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https://github.com/mohd-faizy/hyperparameter-tuning-with-microsoft-network-intelligence-toolkit-nni
Hyperparameter Tuning with Microsoft NNI to automated machine learning (AutoML) experiments. The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
https://github.com/mohd-faizy/hyperparameter-tuning-with-microsoft-network-intelligence-toolkit-nni
automl feature-engineering hyperparameter-optimization hyperparameter-tuning microsoft-nni model-compression neural-architecture-search neural-network-intelligence nni nnictl
Last synced: 4 months ago
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Hyperparameter Tuning with Microsoft NNI to automated machine learning (AutoML) experiments. The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
- Host: GitHub
- URL: https://github.com/mohd-faizy/hyperparameter-tuning-with-microsoft-network-intelligence-toolkit-nni
- Owner: mohd-faizy
- License: mit
- Created: 2020-11-25T19:17:19.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T09:02:25.000Z (9 months ago)
- Last Synced: 2024-05-22T22:27:01.696Z (8 months ago)
- Topics: automl, feature-engineering, hyperparameter-optimization, hyperparameter-tuning, microsoft-nni, model-compression, neural-architecture-search, neural-network-intelligence, nni, nnictl
- Language: Python
- Homepage:
- Size: 3.02 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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---
Hyperparameter Tuning with Microsoft Neural Network Intelligence Toolkit
This repository demonstrates how to perform hyperparameter tuning using Microsoft’s NNI toolkit. NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning tasks.
#### **The below diagram illustrates high-level Architecture of NNI**
## 🌱 ➤ Prerequisites
- NNI requires `Python >= 3.7`. It is tested and supported on `Ubuntu >= 18.04`, `Windows 10 >= 21H2`, and `macOS >= 11`
- Pip (Python package manager)
## 👁️ ➤ Setup Instructions
### 👉 Create a Virtual Environment
1. Open your terminal or command prompt.
2. Clone the repository `git clone https://github.com/mohd-faizy/Hyperparameter-Tuning-with-Microsoft-Network-Intelligence-Toolkit-NNI.git`
`
3. Navigate to your project directory.
4. Run the following command to create a virtual environment (replace `` with your desired name):
```sh
python -m venv venv
```
### 👉 Activate the Virtual Environment
- On Windows:
```sh
.\venv\Scripts\activate
```
- On macOS/Linux:
```sh
source ./venv/bin/activate
```
### 👉 Install Dependencies
```sh
pip install -r requirements.txt
```
## ➤ Run the Hyperparameter Tuning Experiment
1. Creating the Hyperparameter Search Space:
1. In order to perform the hyper-parameter tunning, we first need to create the **search space** that describs the value range of each hyper-parameter.
2. we can use the `.json` code to describe the range & this is the dictionary of all the hyper-parameter values that we want to run for our experiment.
🔸`search_space.json`
```json
{
"dropout_rate": {
"_type": "uniform",
"_value": [0.1, 0.9]
},
"num_units": {
"_type": "choice",
"_value": [ 32, 64, 128, 256, 512]
},
"lr": {
"_type": "choice",
"_value": [ 0.1, 0.01, 0.001, 0.03, 0.003, 0.06, 0.006]
},
"batch_size": {
"_type": "choice",
"_value": [ 16, 32, 64, 128, 256, 512]
},
"activation": {
"_type": "choice",
"_value": [ "relu", "sigmoid", "tanh"]
}
}
```
2. Then we need to create another file called `config.yml` & which contain all the information regarding the configuration information of our experiment.
🔸`config.yml`
```yml
experimentName: mnist
trialConcurrency: 1
maxExperimentDuration: 1h
maxTrialNumber: 10
searchSpaceFile: search_space.json
useAnnotation: false
trialCommand: python main.py
trialCodeDirectory: .
tuner:
name: TPE
classArgs:
optimize_mode: maximize
trainingService:
platform: local
```
3. Then at last we need to Execute the following command to start the NNI experiment:
👇
```sh
nnictl create --config config.yml
```
## 🛠️ Monitor the Experiment
1. Open the NNI dashboard in your web browser (usually at ).
```
[2024-05-22 00:52:19] Web portal URLs: http://192.168.0.106:8080 http://172.25.208.1:8080 http://127.0.0.1:8080
[2024-05-22 00:52:19] To stop experiment run "nnictl stop c8usp7bz" or "nnictl stop --all"
[2024-05-22 00:52:19] Reference:
```
2. Observe the trial jobs, intermediate results, and final results.
## 📄 ➤ Launching the NNI Dashboard
### **Hyperparameter Visualization**
#### 🔸**Top 100% percent**🔸
#### 🔸**Top 20% percent**🔸
#### 🔸**Top 5% percent**🔸
#### 🔸**Top 1% percent**🔸
- ` default metric: 0.9793`
#### 🔸**Duration**🔸
#### 🔸**Intermediate result**🔸
## 🍰 ➤ Contributing
Contributions are welcome!
## 🗂️ ➤ Additional Resources
- 👉[NNI Documentation](https://nni.readthedocs.io/en/latest/index.html)
- 👉[NNI GitHub Repository](https://github.com/microsoft/nni)
- 👉[NNI Experiment Config Reference](https://nni.readthedocs.io/en/latest/reference/experiment_config.html)
- 👉[nnictl Commands](https://nni.readthedocs.io/en/latest/reference/nnictl.html)
## ⚖ ➤ License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
## ❤️ ➤ Support
If you find this repository helpful, show your support by starring it! For questions or feedback, reach out on [Twitter(`X`)](https://twitter.com/F4izy).
#### $\color{skyblue}{\textbf{Connect with me:}}$
🔃 ➤ If you have questions or feedback, feel free to reach out!!!
[
][twitter]
[
][linkedin]
[
][Portfolio]
[twitter]: https://twitter.com/F4izy
[linkedin]: https://www.linkedin.com/in/mohd-faizy/
[Portfolio]: https://ai.stackexchange.com/users/36737/faizy?tab=profile
---
