@@ -56,7 +56,7 @@ If users want to apply both, a sequential mode is recommended as common practise
.. note::
Note that NNI pruners or quantizers are not meant to physically compact the model but for simulating the compression effect. Whereas NNI speedup tool can truly compress model by changing the network architecture and therefore reduce latency.
To obtain a truly compact model, users should conduct :doc:`pruning speedup <../tutorials/cp_pruning_speedup>` or :doc:`quantizaiton speedup <../tutorials/cp_quantization_speedup>`.
To obtain a truly compact model, users should conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or :doc:`quantizaiton speedup <../tutorials/quantization_speedup>`.
The interface and APIs are unified for both PyTorch and TensorFlow. Currently only PyTorch version has been supported, and TensorFlow version will be supported in future.
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@@ -75,8 +75,8 @@ The following figure shows how NNI prunes and speeds up your models.
:align: center
:alt:
The detailed tutorial of Speedup Model with Mask can be found :doc:`here <../tutorials/cp_pruning_speedup>`.
The detailed tutorial of Speedup Model with Calibration Config can be found :doc:`here <../tutorials/cp_quantization_speedup>`.
The detailed tutorial of Speedup Model with Mask can be found :doc:`here <../tutorials/pruning_speedup>`.
The detailed tutorial of Speedup Model with Calibration Config can be found :doc:`here <../tutorials/quantization_speedup>`.
@@ -6,7 +6,7 @@ An experiment can be created with command line tool ``nnictl`` or python APIs. N
Management with ``nnictl``
--------------------------
The ability of ``nnictl`` on experiment management is almost equivalent to :doc:`./webui`. Users can refer to :doc:`../reference/nnictl` for detailed usage. It is highly suggested when visualization is not well supported in your environment (e.g., no GUI on your machine).
The ability of ``nnictl`` on experiment management is almost equivalent to :doc:`web_portal/web_portal`. Users can refer to :doc:`../reference/nnictl` for detailed usage. It is highly suggested when visualization is not well supported in your environment (e.g., no GUI on your machine).
An NNI experiment is a unit of one tuning process. For example, it is one run of hyper-parameter tuning on a specific search space, it is one run of neural architecture search on a search space, or it is one run of automatic model compression on user specified goal on latency and accuracy. Usually, the tuning process requires many trials to explore feasible and potentially good-performing models. Thus, an important component of NNI experiment is **training service**, which is a unified interface to abstract diverse computation resources (e.g., local machine, remote servers, AKS). Users can easily run the tuning process on their prefered computation resource and platform. On the other hand, NNI experiment provides **WebUI** to visualize the tuning process to users.
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@@ -11,11 +11,8 @@ The relation of the components in NNI experiment is illustrated in the following
:scale: 80 %
:align: center
Before reading the following content, you are recommended to go through the quick start first.
Before reading the following content, you are recommended to go through either :doc:`the quickstart of HPO </tutorials/hpo_quickstart_pytorch/main>` or :doc:`quickstart of NAS </tutorials/hello_nas>` first.
.. toctree::
:maxdepth: 2
Training Services <training_service>
Web Portal <web_portal>
Experiment Management <exp_management>
\ No newline at end of file
* :doc:`Overview of NNI training service <training_service/overview>`
* :doc:`Introduction to Web Portal <web_portal/web_portal>`
4. Log into your Azure account with a web browser and create a Machine Learning resource. You will need to choose a resource group and specific a workspace name. Then download ``config.json`` which will be used later.
.. image:: ../../img/aml_workspace.png
.. image:: ../../../img/aml_workspace.png
5. Create an AML cluster as the compute target.
.. image:: ../../img/aml_cluster.png
.. image:: ../../../img/aml_cluster.png
6. Open a command line and install AML package environment.
The ``Preview container paths`` is the NFS host and path that OpenPAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the OpenPAI's NFS storage to upload data/code to or download from OpenPAI cluster. To mount the storage, please use ``mount`` command, for example:
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@@ -69,13 +69,13 @@ Once completing the configuration and run nnictl / use Python to launch the expe
The job name format is something like ``nni_exp_{experiment_id}_trial_{trial_id}``. You can see jobs created by NNI on the OpenPAI cluster's web portal, like:
.. image:: ../../img/nni_pai_joblist.jpg
.. image:: ../../../img/nni_pai_joblist.jpg
.. note:: For OpenPAI training service, NNI will start an additional rest server and listen on a port which is your NNI WebUI's port plus 1. For example, if your WebUI port is ``8080``, the rest server will listen on ``8081``, to receive metrics from trial job running in Kubernetes. So you should ``enable 8081`` TCP port in your firewall rule to allow incoming traffic.
Once a trial job is completed, you can go to NNI WebUI's overview page (like ``http://localhost:8080/oview``) to check trial's information. For example, you can expand a trial information in trial list view, click the logPath link like:
.. image:: ../../img/nni_webui_joblist.png
.. image:: ../../../img/nni_webui_joblist.png
:scale: 30%
Configuration References
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@@ -137,7 +137,7 @@ Check policy:
If you could not run your experiment and want to know if it is caused by version check, you could check your webUI, and there will be an error message about version check.
With local training service, the whole experiment (e.g., tuning algorithms, trials) runs on a single machine, i.e., user's dev machine. The generated trials run on this machine following ``trialConcurrency`` set in the configuration yaml file. If GPUs are used by trial, local training service will allocate required number of GPUs for each trial, like a resource scheduler.
