* 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`.
CNN MNIST classifier for deep learning is similar to ``hello world`` for programming languages. Thus, we use MNIST as example to introduce different features of NNI. The examples are listed below:
* `MNIST with NNI API (TensorFlow v1.x) <#mnist-tfv1>`__
* `MNIST with NNI API (PyTorch) <#mnist-pytorch>`__
* `MNIST with NNI API (TensorFlow v2.x) <#mnist-tfv2>`__
* `MNIST with NNI API (TensorFlow v1.x) <#mnist-tfv1>`__
* `MNIST with NNI annotation <#mnist-annotation>`__
* `MNIST in keras <#mnist-keras>`__
* `MNIST -- tuning with batch tuner <#mnist-batch>`__
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@@ -18,20 +19,30 @@ CNN MNIST classifier for deep learning is similar to ``hello world`` for program
* `distributed MNIST (tensorflow) using kubeflow <#mnist-kubeflow-tf>`__
* `distributed MNIST (pytorch) using kubeflow <#mnist-kubeflow-pytorch>`__
:raw-html:`<a name="mnist-tfv1"></a>`
**MNIST with NNI API (TensorFlow v1.x)**
:raw-html:`<a name="mnist-pytorch"></a>`
**MNIST with NNI API (PyTorch)**
This is a simple network which has two convolutional layers, two pooling layers and a fully connected layer. We tune hyperparameters, such as dropout rate, convolution size, hidden size, etc. It can be tuned with most NNI built-in tuners, such as TPE, SMAC, Random. We also provide an exmaple YAML file which enables assessor.
This is a simple network which has two convolutional layers, two pooling layers and a fully connected layer.
We tune hyperparameters, such as dropout rate, convolution size, hidden size, etc.
It can be tuned with most NNI built-in tuners, such as TPE, SMAC, Random.
We also provide an exmaple YAML file which enables assessor.
This example is implemented in keras. It is also a network for MNIST dataset, with two convolution layers, one pooling layer, and two fully connected layers.
@@ -167,7 +167,7 @@ NNI supports a standalone mode for trial code to run without starting an NNI exp
nni.get_trial_id # return "STANDALONE"
nni.get_sequence_id # return 0
You can try standalone mode with the :githublink:`mnist example <examples/trials/mnist-tfv1>`. Simply run ``python3 mnist.py`` under the code directory. The trial code should successfully run with the default hyperparameter values.
You can try standalone mode with the :githublink:`mnist example <examples/trials/mnist-pytorch>`. Simply run ``python3 mnist.py`` under the code directory. The trial code should successfully run with the default hyperparameter values.
For more information on debugging, please refer to `How to Debug <../Tutorial/HowToDebug.rst>`__
@@ -71,4 +71,4 @@ Our documentation is built with :githublink:`sphinx <docs>`.
* It's an image link which needs to be formatted with embedded html grammar, please use global URL like ``https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png``, which can be automatically generated by dragging picture onto `Github Issue <https://github.com/Microsoft/nni/issues/new>`__ Box.
* It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v1.9/`` (:githublink:`mnist.py <examples/trials/mnist-tfv1/mnist.py>` for example).
* It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v1.9/`` (:githublink:`mnist.py <examples/trials/mnist-pytorch/mnist.py>` for example).
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``.
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.
@@ -36,43 +36,32 @@ After the installation, you may want to enable the auto-completion feature for *
NNIisatoolkittohelpusersrunautomatedmachinelearningexperiments.Itcanautomaticallydothecyclicprocessofgettinghyperparameters,runningtrials,testingresults,andtuninghyperparameters.Here,we'll show how to use NNI to help you find the optimal hyperparameters for a MNIST model.
Here is an example script to train a CNN on the MNIST dataset **without NNI**\ :
Here is an example script to train a CNN on the MNIST dataset **without NNI**:
If you want to see the full implementation, please refer to :githublink:`examples/trials/mnist-tfv1/mnist_before.py <examples/trials/mnist-tfv1/mnist_before.py>`.
The above code can only try one set of parameters at a time; if we want to tune learning rate, we need to manually modify the hyperparameter and start the trial again and again.
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@@ -96,46 +85,48 @@ If you want to use NNI to automatically train your model and find the optimal hy
Three steps to start an experiment
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**Step 1**\ : Write a ``Search Space`` file in JSON, including the ``name`` and the ``distribution`` (discrete-valued or continuous-valued) of all the hyperparameters you need to search.
**Step 1**: Write a ``Search Space`` file in JSON, including the ``name`` and the ``distribution`` (discrete-valued or continuous-valued) of all the hyperparameters you need to search.
**Step 3**\ : Define a ``config`` file in YAML which declares the ``path`` to the search space and trial files. It also gives other information such as the tuning algorithm, max trial number, and max duration arguments.
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@@ -160,9 +151,9 @@ Three steps to start an experiment
.. Note:: If you are planning to use remote machines or clusters as your :doc:`training service <../TrainingService/Overview>`, to avoid too much pressure on network, we limit 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.yml <examples/trials/mnist-tfv1/config.yml>` and :githublink:`.nniignore <examples/trials/mnist-tfv1/.nniignore>`
*Example:* :githublink:`config.yml <examples/trials/mnist-pytorch/config.yml>` and :githublink:`.nniignore <examples/trials/mnist-pytorch/.nniignore>`
All the code above is already prepared and stored in :githublink:`examples/trials/mnist-tfv1/ <examples/trials/mnist-tfv1>`.
All the code above is already prepared and stored in :githublink:`examples/trials/mnist-pytorch/ <examples/trials/mnist-pytorch>`.
Linux and macOS
^^^^^^^^^^^^^^^
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@@ -171,7 +162,7 @@ Run the **config.yml** file from your command line to start an MNIST experiment.
.. Note:: If you'reusingNNIonWindows,youprobablyneedtochange``python3``to``python``intheconfig.ymlfileorusetheconfig_windows.ymlfiletostarttheexperiment.
@@ -6,8 +6,6 @@ NNI development environment supports Ubuntu 1604 (or above), and Windows 10 with
Installation
------------
The installation steps are similar with installing from source code. But the installation links to code directory, so that code changes can be applied to installation as easy as possible.
1. Clone source code
^^^^^^^^^^^^^^^^^^^^
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@@ -20,19 +18,13 @@ Note, if you want to contribute code back, it needs to fork your own NNI repo, a
Nothing to do, the code is already linked to package folders.
TypeScript
^^^^^^^^^^
TypeScript (Linux and macOS)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
* If ``ts/nni_manager`` is changed, run ``yarn watch`` under this folder. It will watch and build code continually. The ``nnictl`` need to be restarted to reload NNI manager.
* If ``ts/webui`` is changed, run ``yarn dev``\ , which will run a mock API server and a webpack dev server simultaneously. Use ``EXPERIMENT`` environment variable (e.g., ``mnist-tfv1-running``\ ) to specify the mock data being used. Built-in mock experiments are listed in ``src/webui/mock``. An example of the full command is ``EXPERIMENT=mnist-tfv1-running yarn dev``.
* If ``ts/nasui`` is changed, run ``yarn start`` under the corresponding folder. The web UI will refresh automatically if code is changed. There is also a mock API server that is useful when developing. It can be launched via ``node server.js``.
TypeScript (Windows)
^^^^^^^^^^^^^^^^^^^^
* If ``src/nni_manager`` is changed, run ``yarn watch`` under this folder. It will watch and build code continually. The ``nnictl`` need to be restarted to reload NNI manager.
* If ``src/webui`` is changed, run ``yarn dev``\ , which will run a mock API server and a webpack dev server simultaneously. Use ``EXPERIMENT`` environment variable (e.g., ``mnist-tfv1-running``\ ) to specify the mock data being used. Built-in mock experiments are listed in ``src/webui/mock``. An example of the full command is ``EXPERIMENT=mnist-tfv1-running yarn dev``.
* If ``src/nasui`` is changed, run ``yarn start`` under the corresponding folder. The web UI will refresh automatically if code is changed. There is also a mock API server that is useful when developing. It can be launched via ``node server.js``.
Currently you must rebuild TypeScript modules with `python3 setup.py build_ts` after edit.
@@ -7,7 +7,7 @@ Assessor receives the intermediate result from a trial and decides whether the t
Here is an experimental result of MNIST after using the 'Curvefitting' Assessor in 'maximize' mode. You can see that Assessor successfully **early stopped** many trials with bad hyperparameters in advance. If you use Assessor, you may get better hyperparameters using the same computing resources.
