Export and update data

Export data

Use the Public API to export or update data that you have saved to W&B. Before using this API, log data from your script. Check the Quickstart for more details. Use Cases for the Public API

Export Data: Pull down a dataframe for custom analysis in a Jupyter Notebook. Once you have explored the data, you can sync your findings by creating a new analysis run and logging results, for example: wandb.init(job_type="analysis")
Update Existing Runs: You can update the data logged in association with a W&B run. For example, you might want to update the config of a set of runs to include additional information, like the architecture or a hyperparameter that wasn’t originally logged.

See the Generated Reference Docs for details on available functions.

Export run data

Download data from a finished or active run. Common usage includes downloading a dataframe for custom analysis in a Jupyter notebook, or using custom logic in an automated environment.

import wandb

api = wandb.Api()
run = api.run("<entity>/<project>/<run_id>")

The most commonly used attributes of a run object are:

Attribute	Meaning
`run.config`	A dictionary of the run’s configuration information, such as the hyperparameters for a training run or the preprocessing methods for a run that creates a dataset Artifact. Think of these as the run’s inputs.
`run.history()`	A list of dictionaries meant to store values that change while the model is training such as loss. The command `run.log()` appends to this object.
`run.summary`	A dictionary of information that summarizes the run’s results. This can be scalars like accuracy and loss, or large files. By default, `run.log()` sets the summary to the final value of a logged time series. The contents of the summary can also be set directly. Think of the summary as the run’s outputs.

You can also modify or update the data of past runs. By default a single instance of an api object will cache all network requests. If your use case requires real time information in a running script, call api.flush() to get updated values.

Querying multiple runs

DataFrame and CSVs
MongoDB Style

This example script finds a project and outputs a CSV of runs with name, configs and summary stats. Replace <entity> and <project> with your W&B entity and the name of your project, respectively.

import pandas as pd
import wandb

api = wandb.Api()
entity, project = "<entity>", "<project>"
runs = api.runs(entity + "/" + project)

summary_list, config_list, name_list = [], [], []
for run in runs:
    # .summary contains output keys/values for
    # metrics such as accuracy.
    #  We call ._json_dict to omit large files
    summary_list.append(run.summary._json_dict)

    # .config contains the hyperparameters.
    #  We remove special values that start with _.
    config_list.append({k: v for k, v in run.config.items() if not k.startswith("_")})

    # .name is the human-readable name of the run.
    name_list.append(run.name)

runs_df = pd.DataFrame(
    {"summary": summary_list, "config": config_list, "name": name_list}
)

runs_df.to_csv("project.csv")

run.finish()

The W&B API also provides a way for you to query across runs in a project with api.runs(). The most common use case is exporting runs data for custom analysis. The query interface is the same as the one MongoDB uses.

runs = api.runs(
    "username/project",
    {"$or": [{"config.experiment_name": "foo"}, {"config.experiment_name": "bar"}]},
)
print(f"Found {len(runs)} runs")

Calling api.runs returns a Runs object that is iterable and acts like a list. By default the object loads 50 runs at a time in sequence as required, but you can change the number loaded per page with the per_page keyword argument. api.runs also accepts an order keyword argument. The default order is -created_at. To order results ascending, specify +created_at. You can also sort by config or summary values. For example, summary.val_acc or config.experiment_name.

Error handling

If errors occur while talking to W&B servers a wandb.CommError will be raised. The original exception can be introspected via the exc attribute.

Get a run’s name and ID during an active run

After calling wandb.init() you can access the random run ID or the human readable run name from your script like this:

Unique run ID (8 character hash): run.id
Random run name (human readable): run.name

If you’re thinking about ways to set useful identifiers for your runs, here’s what we recommend:

Run ID: leave it as the generated hash. This needs to be unique across runs in your project.
Run name: This should be something short, readable, and preferably unique so that you can tell the difference between different lines on your charts.
Run notes: This is a great place to put a quick description of what you’re doing in your run. You can set this with wandb.init(notes="your notes here")
Run tags: Track things dynamically in run tags, and use filters in the UI to filter your table down to just the runs you care about. You can set tags from your script and then edit them in the UI, both in the runs table and the overview tab of the run page. See the detailed instructions here.

Public API Examples

Read metrics from a run

This example outputs timestamp and accuracy saved with run.log({"accuracy": acc}) for a run saved to "<entity>/<project>/<run_id>".

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
if run.state == "finished":
    for i, row in run.history().iterrows():
        print(row["_timestamp"], row["accuracy"])

Read specific metrics from a run

To pull specific metrics from a run, use the keys argument. The default number of samples when using run.history() is 500. Logged steps that do not include a specific metric will appear in the output dataframe as NaN. The keys argument will cause the API to sample steps that include the listed metric keys more frequently.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
if run.state == "finished":
    for i, row in run.history(keys=["accuracy"]).iterrows():
        print(row["_timestamp"], row["accuracy"])

Compare two runs

This will output the config parameters that are different between run1 and run2.

import pandas as pd
import wandb

api = wandb.Api()

# replace with your <entity>, <project>, and <run_id>
run1 = api.run("<entity>/<project>/<run_id>")
run2 = api.run("<entity>/<project>/<run_id>")


df = pd.DataFrame([run1.config, run2.config]).transpose()

df.columns = [run1.name, run2.name]
print(df[df[run1.name] != df[run2.name]])

Outputs:

              c_10_sgd_0.025_0.01_long_switch base_adam_4_conv_2fc
batch_size                                 32                   16
n_conv_layers                               5                    4
optimizer                             rmsprop                 adam

Update metrics for a run, after the run has finished

This example sets the accuracy of a previous run to 0.9:

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
run.summary["accuracy"] = 0.9
run.summary.update()

Rename a metric in a completed run

This example renames a summary column in your tables.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
run.summary["new_name"] = run.summary["old_name"]
del run.summary["old_name"]
run.summary.update()

Renaming a column only applies to tables. Charts will still refer to metrics by their original names.

Update config for an existing run

This example updates one of your configuration settings.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
run.config["key"] = updated_value
run.update()

See Configure experiments for more information.

Export system resource consumptions to a CSV file

The snippet below would find the system resource consumptions and then, save them to a CSV.

import wandb

run = wandb.Api().run("<entity>/<project>/<run_id>")
system_metrics = run.history(stream="events")
system_metrics.to_csv("sys_metrics.csv")

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("<entity>/<project>/<run_id>")
history = run.scan_history()
losses = [row["loss"] for row in history]

Get paginated data from history

To retrieve a run’s complete, unsampled history, use run.scan_history(). Unsampled history includes every history record for the run. By contrast, the sampled view from run.history() can omit individual records. The page_size parameter controls the maximum number of history records fetched per API request and defaults to 1000. If requests are slow or time out, try a smaller page size. Smaller pages can reduce timeout risk but require more API requests. The keys parameter independently filters which metrics are returned.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
run.scan_history(keys=sorted(cols), page_size=100)

Export metrics from all runs in a project to a CSV file

This script pulls down the runs in a project and produces a dataframe and a CSV of runs including their names, configs, and summary stats. Replace <entity> and <project> with your W&B entity and the name of your project, respectively.

import pandas as pd
import wandb

api = wandb.Api()
entity, project = "<entity>", "<project>"
runs = api.runs(entity + "/" + project)

summary_list, config_list, name_list = [], [], []
for run in runs:
    # .summary contains the output keys/values
    #  for metrics such as accuracy.
    #  We call ._json_dict to omit large files
    summary_list.append(run.summary._json_dict)

    # .config contains the hyperparameters.
    #  We remove special values that start with _.
    config_list.append({k: v for k, v in run.config.items() if not k.startswith("_")})

    # .name is the human-readable name of the run.
    name_list.append(run.name)

runs_df = pd.DataFrame(
    {"summary": summary_list, "config": config_list, "name": name_list}
)

runs_df.to_csv("project.csv")

Get the starting time for a run

This code snippet retrieves the time at which the run was created.

import wandb

api = wandb.Api()

run = api.run("entity/project/run_id")
start_time = run.created_at

Upload files to a finished run

The code snippet below uploads a selected file to a finished run.

import wandb

api = wandb.Api()

run = api.run("entity/project/run_id")
run.upload_file("file_name.extension")

Download a file from a run

This finds the file “model-best.h5” associated with run ID uxte44z7 in the cifar project and saves it locally.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
run.file("model-best.h5").download()

Download all files from a run

This finds all files associated with a run and saves them locally.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
for file in run.files():
    file.download()

Get runs from a specific sweep

This snippet downloads all the runs associated with a particular sweep.

import wandb

api = wandb.Api()

sweep = api.sweep("<entity>/<project>/<sweep_id>")
sweep_runs = sweep.runs

Get the best run from a sweep

The following snippet gets the best run from a given sweep.

import wandb

api = wandb.Api()

sweep = api.sweep("<entity>/<project>/<sweep_id>")
best_run = sweep.best_run()

The best_run is the run with the best metric as defined by the metric parameter in the sweep config.

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("<entity>/<project>/<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}% val accuracy")

runs[0].file("model.h5").download(replace=True)
print("Best model saved to model-best.h5")

Delete all files with a given extension from a run

This snippet deletes files with a given extension from a run.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")

extension = ".png"
files = run.files()
for file in files:
    if file.name.endswith(extension):
        file.delete()

Download system metrics data

This snippet produces a dataframe with all the system resource consumption metrics for a run and then saves it to a CSV.

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")
system_metrics = run.history(stream="events")
system_metrics.to_csv("sys_metrics.csv")

Update summary metrics

You can pass a dictionary to update summary metrics.

summary.update({"key": val})

Get the command that ran the run

Each run captures the command that launched it on the run overview page. To pull this command down from the API, you can run:

import wandb

api = wandb.Api()

run = api.run("<entity>/<project>/<run_id>")

meta = json.load(run.file("wandb-metadata.json").download())
program = ["python"] + [meta["program"]] + meta["args"]

Guides

Integrations

Reference

Export data

Export run data

Querying multiple runs

Error handling

Get a run’s name and ID during an active run

Public API Examples

Read metrics from a run

Read specific metrics from a run

Compare two runs

Update metrics for a run, after the run has finished

Rename a metric in a completed run

Update config for an existing run

Export system resource consumptions to a CSV file

Get unsampled metric data

Get paginated data from history

Export metrics from all runs in a project to a CSV file

Get the starting time for a run

Upload files to a finished run

Download a file from a run

Download all files from a run

Get runs from a specific sweep

Get the best run from a sweep

Download the best model file from a sweep

Delete all files with a given extension from a run

Download system metrics data

Update summary metrics

Get the command that ran the run

​Export data

​Export run data

​Querying multiple runs

​Error handling

​Get a run’s name and ID during an active run

​Public API Examples

​Read metrics from a run

​Read specific metrics from a run

​Compare two runs

​Update metrics for a run, after the run has finished

​Rename a metric in a completed run

​Update config for an existing run

​Export system resource consumptions to a CSV file

​Get unsampled metric data

​Get paginated data from history

​Export metrics from all runs in a project to a CSV file

​Get the starting time for a run

​Upload files to a finished run

​Download a file from a run

​Download all files from a run

​Get runs from a specific sweep

​Get the best run from a sweep

​Download the best model file from a sweep

​Delete all files with a given extension from a run

​Download system metrics data

​Update summary metrics

​Get the command that ran the run

Export data

Export run data

Querying multiple runs

Error handling

Get a run’s name and ID during an active run

Public API Examples

Read metrics from a run

Read specific metrics from a run

Compare two runs

Update metrics for a run, after the run has finished

Rename a metric in a completed run

Update config for an existing run

Export system resource consumptions to a CSV file

Get unsampled metric data

Get paginated data from history

Export metrics from all runs in a project to a CSV file

Get the starting time for a run

Upload files to a finished run

Download a file from a run

Download all files from a run

Get runs from a specific sweep

Get the best run from a sweep

Download the best model file from a sweep

Delete all files with a given extension from a run

Download system metrics data

Update summary metrics

Get the command that ran the run