Easy-to-use, offline-first ML experiment management solution.
Project description
tidyexp
Easy-to-use, offline-first ML experiment management solution.
What does it solve?
Other competitors are complex in nature – they have a slight steep learning curve and aren’t often beginner-friendly. Some of them require you to create an account on their platform to get started.
tidyexp aims to provide a free, easy-to-use platform for tracking ML experiment metadata.
Installation
Through pip
:
pip install tidyexp
Usage
Import tidyexp:
import tidyexp
Create a Logger instance with the experiment metadata:
log = tidyexp.Logger(experiment_id="1", experiment_dir=".", time_track=["num_epochs"], stats_track=["mse"], overwrite=True, model_type="torch")
Track experiment metadata in the training loop:
for i in range(epochs):
....
time_dict = {"num_epochs": i}
stats_dict = {"mse": curr_loss}
log.update(time_dict, stats_dict)
Save logs:
log.save()
Load logs:
from tidyexp.load.load_log import load_log, load_stats, load_time
logs = load_log("abcd/logs/log_1.hdf5")
stats = load_stats("abcd/logs/log_1.hdf5", "1")
time_stats = load_time("abcd/logs/log_1.hdf5", "1")
Save model:
log.save_model(model)
Load model:
from tidyexp.load.load_model import load_model
ckpt = load_model("abcd/models/final/final_1.pt", "torch")
Create archive (.zip
):
log.archive("archive")
Upload to Google Drive:
log.upload_gdrive("./credentials.json", "MyExperiment", "archive.zip")
Push to local Git repository:
log.commit("C:\\Users\\ExampleUser\\Experiments", ".\abcd")
License
tidyexp is licensed under the MIT License.
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