spaCy

A Weights & Biases integration for the spaCy library: industrial strength NLP, logged with W&B

​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
Notebook
Command Line
Notebook
!pip install wandb
​
import wandb
wandb.login()
Command Line
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.v2", plus a project_name. You can also turn on dataset and model versioning by just adding a line to the config file.
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.
config.cfg
config.cfg
[training.logger]
@loggers = "spacy.WandbLogger.v2"
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 Weights & Biases 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 Artifacts will 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.
3) Start training
Once you have added the WandbLogger to your spaCy training config you can run spacy train as usual.
Notebook
Command Line
Notebook
!python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev
Command Line
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.

Hugging Face

YOLOv5

Last updated 2 weeks ago

Name	Description
`project_name`	`str`. The name of the Weights & Biases 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 Artifacts will 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.

spaCy

Getting Started: Track and Save your Models

1) Install the `wandb` library and log in

2) Add the `WandbLogger` to your spaCy config file

3) Start training