def test_dask_bfs(dask_client):
gc.collect()
input_data_path = (RAPIDS_DATASET_ROOT_DIR_PATH /
"netscience.csv").as_posix()
print(f"dataset={input_data_path}")
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
def modify_dataset(df):
temp_df = cudf.DataFrame()
temp_df['src'] = df['src'] + 1000
temp_df['dst'] = df['dst'] + 1000
temp_df['value'] = df['value']
return cudf.concat([df, temp_df])
meta = ddf._meta
ddf = ddf.map_partitions(modify_dataset, meta=meta)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = modify_dataset(df)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
expected_dist = cugraph.bfs(g, [0, 1000])
result_dist = dcg.bfs(dg, [0, 1000])
result_dist = result_dist.compute()
compare_dist = expected_dist.merge(result_dist,
on="vertex",
suffixes=["_local", "_dask"])
err = 0
for i in range(len(compare_dist)):
if (compare_dist["distance_local"].iloc[i] !=
compare_dist["distance_dask"].iloc[i]):
err = err + 1
assert err == 0
def test_dask_bfs_multi_column_depthlimit(dask_client):
gc.collect()
input_data_path = (RAPIDS_DATASET_ROOT_DIR_PATH /
"netscience.csv").as_posix()
print(f"dataset={input_data_path}")
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src_a", "dst_a", "value"],
dtype=["int32", "int32", "float32"],
)
ddf['src_b'] = ddf['src_a'] + 1000
ddf['dst_b'] = ddf['dst_a'] + 1000
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src_a", "dst_a", "value"],
dtype=["int32", "int32", "float32"],
)
df['src_b'] = df['src_a'] + 1000
df['dst_b'] = df['dst_a'] + 1000
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, ["src_a", "src_b"], ["dst_a", "dst_b"])
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, ["src_a", "src_b"], ["dst_a", "dst_b"])
start = cudf.DataFrame()
start['a'] = [0]
start['b'] = [1000]
depth_limit = 18
expected_dist = cugraph.bfs(g, start, depth_limit=depth_limit)
result_dist = dcg.bfs(dg, start, depth_limit=depth_limit)
result_dist = result_dist.compute()
compare_dist = expected_dist.merge(result_dist,
on=["0_vertex", "1_vertex"],
suffixes=["_local", "_dask"])
err = 0
for i in range(len(compare_dist)):
if (compare_dist["distance_local"].iloc[i] <= depth_limit
and compare_dist["distance_dask"].iloc[i] <= depth_limit
and compare_dist["distance_local"].iloc[i] !=
compare_dist["distance_dask"].iloc[i]):
err = err + 1
assert err == 0
def test_dask_bfs(client_connection):
gc.collect()
# FIXME: update this to allow dataset to be parameterized and have dataset
# part of test param id (see other tests)
input_data_path = r"../datasets/netscience.csv"
print(f"dataset={input_data_path}")
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst", renumber=True)
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
expected_dist = cugraph.bfs(g, 0)
result_dist = dcg.bfs(dg, 0, True)
result_dist = result_dist.compute()
compare_dist = expected_dist.merge(result_dist,
on="vertex",
suffixes=["_local", "_dask"])
err = 0
for i in range(len(compare_dist)):
if (compare_dist["distance_local"].iloc[i] !=
compare_dist["distance_dask"].iloc[i]):
err = err + 1
assert err == 0
def test_dask_bfs():
gc.collect()
cluster = LocalCUDACluster()
client = Client(cluster)
Comms.initialize()
input_data_path = r"../datasets/netscience.csv"
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(input_data_path,
chunksize=chunksize,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
df = cudf.read_csv(input_data_path,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, 'src', 'dst', renumber=True)
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, renumber=True)
expected_dist = cugraph.bfs(g, 0)
result_dist = dcg.bfs(dg, 0, True)
compare_dist = expected_dist.merge(result_dist,
on="vertex",
suffixes=['_local', '_dask'])
err = 0
for i in range(len(compare_dist)):
if (compare_dist['distance_local'].iloc[i] !=
compare_dist['distance_dask'].iloc[i]):
err = err + 1
assert err == 0
Comms.destroy()
client.close()
cluster.close()