. When a value is one of these types we persist the entire tensor in a binary file and store high level metrics in the summary object such as min, mean, variance, 95th percentile, etc.
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api = wandb.Api()
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run = api.run("username/project/run_id")
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run.summary["tensor"] = np.random.random(1000)
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run.summary.update()
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Customize axes and summaries with define_metric
define_metricUse
define_metric to set a custom x axis or capture a custom summary of a metric.- Custom x-axes are useful in contexts where you need to log to different time steps in the past during training, asynchronously. For example, this can be useful in RL where you may track the per-episode reward and a per-step reward.
- Custom metric summaries are useful to capture model performance at the best step, instead of the last step, of training in your
wandb.summary. For example, you might want to capture the maximum accuracy or the minimum loss value, instead of the final value.
Customize axes
By default, all metrics are logged against the same x-axis, which is the W&B internal
step. Sometimes, you might want to log to a previous step, or use a different x-axis.Here's an example of setting a custom x-axis metric, instead of the default step.
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import wandb
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​
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wandb.init()
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# define our custom x axis metric
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wandb.define_metric("custom_step")
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# define which metrics will be plotted against it
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wandb.define_metric("validation_loss", step_metric="custom_step")
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​
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for i in range(10):
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log_dict = {
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"train_loss": 1/(i+1),
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"custom_step": i**2,
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"validation_loss": 1/(i+1)
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}
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wandb.log(log_dict)
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The x axis can be set using globs as well. Currently, only globs that have string prefixes are available. The following example will plot all logged metrics with the prefix
"train/" to the x-axis "train/step":1
import wandb
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​
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wandb.init()
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# define our custom x axis metric
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wandb.define_metric("train/step")
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# set all other train/ metrics to use this step
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wandb.define_metric("train/*", step_metric="train/step")
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​
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for i in range(10):
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log_dict = {
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"train/step": 2 ** i # grows exponentially with internal wandb step
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"train/loss": 1/(i+1), # x-axis is train/step
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"train/accuracy": 1 - (1/(1+i)), # x-axis is train/step
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"val/loss": 1/(1+i), # x-axis is internal wandb step
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}
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wandb.log(log_dict)
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Customize the summary
Summary metrics can be controlled using the
summary argument in define_metric which accepts the following values: "min", "max", "mean" ,"best", "last" and "none". The "best" parameter can only be used in conjunction with the optional objective argument which accepts values "minimize" and "maximize". Here's an example of capturing the lowest value of loss and the maximum value of accuracy in the summary, instead of the default summary behavior, which uses the final value from history.1
import wandb
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import random
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​
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random.seed(1)
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wandb.init()
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# define a metric we are interested in the minimum of
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wandb.define_metric("loss", summary="min")
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# define a metric we are interested in the maximum of
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wandb.define_metric("acc", summary="max")
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for i in range(10):
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log_dict = {
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"loss": random.uniform(0,1/(i+1)),
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"acc": random.uniform(1/(i+1),1),
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}
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wandb.log(log_dict)
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Here's what the resulting min and max summary values look like, in pinned columns in the sidebar on the Project Page workspace:
Project Page Sidebar
Last modified 3mo ago