fix dependenci and improve doc (#1535)

apps/android_rpc/README.md

@@ -85,10 +85,33 @@ If everything goes well, you will find compile tools in `/opt/android-toolchain-
 
 ### Cross Compile and Upload to the Android Device
 
-First start an RPC tracker using `python -m tvm.exec.rpc_tracker --port [PORT]` and connect your Android device to this RPC tracker via the TVM RPC application.
-Set the `Address` and `Port` fields to the address and port of the RPC tracker respectively.
+First start an RPC tracker using 
+
+```python -m tvm.exec.rpc_tracker --port [PORT]``` 
+
+and connect your Android device to this RPC tracker via the TVM RPC application. Open the app,
+set the `Address` and `Port` fields to the address and port of the RPC tracker respectively.
 The key should be set to "android" if you wish to avoid modifying the default test script.
 
+After pushing "START RPC" button on the app, you can check the connect by run 
+
+```python -m tvm.exec.query_rpc_tracker --port [PORT]``` 
+
+on your host machine. 
+You are supposed to find a free "android" in the queue status.
+
+```
+...
+
+Queue Status
+----------------------------
+key    	free	pending
+----------------------------
+android	1	0
+----------------------------
+```
+
+
 Then checkout [android\_rpc/tests/android\_rpc\_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py) and run,
 
 ```bash

docs/api/python/autotvm.rst

@@ -44,9 +44,6 @@ tvm.autotvm.tuner
 .. automodule:: tvm.autotvm.tuner.callback
     :members:
 
-.. automodule:: tvm.autotvm.tuner.graph_tuning
-    :members:
-
 tvm.autotvm.task
 ~~~~~~~~~~~~~~~~
 .. automodule:: tvm.autotvm.task

docs/install/from_source.rst

@@ -120,7 +120,10 @@ Currently, ROCm is supported only on linux, so all the instructions are written
 Python Package Installation
 ---------------------------
 
-The python package is located at python
+TVM package
+~~~~~~~~~~~
+
+The python package is located at `tvm/python`
 There are several ways to install the package:
 
 1. Set the environment variable `PYTHONPATH` to tell python where to find
@@ -131,7 +134,8 @@ There are several ways to install the package:
 
    .. code:: bash
 
-       export PYTHONPATH=/path/to/tvm/python:/path/to/tvm/topi/python:/path/to/tvm/nnvm/python:${PYTHONPATH}
+       export TVM_HOME=/path/to/tvm
+       export PYTHONPATH=$TVM_HOME/python:$TVM_HOME/topi/python:$TVM_HOME/nnvm/python:${PYTHONPATH}
 
 
 2. Install tvm python bindings by `setup.py`:
@@ -148,6 +152,27 @@ There are several ways to install the package:
        cd nnvm/python; python setup.py install --user; cd ../..
 
 
+Python dependencies
+~~~~~~~~~~~~~~~~~~~
+   * Necessary dependencies:
+
+   .. code:: bash
+
+       pip install --user numpy decorator
+
+   * If you want to use RPC Tracker
+
+   .. code:: bash
+
+       pip install --user tornado
+
+   * If you want to use auto-tuning module
+
+   .. code:: bash
+
+       pip install --user tornado psutil xgboost
+
+
 Install Contrib Libraries
 -------------------------
 

docs/vta/dev/hardware.rst

@@ -199,7 +199,7 @@ Adding more stages has a cost however: it can add storage and extra logic overhe
 
 .. _vta-uarch:
 Microarchitectural Overview
-----------------------
+---------------------------
 
 We describe the modules that compose the VTA design.
 The module definitions are contained in ``vta/hardware/xilinx/sources/vta.cc``.

python/tvm/autotvm/measure/measure_methods.py

@@ -95,12 +95,15 @@ def create_measure_batch(task, option):
     attach_objects = None
     if measure_func == 'local':
         # start temporary rpc tracker and rpc server for the user
-        tracker = rpc.Tracker('localhost', port=9000, port_end=10000, silent=True)
+        from ...rpc.tracker import Tracker
+        from ...rpc.server import Server
+
+        tracker = Tracker('localhost', port=9000, port_end=10000, silent=True)
         device_key = '$local$device$%d' % tracker.port
-        server = rpc.Server('localhost', port=9000, port_end=10000,
-                            key=device_key,
-                            use_popen=True, silent=True,
-                            tracker_addr=(tracker.host, tracker.port))
+        server = Server('localhost', port=9000, port_end=10000,
+                        key=device_key,
+                        use_popen=True, silent=True,
+                        tracker_addr=(tracker.host, tracker.port))
 
         measure_func = use_rpc(device_key, tracker.host, tracker.port)
         attach_objects = (server, tracker)

python/tvm/autotvm/record.py

@@ -163,7 +163,8 @@ def load_from_file(filename):
     result: autotvm.tuner.MeasureResult
     """
     for row in open(filename):
-        yield decode(row)
+        if row and not row.startswith('#'):
+            yield decode(row)
 
 
 def split_workload(in_file, clean=True):

python/tvm/exec/rpc_tracker.py

@@ -6,12 +6,12 @@ import logging
 import argparse
 import multiprocessing
 import sys
-from .. import rpc
+from ..rpc.tracker import Tracker
 
 def main(args):
     """Main funciton"""
-    tracker = rpc.Tracker(args.host, port=args.port, port_end=args.port_end,
-                          silent=args.silent)
+    tracker = Tracker(args.host, port=args.port, port_end=args.port_end,
+                      silent=args.silent)
     tracker.proc.join()
 
 

python/tvm/rpc/__init__.py

@@ -10,6 +10,4 @@ upload and run remote RPC server, get the result back to verify correctness.
 """
 
 from .server import Server
-from .tracker import Tracker
-from .proxy import Proxy
 from .client import RPCSession, LocalSession, TrackerSession, connect, connect_tracker

tutorials/autotvm/tune_conv2d_cuda.py

@@ -16,7 +16,7 @@ vendor provided library CuDNN in many cases.
 #
 # .. code-block:: bash
 #
-#   pip3 install --user psutil xgboost
+#   pip3 install --user psutil xgboost tornado
 #
 # To make tvm run faster in tuning, it is recommended to use cython
 # as FFI of tvm. In the root directory of tvm, execute

tutorials/autotvm/tune_nnvm_arm.py

@@ -27,7 +27,7 @@ to see the results.
 #
 # .. code-block:: bash
 #
-#   pip3 install --user psutil xgboost
+#   pip3 install --user psutil xgboost tornado
 #
 # To make tvm run faster in tuning, it is recommended to use cython
 # as FFI of tvm. In the root directory of tvm, execute

tutorials/cross_compilation_and_rpc.py

@@ -110,7 +110,7 @@ func.export_library(path)
 ######################################################################
 # .. note::
 #
-#   To run this tutorial with real remote device, change :code:`local_demo`
+#   To run this tutorial with a real remote device, change :code:`local_demo`
 #   to False and replace :code:`target` in :code:`build` with the true
 #   target triple of your device. The target triple which might be
 #   different for different devices. For example, it is