Install on Windows¶

Prerequires¶

Python 3.6 (or above) 64-bit. Anaconda or Miniconda is highly recommended to manage multiple Python environments on Windows.
If it’s a newly installed Python environment, it needs to install Microsoft C++ Build Tools to support build NNI dependencies like scikit-learn.
```
pip install cython wheel
```
git for verifying installation.

Install NNI¶

In most cases, you can install and upgrade NNI from pip package. It’s easy and fast.

If you are interested in special or the latest code versions, you can install NNI through source code.

If you want to contribute to NNI, refer to setup development environment.

From pip package
```
python -m pip install --upgrade nni
```

From source code

git clone -b v2.2 https://github.com/Microsoft/nni.git
cd nni
python setup.py develop

Verify installation¶

Clone examples within source code.

git clone -b v2.2 https://github.com/Microsoft/nni.git

Run the MNIST example.

   nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml

Note:  If you are familiar with other frameworks, you can choose corresponding example under ``examples\trials``. It needs to change trial command ``python3`` to ``python`` in each example YAML, since default installation has ``python.exe``\ , not ``python3.exe`` executable.

Wait for the message INFO: Successfully started experiment! in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the Web UI url.

INFO: Starting restful server...
INFO: Successfully started Restful server!
INFO: Setting local config...
INFO: Successfully set local config!
INFO: Starting experiment...
INFO: Successfully started experiment!
-----------------------------------------------------------------------
The experiment id is egchD4qy
The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
-----------------------------------------------------------------------

You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
         commands                       description
1. nnictl experiment show        show the information of experiments
2. nnictl trial ls               list all of trial jobs
3. nnictl top                    monitor the status of running experiments
4. nnictl log stderr             show stderr log content
5. nnictl log stdout             show stdout log content
6. nnictl stop                   stop an experiment
7. nnictl trial kill             kill a trial job by id
8. nnictl --help                 get help information about nnictl
-----------------------------------------------------------------------

Open the Web UI url in your browser, you can view detailed information about the experiment and all the submitted trial jobs as shown below. Here are more Web UI pages.

System requirements¶

Below are the minimum system requirements for NNI on Windows, Windows 10.1809 is well tested and recommend. Due to potential programming changes, the minimum system requirements for NNI may change over time.

	Recommended	Minimum
Operating System	Windows 10 1809 or above
CPU	Intel® Core™ i5 or AMD Phenom™ II X3 or better	Intel® Core™ i3 or AMD Phenom™ X3 8650
GPU	NVIDIA® GeForce® GTX 660 or better	NVIDIA® GeForce® GTX 460
Memory	6 GB RAM	4 GB RAM
Storage	30 GB available hare drive space
Internet	Boardband internet connection
Resolution	1024 x 768 minimum display resolution

FAQ¶

simplejson failed when installing NNI¶

Make sure a C++ 14.0 compiler is installed.

building ‘simplejson._speedups’ extension error: [WinError 3] The system cannot find the path specified

Trial failed with missing DLL in command line or PowerShell¶

This error is caused by missing LIBIFCOREMD.DLL and LIBMMD.DLL and failure to install SciPy. Using Anaconda or Miniconda with Python(64-bit) can solve it.

ImportError: DLL load failed

Trial failed on webUI¶

Please check the trial log file stderr for more details.

If there is a stderr file, please check it. Two possible cases are:

forgetting to change the trial command python3 to python in each experiment YAML.
forgetting to install experiment dependencies such as TensorFlow, Keras and so on.

Fail to use BOHB on Windows¶

Make sure a C++ 14.0 compiler is installed when trying to run pip install nni[BOHB] to install the dependencies.

Not supported tuner on Windows¶

SMAC is not supported currently; for the specific reason refer to this GitHub issue.

Use Windows as a remote worker¶

Refer to Remote Machine mode.

Segmentation fault (core dumped) when installing¶

Refer to FAQ.