Feature/sklearnex and remove faiss#33
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The FAISS KMeans backend added meaningful installation weight and startup import noise for a marginal benefit. Removing it simplies the backend selection logic to two cases: - cuML if GPU available - else sklearn Changes - Drop `faiss-cpu` & `faiss-gpu-cu12` from main deps and `gpu-*` extras - Remove FAISS from backend scripts `resolve_brackend()`, `run_kmeans()` dispatch - Remove "faiss" from clustering backend dropdowns in the webUI - Update README & BACKEND_PIPELINE doc to reflect the changes
Add `scikit-learn-intelex` as default dependency and patch sklearn at import time in `shared/utils/clustering.py`. Accelerates the existing `sklearn` PCA / TSNE / KMeans calls on CPU. UMAP is unaffected as `umap-learn` is not part of the `sklearn` algorithm. Set Set EMB_EXPLORER_DISABLE_SKLEARNEX=1 to opt out for debugging vanilla sklearn behavior.
NetZissou
commented
Jun 3, 2026
NetZissou
commented
Jun 3, 2026
Co-authored-by: Net Zhang <48858129+NetZissou@users.noreply.github.com>
egrace479
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Jun 3, 2026
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| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex` [^1]) | ||
| │ | ||
| ├─► Step 2: Dimensionality Reduction to 2D | ||
| │ Method: PCA / t-SNE / UMAP | ||
| │ Backend: cuML → sklearn | ||
| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex` for PCA/TSNE [^1]) |
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| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex` [^1]) | |
| │ | |
| ├─► Step 2: Dimensionality Reduction to 2D | |
| │ Method: PCA / t-SNE / UMAP | |
| │ Backend: cuML → sklearn | |
| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex` for PCA/TSNE [^1]) | |
| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex`) | |
| │ | |
| ├─► Step 2: Dimensionality Reduction to 2D | |
| │ Method: PCA / t-SNE / UMAP | |
| │ Backend: cuML (GPU) → sklearn (CPU, auto-accelerated by `sklearn-intelex` for PCA/TSNE) |
Wrong spot for footnotes (they won't function in the codeblock)
| └─► Scatter Plot (Altair) | ||
| Color = cluster, position = 2D projection | ||
| ``` | ||
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| Note that `sklearn-intelex` acceleration is used for CPU operations where available[^1]. | |
| | **cuML** | GPU available + >500 samples | GPU-accelerated KMeans via RAPIDS. Runs on CuPy arrays. Falls back to sklearn on any error. | | ||
| | **FAISS** | No GPU + >500 samples | Facebook's optimized CPU KMeans using L2 index. Fast for medium datasets. Falls back to sklearn on error. | | ||
| | **sklearn** | Small datasets or fallback | Standard scikit-learn KMeans. Always works, no special dependencies. | | ||
| | **sklearn** | CPU path (default on machines without a GPU) | Standard scikit-learn KMeans, auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex) (Intel oneDAL) when installed — typically 10–17× faster than vanilla sklearn on CPU. Disable with `EMB_EXPLORER_DISABLE_SKLEARNEX=1`. | |
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| | **sklearn** | CPU path (default on machines without a GPU) | Standard scikit-learn KMeans, auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex) (Intel oneDAL) when installed — typically 10–17× faster than vanilla sklearn on CPU. Disable with `EMB_EXPLORER_DISABLE_SKLEARNEX=1`. | | |
| | **sklearn** | CPU path (default on machines without a GPU) | Standard scikit-learn KMeans, auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex) (Intel oneDAL) when installed[^1] — typically 10–17× faster than vanilla sklearn on CPU. Disable with `EMB_EXPLORER_DISABLE_SKLEARNEX=1`. | |
| |-----------|-----------| | ||
| | KMeans | cuML if GPU + >500 samples, else FAISS if available + >500 samples, else sklearn | | ||
| | Dim. Reduction | cuML if GPU + >5000 samples, else sklearn | | ||
| | KMeans | cuML if GPU + >500 samples, else sklearn (auto-accelerated by `sklearn-intelex` when installed [^1]) | |
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| | KMeans | cuML if GPU + >500 samples, else sklearn (auto-accelerated by `sklearn-intelex` when installed [^1]) | | |
| | KMeans | cuML if GPU + >500 samples, else sklearn (auto-accelerated by `sklearn-intelex` when installed[^1]) | |
| ``` | ||
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| The app auto-detects GPU availability at runtime and falls back to CPU if anything goes wrong — no configuration needed. You can also manually select backends (cuML, FAISS, sklearn) in the sidebar. | ||
| The app auto-detects GPU availability at runtime and falls back to CPU if anything goes wrong — no configuration needed. The CPU sklearn path is auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex) [^1]. You can also manually select backends (`cuML`, `sklearn`) in the sidebar. |
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| The app auto-detects GPU availability at runtime and falls back to CPU if anything goes wrong — no configuration needed. The CPU sklearn path is auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex) [^1]. You can also manually select backends (`cuML`, `sklearn`) in the sidebar. | |
| The app auto-detects GPU availability at runtime and falls back to CPU if anything goes wrong — no configuration needed. The CPU sklearn path is auto-accelerated by [scikit-learn-intelex](https://github.com/uxlfoundation/scikit-learn-intelex)[^1]. You can also manually select backends (`cuML`, `sklearn`) in the sidebar. |
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Closes issue #32
sklearnwithsklearnex.faissentirely from backend.About
sklearnexGood API stability:
sklearnexis powered by the oneDAL library that provides accelerations on x86_64 Linux and Windows machines, and silently fall back to vanillasklearnon unsupported architectures like Apple Silicon and ARM Linux. The package is under the UXL Foundation (a Linux Foundation project) so cross-vendor support is a stated goal.