Milano (Machine learning autotuner and network optimizer) is a tool
for enabling machine learning researchers and practitioners to perform
massive hyperparameters
and architecture searches.
Your script can use any framework of your choice, for example,
TensorFlow, PyTorch, Microsoft Cognitive Toolkit etc. or no framework at
all.
Milano only requires minimal changes to what your script accepts via
command line and what it returns to stdout.
Currently supported backends:
Azkaban - on a single multi-GPU machine or server with Azkaban installed
AWS - Amazon cloud using GPU instances
SLURM - any cluster which is running SLURM
Prerequisites
Linux
Python 3
Ensure you have Python version 3.5 or later with packages listed in the requirements.txt file.
Backend with NVIDIA GPU
How to Get Started
Install all dependencies with the following command
pip install -r requirements.txt.
We provide a script to convert the csv file output into two kinds of graphs:
Graphs of each hyperparameter with the benchmark (e.g. valid perplexity)
Color graphs that show the relationship between any two hyperparameters and the benchmark
To run the script, use:
python3 visualize.py --file [the name of the results csv file]
--n [the number of samples to visualize]
--subplots [the number of subplots to show in a plot]
--max [the max value of benchmark you care about]