Tiny yet useful tool for consistent model training logs generation.
To install through pip use the following command:
pip install traice
The tool requires only pandas
package to be installed. However, there is environment.yml
file which can be used for the same environment which is used for developing the tool:
conda env create -f environment.yml
The tool may be used as follows (see examples/dummy.py
):
from random import seed, uniform
from time import time, sleep
from traice import Traicer
traicer = Traicer()
def train_step():
sleep(uniform(0, 1))
seed(17)
init_timestamp = time()
for i in range(1, 5):
start_timestamp = time()
train_step()
traicer.push(i, uniform(0, 1 / i), (time_ := time()) - start_timestamp, time_ - init_timestamp)
print(traicer.df)
Essentially, it accumulates all push
arguments in a list which is then converted to a dataframe. The example produces the following log (the last two columns may differ a bit):
epoch loss time cumulative_time
0 1 0.806691 0.522609 0.522609
1 2 0.144813 0.961565 1.484184
2 3 0.234740 0.767061 2.251254
3 4 0.027541 0.661659 2.912921
To run test execute the following statement in your terminal:
python -m unittest discover test