update markdown docs (english) (#60)

README.md

@@ -42,21 +42,56 @@ pip install -v --no-cache-dir --global-option="--cuda_ext" .
 
 ```python
 import colossalai
-from colossalai.trainer import Trainer
-from colossalai.core import global_context as gpc
+from colossalai.utils import get_dataloader
 
-engine, train_dataloader, test_dataloader = colossalai.initialize()
 
-trainer = Trainer(engine=engine,
-                  verbose=True)
-trainer.fit(
-    train_dataloader=train_dataloader,
-    test_dataloader=test_dataloader,
-    epochs=gpc.config.num_epochs,
-    hooks_cfg=gpc.config.hooks,
-    display_progress=True,
-    test_interval=5
+# my_config can be path to config file or a dictionary obj
+# 'localhost' is only for single node, you need to specify
+# the node name if using multiple nodes
+colossalai.launch(
+    config=my_config,
+    rank=rank,
+    world_size=world_size,
+    backend='nccl',
+    port=29500,
+    host='localhost'
 )
+
+# build your model
+model = ... 
+
+# build you dataset, the dataloader will have distributed data 
+# sampler by default
+train_dataset = ... 
+train_dataloader = get_dataloader(dataset=dataset,
+                            shuffle=True,
+                            )
+
+
+# build your 
+optimizer = ... 
+
+# build your loss function
+criterion = ...
+
+# build your lr_scheduler
+engine, train_dataloader, _, _ = colossalai.initialize(
+    model=model,
+    optimizer=optimizer,
+    criterion=criterion,
+    train_dataloader=train_dataloader
+)
+
+# start training
+engine.train()
+for epoch in range(NUM_EPOCHS):
+    for data, label in train_dataloader:
+        engine.zero_grad()
+        output = engine(data)
+        loss = engine.criterion(output, label)
+        engine.backward(loss)
+        engine.step()
+
 ```
 
 ### Write a Simple 2D Parallel Model