def test_metrics_union(ray_start_regular_shared):
it1 = from_items([1, 2, 3, 4], num_shards=1)
it2 = from_items([1, 2, 3, 4], num_shards=1)
def foo_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["foo"] += x
return metrics.counters["foo"]
def bar_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["bar"] += 100
return metrics.counters["bar"]
def verify_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["n"] += 1
# Check the metrics context is shared.
if metrics.counters["n"] >= 2:
assert "foo" in metrics.counters
assert "bar" in metrics.counters
return x
it1 = it1.gather_async().for_each(foo_metrics)
it2 = it2.gather_async().for_each(bar_metrics)
it3 = it1.union(it2, deterministic=True)
it3 = it3.for_each(verify_metrics)
assert it3.take(10) == [1, 100, 3, 200, 6, 300, 10, 400]
def test_union(ray_start_regular_shared):
it1 = from_items(["a", "b", "c"], 1)
it2 = from_items(["x", "y", "z"], 1)
it = it1.union(it2)
assert (repr(it) == "ParallelIterator[ParallelUnion[ParallelIterator["
"from_items[str, 3, shards=1]], ParallelIterator["
"from_items[str, 3, shards=1]]]]")
assert list(it.gather_sync()) == ["a", "x", "b", "y", "c", "z"]
def test_metrics(ray_start_regular_shared):
it = from_items([1, 2, 3, 4], num_shards=1)
it2 = from_items([1, 2, 3, 4], num_shards=1)
def f(x):
metrics = LocalIterator.get_metrics()
metrics.counters["foo"] += x
return metrics.counters["foo"]
it = it.gather_sync().for_each(f)
it2 = it2.gather_sync().for_each(f)
# Tests iterators have isolated contexts.
assert it.take(4) == [1, 3, 6, 10]
assert it2.take(4) == [1, 3, 6, 10]
def test_local_shuffle(ray_start_regular_shared):
# confirm that no data disappears, and they all stay within the same shard
it = from_range(8, num_shards=2).local_shuffle(shuffle_buffer_size=2)
assert repr(it) == ("ParallelIterator[from_range[8, shards=2]" +
".local_shuffle(shuffle_buffer_size=2, seed=None)]")
shard_0 = it.get_shard(0)
shard_1 = it.get_shard(1)
assert set(shard_0) == {0, 1, 2, 3}
assert set(shard_1) == {4, 5, 6, 7}
# check that shuffling results in different orders
it1 = from_range(100, num_shards=10).local_shuffle(shuffle_buffer_size=5)
it2 = from_range(100, num_shards=10).local_shuffle(shuffle_buffer_size=5)
assert list(it1.gather_sync()) != list(it2.gather_sync())
# buffer size of 1 should not result in any shuffling
it3 = from_range(10, num_shards=1).local_shuffle(shuffle_buffer_size=1)
assert list(it3.gather_sync()) == list(range(10))
# statistical test
it4 = from_items([0, 1] * 10000,
num_shards=1).local_shuffle(shuffle_buffer_size=100)
result = "".join(it4.gather_sync().for_each(str))
freq_counter = Counter(zip(result[:-1], result[1:]))
assert len(freq_counter) == 4
for key, value in freq_counter.items():
assert value / len(freq_counter) > 0.2
def main():
num_points = 32 * 100 * 2
data = [i * (1 / num_points) for i in range(num_points)]
it = parallel_it.from_items(data, 2, False).for_each(lambda x: [x, x])
# this will create MLDataset with column RangeIndex(range(2))
ds = ml_data.from_parallel_iter(it, True, batch_size=32, repeated=False)
tf_ds = ds.to_tf(feature_columns=[0], label_column=1)
trainer = TFTrainer(
model_creator=model_creator,
data_creator=make_data_creator(tf_ds),
num_replicas=2,
config={
"batch_size": 32,
"fit_config": {
"steps_per_epoch": 100,
},
},
)
for _ in range(10):
trainer.train()
model = trainer.get_model()
print("f(0.5)=", float(model.predict([0.5])))
def test_for_each_concur_sync(ray_start_regular_shared):
main_wait = Semaphore.remote(value=0)
test_wait = Semaphore.remote(value=0)
def task(x):
i, main_wait, test_wait = x
ray.get(main_wait.release.remote())
ray.get(test_wait.acquire.remote())
return i + 10
@ray.remote(num_cpus=0.01)
def to_list(it):
return list(it)
it = from_items([(i, main_wait, test_wait) for i in range(8)],
num_shards=2)
it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.01})
list_promise = to_list.remote(it.gather_sync())
for i in range(4):
assert i in [0, 1, 2, 3]
ray.get(main_wait.acquire.remote())
# There should be exactly 4 tasks executing at this point.
assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
for i in range(8):
ray.get(test_wait.release.remote())
assert repr(
it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
result_list = ray.get(list_promise)
assert set(result_list) == set(range(10, 18))
def test_tf_dataset(ray_start_4_cpus): # noqa: F811
num_points = 32 * 100 * 2
data = [i * (1 / num_points) for i in range(num_points)]
it = parallel_it.from_items(data, 2, False).for_each(lambda x: [x, x])
# this will create MLDataset with column RangeIndex(range(2))
ds = ml_data.from_parallel_iter(it, True, batch_size=32, repeated=False)
tf_ds = ds.to_tf(feature_columns=[0], label_column=1)
trainer = TFTrainer(
model_creator=model_creator,
data_creator=make_data_creator(tf_ds),
num_replicas=2,
config={
"batch_size": 32,
"fit_config": {
"steps_per_epoch": 100,
},
},
)
for _ in range(10):
trainer.train()
model = trainer.get_model()
prediction = model.predict([0.5])[0][0]
assert 0.4 <= prediction <= 0.6
trainer.shutdown()
def test_zip_with_source_actor(ray_start_regular_shared):
it = from_items([1, 2, 3, 4], num_shards=2)
counts = collections.defaultdict(int)
for actor, value in it.gather_async().zip_with_source_actor():
counts[actor] += 1
assert len(counts) == 2
for a, count in counts.items():
assert count == 2
def test_serialization(ray_start_regular_shared):
it = (from_items([1, 2, 3, 4]).gather_sync().for_each(lambda x: x).filter(
lambda x: True).batch(2).flatten())
assert (repr(it) == "LocalIterator[ParallelIterator["
"from_items[int, 4, shards=2]].gather_sync()."
"for_each().filter().batch(2).flatten()]")
@ray.remote
def get(it):
return list(it)
assert ray.get(get.remote(it)) == [1, 2, 3, 4]
def test_metrics_union_recursive(ray_start_regular_shared):
it1 = from_items([1, 2, 3, 4], num_shards=1)
it2 = from_items([1, 2, 3, 4], num_shards=1)
it3 = from_items([1, 2, 3, 4], num_shards=1)
def foo_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["foo"] += 1
return metrics.counters["foo"]
def bar_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["bar"] += 1
return metrics.counters["bar"]
def baz_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["baz"] += 1
return metrics.counters["baz"]
def verify_metrics(x):
metrics = LocalIterator.get_metrics()
metrics.counters["n"] += 1
# Check the metrics context is shared recursively.
print(metrics.counters)
if metrics.counters["n"] >= 3:
assert "foo" in metrics.counters
assert "bar" in metrics.counters
assert "baz" in metrics.counters
return x
it1 = it1.gather_async().for_each(foo_metrics)
it2 = it2.gather_async().for_each(bar_metrics)
it3 = it3.gather_async().for_each(baz_metrics)
it12 = it1.union(it2, deterministic=True)
it123 = it12.union(it3, deterministic=True)
out = it123.for_each(verify_metrics)
assert out.take(20) == [1, 1, 1, 2, 2, 3, 2, 4, 3, 3, 4, 4]
def test_metrics(ray_start_regular_shared):
it = from_items([1, 2, 3, 4], num_shards=1)
it2 = from_items([1, 2, 3, 4], num_shards=1)
def f(x):
metrics = LocalIterator.get_metrics()
metrics.counters["foo"] += x
return metrics.counters["foo"]
it = it.gather_sync().for_each(f)
it2 = it2.gather_sync().for_each(f)
# Context cannot be accessed outside the iterator.
with pytest.raises(ValueError):
LocalIterator.get_metrics()
# Tests iterators have isolated contexts.
assert it.take(4) == [1, 3, 6, 10]
assert it2.take(4) == [1, 3, 6, 10]
# Context cannot be accessed outside the iterator.
with pytest.raises(ValueError):
LocalIterator.get_metrics()
def main():
num_points = 32 * 100 * 2
data = [i * (1 / num_points) for i in range(num_points)]
it = parallel_it.from_items(data, 2, False).for_each(lambda x: [x, x])
# this will create MLDataset with column RangeIndex(range(2))
ds = ml_data.from_parallel_iter(it, True, batch_size=32, repeated=False)
torch_ds = ds.to_torch(feature_columns=[0], label_column=1)
trainer = TorchTrainer(
num_workers=2,
training_operator_cls=make_train_operator(torch_ds),
add_dist_sampler=False,
config={"batch_size": 32})
for i in range(10):
trainer.train(num_steps=100)
model = trainer.get_model()
print("f(0.5)=", float(model(torch.tensor([[0.5]]).float())[0][0]))
def from_modin(cls, df, num_shards: int = 2):
"""Create a MLDataset from a Modin Dataframe.
Args:
df (modin.pandas.DataFrame): A Modin Dataframe.
num_shards (int): The number of worker actors to create.
"""
try:
import modin.pandas as pd
except ImportError:
raise ImportError("Cannot convert from Modin because "
"Modin is not installed.") from None
if not isinstance(df, (pd.DataFrame, pd.Series)):
raise ValueError("Must provide a modin.pandas DataFrame or Series")
from modin.distributed.dataframe.pandas.partitions import unwrap_partitions
parts = unwrap_partitions(df)
modin_iter = from_items(parts, num_shards=num_shards, repeat=False)
return cls.from_parallel_it(modin_iter, batch_size=0, repeated=False)
def test_torch_dataset(ray_start_4_cpus, use_local):
num_points = 32 * 100 * 2
data = [i * (1 / num_points) for i in range(num_points)]
para_it = parallel_it.from_items(data, 2, False).for_each(lambda x: [x, x])
ds = ml_data.from_parallel_iter(para_it, batch_size=32)
torch_ds = ds.to_torch(feature_columns=[0], label_column=1)
operator = make_train_operator(torch_ds)
trainer = TorchTrainer(training_operator_cls=operator,
num_workers=2,
use_local=use_local,
add_dist_sampler=False,
config={"batch_size": 32})
for i in range(10):
trainer.train(num_steps=100)
model = trainer.get_model()
prediction = float(model(torch.tensor([[0.5]]).float())[0][0])
assert 0.4 <= prediction <= 0.6
trainer.shutdown()
def process_data(data_set_type: str, parallel=True):
files = recurse_files(path.join("./data/webnlg", "raw", data_set_type))
xml_objs = [parse_xml_file(f) for f in files]
entries = []
if not parallel:
print(f"[Info] Processing data...")
chunks = [RDFFileReader(x).data for x in xml_objs]
entries = flatten_list(chunks)
else:
num_shards: int = num_cpus if parallel else 1
print(f"[Info] Processing data in {num_shards} shards...")
iterator = (
pariter.from_items(xml_objs[0:5], num_shards=num_shards)
# .for_each(lambda f: cleaner.clean)
.for_each(lambda xmldata: RDFFileReader(xmldata).data).flatten())
entries = iterator.gather_async()
return tqdm(entries, desc="WebNLG", unit="entry")
def test_for_each_concur(ray_start_regular_shared):
main_wait = Semaphore.remote(value=0)
test_wait = Semaphore.remote(value=0)
def task(x):
i, main_wait, test_wait = x
ray.get(main_wait.release.remote())
ray.get(test_wait.acquire.remote())
return i + 10
@ray.remote(num_cpus=0.1)
def to_list(it):
return list(it)
it = from_items([(i, main_wait, test_wait) for i in range(8)],
num_shards=2)
it = it.for_each(task, max_concurrency=2, resources={"num_cpus": 0.1})
for i in range(4):
ray.get(main_wait.acquire.remote())
# There should be exactly 4 tasks executing at this point.
assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
# When we finish one task, exactly one more should start.
ray.get(test_wait.release.remote())
ray.get(main_wait.acquire.remote())
assert ray.get(main_wait.locked.remote()) is True, "Too much parallelism"
# Finish everything and make sure the output matches a regular iterator.
for i in range(3):
ray.get(test_wait.release.remote())
assert repr(
it) == "ParallelIterator[from_items[tuple, 8, shards=2].for_each()]"
assert ray.get(to_list.remote(it.gather_sync())) == list(range(10, 18))
def test_from_items_repeat(ray_start_regular_shared):
it = from_items([1, 2, 3, 4], repeat=True)
assert repr(
it) == "ParallelIterator[from_items[int, 4, shards=2, repeat=True]]"
assert it.take(8) == [1, 2, 3, 4, 1, 2, 3, 4]
def test_select_shards(ray_start_regular_shared):
it = from_items([1, 2, 3, 4], num_shards=4)
it1 = it.select_shards([0, 2])
it2 = it.select_shards([1, 3])
assert it1.take(4) == [1, 3]
assert it2.take(4) == [2, 4]
def test_union_local(ray_start_regular_shared):
it1 = from_items(["a", "b", "c"], 1).gather_async()
it2 = from_range(5, 2).for_each(str).gather_async()
it = it1.union(it2)
assert sorted(it) == ["0", "1", "2", "3", "4", "a", "b", "c"]
def test_from_items(ray_start_regular_shared):
it = from_items([1, 2, 3, 4])
assert repr(it) == "ParallelIterator[from_items[int, 4, shards=2]]"
assert list(it.gather_sync()) == [1, 2, 3, 4]
assert next(it.gather_sync()) == 1
def test_flatten(ray_start_regular_shared):
it = from_items([[1, 2], [3, 4]], 1).flatten()
assert repr(
it) == "ParallelIterator[from_items[list, 2, shards=1].flatten()]"
assert list(it.gather_sync()) == [1, 2, 3, 4]
