Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[REVIEW] Fixing dask tsvd stress test failure #2941

Merged
merged 5 commits into from
Oct 15, 2020
Merged
Show file tree
Hide file tree
Changes from 3 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -81,6 +81,7 @@
- PR #2932: Marking KBinsDiscretizer pytests as xfail
- PR #2925: Fixing Owner Bug When Slicing CumlArray Objects
- PR #2931: Fix notebook error handling in gpuCI
- PR #2941: Fixing dask tsvd stress test failure
- PR #2943: Remove unused shuffle_features parameter
- PR #2940: Correcting labels meta dtype for `cuml.dask.make_classification`

Expand Down
18 changes: 9 additions & 9 deletions python/cuml/test/dask/test_tsvd.py
Original file line number Diff line number Diff line change
Expand Up @@ -35,10 +35,10 @@ def test_pca_fit(data_info, input_type, client):

from cuml.dask.datasets import make_blobs

X, _ = make_blobs(n_samples=nrows,
n_features=ncols,
X, _ = make_blobs(n_samples=int(nrows),
Nanthini10 marked this conversation as resolved.
Show resolved Hide resolved
n_features=int(ncols),
centers=1,
n_parts=n_parts,
n_parts=int(n_parts),
cluster_std=0.5,
random_state=10, dtype=np.float32)

Expand Down Expand Up @@ -79,10 +79,10 @@ def test_pca_fit_transform_fp32(data_info, client):
from cuml.dask.decomposition import TruncatedSVD as daskTPCA
from cuml.dask.datasets import make_blobs

X_cudf, _ = make_blobs(n_samples=nrows,
n_features=ncols,
X_cudf, _ = make_blobs(n_samples=int(nrows),
n_features=int(ncols),
centers=1,
n_parts=n_parts,
n_parts=int(n_parts),
cluster_std=1.5,
random_state=10, dtype=np.float32)

Expand All @@ -100,10 +100,10 @@ def test_pca_fit_transform_fp64(data_info, client):
from cuml.dask.decomposition import TruncatedSVD as daskTPCA
from cuml.dask.datasets import make_blobs

X_cudf, _ = make_blobs(n_samples=nrows,
n_features=ncols,
X_cudf, _ = make_blobs(n_samples=int(nrows),
Nanthini10 marked this conversation as resolved.
Show resolved Hide resolved
n_features=int(ncols),
centers=1,
n_parts=n_parts,
n_parts=int(n_parts),
cluster_std=1.5,
random_state=10, dtype=np.float64)

Expand Down