The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. The W&B integration adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use.

🤗 Next-level Hugging Face logging in 2 lines

from transformers import TrainingArguments, Trainer
​
args = TrainingArguments(... , report_to="wandb")
trainer = Trainer(... , args=args)

This guide covers

• how to get started using W&B with Hugging Face Transformers to track your NLP experiments and

• how to use advanced features of the W&B Hugging Face integration to get the most out of experiment tracking.

Getting started: track experiments

1) Install the wandb library and log in

!pip install wandb
​
import wandb
wandb.login()

pip install wandb
wandb login

2) Name the project

A Project is where all of the charts, data, and models logged from related runs are stored. Naming your project helps you organize your work and keep all the information about a single project in one place.

To add a run to a project simply set the WANDB_PROJECT environment variable to the name of your project. The WandbCallback will pick up this project name environment variable and use it when setting up your run.

%env WANDB_PROJECT=amazon_sentiment_analysis

WANDB_PROJECT=amazon_sentiment_analysis

If a project name is not specified the project name defaults to "huggingface".

3) Log your training runs to W&B

This is the most important step: when defining your Trainer training arguments, either inside your code or from the command line, set report_to to "wandb" in order enable logging with Weights & Biases.

You can also give a name to the training run using the run_name argument.

That's it! Now your models will log losses, evaluation metrics, model topology, and gradients to Weights & Biases while they train.

from transformers import TrainingArguments, Trainer
​
args = TrainingArguments(
    # other args and kwargs here
    report_to="wandb",  # enable logging to W&B
    run_name="bert-base-high-lr"  # name of the W&B run (optional)
)
​
trainer = Trainer(
    # other args and kwargs here
    args=args,  # your training args
)
​
trainer.train()  # start training and logging to W&B

python run_glue.py \     # run your Python script
  --report_to wandb \    # enable logging to W&B
  --run_name bert-base-high-lr \   # name of the W&B run (optional)
  # other command line arguments here

(Notebook only) Finish your W&B Run

If your training is encapsulated in a Python script, the W&B run will end when your script finishes.

If you are using a Jupyter or Google Colab notebook, you'll need to tell us when you're done with training by calling wandb.finish().

trainer.train()  # start training and logging to W&B
​
# post-training analysis, testing, other logged code
​
wandb.finish()

4) Visualize your results

Once you have logged your training results you can explore your results dynamically in the W&B Dashboard. It's easy to compare across dozens of runs at once, zoom in on interesting findings, and coax insights out of complex data with flexible, interactive visualizations.

Advanced features

Turn on model versioning

Using Weights & Biases' Artifacts, you can store up to 100GB of models and datasets. Logging your Hugging Face model to W&B Artifacts can be done by setting a W&B environment variable called WANDB_LOG_MODEL to true.

%env WANDB_LOG_MODEL=true

WANDB_LOG_MODEL=true

Any Trainer you initialize from now on will upload models to your W&B project. Your model file will be viewable through the W&B Artifacts UI. See the Weights & Biases' Artifacts guide for more about how to use Artifacts for model and dataset versioning.

How do I save the best model?

If load_best_model_at_end=True is passed to Trainer, then W&B will save the best performing model checkpoint to Artifacts instead of the final checkpoint.

Loading a saved model

If you saved your model to W&B Artifacts with WANDB_LOG_MODEL, you can download your model weights for additional training or to run inference. You just load them back into the same Hugging Face architecture that you used before.

# Create a new run
with wandb.init(project="amazon_sentiment_analysis") as run:
​
  # Connect an Artifact to the run
  my_model_name = "run-bert-base-high-lr:latest"
  my_model_artifact = run.use_artifact(my_model_name)
​
  # Download model weights to a folder and return the path
  model_dir = my_model_artifact.download()
​
  # Load your Hugging Face model from that folder
  #  using the same model class
  model = AutoModelForSequenceClassification.from_pretrained(
      model_dir, num_labels=num_labels)
​
  # Do additional training, or run inference

Additional W&B settings

Further configuration of what is logged with Trainer is possible by setting environment variables. A full list of W&B environment variables can be found here.

%env WANDB_WATCH=all
%env WANDB_SILENT=true

WANDB_WATCH=all
WANDB_SILENT=true

Customize wandb.init

The WandbCallback that Trainer uses will call wandb.init under the hood when Trainer is initialized. You can alternatively set up your runs manually by calling wandb.init before theTrainer is initialized. This gives you full control over your W&B run configuration.

An example of what you might want to pass to init is below. For more details on how to use wandb.init, check out the reference documentation.

wandb.init(project="amazon_sentiment_analysis", 
           name="bert-base-high-lr",
           tags=["baseline", "high-lr"],
           group="bert")

Custom logging

Logging to Weights & Biases via the Transformers Trainer is taken care of by the WandbCallback (reference documentation) in the Transformers library. If you need to customize your Hugging Face logging you can modify this callback.

Data Visualization with W&B Tables

Use W&B Tables to log, query, and analyze your data. You can think of a W&B Table as a DataFrame that you can interact with inside W&B. Tables support rich media types, primitive and numeric types, as well as nested lists and dictionaries.

This pseudo-code shows you how to log images, along with their ground truth and predicted class, to W&B Tables:

# Create a new W&B Run
wandb.init(project="mnist")
​
# Create a W&B Table
my_table = wandb.Table(columns=["id", "image", "labels", "prediction"])
​
# Get your image data and make predictions
image_tensors, labels = get_mnist_data()
predictions = model(image_tensors)
​
# Add your image data and predictions to the W&B Table
for idx, im in enumerate(image_tensors): 
  my_table.add_data(idx, wandb.Image(im), labels[idx], predictions[id])
​
# Log your Table to W&B
wandb.log({"mnist_predictions": my_table})

This is will produce a Table like this:

For more examples of data visualization with W&B Tables, please see the documentation.

Issues, questions, feature requests

For any issues, questions, or feature requests for the Hugging Face W&B integration, feel free to post in this thread on the Hugging Face forums or open an issue on the Hugging Face Transformers GitHub repo.