README.md

A super-easy way to record, search and compare AI experiments.

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This project hosts the Aim UI code. Please file issues at Aim primary repo.

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PLAY with live demo and check out a short INTRO VIDEO

Integrate seamlessly with your favorite tools

Getting started in three steps

1. Install Aim in your training environment

$ pip install aim

2. Integrate Aim with your code

Flexible integration for any Python script

import aim

# Save inputs, hparams or any other `key: value` pairs
aim.set_params(hyperparam_dict, name='hparams') # Passing name argument is optional

...
for step in range(10):
    # Log metrics to visualize performance
    aim.track(metric_value, name='metric_name', epoch=epoch_number)
...

See documentation here.

PyTorch Lightning integration

from aim.pytorch_lightning import AimLogger

...
trainer = pl.Trainer(logger=AimLogger(experiment='experiment_name'))
...

See documentation here.

Keras & tf.keras integrations

import aim

# Save inputs, hparams or any other `key: value` pairs
aim.set_params(param_dict, name='params_name') # Passing name argument is optional

...
model.fit(x_train, y_train, epochs=epochs, callbacks=[
    aim.keras.AimCallback(aim.Session(experiment='experiment_name'))
    
    # Use aim.tensorflow.AimCallback in case of tf.keras
    aim.tensorflow.AimCallback(aim.Session(experiment='experiment_name'))
])
...

See documentation here.

3. Run the training like you are used to and start Aim UI

$ aim up

Docs

See the docs at Aim.