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Please use `run.define_metric(...)` to define a custom metric to log your step values.r0)rWrranykeysrrrrrPrsrrrrw)rrXrbrWrararb_logs*   zRun._logsynccCs|durtj|d }d|j_Wdn1swY|dur+tjtjjdd|jjrA|durAt j dt ddd d |j |||d dS) a!Upload run data. Use `log` to log data from runs, such as scalars, images, video, histograms, plots, and tables. See our [guides to logging](https://docs.wandb.ai/guides/track/log) for live examples, code snippets, best practices, and more. The most basic usage is `run.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](https://wandb.ai), or locally on a [self-hosted instance](https://docs.wandb.ai/guides/hosting) of the W&B app, or export data to visualize and explore locally, e.g. in Jupyter notebooks, with [our API](https://docs.wandb.ai/guides/track/public-api-guide). Logged values don't have to be scalars. Logging any wandb object is supported. For example `run.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](https://docs.wandb.com/ref/python/data-types) for all of the different supported types or check out our [guides to logging](https://docs.wandb.ai/guides/track/log) for examples, from 3D molecular structures and segmentation masks to PR curves and histograms. You can use `wandb.Table` to log structured data. See our [guide to logging tables](https://docs.wandb.ai/guides/tables/tables-walkthrough) for details. The W&B UI organizes metrics with a forward slash (`/`) in their name into sections named using the text before the final slash. For example, the following results in two sections named "train" and "validate": ``` run.log( { "train/accuracy": 0.9, "train/loss": 30, "validate/accuracy": 0.8, "validate/loss": 20, } ) ``` Only one level of nesting is supported; `run.log({"a/b/c": 1})` produces a section named "a/b". `run.log` is not intended to be called more than a few times per second. For optimal performance, limit your logging to once every N iterations, or collect data over multiple iterations and log it in a single step. ### The W&B step With basic usage, each call to `log` creates a new "step". The step must always increase, and it is not possible to log to a previous step. Note that you can use any metric as the X axis in charts. In many cases, it is better to treat the W&B step like you'd treat a timestamp rather than a training step. ``` # Example: log an "epoch" metric for use as an X axis. run.log({"epoch": 40, "train-loss": 0.5}) ``` See also [define_metric](https://docs.wandb.ai/ref/python/run#define_metric). It is possible to use multiple `log` invocations to log to the same step with the `step` and `commit` parameters. The following are all equivalent: ``` # Normal usage: run.log({"train-loss": 0.5, "accuracy": 0.8}) run.log({"train-loss": 0.4, "accuracy": 0.9}) # Implicit step without auto-incrementing: run.log({"train-loss": 0.5}, commit=False) run.log({"accuracy": 0.8}) run.log({"train-loss": 0.4}, commit=False) run.log({"accuracy": 0.9}) # Explicit step: run.log({"train-loss": 0.5}, step=current_step) run.log({"accuracy": 0.8}, step=current_step) current_step += 1 run.log({"train-loss": 0.4}, step=current_step) run.log({"accuracy": 0.9}, step=current_step) ``` Args: data: A `dict` with `str` keys and values that are serializable Python objects including: `int`, `float` and `string`; any of the `wandb.data_types`; lists, tuples and NumPy arrays of serializable Python objects; other `dict`s of this structure. step: The step number to log. If `None`, then an implicit auto-incrementing step is used. See the notes in the description. commit: If true, finalize and upload the step. If false, then accumulate data for the step. See the notes in the description. If `step` is `None`, then the default is `commit=True`; otherwise, the default is `commit=False`. sync: This argument is deprecated and does nothing. Examples: For more and more detailed examples, see [our guides to logging](https://docs.wandb.com/guides/track/log). ### Basic usage ```python import wandb run = wandb.init() run.log({"accuracy": 0.9, "epoch": 5}) ``` ### Incremental logging ```python import wandb run = wandb.init() run.log({"loss": 0.2}, commit=False) # Somewhere else when I'm ready to report this step: run.log({"accuracy": 0.8}) ``` ### Histogram ```python import numpy as np import wandb # sample gradients at random from normal distribution gradients = np.random.randn(100, 100) run = wandb.init() run.log({"gradients": wandb.Histogram(gradients)}) ``` ### Image from numpy ```python import numpy as np import wandb run = 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) run.log({"examples": examples}) ``` ### Image from PIL ```python import numpy as np from PIL import Image as PILImage import wandb run = 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) run.log({"examples": examples}) ``` ### Video from numpy ```python import numpy as np import wandb run = wandb.init() # axes are (time, channel, height, width) frames = np.random.randint( low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8 ) run.log({"video": wandb.Video(frames, fps=4)}) ``` ### Matplotlib Plot ```python from matplotlib import pyplot as plt import numpy as np import wandb run = wandb.init() fig, ax = plt.subplots() x = np.linspace(0, 10) y = x * x ax.plot(x, y) # plot y = x^2 run.log({"chart": fig}) ``` ### PR Curve ```python import wandb run = wandb.init() run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)}) ``` ### 3D Object ```python import wandb run = wandb.init() run.log( { "generated_samples": [ wandb.Object3D(open("sample.obj")), wandb.Object3D(open("sample.gltf")), wandb.Object3D(open("sample.glb")), ] } ) ``` Raises: wandb.Error: if called before `wandb.init` ValueError: if invalid data is passed NrTzN`sync` argument is deprecated and does not affect the behaviour of `wandb.log`rztIn shared mode, the use of `wandb.log` with the step argument is not supported and will be ignored. 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Args: 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. NzMCannot upsert artifact unless run is in a group or distributed_id is providedrF)rr|rrfinalizerrrr!rrrr|rrrararbupsert_artifact) s' zRun.upsert_artifactcCr )a,Finishes a non-finalized artifact as output of a run. Subsequent "upserts" with the same distributed ID will result in a new version. Args: 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. 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This will default to the basename of the path prepended with the current run id if not specified. aliases: (list, optional) Aliases to apply to the created model artifact, defaults to `["latest"]` Examples: ```python run.log_model( path="/local/directory", name="my_model_artifact", aliases=["production"], ) ``` Invalid usage ```python run.log_model( path="/local/directory", name="my_entity/my_project/my_model_artifact", aliases=["production"], ) ``` Raises: ValueError: if name has invalid special characters Returns: None model)rrr|rNr)rrrrrararbr4# s/ z Run.log_modelr&cCs^|j|d}dt|jvsJd|}t|}t|dkr-ttj ||dS|S)aDownload the files logged in a model artifact 'name'. Args: name: (str) A model artifact name. 'name' must match the name of an existing logged model artifact. May be prefixed with entity/project/. 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The linked model version will be visible in the UI for the specified registered model. Steps: - Check if 'name' model artifact has been logged. If so, use the artifact version that matches the files located at 'path' or log a new version. Otherwise log files under 'path' as a new model artifact, 'name' of type 'model'. - Check if registered model with name 'registered_model_name' exists in the 'model-registry' project. If not, create a new registered model with name 'registered_model_name'. - Link version of model artifact 'name' to registered model, 'registered_model_name'. - Attach aliases from 'aliases' list to the newly linked model artifact version. Args: path: (str) A path to the contents of this model, can be in the following forms: - `/local/directory` - `/local/directory/file.txt` - `s3://bucket/path` registered_model_name: (str) - the name of the registered model that the model is to be linked to. A registered model is a collection of model versions linked to the model registry, typically representing a team's specific ML Task. The entity that this registered model belongs to will be derived from the run name: (str, optional) - the name of the model artifact that files in 'path' will be logged to. This will default to the basename of the path prepended with the current run id if not specified. aliases: (List[str], optional) - alias(es) that will only be applied on this linked artifact inside the registered model. The alias "latest" will always be applied to the latest version of an artifact that is linked. Examples: ```python run.link_model( path="/local/directory", registered_model_name="my_reg_model", name="my_model_artifact", aliases=["production"], ) ``` Invalid usage ```python run.link_model( path="/local/directory", registered_model_name="my_entity/my_project/my_reg_model", name="my_model_artifact", aliases=["production"], ) run.link_model( path="/local/directory", registered_model_name="my_reg_model", name="my_entity/my_project/my_model_artifact", aliases=["production"], ) ``` Raises: AssertionError: if registered_model_name is a path or if model artifact 'name' is of a type that does not contain the substring 'model' ValueError: if name has invalid special characters Returns: None rr0z_Please provide only the name of the registered model. 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