wandb.Run

View source on GitHub

A unit of computation logged by wandb. Typically this is an ML experiment.

Run(
    settings: Settings,
    config: Optional[Dict[str, Any]] = None,
    sweep_config: Optional[Dict[str, Any]] = None
) -> None

Create a run with wandb.init().

In distributed training, use wandb.init() to create a run for each process, and set the group argument to organize runs into a larger experiment.

Currently there is a parallel Run object in the wandb.Api. Eventually these two objects will be merged.

Attributes

history

(History) Time series values, created with wandb.log(). History can contain scalar values, rich media, or even custom plots across multiple steps.

summary

(Summary) Single values set for each wandb.log() key. By default, summary is set to the last value logged. You can manually set summary to the best value, like max accuracy, instead of the final value.

config

Returns: (Config): A config object (similar to a nested dict) of key value pairs associated with the hyperparameters of the run.

dir

Returns: (str): The directory where all of the files associated with the run are placed.

entity

Returns: (str): name of W&B entity associated with run. Entity is either a user name or an organization name.

group

Setting a group helps the W&B UI organize runs in a sensible way. If you are doing a distributed training you should give all of the runs in the training the same group. If you are doing crossvalidation you should give all the crossvalidation folds the same group.

id

id property.

mode

For compatibility with 0.9.x and earlier, deprecate eventually.

name

Returns: (str): the display name of the run. It does not need to be unique and ideally is descriptive.

notes

Returns: (str): notes associated with the run. Notes can be a multiline string and can also use markdown and latex equations inside $$ like $\{x}

path

Returns: (str): the path to the run [entity]/[project]/[run_id]

project

Returns: (str): name of W&B project associated with run.

resumed

Returns: (bool): whether or not the run was resumed

start_time

Returns: (int): the unix time stamp in seconds when the run started

starting_step

Returns: (int): the first step of the run

step

Every time you call wandb.log() it will by default increment the step counter.

sweep_id

Returns: (str, optional): the sweep id associated with the run or None

tags

Returns: (Tuple[str]): tags associated with the run

url

Returns: (str): name of W&B url associated with run.

Methods

alert

View source

alert(
    title: str,
    text: str,
    level: Union[str, None] = None,
    wait_duration: Union[int, float, timedelta, None] = None
) -> None

Launch an alert with the given title and text.

Arguments

title

(str) The title of the alert, must be less than 64 characters long.

text

(str) The text body of the alert.

level

(str or wandb.AlertLevel, optional) The alert level to use, either: INFO, WARN, or ERROR.

wait_duration

(int, float, or timedelta, optional) The time to wait (in seconds) before sending another alert with this title.

define_metric

View source

define_metric(
    name: str,
    step_metric: Union[str, wandb_metric.Metric, None] = None,
    step_sync: bool = None,
    hidden: bool = None,
    summary: str = None,
    goal: str = None,
    overwrite: bool = None,
    **kwargs
) -> wandb_metric.Metric

Define metric properties which will later be logged with wandb.log().

Arguments

name

Name of the metric.

step_metric

Independent variable associated with the metric.

step_sync

Automatically add step_metric to history if needed. Defaults to True if step_metric is specified.

hidden

Hide this metric from automatic plots.

summary

Specify aggregate metrics added to summary. Supported aggregations: "min,max,mean,best,last,none" Default aggregation is copy Aggregation best defaults to goal==minimize

goal

Specify direction for optimizing the metric. Supported direections: "minimize,maximize"

Returns

A metric object is returned that can be further specified.

finish

View source

finish(
    exit_code: int = None
) -> None

Marks a run as finished, and finishes uploading all data. This is used when creating multiple runs in the same process. We automatically call this method when your script exits.

finish_artifact

View source

finish_artifact(
    artifact_or_path: Union[wandb_artifacts.Artifact, str],
    name: Optional[str] = None,
    type: Optional[str] = None,
    aliases: Optional[List[str]] = None,
    distributed_id: Optional[str] = None
) -> wandb_artifacts.Artifact

Finish a non-finalized artifact as output of a run. Subsequent "upserts" with the same distributed ID will result in a new version

Arguments

artifact_or_path

(str or Artifact) A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.

name

(str, optional) An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.

type

(str) The type of artifact to log, examples include dataset, model

aliases

(list, optional) Aliases to apply to this artifact, defaults to ["latest"]

distributed_id

(string, optional) Unique string that all distributed jobs share. If None, defaults to the run's group name.

Returns

An Artifact object.

get_project_url

View source

get_project_url() -> Optional[str]

Returns: A url (str, optional) for the W&B project associated with the run or None if the run is offline

get_sweep_url

View source

get_sweep_url() -> Optional[str]

Returns: A url (str, optional) for the sweep associated with the run or None if there is no associated sweep or the run is offline.

get_url

View source

get_url() -> Optional[str]

Returns: A url (str, optional) for the W&B run or None if the run is offline

join

View source

join(
    exit_code: int = None
) -> None

Deprecated alias for finish() - please use finish

log

View source

log(
    data: Dict[str, Any],
    step: int = None,
    commit: bool = None,
    sync: bool = None
) -> None

Log a dict to the global run's history.

Use wandb.log to log data from runs, such as scalars, images, video, histograms, and matplotlib plots.

The most basic usage is wandb.log({'train-loss': 0.5, 'accuracy': 0.9}). This will save a history row associated with the run with train-loss=0.5 and accuracy=0.9. Visualize logged data in the workspace at wandb.ai, or locally on a self-hosted instance of the W&B app: https://docs.wandb.ai/self-hosted

Export data to explore in a Jupyter notebook, for example, with the API: https://docs.wandb.ai/ref/public-api

Each time you call wandb.log(), this adds a new row to history and updates the summary values for each key logged. In the UI, summary values show up in the run table to compare single values across runs. You might want to update summary manually to set the best value instead of the last value for a given metric. After you finish logging, you can set summary: wandb.run.summary["accuracy"] = 0.9.

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 wandb UI. See https://docs.wandb.com/library/reference/data_types for all of the different supported types.

Logging nested metrics is encouraged and is supported in the wandb API, so you could log multiple accuracy values with wandb.log({'dataset-1': {'acc': 0.9, 'loss': 0.3} ,'dataset-2': {'acc': 0.8, 'loss': 0.2}}) and the metrics will be organized in the wandb UI.

W&B keeps track of a global step so logging related metrics together is encouraged, so by default each time wandb.log is called a global step is incremented. 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

row

(dict, optional) A dict of serializable python objects i.e str, ints, floats, Tensors, dicts, or 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 row 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.

Examples:

Basic usage

wandb.log({'accuracy': 0.9, 'epoch': 5})

Incremental logging

wandb.log({'loss': 0.2}, commit=False)
# Somewhere else when I'm ready to report this step:
wandb.log({'accuracy': 0.8})

Histogram

wandb.log({"gradients": wandb.Histogram(numpy_array_or_sequence)})

Image

wandb.log({"examples": [wandb.Image(numpy_array_or_pil, caption="Label")]})

Video

wandb.log({"video": wandb.Video(numpy_array_or_video_path, fps=4,
    format="gif")})

Matplotlib Plot

wandb.log({"chart": plt})

PR Curve

wandb.log({'pr': wandb.plots.precision_recall(y_test, y_probas, labels)})

3D Object

wandb.log({"generated_samples":
[wandb.Object3D(open("sample.obj")),
    wandb.Object3D(open("sample.gltf")),
    wandb.Object3D(open("sample.glb"))]})

For more examples, see https://docs.wandb.com/library/log

Raises

wandb.Error

if called before wandb.init

ValueError

if invalid data is passed

log_artifact

View source

log_artifact(
    artifact_or_path: Union[wandb_artifacts.Artifact, str],
    name: Optional[str] = None,
    type: Optional[str] = None,
    aliases: Optional[List[str]] = None
) -> wandb_artifacts.Artifact

Declare an artifact as output of a run.

Arguments

artifact_or_path

(str or Artifact) A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.

name

(str, optional) An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.

type

(str) The type of artifact to log, examples include dataset, model

aliases

(list, optional) Aliases to apply to this artifact, defaults to ["latest"]

Returns

An Artifact object.

log_code

View source

log_code(
    root: str = ".",
    name: str = None,
    include_fn: Callable[[str], bool] = (lambda path: path.endswith(".py")),
    exclude_fn: Callable[[str], bool] = (lambda path: os.sep + "wandb" + os.sep in path)
) -> Optional[Artifact]

log_code() saves the current state of your code to a W&B artifact. By default it walks the current directory and logs all files that end with ".py".

Arguments

root (str, optional): The relative (to os.getcwd()) or absolute path to recursively find code from. name (str, optional): The name of our code artifact. By default we'll name the artifact "source-$RUN_ID". There may be scenarios where you want many runs to share the same artifact. Specifying name allows you to achieve that. include_fn (callable, optional): A callable that accepts a file path and returns True when it should be included and False otherwise. This defaults to: lambda path: path.endswith(".py") exclude_fn (callable, optional): A callable that accepts a file path and returns True when it should be excluded and False otherwise. This defaults to: lambda path: False

Examples:

Basic usage

run.log_code()

Advanced usage

run.log_code("../", include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb"))

Returns

An Artifact object if code was logged

mark_preempting

View source

mark_preempting() -> None

Mark this run as preempting and tell the internal process to immediately report this to the server.

plot_table

View source

plot_table(
    vega_spec_name, data_table, fields, string_fields=None
)

Creates a custom plot on a table.

Arguments

vega_spec_name

the name of the spec for the plot

table_key

the key used to log the data table

data_table

a wandb.Table object containing the data to be used on the visualization

fields

a dict mapping from table keys to fields that the custom visualization needs

string_fields

a dict that provides values for any string constants the custom visualization needs

project_name

View source

project_name() -> str

restore

View source

restore(
    name: str,
    run_path: Optional[str] = None,
    replace: bool = (False),
    root: Optional[str] = None
) -> Union[None, TextIO]

Downloads the specified file from cloud storage into the current directory or run directory. By default this will only download the file if it doesn't already exist.

Arguments

name

the name of the file

run_path

optional path to a run to pull files from, i.e. username/project_name/run_id if wandb.init has not been called, this is required.

replace

whether to download the file even if it already exists locally

root

the directory to download the file to. Defaults to the current directory or the run directory if wandb.init was called.

Returns

None if it can't find the file, otherwise a file object open for reading

Raises

wandb.CommError

if we can't connect to the wandb backend

ValueError

if the file is not found or can't find run_path

save

View source

save(
    glob_str: Optional[str] = None,
    base_path: Optional[str] = None,
    policy: str = "live"
) -> Union[bool, List[str]]

Ensure all files matching glob_str are synced to wandb with the policy specified.

Arguments

glob_str

(string) a relative or absolute path to a unix glob or regular path. If this isn't specified the method is a noop.

base_path

(string) the base path to run the glob relative to

policy

(string) on of live, now, or end - live: upload the file as it changes, overwriting the previous version - now: upload the file once now - end: only upload file when the run ends

upsert_artifact

View source

upsert_artifact(
    artifact_or_path: Union[wandb_artifacts.Artifact, str],
    name: Optional[str] = None,
    type: Optional[str] = None,
    aliases: Optional[List[str]] = None,
    distributed_id: Optional[str] = None
) -> wandb_artifacts.Artifact

Declare (or append tp) a non-finalized artifact as output of a run. Note that you must call run.finish_artifact() to finalize the artifact. This is useful when distributed jobs need to all contribute to the same artifact.

Arguments

artifact_or_path

(str or Artifact) A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.

name

(str, optional) An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.

type

(str) The type of artifact to log, examples include dataset, model

aliases

(list, optional) Aliases to apply to this artifact, defaults to ["latest"]

distributed_id

(string, optional) Unique string that all distributed jobs share. If None, defaults to the run's group name.

Returns

An Artifact object.

use_artifact

View source

use_artifact(
    artifact_or_name, type=None, aliases=None
)

Declare an artifact as an input to a run, call download or file on the returned object to get the contents locally.

Arguments

artifact_or_name

(str or Artifact) An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest You can also pass an Artifact object created by calling wandb.Artifact

type

(str, optional) The type of artifact to use.

aliases

(list, optional) Aliases to apply to this artifact

Returns

An Artifact object.

watch

View source

watch(
    models, criterion=None, log="gradients", log_freq=100, idx=None
) -> None

__enter__

View source

__enter__() -> "Run"

__exit__

View source

__exit__(
    exc_type: Type[BaseException],
    exc_val: BaseException,
    exc_tb: TracebackType
) -> bool
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