wandb.Run
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,
launch_config: Optional[Dict[str, Any]] = None
) -> None
Create a run with
wandb.init()
:import wandb
run = wandb.init()
There is only ever at most one active
wandb.Run
in any process, and it is accessible as wandb.run
:import wandb
assert wandb.run is None
wandb.init()
assert wandb.run is not None
anything you log with
wandb.log
will be sent to that run.If you want to start more runs in the same script or notebook, you'll need to finish the run that is in-flight. Runs can be finished with
wandb.finish
or by using them in a with
block:import wandb
wandb.init()
wandb.finish()
assert wandb.run is None
with wandb.init() as run:
pass # log data here
assert wandb.run is None
See the documentation for
wandb.init
for more on creating runs, or check out our guide to wandb.init
.In distributed training, you can either create a single run in the rank 0 process and then log information only from that process, or you can create a run in each process, logging from each separately, and group the results together with the
group
argument to wandb.init
. For more details on distributed training with W&B, check out our guide.Currently, there is a parallel
Run
object in the wandb.Api
. Eventually these two objects will be merged.Attributes | Text |
---|---|
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 the config object associated with this run. |
dir | Returns the directory where files associated with the run are saved. |
entity | Returns the name of the W&B entity associated with the run. Entity can be a user name or the name of a team or organization. |
group | Returns the name of the group associated with the run. 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 | Returns the identifier for this run. |
mode | For compatibility with 0.9.x and earlier, deprecate eventually. |
name | Returns the display name of the run. Display names are not guaranteed to be unique and may be descriptive. By default, they are randomly generated. |
notes | Returns the notes associated with the run, if there are any. Notes can be a multiline string and can also use markdown and latex equations inside $$ , like $x + 3$ . |
path | Returns the path to the run. Run paths include entity, project, and run ID, in the format entity/project/run_id . |
project | Returns the name of the W&B project associated with the run. |
resumed | Returns True if the run was resumed, False otherwise. |
settings | Returns a frozen copy of run's Settings object. |
start_time | Returns the unix time stamp, in seconds, when the run started. |
starting_step | Returns the first step of the run. |
step | Returns the current value of the step. This counter is incremented by wandb.log . |
sweep_id | Returns the ID of the sweep associated with the run, if there is one. |
tags | Returns the tags associated with the run, if there are any. |
url | Returns the W&B url associated with the run. |
alert(
title: str,
text: str,
level: Union[str, 'AlertLevel'] = None,
wait_duration: Union[int, float, timedelta, None] = None
) -> None
Launch an alert with the given title and text.
Arguments | Text |
---|---|
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(
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 | Text |
---|---|
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 directions: "minimize,maximize" |
Returns | Text |
---|---|
A metric object is returned that can be further specified. | |
detach() -> None
display(
height: int = 420,
hidden: bool = (False)
) -> bool
Displays this run in jupyter.
finish(
exit_code: int = None,
quiet: Optional[bool] = 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 or if you use the run context manager.
Arguments | Text |
---|---|
exit_code | Set to something other than 0 to mark a run as failed |
quiet | Set to true to minimize log output |
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
Finishes a non-finalized artifact as output of a run.
Subsequent "upserts" with the same distributed ID will result in a new version.
Arguments | Text |
---|---|
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 | Text |
---|---|
An Artifact object. | |
get_project_url() -> Optional[str]
Returns the url for the W&B project associated with the run, if there is one.
Offline runs will not have a project url.
get_sweep_url() -> Optional[str]
Returns the url for the sweep associated with the run, if there is one.
get_url() -> Optional[str]
Returns the url for the W&B run, if there is one.
Offline runs will not have a url.
join(
exit_code: int = None
) -> None
Deprecated alias for
finish()
- please use finish.link_artifact(
artifact: Union[public.Artifact, Artifact],
target_path: str,
aliases: Optional[List[str]] = None
) -> None
Links the given artifact to a portfolio (a promoted collection of artifacts).
The linked artifact will be visible in the UI for the specified portfolio.
Arguments | Text |
---|---|
artifact | the (public or local) artifact which will be linked |
target_path | str - takes the following forms: {portfolio}, {project}/{portfolio}, or {entity}/{project}/{portfolio} |
aliases | List[str] - optional alias(es) that will only be applied on this linked artifact inside the portfolio. The alias "latest" will always be applied to the latest version of an artifact that is linked. |
Returns | Text |
---|---|
None | |
log(
data: Dict[str, Any],
step: Optional[int] = None,
commit: Optional[bool] = None,
sync: Optional[bool] = None
) -> None
Logs a dictonary of data to the current run's history.
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.Table
s 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 |
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 an output of a run.
Arguments | Text |
---|---|
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 | Text |
---|---|
An Artifact object. | |
log_code(
root: str = ".",
name: str = None,
include_fn: Callable[[str], bool] = _is_py_path,
exclude_fn: Callable[[str], bool] = filenames.exclude_wandb_fn
) -> Optional[Artifact]
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 | Text |
---|---|
root | 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-$PROJECT_ID-$ENTRYPOINT_RELPATH . There may be scenarios where you want many runs to share the same artifact. Specifying name allows you to achieve that. |
include_fn | 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 | A callable that accepts a file path and returns True when it should be excluded and False otherwise. This defaults to: lambda path: False |
Basic usage
run.log_code()
Advanced usage
run.log_code(
"../", include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb")
)
Returns | Text |
---|---|
An Artifact object if code was logged | |
mark_preempting() -> None
Marks this run as preempting.
Also tells the internal process to immediately report this to server.
@staticmethod
plot_table(
vega_spec_name: str,
data_table: "wandb.Table",
fields: Dict[str, Any],
string_fields: Optional[Dict[str, Any]] = None
) -> CustomChart
Creates a custom plot on a table.
Arguments | Text |
---|---|
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() -> str
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.
File is placed into the current directory or run directory. By default, will only download the file if it doesn't already exist.
Arguments | Text |
---|---|
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 | Text |
---|---|
None if it can't find the file, otherwise a file object open for reading | |
Raises | Text |
---|---|
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(
glob_str: Optional[str] = None,
base_path: Optional[str] = None,
policy: "PolicyName" = "live"
) -> Union[bool, List[str]]
Ensure all files matching
glob_str
are synced to wandb with the policy specified.Arguments | Text |
---|---|
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 |
to_html(
height: int = 420,
hidden: bool = (False)
) -> str
Generates HTML containing an iframe displaying the current run.
unwatch(
models=None
) -> None
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 to) 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 | Text |
---|---|
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 | Text |
---|---|
An Artifact object. | |
use_artifact(
artifact_or_name: Union[str, public.Artifact, Artifact],
type: Optional[str] = None,
aliases: Optional[List[str]] = None,
use_as: Optional[str] = None
) -> Union[public.Artifact, Artifact]
Declare an artifact as an input to a run.
Call
download
or file
on the returned object to get the contents locally.Arguments | Text |
---|---|
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 |
use_as | (string, optional) Optional string indicating what purpose the artifact was used with. Will be shown in UI. |
Returns | Text |
---|---|
An Artifact object. | |
watch(
models, criterion=None, log="gradients", log_freq=100, idx=None,
log_graph=(False)
) -> None
__enter__() -> "Run"