cuml.accel: Zero Code Change Acceleration for Scikit-Learn, UMAP and HDBSCAN#

Starting in RAPIDS 25.02.01, cuML offers a new way to accelerate existing code based on Scikit-Learn, UMAP-Learn, and HDBSCAN. Instead of rewriting that code to import equivalent cuML functionality, simply invoke your existing, unaltered Python script as follows, and cuML will accelerate as much of the code as possible with NVIDIA GPUs, falling back to CPU where necessary:

python -m cuml.accel unchanged_script.py

The same functionality is available in Jupyter notebooks using the following magic at the beginning of the notebook (before other imports):

%load_ext cuml.accel
import sklearn

You can see an example of this in KMeans Digits Notebook, where an unmodified Scikit-Learn example notebook is used to demonstrate how cuml.accel can be used in Jupyter.

In any Python environment, the following code snippet can also be used to activate cuml.accel if it is run prior to importing the module you wish to accelerate:

from cuml.accel import install
install()
import sklearn

``cuml.accel`` is currently a beta feature and will continue to improve over time.

Contents:

FAQs#

1. Why use cuml.accel instead of using cuML directly?#

Many software lifecycles involve running code on a variety of hardware. Maybe the data scientists developing a pipeline do not have access to NVIDIA GPUs, but you want the cost and time savings of running that pipeline on NVIDIA GPUs in production. Rather than going through a manual migration to cuML every time the pipeline is updated, cuml.accel allows you to immediately deploy unaltered Scikit-Learn, UMAP-Learn, and HDBSCAN code on NVIDIA GPUs. Furthermore, cuml.accel will automatically fall back to CPU execution for anything which is implemented in Scikit-Learn but not yet accelerated by cuML.

Additionally, cuml.accel offers a quick way to evaluate the minimum acceleration cuML can provide for your workload without touching a line of code.

2. Why use cuML directly instead of cuml.accel?#

In many cases, cuml.accel offers enough of a performance boost on its own that there is no need to migrate code to cuML. However, cuML’s API offers a variety of additional parameters that let you fine-tune GPU execution in order to get the maximum possible performance out of NVIDIA GPUs. So for software that will always be run with NVIDIA GPUs available, it may be worthwhile to write your code directly with cuML.

Additionally, running code directly with cuML offers finer control over GPU memory usage. cuml.accel will automatically use unified or managed memory for allocations in order to reduce the risk of CUDA OOM errors. In contrast, cuML defaults to ordinary device memory, which can offer improved performance but requires slightly more care to avoid exhausting the GPU VRAM. If you experience unexpectedly slow performance with cuml.accel, you can try disabling the use of unified memory with the --disable-uvm flag.

3. What does cuml.accel accelerate?#

cuml.accel is designed to provide zero code change acceleration of any Scikit-Learn-like estimator which has an equivalent cuML implementation, including estimators from Scikit-Learn, UMAP-Learn, and HDBSCAN. Currently, the following estimators are mostly or entirely accelerated when run with cuml.accel:

  • UMAP-Learn

    • umap.UMAP

  • HDBSCAN

    • hdbscan.HDBSCAN

  • Scikit-Learn

    • sklearn.cluster.KMeans

    • sklearn.cluster.DBSCAN

    • sklearn.decomposition.PCA

    • sklearn.decomposition.TruncatedSVD

    • sklearn.ensemble.RandomForestClassifier

    • sklearn.ensemble.RandomForestRegressor

    • sklearn.kernel_ridge.KernelRidge

    • sklearn.linear_model.LinearRegression

    • sklearn.linear_model.LogisticRegression

    • sklearn.linear_model.ElasticNet

    • sklearn.linear_model.Ridge

    • sklearn.linear_model.Lasso

    • sklearn.manifold.TSNE

    • sklearn.neighbors.NearestNeighbors

    • sklearn.neighbors.KNeighborsClassifier

    • sklearn.neighbors.KNeighborsRegressor

This list will continue to expand as cuml.accel development continues to cover all algorithms present in cuML. Please see Zero Code Change Limitations for known limitations.

If there are specific models that you are particularly interested in seeing prioritized for cuML, please request them via an issue in the cuML Githb repository.

4. Will I get the same results as I do without cuml.accel?#

cuml.accel is designed to provide equivalent results to the estimators it acelerates, but the output may have small numerical differences. To be more specific, measures of the quality of the results (accuracy, trustworthiness, etc.) should be approximately as good or better than those obtained without cuml.accel, even if the exact output varies.

A baseline limitation for obtaining exact numerical equality is that in highly parallel execution environments (e.g. GPUs), there is no guarantee that floating point operations will happen in exactly the same order as in non-parallel environments. This means that floating point arithmetic error may propagate differently and lead to different outcomes. This can be exacerbated by discretization operations in which values end up in different categories based on floating point values.

Secondarily, some algorithms are implemented in a fundamentally different way on GPU than on CPU in order to make efficient use of the GPU’s highly parallel compute capabilities. In such cases, cuml.accel will translate hyperparameters appropriately to maintain equivalence with the CPU implementation. Differences of this kind are noted in the corresponding entry of Zero Code Change Limitations for that estimator.

If you discover a use case where the quality of results obtained with cuml.accel is worse than that obtained without, please report it as a bug, and the RAPIDS team will investigate.

5. How much faster is cuml.accel?#

This depends on the individual algorithm being accelerated and the dataset being processed. As with cuML itself, you will generally see the most benefit when cuml.accel is used on large datasets. Please see Zero Code Change Benchmarks for some representative benchmarks.

Please note that the first time an estimator method is called in a Python process, there may be some overhead due to JIT compilation of cupy kernels. To get an accurate sense of performance, run the method once on a small subset of data before measuring runtime on a full-scale dataset.

6. Will I run out of GPU memory if I use cuml.accel?#

cuml.accel will use CUDA managed memory for allocations on NVIDIA GPUs. This means that host memory can be used to augment GPU memory, and data will be migrated automatically as necessary. This does not mean that cuml.accel is entirely impervious to OOM errors, however. Very large datasets can exhaust the entirety of both host and device memory. Additionally, if device memory is heavily oversubscribed, it can lead to slow execution. cuml.accel is designed to minimize both possibilities, but if you observe OOM errors or slow execution on data that should fit in combined host plus device memory for your system, please report it, and the RAPIDS team will investigate.

7. What is the relationship between cuml.accel and cudf.pandas?#

Both projects serve a similar role. Just as cuml.accel offers zero code change acceleration for Scikit-Learn and similar packages, cudf.pandas offers zero code change acceleration for Pandas.

Using them together is supported as an experimental feature. To do this from the CLI, the flag -cudf-pandas can be added to the cuml.accel call:

python -m cuml.accel --cudf-pandas

For Jupyter notebooks, use the following to turn on both:

%load_ext cudf.pandas
from cuml.experimental.accel import install
install()

A single magic invocation will be available in the next version of cuML.

8. What happens if something in my script is not implemented in cuML?#

cuml.accel should cleanly and transparently fall back to the CPU implementation for any methods or estimators which are not implemented in cuML. If it does not do so, please report it as a bug, and the RAPIDS team will investigate.

9. I’ve discovered a bug in cuml.accel. How do I report it?#

Bugs affecting cuml.accel can be reported via the cuML issue tracker. If you observe a significant difference in the quality of output with and without cuml.accel, please report it as a bug. These issues will be taken especially seriously. Similarly, if runtime slows down for your estimator when using cuml.accel, the RAPIDS team will try to triage and fix the issue as soon as possible. Note that library import time will be longer when using cuml.accel, so please exclude that from runtime. Long import time is a known issue and will be improved with subsequent releases of cuML.

10. If I serialize a model using cuml.accel, can I load it without cuml.accel?#

This is a common use case for cuml.accel and cuML in general, since it may be useful to train a model using NVIDIA GPUs but deploy it for inference in an environment that does not have access to NVIDIA GPUs.

Currently, models serialized with cuml.accel need to be converted to pure Scikit-Learn (or UMAP/HDBSCAN/…). After serializing a model, using either pickle or joblib, for example to model_pickled.pkl, that model can then be converted to a regular sklearn/umap-learn/hdbscan pickled model with:

python -m cuml.accel --convert-to-sklearn model_pickled.pkl --format pickle --output converted_model.pkl

The converted_model.pkl is now a regular pickled/joblib serialized model, that can be deserialized and used in a computer/environment without cuML or GPUs.

This conversion step should become unnecessary in a future release of cuML.