def test_distributed_basic(self):
checker = acp._get_checker()
fs = HDFSClient(checker.hdfs_home, None)
fs.delete(checker.hdfs_checkpoint_path)
self._reset_generator()
logger.info("begin test_distributed_basic")
fs = LocalFS()
save_dir = "./run_save_0"
fs.delete(save_dir)
#basic
exe, main_prog, startup_prog = self._generate()
compiled, data_loader, optimizer, loss, image, label = \
self._init_env(exe, main_prog, startup_prog, minimize=False)
#fleet
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ID"] = "0"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:6070"
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
with fluid.program_guard(main_prog, startup_prog):
dist_optimizer = fleet.distributed_optimizer(optimizer)
dist_optimizer.minimize(loss)
exe.run(startup_prog)
o = None
i = 0
name = None
for i in acp.train_epoch_range(3, 0):
o = acp._get_train_epoch_range()
name = o.name
logger.info("_run_save_0 name:{} epoch_no:{}".format(o.name, i))
for data in data_loader():
fetch = exe.run(fleet.main_program,
feed=data,
fetch_list=[loss])
self.assertEqual(len(o._exe_status), 1)
o = acp._get_train_epoch_range()
assert o == None, "now train epoch must not exits now"
self.assertEqual(i, 2)
fs.delete(save_dir)
logger.info("end test_distributed_basic")
def test_normal(self):
logger.info("begin test_normal")
checker = acp._get_checker()
fs = HDFSClient(checker.hdfs_home, None)
fs.delete(checker.hdfs_checkpoint_path)
self._clear_envs()
self._reset_generator()
self._run_normal()
self._readd_envs()
logger.info("end test_normal")
def _test_corner_epoch_no(self, break_epoch_no):
logger.info("begin test_corener_epoch_no")
checker = acp._get_checker()
fs = HDFSClient(checker.hdfs_home, None)
fs.delete(checker.hdfs_checkpoint_path)
self._reset_generator()
self._run_save_0(break_epoch_no=break_epoch_no)
self._reset_generator()
self._run_load_0(break_epoch_no=break_epoch_no)
fs.delete(checker.hdfs_checkpoint_path)
logger.info("end test_corener_epoch_no")
def test_basic(self):
logger.info("begin test_basic")
checker = acp._get_checker()
self.assertEqual(checker.run_env, "PADDLE_EDL_AUTO_CHECKPOINT")
self.assertEqual(checker.platform, "PADDLE_CLOUD")
self.assertEqual(checker.save_checkpoint_inter, 0)
print(checker)
fs = HDFSClient(checker.hdfs_home, None)
fs.delete(checker.hdfs_checkpoint_path)
self._reset_generator()
self._run_save_0()
self._reset_generator()
self._run_load_0()
logger.info("end test_basic")
def test_multiple(self):
checker = acp._get_checker()
fs = HDFSClient(checker.hdfs_home, None)
fs.delete(checker.hdfs_checkpoint_path)
self._reset_generator()
logger.info("begin test_multiple")
fs = LocalFS()
save_dir = "./run_save_0"
fs.delete(save_dir)
exe, main_prog1, startup_prog1 = self._generate()
_, main_prog2, startup_prog2 = self._generate()
compiled1, data_loader1, optimizer1, loss1, image1, label1 = \
self._init_env(exe, main_prog1, startup_prog1)
compiled2, data_loader2, optimizer2, loss2, image2, label2 = \
self._init_env(exe, main_prog2, startup_prog2)
o = None
epochs = []
for i in acp.train_epoch_range(3, 0):
for data in data_loader1():
fetch = exe.run(compiled1, feed=data, fetch_list=[loss1])
for data in data_loader2():
fetch = exe.run(compiled2, feed=data, fetch_list=[loss2])
o = acp._get_train_epoch_range()
self.assertEqual(len(o._exe_status), 2)
print(o._exe_status)
epochs.append(i)
o = acp._get_train_epoch_range()
self.assertTrue(o == None, "now train epoch must not exits now")
self.assertEqual(i, 2)
self.assertEqual(epochs, [0, 1, 2])
fs.delete(save_dir)
logger.info("end test_multiple")