wandbot-dev
Project visibility
Public
Last active
7/29/2025, 11:51:13 AM
Owner
Contributors
11 users
Total runs
3855
Total traces
1,958,703
Traces ingestion size
363.2GB
Objects ingestion size
66.3MB
Tables ingestion size
271.3kB
Files ingestion size
1.2MB
Total compute
95 days
Export & update data
Use our Public API to export data or update data, such as editing the config of existing runs. Learn more in the docs →
Find the run path
To use the public API, you'll often need the run path which is <entity>/<project>/<run_id>. In the app UI, open a run page, then click on the Overview tab to see the run path.
Update config for an existing run
This example updates one of your configuration settings.
import wandb
api = wandb.Api()
run = api.run("wandbot/wandbot-dev/<run_id>")
run.config["key"] = updated_value
run.update()
Export metrics from a single run to a CSV file
This snippet finds all the metrics saved for a single run and saves them to a CSV file.
import wandb
api = wandb.Api()
# run is specified by <entity>/<project>/<run_id>
run = api.run("wandbot/wandbot-dev/<run_id>")
# save the metrics for the run to a csv file
metrics_dataframe = run.history()
metrics_dataframe.to_csv("metrics.csv")
Read metrics for a run
Pull down the accuracy and timestamps for logged metric data. In this example, data was logged with wandb.log({"accuracy": acc}) to a run with the run path <entity>/<project>/<run_id>.
import wandb
api = wandb.Api()
run = api.run("wandbot/wandbot-dev/<run_id>")
if run.state == "finished":
for i, row in run.history().iterrows():
print(row["_timestamp"], row["accuracy"])
Get unsampled metric data
When you pull data from history, by default it's sampled to 500 points. Get all the logged data points using run.scan_history(). Here's an example downloading all the loss data points logged in history.
import wandb
api = wandb.Api()
run = api.run("wandbot/wandbot-dev/<run_id>")
history = run.scan_history()
losses = [row["loss"] for row in history]
Download the best model file from a sweep
This snippet downloads the model file with the highest validation accuracy from a sweep with runs that saved model files to model.h5.
import wandb
api = wandb.Api()
sweep = api.sweep("wandbot/wandbot-dev/<sweep_id>")
runs = sorted(sweep.runs,
key=lambda run: run.summary.get("val_acc", 0), reverse=True)
val_acc = runs[0].summary.get("val_acc", 0)
print(f"Best run {runs[0].name} with {val_acc}% validation accuracy")
runs[0].file("model.h5").download(replace=True)
print("Best model saved to model-best.h5")