Describe the bug
Running UMAP with a random state seed set results in extremely noisy output embeddings. These embeddings are not just noisier than what is expected from CPU UMAP; they are noisier than non-deterministic cuML UMAP.
Steps/Code to reproduce bug
Running a seeded embedding on any datasets larger than ~100k data points will do. A minimal example is provided below:
import matplotlib.pyplot as plt
from cuml.manifold import UMAP
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=100_000, random_state=42)
mapper = UMAP(random_state=42)
embedding = mapper.fit_transform(X)
plt.scatter(embedding[:,0], embedding[:,1], s=0.1)
Expected behavior
Setting a random state should not interfere with UMAP convergence. Results should have similar global structure characteristics to non-deterministic embeddings. Setting a random state should only affect the consistency of results between fit calls.
Environment details (please complete the following information):
- Environment location: Bare metal
- Linux Distro/Architecture: Pop!_OS 22.04 LTS x86_64
- GPU Model/Driver: NVIDIA GeForce RTX 4070 / Driver 570
- CUDA: 12.8
- Method of cuDF & cuML install: conda (environment details below)
Full environment
# Name Version Build Channel
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anyio 4.10.0 pyhe01879c_0 conda-forge
aom 3.9.1 hac33072_0 conda-forge
argon2-cffi 25.1.0 pyhd8ed1ab_0 conda-forge
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azure-storage-blobs-cpp 12.14.0 hb1c9500_1 conda-forge
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pylibcudf 25.08.00 cuda12_py313_250806_6cea3743 rapidsai
pylibcugraph 25.08.00 cuda12_py313_250806_e0f09e50 rapidsai
pylibraft 25.08.00 cuda12_py313_250806_2fb92393 rapidsai
pynvjitlink 0.7.0 12.9_py313_250625_3693c9cd_hd6618c4 rapidsai
pynvml 12.0.0 pyhd8ed1ab_0 conda-forge
pyogrio 0.11.1 py313h8b61037_1 conda-forge
pyparsing 3.2.3 pyhe01879c_2 conda-forge
pyproj 3.7.2 py313hcfca4fd_1 conda-forge
pysocks 1.7.1 pyha55dd90_7 conda-forge
python 3.13.5 hec9711d_102_cp313 conda-forge
python-confluent-kafka 2.8.0 py313h536fd9c_0 conda-forge
python-dateutil 2.9.0.post0 pyhe01879c_2 conda-forge
python-fastjsonschema 2.21.2 pyhe01879c_0 conda-forge
python-gil 3.13.5 h4df99d1_102 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python-tzdata 2025.2 pyhd8ed1ab_0 conda-forge
python_abi 3.13 8_cp313 conda-forge
pytz 2025.2 pyhd8ed1ab_0 conda-forge
pyviz_comms 3.0.6 pyhd8ed1ab_0 conda-forge
pywavelets 1.9.0 py313h29aa505_1 conda-forge
pyyaml 6.0.2 py313h8060acc_2 conda-forge
pyzmq 27.0.2 py312hfb55c3c_2 conda-forge
qhull 2020.2 h434a139_5 conda-forge
raft-dask 25.08.00 cuda12_py313_250806_2fb92393 rapidsai
rapids 25.08.00 cuda12_py313_250806_6825ee7c rapidsai
rapids-dask-dependency 25.08.00 250806_fb4cb9f6 rapidsai
rapids-logger 0.1.1 h98325ef_0 rapidsai
rapids-xgboost 25.08.00 cuda12_py313_250806_6825ee7c rapidsai
rav1e 0.7.1 h8fae777_3 conda-forge
rdma-core 59.0 hecca717_0 conda-forge
re2 2025.07.22 h5a314c3_0 conda-forge
readline 8.2 h8c095d6_2 conda-forge
referencing 0.36.2 pyh29332c3_0 conda-forge
requests 2.32.5 pyhd8ed1ab_0 conda-forge
rfc3339-validator 0.1.4 pyhd8ed1ab_1 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rfc3987-syntax 1.1.0 pyhe01879c_1 conda-forge
rich 14.1.0 pyhe01879c_0 conda-forge
rmm 25.08.00 cuda12_py313_250806_63a4d366 rapidsai
rpds-py 0.27.1 py313h843e2db_1 conda-forge
s2n 1.5.23 h8e187f5_0 conda-forge
scikit-image 0.24.0 py313ha87cce1_3 conda-forge
scikit-learn 1.7.1 py313h06d4379_0 conda-forge
scipy 1.16.1 py313h11c21cd_1 conda-forge
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setuptools 80.9.0 pyhff2d567_0 conda-forge
shapely 2.1.1 py313h393e2d1_1 conda-forge
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six 1.17.0 pyhe01879c_1 conda-forge
snappy 1.2.2 h03e3b7b_0 conda-forge
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sortedcontainers 2.4.0 pyhd8ed1ab_1 conda-forge
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sqlite 3.50.4 hbc0de68_0 conda-forge
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tk 8.6.13 noxft_hd72426e_102 conda-forge
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types-python-dateutil 2.9.0.20250822 pyhd8ed1ab_0 conda-forge
typing-extensions 4.15.0 h396c80c_0 conda-forge
typing_extensions 4.15.0 pyhcf101f3_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_1 conda-forge
tzdata 2025b h78e105d_0 conda-forge
uc-micro-py 1.0.3 pyhd8ed1ab_1 conda-forge
ucx 1.18.1 h990bcc0_2 conda-forge
ucx-py 0.45.00 py313_250806_d53bdda0 rapidsai
ucxx 0.45.01 cuda12_py313_250806_c25d2cdb rapidsai
uri-template 1.3.0 pyhd8ed1ab_1 conda-forge
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websocket-client 1.8.0 pyhd8ed1ab_1 conda-forge
xarray 2025.8.0 pyhd8ed1ab_0 conda-forge
xerces-c 3.2.5 h988505b_2 conda-forge
xgboost 3.0.3 rapidsai_pyh452a892_0 rapidsai
xorg-libxau 1.0.12 hb9d3cd8_0 conda-forge
xorg-libxdmcp 1.1.5 hb9d3cd8_0 conda-forge
xyzservices 2025.4.0 pyhd8ed1ab_0 conda-forge
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Additional context
See below for a handful of examples illustrating the difference between non-seeded and seeded UMAP for a handful of toy datasets of varying size. Problematic seeded embeddings are framed in red:
Circles:
Gaussian blobs:
Moons with noise:
Moons:
There are a couple of other apparent convergence issues in the non-deterministic embeddings for the largest datasets above (e.g. blobs, moons), but they are not consistently problematic in the way that the seeded embeddings are.
Describe the bug
Running UMAP with a random state seed set results in extremely noisy output embeddings. These embeddings are not just noisier than what is expected from CPU UMAP; they are noisier than non-deterministic cuML UMAP.
Steps/Code to reproduce bug
Running a seeded embedding on any datasets larger than ~100k data points will do. A minimal example is provided below:
Expected behavior
Setting a random state should not interfere with UMAP convergence. Results should have similar global structure characteristics to non-deterministic embeddings. Setting a random state should only affect the consistency of results between fit calls.
Environment details (please complete the following information):
Full environment
Additional context
See below for a handful of examples illustrating the difference between non-seeded and seeded UMAP for a handful of toy datasets of varying size. Problematic seeded embeddings are framed in red:
Circles:
Gaussian blobs:
Moons with noise:
Moons:
There are a couple of other apparent convergence issues in the non-deterministic embeddings for the largest datasets above (e.g. blobs, moons), but they are not consistently problematic in the way that the seeded embeddings are.