feat: fix travis ci test (kleveross#75)

Author: zw0610

.isort.cfg

@@ -1,4 +1,4 @@
 [settings]
-known_third_party = data,grpc,numpy,setuptools,torch
+known_third_party = grpc,numpy,setuptools,torch
 multi_line_output=3
 include_trailing_comma=True

test/deprecated-tests/pytorch_test.py

@@ -13,7 +13,7 @@
 from torch.optim.lr_scheduler import StepLR
 
 from ftlib import BasicFTLib
-from ftlib.ftlib_status import FTAllReduceStatus
+from ftlib.ftlib_status import FTCollectiveStatus
 
 root_dir = os.path.join(os.path.dirname(__file__), os.path.pardir)
 sys.path.insert(0, os.path.abspath(root_dir))
@@ -106,15 +106,15 @@ def forward(self, x):
                 continue
             else:
                 res = ftlib.wait_gradients_ready(model)
-            if res == FTAllReduceStatus.NO_NEED:
+            if res == FTCollectiveStatus.NO_NEED:
                 logging.critical(
                     "cannot use average_gradient when there is no need"
                 )
                 exit(2)
-            if res == FTAllReduceStatus.SUCCESS:
+            if res == FTCollectiveStatus.SUCCESS:
                 logging.info("average succeed")
                 optimizer.step()
-            if res == FTAllReduceStatus.ABORT:
+            if res == FTCollectiveStatus.ABORT:
                 logging.info("average failed, abort")
                 continue
         scheduler.step()

test/deprecated-tests/tricky-data/pytorch-gossip-tricky-data.py

@@ -10,11 +10,12 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from data import TrickySampler
 from torch.optim.lr_scheduler import StepLR
 
 from ftlib import BasicFTLib
-from ftlib.ftlib_status import FTAllReduceStatus
+from ftlib.ftlib_status import FTCollectiveStatus
+
+from .data import TrickySampler
 
 LOGLEVEL = os.environ.get("LOGLEVEL", "WARNING").upper()
 logging.basicConfig(level=LOGLEVEL)
@@ -145,15 +146,15 @@ def forward(self, x):
                 continue
             else:
                 res = ftlib.wait_gradients_ready(model)
-            if res == FTAllReduceStatus.NO_NEED:
+            if res == FTCollectiveStatus.NO_NEED:
                 logging.critical(
                     "cannot use average_gradient when there is no need"
                 )
                 exit(2)
-            elif res == FTAllReduceStatus.SUCCESS:
+            elif res == FTCollectiveStatus.SUCCESS:
                 logging.info("average succeed")
                 optimizer.step()
-            elif res == FTAllReduceStatus.ABORT:
+            elif res == FTCollectiveStatus.ABORT:
                 logging.info("average failed, abort")
                 continue
             else: