https://nvidia.github.io/Milano/

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
https://nvidia.github.io/Milano/

automl deep-learning deep-neural-networks hyperparameter-optimization hyperparameter-tuning machine-learning

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Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

• Host: GitHub
• URL: https://nvidia.github.io/Milano/
• Owner: NVIDIA
• License: apache-2.0
• Archived: true
• Created: 2018-09-04T21:58:58.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-11-07T22:32:56.000Z (about 7 years ago)
• Last Synced: 2025-10-24T16:34:24.028Z (28 days ago)
• Topics: automl, deep-learning, deep-neural-networks, hyperparameter-optimization, hyperparameter-tuning, machine-learning
• Language: Python
• Homepage:
• Size: 351 KB
• Stars: 155
• Watchers: 7
• Forks: 25
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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# Milano 

(*This is a research project, not an official NVIDIA product.*)



  

  




## Documentation

[https://nvidia.github.io/Milano](https://nvidia.github.io/Milano)

Milano (Machine learning autotuner and network optimizer) is a tool for enabling machine learning researchers and practitioners to perform massive hyperparameters 

and architecture searches.

You can use it to:

* Tune your model on a cloud backend of your choice

* Benchmark Auto-ML algorithms (see [how to add new search algorithm](docs/how-to-add-new-search-algorithm.md))

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

1. Install all dependencies with the following command

   pip install -r requirements.txt.

2. Follow this [mini-tutorial for local machine](docs/Quick_start.md) or this [mini-tutorial for AWS](docs/Quick_start_aws.md) 

## Visualize

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:

```bash

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]

```