Engine docs (catalyst-team#1141)

* typings & few docs

* typing fix; disabled `dist.barrier()` in optimizer step for ddp

* docs

* docs: fixed long lines with docs

* docs fixes

* optimizer args

* removed empty line

Co-authored-by: Dmytro Doroshenko <dimdoroshenko@gmail.com>

catalyst/core/engine.py

@@ -6,7 +6,7 @@
 
 
 @contextmanager
-def nullcontext(enter_result=None):
+def nullcontext(enter_result: Any = None):
     """Context handler."""
     yield enter_result
 
@@ -37,8 +37,7 @@ def rank(self) -> int:
     @property
     @abstractmethod
     def world_size(self) -> int:
-        """Process world size  for distributed training."""
-        # only for ddp
+        """Process world size for distributed training."""
         pass
 
     @property
@@ -49,26 +48,28 @@ def is_ddp(self) -> bool:
     @property
     def is_master_process(self) -> bool:
         """Checks if a process is master process.
-        Should be implemented only for DDP setup in other cases should always return True.
+        Should be implemented only for distributed training (ddp).
+        For non distributed training should always return `True`.
 
         Returns:
-            `True` if current process is a master process, otherwise `False`.
+            `True` if current process is a master process in other cases return `False`.
         """
         return True
 
     @property
     def is_worker_process(self) -> bool:
         """Checks if a process is worker process.
-        Should be implemented only for DDP setup in other cases should always return False.
+        Should be implemented only for distributed training (ddp).
+        For non distributed training should always return `False`.
 
         Returns:
-            `True` if current process is a worker process, otherwise `False`.
+            `True` if current process is a worker process in other cases return `False`.
         """
         return False
 
     @abstractmethod
     def sync_device(self, tensor_or_module: Any) -> Any:
-        """Moves ``tensor_or_module`` to Engine's deivce.
+        """Moves ``tensor_or_module`` to Engine's device.
 
         Args:
             tensor_or_module: tensor to mode
@@ -89,23 +90,50 @@ def init_components(
 
     @abstractmethod
     def deinit_components(self):
-        """Deinits the runs components."""
-        # only for ddp
+        """Deinits the runs components.
+        In distributed mode should destroy process group.
+        """
         pass
 
     @abstractmethod
     def zero_grad(self, loss, model, optimizer) -> None:
-        """Abstraction over ``model.zero_grad()`` step."""
+        """Abstraction over ``model.zero_grad()`` step.
+        Should be overloaded in cases when required to set arguments
+        for ``model.zero_grad()`` like `set_to_none=True` or
+        you need to use custom scheme which replaces/improves
+        `.zero_grad()` method.
+
+        Args:
+            loss: tensor with loss value.
+            model: model module.
+            optimizer: model optimizer.
+        """
         pass
 
     @abstractmethod
     def backward_loss(self, loss, model, optimizer) -> None:
-        """Abstraction over ``loss.backward()`` step."""
+        """Abstraction over ``loss.backward()`` step.
+        Should be overloaded in cases when required loss scaling.
+        Examples - APEX and AMP.
+
+        Args:
+            loss: tensor with loss value.
+            model: model module.
+            optimizer: model optimizer.
+        """
         pass
 
     @abstractmethod
     def optimizer_step(self, loss, model, optimizer) -> None:
-        """Abstraction over ``optimizer.step()`` step."""
+        """Abstraction over ``optimizer.step()`` step.
+        Should be overloaded in cases when required gradient scaling.
+        Example - AMP.
+
+        Args:
+            loss: tensor with loss value.
+            model: model module.
+            optimizer: model optimizer.
+        """
         pass
 
     @abstractmethod
@@ -174,7 +202,8 @@ def load_checkpoint(self, path: str) -> Dict:
         pass
 
     def autocast(self, *args, **kwargs):
-        """AMP scaling context. Default autocast context does not scale anything.
+        """AMP scaling context.
+        Default autocast context does not scale anything.
 
         Args:
             *args: some args

catalyst/engines/amp.py

@@ -10,6 +10,35 @@ class AMPEngine(DeviceEngine):
 
     Args:
         device: used device, default is `"cuda"`.
+
+    Examples:
+
+    .. code-block:: python
+
+        from catalyst import dl
+
+        class MyRunner(dl.IRunner):
+            # ...
+            def get_engine(self):
+                return dl.AMPEngine("cuda:1")
+            # ...
+
+    .. code-block:: yaml
+
+        args:
+            logs: ...
+
+        model:
+            _target_: ...
+            ...
+
+        engine:
+            _target_: AMPEngine
+            device: cuda:1
+
+        stages:
+            ...
+
     """
 
     def __init__(self, device: str = "cuda"):
@@ -36,7 +65,36 @@ def autocast(self):
 
 
 class DataParallelAMPEngine(AMPEngine):
-    """AMP multi-gpu training device engine."""
+    """AMP multi-gpu training device engine.
+
+    Examples:
+
+    .. code-block:: python
+
+        from catalyst import dl
+
+        class MyRunner(dl.IRunner):
+            # ...
+            def get_engine(self):
+                return dl.DataParallelAMPEngine()
+            # ...
+
+    .. code-block:: yaml
+
+        args:
+            logs: ...
+
+        model:
+            _target_: ...
+            ...
+
+        engine:
+            _target_: DataParallelAMPEngine
+
+        stages:
+            ...
+
+    """
 
     def __init__(self):
         """Init."""
@@ -75,10 +133,42 @@ class DistributedDataParallelAMPEngine(DistributedDataParallelEngine):
     """Distributed AMP multi-gpu training device engine.
 
     Args:
-        address: process address to use (required for PyTorch backend), default is `"localhost"`.
-        port: process port to listen (required for PyTorch backend), default is `"12345"`.
-        backend: multiprocessing backend to use, default is `"nccl"`.
+        address: process address to use
+            (required for PyTorch backend), default is `"localhost"`.
+        port: process port to listen
+            (required for PyTorch backend), default is `"12345"`.
+        backend: multiprocessing backend to use,
+            default is `"nccl"`.
         world_size: number of processes.
+
+    Examples:
+
+    .. code-block:: python
+
+        from catalyst import dl
+
+        class MyRunner(dl.IRunner):
+            # ...
+            def get_engine(self):
+                return dl.DistributedDataParallelAMPEngine(port=12345)
+            # ...
+
+    .. code-block:: yaml
+
+        args:
+            logs: ...
+
+        model:
+            _target_: ...
+            ...
+
+        engine:
+            _target_: DistributedDataParallelAMPEngine
+            port: 12345
+
+        stages:
+            ...
+
     """
 
     def __init__(