Easy to useopen source

Just add the following to your spaCy training config file


[training.logger]
@loggers = "spacy.AimLogger.v1"
repo = "path/to/your/save/directory"
experiment_name = "name_of_your_experiment"

When, where and how

Use aim-spaCy to detect when, where and how your models have done well or failed. In a couple of clicks!

8 major features to speed up your work

1.

Powerful pythonic search to select the runs for analysis and comparison.

Automatically track displaCy visualizations and compare them at scale.

Group images by hyperparameters to visualize them in the needed order

Group images by step or epoch to see the evolution

Group metrics by hyperparameters to analyze hyperparameters’ influence on run performance

Select multiple metrics and analyze them side by side

Aggregate metrics by std.dev, std.err, conf.interval, to observe average, min and max values of metrics you track

Deep dive into details of each run for easy debugging

We are building more spaCy features.
If you are interested, sign up to access the project roadmap.

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