spaCy

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

Command Line

pip install wandb
wandb login

Notebook

!pip install wandb

import 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.

info

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.

[training.logger]
@loggers = "spacy.WandbLogger.v3"
project_name = "my_spacy_project"
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
log_dataset_dir = "./corpus"
model_log_interval = 1000

Name	Description
project_name	str. The name of the W&B Project. The project will be created automatically if it doesn’t exist yet.
remove_config_values	List[str] . A list of values to exclude from the config before it is uploaded to W&B. [] by default.
model_log_interval	Optional 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_dir	Optional str. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. None by default.
entity	Optional str . If passed, the run will be created in the specified entity
run_name	Optional 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.

Command Line

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

Notebook

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