* trainingServicePlatforms. required key. This field specify the platforms used in hybrid mode, the values using yaml list format. NNI support setting ``local``, ``remote``, ``aml``, ``pai`` in this field.
.. Note:: If setting a platform in trainingServicePlatforms mode, users should also set the corresponding configuration for the platform. For example, if set ``remote`` as one of the platform, should also set ``machineList`` and ``remoteConfig`` configuration. Local platform in hybrid mode does not support windows for now.
.. Note:: If setting a platform in trainingServicePlatforms mode, users should also set the corresponding configuration for the platform. For example, if set ``remote`` as one of the platform, should also set ``machineList`` and ``remoteConfig`` configuration.
We should have a concrete rule for specifying nodes in graph with namespace.
Each node has a name, either specified or generated. The nodes in the same hierarchy cannot have the same name.
* The name of module node natively follows this rule, because we use variable name for instantiated modules like what PyTorch graph does.
* For the nodes created in `forward` function, we use a global sequence number.
### Namespace for mutated (new) nodes
TBD
## Graph Simplification
TBD
## Node Types
We define concrete type string for each node type.
## Module's Input Arguments
We use wrapper to obtain the input arguments of modules. Users need to use our wrapped "nn" and wrapped "Module".
## Control Flow
### for loop
Currently, we only support `ModuleList` (`ModuleDict`) based for loop, which is automatically unfolded by TorchScript. That is to say, we do not support loop in TorchScript for now.
### if/else
For now, we only deal with the case that the condition is constant or attribute. In this case, only one branch is kept during generating the graph.
