NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([简体中文版点这里](https://nni.readthedocs.io/zh/))**. Quick links:
NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([简体中文版点这里](https://nni.readthedocs.io/zh/stable))**. Quick links:
@@ -50,18 +50,20 @@ To update NNI to the latest version, add `--upgrade` flag to the above commands.
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@@ -50,18 +50,20 @@ To update NNI to the latest version, add `--upgrade` flag to the above commands.
## Run your first experiment
## Run your first experiment
To run this experiment, you need to have [XXX](link) installed.
To run this experiment, you need to have [PyTorch](https://pytorch.org/)(as well as [torchvision](https://pytorch.org/vision/stable/index.html)) installed.
```shell
```shell
$ nnictl hello-world
$ nnictl hello
```
```
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` shown in the console.
It will generate `nni_hello_hpo` folder in your current working directory, which contains a minimum hyper-parameter tuning example. It will also prompt you to run
```text
```shell
TBD
python nni_hello_hpo/main.py
```
```
to launch your first NNI experiment. Use the web portal URL shown in the console to monitor the running status of your experiment.
NNIisatoolkittohelpusersrunautomatedmachinelearningexperiments.Itcanautomaticallydothecyclicprocessofgettinghyperparameters,runningtrials,testingresults,andtuninghyperparameters.Here,we'll show how to use NNI to help you find the optimal hyperparameters on the MNIST dataset.
Here is an example script to train a CNN on the MNIST dataset **without NNI**:
The above code can only try one set of parameters at a time. If you want to tune the learning rate, you need to manually modify the hyperparameter and start the trial again and again.
NNI is born to help users tune jobs, whose working process is presented below:
.. code-block:: text
input: search space, trial code, config file
output: one optimal hyperparameter configuration
1: For t = 0, 1, 2, ..., maxTrialNum,
2: hyperparameter = chose a set of parameter from search space
3: final result = run_trial_and_evaluate(hyperparameter)
4: report final result to NNI
5: If reach the upper limit time,
6: Stop the experiment
7: return hyperparameter value with best final result
.. note::
If you want to use NNI to automatically train your model and find the optimal hyper-parameters, there are two approaches:
1. Write a config file and start the experiment from the command line.
2. Config and launch the experiment directly from a Python file
In the this part, we will focus on the first approach. For the second approach, please refer to `this tutorial <HowToLaunchFromPython.rst>`__\ .
Step 1: Modify the ``Trial`` Code
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Modify your ``Trial`` file to get the hyperparameter set from NNI and report the final results to NNI.
Define a ``Search Space`` in a YAML file, including the ``name`` and the ``distribution`` (discrete-valued or continuous-valued) of all the hyperparameters you want to search.
You can also write your search space in a JSON file and specify the file path in the configuration. For detailed tutorial on how to write the search space, please see `here <SearchSpaceSpec.rst>`__.
Step 3: Config the Experiment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition to the search_space defined in the `step2 <step-2-define-the-search-space>`__, you need to config the experiment in the YAML file. It specifies the key information of the experiment, such as the trial files, tuning algorithm, max trial number, and max duration, etc.
.. code-block:: yaml
experimentName: MNIST # An optional name to distinguish the experiments
trialCommand: python3 mnist.py # NOTE: change "python3" to "python" if you are using Windows
trialConcurrency: 2 # Run 2 trials concurrently
maxTrialNumber: 10 # Generate at most 10 trials
maxExperimentDuration: 1h # Stop generating trials after 1 hour
tuner: # Configure the tuning algorithm
name: TPE
classArgs: # Algorithm specific arguments
optimize_mode: maximize
trainingService: # Configure the training platform
platform: local
Experiment config reference could be found `here <../reference/experiment_config.rst>`__.
.. _nniignore:
.. Note:: If you are planning to use remote machines or clusters as your training service, to avoid too much pressure on network, NNI limits the number of files to 2000 and total size to 300MB. If your codeDir contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation <https://git-scm.com/docs/gitignore#_pattern_format>`__.
*Example:* :githublink:`config_detailed.yml <examples/trials/mnist-pytorch/config_detailed.yml>` and :githublink:`.nniignore <examples/trials/mnist-pytorch/.nniignore>`
All the code above is already prepared and stored in :githublink:`examples/trials/mnist-pytorch/<examples/trials/mnist-pytorch>`.
Step 4: Launch the Experiment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Linux and macOS
***************
Run the **config_detailed.yml** file from your command line to start the experiment.
Change ``python3`` to ``python`` of the ``trialCommand`` field in the **config_detailed.yml** file, and run the **config_detailed.yml** file from your command line to start the experiment.
.. Note:: ``nnictl`` is a command line tool that can be used to control experiments, such as start/stop/resume an experiment, start/stop NNIBoard, etc. Click :doc:`here <../reference/nnictl>` for more usage of ``nnictl``.
Wait for the message ``INFO: Successfully started experiment!`` in the command line. This message indicates that your experiment has been successfully started. And this is what we expect to get:
If you prepared ``trial``\ , ``search space``\ , and ``config`` according to the above steps and successfully created an NNI job, NNI will automatically tune the optimal hyper-parameters and run different hyper-parameter sets for each trial according to the defined search space. You can see its progress through the WebUI clearly.
Step 5: View the Experiment
^^^^^^^^^^^^^^^^^^^^^^^^^^^
After starting the experiment successfully, you can find a message in the command-line interface that tells you the ``Web UI url`` like this:
.. code-block:: text
The Web UI urls are: [Your IP]:8080
Open the ``Web UI url`` (Here it's:``[YourIP]:8080``\)inyourbrowser,youcanviewdetailedinformationabouttheexperimentandallthesubmittedtrialjobsasshownbelow.IfyoucannotopentheWebUIlinkinyourterminal,pleaserefertothe`FAQ<FAQ.rst#could-not-open-webui-link>`__.
