blessingchitra/aim

Aim — an easy-to-use and performant open-source experiment tracker.

⚡ Try out Aim at play.aimstack.io ⚡

Aim logs your training runs, enables a beautiful UI to compare them and an API to query them programmatically.

Documentation • Aim in 3 steps • API Reference • Slack Community • Twitter

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Integrate seamlessly with your favorite tools

Aim is open-source, self-hosted AI experiment tracking tool. Use Aim to deeply inspect hundreds of hyperparameter-sensitive training runs at once.

Getting Started in 3 Steps

Follow the steps below to get started with Aim.

1. Install Aim on your training environment

Prerequisite: You need to have python3 and pip3 installed in your environment before installing Aim

$ pip3 install aim

2. Integrate Aim with your code

Integrate your Python script

from aim import Run

# Initialize a new run
run = Run()

# Log run parameters
run["hparams"] = {
    "learning_rate": 0.001,
    "batch_size": 32,
}

# Log metrics
for step, sample in enumerate(train_loader):
    # ...
    run.track(loss_val, name='loss', step=step, epoch=epoch, context={ "subset": "train" })
    run.track(acc_val, name='acc', step=step, epoch=epoch, context={ "subset": "train" })
    # ...

See documentation here.

Integrate PyTorch Lightning

from aim.pytorch_lightning import AimLogger

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

See documentation here.

Integrate Hugging Face

from aim.hugging_face import AimCallback

# ...
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='mnli')
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    callbacks=[aim_callback],
    # ...
)
# ...

See documentation here.

Integrate Keras & tf.keras

import aim

# ...
model.fit(x_train, y_train, epochs=epochs, callbacks=[
    aim.keras.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
    
    # Use aim.tensorflow.AimCallback in case of tf.keras
    aim.tensorflow.AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
])
# ...

See documentation here.

Integrate XGBoost

from aim.xgboost import AimCallback

# ...
aim_callback = AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
bst = xgb.train(param, xg_train, num_round, watchlist, callbacks=[aim_callback])
# ...

See documentation here.

3. Run the training as usual and start Aim UI

$ aim up

Roadmap

In progress (Oct 18 - Oct 24):

• Images tracking and visualization
• Centralized tracking server

TODO: Nov 1 - Feb 1

Track and Explore:

• Distributions tracking and visualization
• Transcripts tracking and visualization
• Runs side-by-side comparison

Data Backup:

• Cloud storage support: aws s3, gsc, azure storage

Reproducibility:

• Track git info, env vars, CLI arguments, dependencies
• Collect stdout, stderr logs

Integrations:

• Colab integration
• Jupyter integration
• plotly integration
• Kubeflow integration
• Streamlit integration
• Raytune integration

Community

If you have questions please:

1. Read the docs
2. Open a feature request or report a bug
3. Join our slack