[AUTOTVM] Improve tutorial and logging (#1544)

python/tvm/autotvm/measure/__init__.py

 """Distributed executor infrastructure to scale up the tuning"""
 
 from .measure import MeasureInput, MeasureResult, MeasureErrorNo, measure_option
-from .measure_methods import request_remote, create_measure_batch, use_rpc
+from .measure_methods import request_remote, check_remote, create_measure_batch, use_rpc
 
 from .local_executor import LocalExecutor
 from .executor import Future, Executor

python/tvm/autotvm/measure/measure_methods.py

@@ -9,6 +9,7 @@ import logging
 import os
 import time
 from random import getrandbits
+import threading
 
 import numpy as np
 
@@ -23,6 +24,7 @@ from ..task.space import InstantiationError
 from .measure import MeasureResult, MeasureErrorNo
 from .local_executor import LocalExecutor
 
+logger = logging.getLogger('autotvm')
 
 class HashMismatchError(ValueError):
     """Raised when the code hash of a submitted config doesn't match that on the
@@ -42,9 +44,9 @@ def request_remote(device_key, tracker_addr=None, priority=1, timeout=60):
         If is none, will use environment variable "TVM_TRACKER_HOST"
         and "TVM_TRACKER_PORT"
     priority: int, optional
-        priority of this request, larger is more prior
+        The priority of this request, larger is more prior
     timeout: float, optional
-        timeout of this session (units: seconds)
+        The timeout of this session (units: seconds)
 
     Returns
     ------
@@ -63,6 +65,33 @@ def request_remote(device_key, tracker_addr=None, priority=1, timeout=60):
                              session_timeout=timeout)
     return remote
 
+def check_remote(target, device_key, tracker_addr=None, priority=2, timeout=10):
+    """
+    Check the availability of a remote device
+
+    Parameters
+    ----------
+    target: Target
+        The wanted compilation target
+    device_key: string
+        device key of registered device in tracker
+    tracker_addr: Tuple(string, int), optional
+        The address of rpc tracker in (host, port) format.
+        If is none, will use environment variable "TVM_TRACKER_HOST"
+        and "TVM_TRACKER_PORT"
+    priority: int, optional
+        The priority of this request, larger is more prior
+    timeout: float, optional
+        The timeout of this check (units: seconds).
+        If time is out, a RuntimerError will be raised.
+    """
+    def _check():
+        remote = request_remote(device_key, tracker_addr, priority)
+        remote.context(str(target))
+    t = threading.Thread(target=_check,)
+    t.start()
+    t.join(timeout)
+    return not t.is_alive()
 
 def create_measure_batch(task, option):
     """Get a standard measure_batch function.
@@ -115,6 +144,17 @@ def create_measure_batch(task, option):
         build_func = default_build_func
         build_kwargs['use_ndk'] = True
 
+    # check the availability of remote devices
+    if hasattr(measure_func, 'rpc_info'):
+        rpc_info = measure_func.rpc_info
+        if check_remote(task.target, rpc_info['key'], (rpc_info['host'], rpc_info['port'])):
+            logger.info("Get devices for measurement successfully!")
+        else:
+            raise RuntimeError("Cannot get remote devices from the tracker. "
+                               "Please check the status of tracker by "
+                               "'python -m tvm.exec.query_rpc_tracker --port [THE PORT YOU USE]' "
+                               "and make sure you have free devices on the queue status.")
+
     # add device info of cuda and opencl target
     if ('cuda' in task.target.keys or 'opencl' in task.target.keys) \
             and hasattr(measure_func, 'rpc_info'):
@@ -313,7 +353,7 @@ def _measure_common(input_pack, build_func, build_kwargs, number, repeat,
             continue
         except InstantiationError as e:
             tstamp = time.time()
-            res_pack.append(MeasureResult((e,),
+            res_pack.append(MeasureResult((InstantiationError(str(e)),),
                                           MeasureErrorNo.INSTANTIATION_ERROR,
                                           tstamp - tic, tstamp))
             continue
@@ -346,7 +386,7 @@ def _measure_common(input_pack, build_func, build_kwargs, number, repeat,
             if ref_output:
                 for expected, real in zip(ref_output, args):
                     if not np.allclose(expected, real.asnumpy(), rtol=1e-4):
-                        logging.warning("Wrong Answer!")
+                        logger.warning("Wrong Answer!")
                         errno = MeasureErrorNo.WRONG_ANSWER
         except TVMError as exc:
             msg = str(exc)

python/tvm/autotvm/record.py

@@ -18,6 +18,7 @@ from .task import ConfigEntity, ApplyHistoryBest
 from .measure import MeasureInput, MeasureResult
 
 AUTOTVM_LOG_VERSION = 0.1
+logger = logging.getLogger('autotvm')
 
 try:  # convert unicode to str for python2
     _unicode = unicode
@@ -181,10 +182,10 @@ def split_workload(in_file, clean=True):
     tic = time.time()
     lines = list(open(in_file).readlines())
 
-    logging.info("start converting...")
+    logger.info("start converting...")
     pool = multiprocessing.Pool()
     lines = pool.map(decode, lines)
-    logging.info("map done %.2f", time.time() - tic)
+    logger.info("map done %.2f", time.time() - tic)
 
     wkl_dict = OrderedDict()
     for inp, res in lines:
@@ -206,13 +207,13 @@ def split_workload(in_file, clean=True):
                 cleaned.append([inp, res])
 
             # write to file
-            logging.info("Key: %s\tValid: %d\tDup: %d\t", k, len(cleaned), len(v) - len(cleaned))
+            logger.info("Key: %s\tValid: %d\tDup: %d\t", k, len(cleaned), len(v) - len(cleaned))
             with open(args.i + ".%03d.wkl" % i, 'w') as fout:
                 for inp, res in cleaned:
                     fout.write(encode(inp, res) + '\n')
     else:
         for i, (k, v) in enumerate(wkl_dict.items()):
-            logging.info("Key: %s\tNum: %d", k, len(v))
+            logger.info("Key: %s\tNum: %d", k, len(v))
             with open(args.i + ".%03d.wkl" % i, 'w') as fout:
                 for inp, res in v:
                     fout.write(encode(inp, res) + '\n')
@@ -238,7 +239,7 @@ def pick_best(in_file, out_file):
     for v in best_context.best_by_targetkey.values():
         best_set.add(measure_str_key(v[0]))
 
-    logging.info("Extract %d best records from the %s", len(best_set), in_file)
+    logger.info("Extract %d best records from the %s", len(best_set), in_file)
     fout = open(out_file, 'w') if isinstance(out_file, str) else out_file
 
     for inp, res in load_from_file(in_file):
@@ -270,7 +271,7 @@ if __name__ == '__main__':
     parser.add_argument("--code", action='store_true')
 
     args = parser.parse_args()
-    logging.basicConfig(level=logging.INFO)
+    logger.basicConfig(level=logger.INFO)
 
     if args.mode == 'pick':
         args.o = args.o or args.i + ".best.log"

python/tvm/autotvm/task/dispatcher.py

@@ -10,6 +10,8 @@ of the DispatchContext base class.
 - During search, we can use it to pass the current proposal from tuner.
 - During evaluation, we can use it to set pick the best policy.
 """
+# pylint: disable=invalid-name
+
 from __future__ import absolute_import as _abs
 
 import logging
@@ -19,6 +21,8 @@ import numpy as np
 
 from tvm import target as _target
 
+logger = logging.getLogger('autotvm')
+
 class DispatchContext(object):
     """
     Base class of dispatch context.
@@ -216,7 +220,7 @@ class ApplyHistoryBest(DispatchContext):
                             best_by_model[key] = (inp, res)
                     break
 
-        logging.debug("Finish loading %d records", counter)
+        logger.debug("Finish loading %d records", counter)
 
     def query(self, target, workload):
         if target is None:

python/tvm/autotvm/tophub.py

@@ -4,6 +4,7 @@ To get the best performance, we typically need auto-tuning for the specific devi
 TVM releases pre-tuned parameters in TopHub for some common networks and hardware targets.
 TVM will download these parameters for you when you create the target for the first time.
 """
+# pylint: disable=invalid-name
 
 import logging
 import os
@@ -16,6 +17,7 @@ from ..contrib.download import download
 
 AUTOTVM_TOPHUB_ROOT_PATH = os.path.join(os.path.expanduser('~'), ".tvm", "tophub")
 
+logger = logging.getLogger('autotvm')
 
 def _alias(name):
     """convert alias for some packages"""
@@ -79,7 +81,7 @@ def download_package(backend):
                 os.mkdir(path)
 
     backend = _alias(backend)
-    logging.info("Download pre-tuned parameters for %s", backend)
+    logger.info("Download pre-tuned parameters for %s", backend)
     download("https://raw.githubusercontent.com/uwsaml/tvm-distro/master/tophub/%s.log" % backend,
              os.path.join(rootpath, backend + ".log"), True, verbose=0)
 
@@ -110,7 +112,7 @@ def list_packages():
     """
     path = tempdir()
     filename = path.relpath("info.json")
-    logging.info("Download meta info for pre-tuned parameters")
+    logger.info("Download meta info for pre-tuned parameters")
     download("https://raw.githubusercontent.com/uwsaml/tvm-distro/master/tophub/info.json",
              filename, True, verbose=0)
 

python/tvm/autotvm/tuner/callback.py

@@ -2,11 +2,13 @@
 """Namespace of callback utilities of AutoTVM"""
 import sys
 import time
+import logging
 
 import numpy as np
 
 from .. import record
 
+logger = logging.getLogger('autotvm')
 
 def log_to_file(file_out, protocol='json'):
     """Log the tuning records into file.
@@ -90,7 +92,7 @@ def progress_bar(total, prefix=''):
     prefix: str
         The prefix of output message
     """
-    class _Context:
+    class _Context(object):
         """Context to store local variables"""
         def __init__(self):
             self.best_flops = 0
@@ -112,11 +114,12 @@ def progress_bar(total, prefix=''):
             if res.error_no == 0:
                 flops = inp.task.flop / np.mean(res.costs)
 
+        if logger.level < logging.DEBUG:  # only print progress bar in non-debug mode
             ctx.cur_flops = flops
             ctx.best_flops = tuner.best_flops
 
-        sys.stdout.write('\r%s Current/Best: %7.2f/%7.2f GFLOPS | Progress: (%d/%d) '
-                         '| %.2f s' %
+            sys.stdout.write('%s Current/Best: %7.2f/%7.2f GFLOPS | Progress: (%d/%d) '
+                             '| %.2f s\r' %
                              (prefix, ctx.cur_flops/1e9, ctx.best_flops/1e9, ctx.ct, ctx.total,
                               time.time() - tic))
             sys.stdout.flush()

python/tvm/autotvm/tuner/sa_model_optimizer.py

-# pylint: disable=consider-using-enumerate
+# pylint: disable=consider-using-enumerate, invalid-name
 """
 Cost model optimizer based on simulated annealing
 """
@@ -12,6 +12,8 @@ import numpy as np
 from ..util import sample_ints
 from .model_based_tuner import ModelOptimizer, knob2point, point2knob
 
+logger = logging.getLogger('autotvm')
+
 class SimulatedAnnealingOptimizer(ModelOptimizer):
     """parallel simulated annealing optimization algorithm
 
@@ -103,16 +105,16 @@ class SimulatedAnnealingOptimizer(ModelOptimizer):
 
             if log_interval and k % log_interval == 0:
                 t_str = "%.2f" % t
-                logging.debug("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\ttemp: %s\t"
+                logger.debug("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\ttemp: %s\t"
                              "elapsed: %.2f",
                              k, k_last_modify, heap_items[0][0],
                              np.max([v for v, _ in heap_items]), t_str,
                              time.time() - tic)
 
         heap_items.sort(key=lambda item: -item[0])
-        logging.debug("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\telapsed: %.2f",
+        logger.debug("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\telapsed: %.2f",
                      k, k_last_modify, heap_items[-1][0], heap_items[0][0], time.time() - tic)
-        logging.debug("SA Maximums: %s", heap_items)
+        logger.debug("SA Maximums: %s", heap_items)
 
         if self.persistent:
             self.points = points

python/tvm/autotvm/tuner/tuner.py

@@ -4,11 +4,12 @@ import logging
 
 import numpy as np
 
-from ..measure import MeasureInput
-from ..measure import create_measure_batch
+from ..measure import MeasureInput, create_measure_batch
 
 from ..env import GLOBAL_SCOPE
 
+logger = logging.getLogger('autotvm')
+
 class Tuner(object):
     """Base class for tuners
 
@@ -86,9 +87,10 @@ class Tuner(object):
         measure_batch = create_measure_batch(self.task, measure_option)
         parallel_num = getattr(measure_batch, 'parallel_num', 1)
         early_stopping = early_stopping or 1e9
+        old_level = logger.level
 
         GLOBAL_SCOPE.in_tuning = True
-        i = 0
+        i = error_ct = 0
         while i < n_trial:
             if not self.has_next():
                 break
@@ -103,15 +105,18 @@ class Tuner(object):
                 config = inp.config
                 if res.error_no == 0:
                     flops = inp.task.flop / np.mean(res.costs)
+                    error_ct = 0
                 else:
                     flops = 0
+                    error_ct += 1
+
                 if flops > self.best_flops:
                     self.best_flops = flops
                     self.best_config = config
                     self.best_measure_pair = (inp, res)
                     self.best_iter = i + k
 
-                logging.debug("No: %d\tGFLOPS: %.2f/%.2f\tresult: %s\t%s",
+                logger.debug("No: %d\tGFLOPS: %.2f/%.2f\tresult: %s\t%s",
                              i + k + 1, flops / 1e9, self.best_flops / 1e9,
                              res, config)
 
@@ -123,11 +128,16 @@ class Tuner(object):
                 callback(self, inputs, results)
 
             if i > self.best_iter + early_stopping:
-                logging.debug("Early stopped. Best iter: %d.", self.best_iter)
+                logger.debug("Early stopped. Best iter: %d.", self.best_iter)
                 break
 
-        GLOBAL_SCOPE.in_tuning = False
+            if error_ct > 50:
+                logger.warning("Too many errors happen in the tuning. Now is in debug mode")
+                logger.setLevel(logging.DEBUG)
+            else:
+                logger.setLevel(old_level)
 
+        GLOBAL_SCOPE.in_tuning = False
         del measure_batch
 
     def reset(self):

python/tvm/autotvm/tuner/xgboost_cost_model.py

@@ -16,6 +16,8 @@ from ..util import get_rank
 from .metric import max_curve, recall_curve, cover_curve
 from .model_based_tuner import CostModel, FeatureCache
 
+logger = logging.getLogger('autotvm')
+
 class XGBoostCostModel(CostModel):
     """XGBoost as cost model
 
@@ -163,7 +165,7 @@ class XGBoostCostModel(CostModel):
                                  ],
                                  verbose_eval=self.log_interval)])
 
-        logging.debug("XGB train: %.2f\tobs: %d\terror: %d\tn_cache: %d",
+        logger.debug("XGB train: %.2f\tobs: %d\terror: %d\tn_cache: %d",
                      time.time() - tic, len(xs),
                      len(xs) - np.sum(valid_index),
                      self.feature_cache.size(self.fea_type))
@@ -173,7 +175,7 @@ class XGBoostCostModel(CostModel):
         self._reset_pool()
 
         args = list(records)
-        logging.debug("XGB load %d entries from history log file", len(args))
+        logger.debug("XGB load %d entries from history log file", len(args))
 
         if self.fea_type == 'itervar':
             feature_extract_func = _extract_itervar_feature_log
@@ -208,7 +210,7 @@ class XGBoostCostModel(CostModel):
                                  ],
                                  verbose_eval=self.log_interval)])
 
-        logging.debug("XGB train: %.2f\tobs: %d", time.time() - tic, len(xs))
+        logger.debug("XGB train: %.2f\tobs: %d", time.time() - tic, len(xs))
 
     def predict(self, xs, output_margin=False):
         feas = self._get_feature(xs)
@@ -403,7 +405,7 @@ def custom_callback(stopping_rounds, metric, fevals, evals=(), log_file=None,
             infos.append("%s: %.6f" % (item[0], item[1]))
 
         if not isinstance(verbose_eval, bool) and verbose_eval and i % verbose_eval == 0:
-            logging.debug("\t".join(infos))
+            logger.debug("\t".join(infos))
         if log_file:
             with open(log_file, "a") as fout:
                 fout.write("\t".join(infos) + '\n')
@@ -435,7 +437,7 @@ def custom_callback(stopping_rounds, metric, fevals, evals=(), log_file=None,
         elif env.iteration - best_iteration >= stopping_rounds:
             best_msg = state['best_msg']
             if verbose_eval and env.rank == 0:
-                logging.debug("XGB stopped. Best iteration: %s ", best_msg)
+                logger.debug("XGB stopped. Best iteration: %s ", best_msg)
             raise EarlyStopException(best_iteration)
 
     return callback

python/tvm/autotvm/util.py

@@ -8,6 +8,7 @@ import numpy as np
 
 from .. import expr, ir_pass
 
+logger = logging.getLogger('autotvm')
 
 class EmptyContext(object):
     """An empty context"""
@@ -92,15 +93,15 @@ def pool_map(func, args, batch_size, verbose=False, pool=None):
     tic = time.time()
     local_pool = pool or multiprocessing.Pool()
     if verbose:
-        logging.info("mapping begin")
+        logger.info("mapping begin")
     for i in range(0, len(args), batch_size):
         if verbose:
-            logging.info("mapping %d/%d elapsed %.2f", i, len(args),
+            logger.info("mapping %d/%d elapsed %.2f", i, len(args),
                         time.time() - tic)
         tmp = np.array(local_pool.map(func, args[i:i+batch_size]))
         ret = tmp if ret is None else np.concatenate((ret, tmp))
     if verbose:
-        logging.info("mapping done")
+        logger.info("mapping done")
     if not pool:
         local_pool.close()
     return ret

python/tvm/rpc/base.py

 """Base definitions for RPC."""
+# pylint: disable=invalid-name
+
 from __future__ import absolute_import
 
 import socket
@@ -23,6 +25,7 @@ RPC_CODE_DUPLICATE = RPC_MAGIC + 1
 # cannot found matched key in server
 RPC_CODE_MISMATCH = RPC_MAGIC + 2
 
+logger = logging.getLogger('RPCServer')
 
 class TrackerCode(object):
     """Enumeration code for the RPC tracker"""
@@ -120,7 +123,7 @@ def random_key(prefix, cmap=None):
         return prefix + str(random.random())
 
 
-def connect_with_retry(addr, timeout=60, retry_period=5, silent=False):
+def connect_with_retry(addr, timeout=60, retry_period=5):
     """Connect to a TPC address with retry
 
     This function is only reliable to short period of server restart.
@@ -135,9 +138,6 @@ def connect_with_retry(addr, timeout=60, retry_period=5, silent=False):
 
     retry_period : float
          Number of seconds before we retry again.
-
-    silent: bool
-        whether run in silent mode
     """
     tstart = time.time()
     while True:
@@ -152,8 +152,7 @@ def connect_with_retry(addr, timeout=60, retry_period=5, silent=False):
             if period > timeout:
                 raise RuntimeError(
                     "Failed to connect to server %s" % str(addr))
-            if not silent:
-                logging.info("Cannot connect to tracker%s, retry in %g secs...",
+            logger.warning("Cannot connect to tracker %s, retry in %g secs...",
                            str(addr), retry_period)
             time.sleep(retry_period)
 

python/tvm/rpc/proxy.py

@@ -23,7 +23,8 @@ try:
     from tornado import ioloop
     from . import tornado_util
 except ImportError as error_msg:
-    raise ImportError("RPCProxy module requires tornado package %s" % error_msg)
+    raise ImportError(
+        "RPCProxy module requires tornado package %s. Try 'pip install tornado'." % error_msg)
 
 from . import base
 from .base import TrackerCode
@@ -540,7 +541,7 @@ def websocket_proxy_server(url, key=""):
     def _connect(key):
         conn = yield websocket.websocket_connect(url)
         on_message = create_on_message(conn)
-        temp = _server_env(None, None)
+        temp = _server_env(None)
         # Start connecton
         conn.write_message(struct.pack('<i', base.RPC_MAGIC), binary=True)
         key = "server:" + key

python/tvm/rpc/server.py

@@ -8,6 +8,8 @@ Server is TCP based with the following protocol:
 - The key is in format
    - {server|client}:device-type[:random-key] [-timeout=timeout]
 """
+# pylint: disable=invalid-name
+
 from __future__ import absolute_import
 
 import os
@@ -30,11 +32,11 @@ from ..contrib import util
 from . import base
 from . base import TrackerCode
 
-def _server_env(load_library, logger):
+logger = logging.getLogger('RPCServer')
+
+def _server_env(load_library):
     """Server environment function return temp dir"""
     temp = util.tempdir()
-    if logger is None:
-        logger = logging.getLogger()
 
     # pylint: disable=unused-variable
     @register_func("tvm.rpc.server.workpath")
@@ -59,13 +61,10 @@ def _server_env(load_library, logger):
     return temp
 
 
-def _serve_loop(sock, addr, load_library, silent):
+def _serve_loop(sock, addr, load_library):
     """Server loop"""
-    logger = logging.getLogger("RPCServer")
-    if silent:
-        logger.disabled = True
     sockfd = sock.fileno()
-    temp = _server_env(load_library, logger)
+    temp = _server_env(load_library)
     base._ServerLoop(sockfd)
     temp.remove()
     logger.info("Finish serving %s", addr)
@@ -79,12 +78,8 @@ def _parse_server_opt(opts):
             ret["timeout"] = float(kv[9:])
     return ret
 
-def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr, silent):
+def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr):
     """Listening loop of the server master."""
-    logger = logging.getLogger("RPCServer")
-    if silent:
-        logger.disabled = True
-
     def _accept_conn(listen_sock, tracker_conn, ping_period=2):
         """Accept connection from the other places.
 
@@ -148,7 +143,7 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr, s
             if arr[0] != expect_header:
                 conn.sendall(struct.pack("<i", base.RPC_CODE_MISMATCH))
                 conn.close()
-                logger.info("mismatch key from %s", addr)
+                logger.warning("mismatch key from %s", addr)
                 continue
             else:
                 conn.sendall(struct.pack("<i", base.RPC_CODE_SUCCESS))
@@ -162,7 +157,7 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr, s
         try:
             # step 1: setup tracker and report to tracker
             if tracker_addr and tracker_conn is None:
-                tracker_conn = base.connect_with_retry(tracker_addr, silent=silent)
+                tracker_conn = base.connect_with_retry(tracker_addr)
                 tracker_conn.sendall(struct.pack("<i", base.RPC_TRACKER_MAGIC))
                 magic = struct.unpack("<i", base.recvall(tracker_conn, 4))[0]
                 if magic != base.RPC_TRACKER_MAGIC:
@@ -182,15 +177,12 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr, s
                 tracker_conn = None
             continue
         except RuntimeError as exc:
-            if silent:
-                return
-            else:
             raise exc
 
         # step 3: serving
         logger.info("connection from %s", addr)
         server_proc = multiprocessing.Process(target=_serve_loop,
-                                              args=(conn, addr, load_library, silent))
+                                              args=(conn, addr, load_library))
         server_proc.deamon = True
         server_proc.start()
         # close from our side.
@@ -202,10 +194,7 @@ def _listen_loop(sock, port, rpc_key, tracker_addr, load_library, custom_addr, s
             server_proc.terminate()
 
 
-def _connect_proxy_loop(addr, key, load_library, silent):
-    logger = logging.getLogger("RPCProxy")
-    if silent:
-        logger.disabled = True
+def _connect_proxy_loop(addr, key, load_library):
     key = "server:" + key
     retry_count = 0
     max_retry = 5
@@ -221,7 +210,7 @@ def _connect_proxy_loop(addr, key, load_library, silent):
             if magic == base.RPC_CODE_DUPLICATE:
                 raise RuntimeError("key: %s has already been used in proxy" % key)
             elif magic == base.RPC_CODE_MISMATCH:
-                logger.info("RPCProxy do not have matching client key %s", key)
+                logger.warning("RPCProxy do not have matching client key %s", key)
             elif magic != base.RPC_CODE_SUCCESS:
                 raise RuntimeError("%s is not RPC Proxy" % str(addr))
             keylen = struct.unpack("<i", base.recvall(sock, 4))[0]
@@ -229,7 +218,7 @@ def _connect_proxy_loop(addr, key, load_library, silent):
             opts = _parse_server_opt(remote_key.split()[1:])
             logger.info("connected to %s", str(addr))
             process = multiprocessing.Process(
-                target=_serve_loop, args=(sock, addr, load_library, silent))
+                target=_serve_loop, args=(sock, addr, load_library))
             process.deamon = True
             process.start()
             sock.close()
@@ -240,7 +229,7 @@ def _connect_proxy_loop(addr, key, load_library, silent):
             retry_count = 0
         except (socket.error, IOError) as err:
             retry_count += 1
-            logger.info("Error encountered %s, retry in %g sec", str(err), retry_period)
+            logger.warning("Error encountered %s, retry in %g sec", str(err), retry_period)
             if retry_count > max_retry:
                 raise RuntimeError("Maximum retry error: last error: %s" % str(err))
             time.sleep(retry_period)
@@ -323,9 +312,8 @@ class Server(object):
         self.custom_addr = custom_addr
         self.use_popen = use_popen
 
-        self.logger = logging.getLogger("RPCServer")
         if silent:
-            self.logger.disabled = True
+            logger.setLevel(logging.WARN)
 
         if use_popen:
             cmd = [sys.executable,
@@ -360,18 +348,18 @@ class Server(object):
                         raise sock_err
             if not self.port:
                 raise ValueError("cannot bind to any port in [%d, %d)" % (port, port_end))
-            self.logger.info("bind to %s:%d", host, self.port)
+            logger.info("bind to %s:%d", host, self.port)
             sock.listen(1)
             self.sock = sock
             self.proc = multiprocessing.Process(
                 target=_listen_loop, args=(
                     self.sock, self.port, key, tracker_addr, load_library,
-                    self.custom_addr, silent))
+                    self.custom_addr))
             self.proc.deamon = True
             self.proc.start()
         else:
             self.proc = multiprocessing.Process(
-                target=_connect_proxy_loop, args=((host, port), key, load_library, silent))
+                target=_connect_proxy_loop, args=((host, port), key, load_library))
             self.proc.deamon = True
             self.proc.start()
 

python/tvm/rpc/tracker.py

@@ -23,6 +23,8 @@ List of available APIs:
   - input: [TrackerCode.REQUEST, [key, user, priority]]
   - return: [TrackerCode.SUCCESS, [url, port, match-key]]
 """
+# pylint: disable=invalid-name
+
 import heapq
 import time
 import logging
@@ -37,12 +39,13 @@ try:
     from . import tornado_util
 except ImportError as error_msg:
     raise ImportError(
-        "RPCTracker module requires tornado package %s" % error_msg)
+        "RPCTracker module requires tornado package %s. Try 'pip install tornado'." % error_msg)
 
 from .._ffi.base import py_str
 from . import base
 from .base import RPC_TRACKER_MAGIC, TrackerCode
 
+logger = logging.getLogger("RPCTracker")
 
 class Scheduler(object):
     """Abstratc interface of scheduler."""
@@ -141,11 +144,11 @@ class TCPEventHandler(tornado_util.TCPHandler):
     def _init_conn(self, message):
         """Initialie the connection"""
         if len(message) != 4:
-            logging.info("Invalid connection from %s", self.name())
+            logger.warning("Invalid connection from %s", self.name())
             self.close()
         magic = struct.unpack('<i', message)[0]
         if magic != RPC_TRACKER_MAGIC:
-            logging.info("Invalid magic from %s", self.name())
+            logger.warning("Invalid magic from %s", self.name())
             self.close()
         self.write_message(struct.pack('<i', RPC_TRACKER_MAGIC), binary=True)
         self._init_req_nbytes = 0
@@ -232,14 +235,14 @@ class TCPEventHandler(tornado_util.TCPHandler):
             status = self._tracker.summary()
             self.ret_value([TrackerCode.SUCCESS, status])
         else:
-            logging.info("Unknown tracker code %d", code)
+            logger.warning("Unknown tracker code %d", code)
             self.close()
 
     def on_close(self):
         self._tracker._connections.remove(self)
 
     def on_error(self, err):
-        logging.info("%s: Error in RPC Tracker: %s", self.name(), err)
+        logger.warning("%s: Error in RPC Tracker: %s", self.name(), err)
         self.close()
 
 
@@ -335,9 +338,8 @@ class Tracker(object):
                  port=9190,
                  port_end=9199,
                  silent=False):
-        self.logger = logging.getLogger("RPCTracker")
         if silent:
-            self.logger.disabled = True
+            logger.setLevel(logging.WARN)
 
         sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
         self.port = None
@@ -354,7 +356,7 @@ class Tracker(object):
                     raise sock_err
         if not self.port:
             raise ValueError("cannot bind to any port in [%d, %d)" % (port, port_end))
-        self.logger.info("bind to %s:%d", host, self.port)
+        logger.info("bind to %s:%d", host, self.port)
         sock.listen(1)
         self.proc = multiprocessing.Process(
             target=_tracker_server, args=(sock, self.stop_key))
@@ -380,7 +382,7 @@ class Tracker(object):
                 self._stop_tracker()
                 self.proc.join(1)
             if self.proc.is_alive():
-                self.logger.info("Terminating Tracker Server...")
+                logger.info("Terminating Tracker Server...")
                 self.proc.terminate()
             self.proc = None
 

tutorials/autotvm/tune_conv2d_cuda.py

@@ -154,7 +154,8 @@ def conv2d_no_batching(N, H, W, CI, CO, KH, KW, stride, padding):
 # for this template
 
 # logging config (for printing tuning log to screen)
-logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)
+logging.getLogger('autotvm').setLevel(logging.DEBUG)
+logging.getLogger('autotvm').addHandler(logging.StreamHandler(sys.stdout))
 
 # the last layer in resnet
 N, H, W, CO, CI, KH, KW, strides, padding = 1, 7, 7, 512, 512, 3, 3, (1, 1), (1, 1)

tutorials/autotvm/tune_nnvm_arm.py

@@ -163,8 +163,10 @@ def get_network(name, batch_size):
 # Set Tuning Options
 # ------------------
 # Before tuning, we should do some configurations. Here I use an RK3399 board
-# in our environment as example. In your setting, you should modify the target
-# and device_key accordingly.
+# as example. In your setting, you should modify the target and device_key accordingly.
+# set :code:`use_android` to True if you use android phone.
+
+#### DEVICE CONFIG ####
 
 # Replace "aarch64-linux-gnu" with the correct target of your board.
 # This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
@@ -173,7 +175,10 @@ target = tvm.target.create('llvm -device=arm_cpu -target=aarch64-linux-gnu')
 # Also replace this with the device key in your tracker
 device_key = 'rk3399'
 
-# tuning option
+# Set this to True if you use android phone
+use_android = False
+
+#### TUNING OPTION ####
 network = 'resnet-18'
 log_file = "%s.%s.log" % (device_key, network)
 dtype = 'float32'
@@ -181,17 +186,17 @@ dtype = 'float32'
 tuning_option = {
    'log_filename': log_file,
 
-   'tuner':'xgb',
+   'tuner': 'xgb',
    'n_trial': 1000,
-   'early_stopping': 200,
+   'early_stopping': 250,
 
    'measure_option': autotvm.measure_option(
        autotvm.use_rpc(device_key, host='localhost', port=9190),
        number=4,
        parallel_num=1,
-       timeout=10),
-
-   'use_transfer_learning': True,
+       timeout=10,
+       build_func='ndk' if use_android else 'default',
+   ),
 }
 
 ####################################################################
@@ -208,9 +213,6 @@ tuning_option = {
 #   If your device is very slow or a single conv2d operator in your network has large FLOPs,
 #   consider setting timeout larger.
 #
-#   **For android phone**, add :code:`build_func='ndk'` to the argument list of
-#   :code:`autotvm.measure_option` to use Android NDK for creating shared library.
-#
 
 ###################################################################
 # Begin Tuning
@@ -280,12 +282,14 @@ def tune_tasks(tasks,
 
 def tune_and_evaluate():
     # extract workloads from nnvm graph
+    print("Extract tasks...")
     net, params, shape, out_shape = get_network(network, batch_size=1)
     tasks = autotvm.task.extract_from_graph(net, shape=shape, dtype=dtype,
                                             symbols=(nnvm.sym.conv2d,),
                                             target=target)
 
     # run tuning tasks
+    print("Tuning...")
     tune_tasks(tasks, **tuning_option)
 
     # compile kernels with history best records
@@ -329,6 +333,7 @@ def tune_and_evaluate():
 
 # We do not run the tuning in our webpage server since it takes too long.
 # Uncomment the following line to run by yourself.
+
 # tune_and_evaluate()
 
 ######################################################################
@@ -341,6 +346,8 @@ def tune_and_evaluate():
 #
 # .. code-block:: bash
 #
+#    Extract tasks...
+#    Tuning...
 #    [Task  1/16]  Current/Best:   13.15/  20.49 GFLOPS | Progress: (297/1000) | 348.51 s Done.
 #    [Task  2/16]  Current/Best:   16.66/  22.64 GFLOPS | Progress: (475/1000) | 415.42 s Done.
 #    [Task  3/16]  Current/Best:   10.33/  14.19 GFLOPS | Progress: (306/1000) | 239.61 s Done.
@@ -362,3 +369,23 @@ def tune_and_evaluate():
 #    Evaluate inference time cost...
 #    Mean inference time (std dev): 156.51 ms (0.89 ms)
 #
+
+
+######################################################################
+#
+# .. note:: **Meet some problems?**
+#
+#   The auto tuning module is error prone. If you always see " 0.00/ 0.00 GFLOPS",
+#   then there must be something wrong.
+#
+#   First, make sure you set the correct configuration of your device.
+#   Then, you can print debug information by adding these lines in the beginning
+#   of the script. It will print every measurement result, where you can find useful
+#   error messages.
+#
+#   .. code-block:: python
+#
+#      import logging
+#      logging.getLogger('autotvm').setLevel(logging.DEBUG)
+#
+#   Finally, always feel free to ask our community for help on https://discuss.tvm.ai

tutorials/autotvm/tune_simple_template.py

@@ -267,8 +267,9 @@ print(task.config_space)
 # We will log the tuning results into a log file. This file can be
 # used to get the best config later.
 
-# logging config (for printing tuning log to screen)
-logging.basicConfig(level=logging.DEBUG, stream=sys.stdout)
+# logging config (for printing tuning log to the screen)
+logging.getLogger('autotvm').setLevel(logging.DEBUG)
+logging.getLogger('autotvm').addHandler(logging.StreamHandler(sys.stdout))
 
 # use local cpu, measure 5 times for every config to reduce variance
 measure_option = autotvm.measure_option('local',