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spaCy is a popular "industrial-strength" NLP library: fast, accurate models with a minimum of fuss. As of spaCy v3, Weights and Biases can now be used with spacy train to track your spaCy model's training metrics as well as to save and version your models and datasets. And all it takes is a few added lines in your configuration!

Getting Started: Track and Save your Models

1. Install the wandb library and log in

pip install wandb
wandb login

2) Add the WandbLogger to your spaCy config file

spaCy config files are used to specify all aspects of training, not just logging -- GPU allocation, optimizer choice, dataset paths, and more. Minimally, under [training.logger] you need to provide the key @loggers with the value "spacy.WandbLogger.v3", plus a project_name.


For more on how spaCy training config files work and on other options you can pass in to customize training, check out spaCy's documentation.

@loggers = "spacy.WandbLogger.v3"
project_name = "my_spacy_project"
remove_config_values = ["paths.train", "", "corpora.train.path", ""]
log_dataset_dir = "./corpus"
model_log_interval = 1000
project_namestr. The name of the Weights & Biases project. The project will be created automatically if it doesn’t exist yet.
remove_config_valuesList[str] . A list of values to exclude from the config before it is uploaded to W&B. [] by default.
model_log_intervalOptional int. None by default. If set, model versioning with Artifactswill be enabled. Pass in the number of steps to wait between logging model checkpoints. None by default.
log_dataset_dirOptional str. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. None by default.
entityOptional str . If passed, the run will be created in the specified entity
run_nameOptional str . If specified, the run will be created with the specified name.

3) Start training

Once you have added the WandbLogger to your spaCy training config you can run spacy train as usual.

python -m spacy train \
config.cfg \
--output ./output \
--paths.train ./train \ ./dev

When training begins, a link to your training run's W&B page will be output which will take you to this run's experiment tracking dashboard in the Weights & Biases web UI.

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