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.
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 v1.9 https://github.com/Microsoft/nni.git cd nni powershell -ExecutionPolicy Bypass -file install.ps1
The following example is built on TensorFlow 1.x. Make sure TensorFlow 1.x is used when running it.
Clone examples within source code.
git clone -b v1.9 https://github.com/Microsoft/nni.git
Run the MNIST example.
nnictl create --config nni\examples\trials\mnist-tfv1\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 theWeb 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.
![overview](/Anon-Artist/nni/raw/master/docs/img/webui_overview_page.png)
![detail](/Anon-Artist/nni/raw/master/docs/img/webui_trialdetail_page.png)
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 |
Make sure a C++ 14.0 compiler is installed.
building 'simplejson._speedups' extension error: [WinError 3] The system cannot find the path specified
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
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
topython
in each experiment YAML. - forgetting to install experiment dependencies such as TensorFlow, Keras and so on.
Make sure a C++ 14.0 compiler is installed when trying to run nnictl package install --name=BOHB
to install the dependencies.
SMAC is not supported currently; for the specific reason refer to this GitHub issue.
Refer to Remote Machine mode.
Refer to FAQ.
- Overview
- Use command line tool nnictl
- Use NNIBoard
- Define search space
- Config an experiment
- How to run an experiment on local (with multiple GPUs)?
- How to run an experiment on multiple machines?
- How to run an experiment on OpenPAI?
- How to run an experiment on Kubernetes through Kubeflow?
- How to run an experiment on Kubernetes through FrameworkController?