🐝 Weights & Biases Addons

Weights & Biases Addons is a repository that provides of integrations and utilities that will supercharge your Weights & Biases workflows. Its a repositpry built and maintained by wandb users for wandb users.

Integrations

TensorFlow Datasets

A set of utilities for easily accessing datasets for various machine learning tasks using Weights & Biases artifacts built on top of TensorFlow Datasets.

In order to install wandb-addons along with the dependencies for the dataset utilities, you can run:

git clone https://github.com/soumik12345/wandb-addons
pip install ./wandb-addons[dataset]

• WandbDatasetBuilder: An abstract class for Dataset builder that enables building a dataset and upload it as a Weights & Biases Artifact.

• upload_dataset: Upload and register a dataset with a TFDS module or a TFDS builder script as a Weights & Biases artifact. This function would verify if a TFDS build/registration is possible with the current specified dataset path and upload it as a Weights & Biases artifact.

• load_dataset: Load a dataset from a wandb artifact. Using this function you can load a dataset hosted as a wandb artifact in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model.

🦄 Keras

Backend-agnostic callbacks integrating Weights & Biases with Keras-Core.

In order to install wandb-addons along with the dependencies for the ciclo callbacks, you can run:

git clone https://github.com/soumik12345/wandb-addons
pip install ./wandb-addons[keras]

Once you've installed wandb-addons, you can import it using:

from wandb_addons.keras import WandbMetricsLogger, WandbModelCheckpoint

callbacks = [
    WandbMetricsLogger(),           # Logs metrics to W&B
    WandbModelCheckpoint(filepath)  # Logs and versions model checkpoints to W&B
]

model.fit(..., callbacks=callbacks)

For more information, check out

• Backend-agnostic callbacks for KerasCore.
• Object-detection integration for KerasCV.

MonAI

Event handlers for experiment tracking on Weights & Biases with MonAI Engine for deep learning in healthcare imaging.

In order to install wandb-addons along with the dependencies for the ciclo callbacks, you can run:

git clone https://github.com/soumik12345/wandb-addons
pip install ./wandb-addons[monai]

Once you've installed wandb-addons, you can import it using:

from wandb_addons.monai import WandbStatsHandler, WandbModelCheckpointHandler

For more information, check out more at the docs.

🧨 Diffusers

Callbacks for logging experiment details, configs and generated images for multi-modal diffusion pipelines from Diffusers 🧨 to your Weights & Biases workspace or Weave Dashboard.

In order to install wandb-addons along with the dependencies for the ciclo callbacks, you can run:

git clone https://github.com/soumik12345/wandb-addons
pip install ./wandb-addons[huggingface]

Once you've installed wandb-addons, you can use the callbacks in the following manner:

import torch
from diffusers import StableDiffusionPipeline

from wandb_addons.diffusers import StableDiffusionCallback


pipeline = StableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
)
pipeline = pipeline.to("cuda")

prompt = [
    "a photograph of an astronaut riding a horse",
    "a photograph of a dragon"
]
negative_prompt = ["ugly, deformed", "ugly, deformed"]
num_images_per_prompt = 2
configs = {
    "eta": 0.0,
    "guidance_rescale": 0.0,
}

# Create the WandB callback for StableDiffusionPipeline
callback = StableDiffusionCallback(
    pipe,
    prompt=prompt,
    negative_prompt=negative_prompt,
    wandb_project="diffusers",
    num_images_per_prompt=num_images_per_prompt,
    configs=configs,
)

# Add the callback to the pipeline
image = pipe(
    prompt,
    negative_prompt=negative_prompt,
    callback=callback,
    num_images_per_prompt=num_images_per_prompt,
    **configs,
)

For more information, check out more at the docs.

Converting IPython Notebooks to Reports

A set of utilities to convert an IPython notebook to a Weights & Biases report.

Simply install wandb-addons using

git clone https://github.com/soumik12345/wandb-addons
pip install ./wandb-addons

You can convert your notebook to a report using the CLI:

nb2report \
    --filepath Use_WandbMetricLogger_in_your_Keras_workflow.ipynb \
    --wandb_project report-to-notebook \
    --wandb_entity geekyrakshit \
    --report_title "Use WandbMetricLogger in your Keras Workflow" \
    --description "A guide to using the WandbMetricLogger callback in your Keras and TensorFlow training worflow" \
    --width "readable"

Alternatively, you can also use the Python function:

from wandb_addons.report import convert_to_wandb_report

convert_to_wandb_report(
    filepath="./Use_WandbMetricLogger_in_your_Keras_workflow.ipynb",
    wandb_project="report-to-notebook",
    wandb_entity="geekyrakshit",
    report_title="Use WandbMetricLogger in your Keras Workflow",
    description="A guide to using the WandbMetricLogger callback in your Keras and TensorFlow training worflow"
)

For more information, check out more at the docs.

Trace

A high level API for Tracing your LLMs workflows with Weights & Biases Prompts.

You can use the Trace class to create objects that can be logged as a WBTraceTree media type. For a more detailed walkthrough please look at the quickstart tutorial in the examples directory

Status

wandb-addons is still in early development, the API for integrations and utilities is subject to change, expect things to break. If you are interested in contributing, please feel free to open an issue and/or raise a pull request.