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Accelerating pipelines

For very large datasets, ATOM offers various ways to accelerate its pipeline:

Warning

Performance improvements are usually noticeable for datasets larger than ~5M rows. For smaller datasets, using other values than the default can even harm performance!

GPU acceleration

Graphics Processing Units (GPUs) can significantly accelerate calculations for preprocessing steps or training machine learning models. Training models involve compute-intensive matrix multiplications and other operations that can take advantage of a GPU's massively parallel architecture. Training on large datasets can take hours to run on a single processor. However, if you offload those tasks to a GPU, you can reduce training time to minutes instead.

Running transformers and models in atom using a GPU is as easy as initializing the instance with parameter device="gpu". The device parameter accepts any string that follows the SYCL_DEVICE_FILTER filter selector. Examples are:

  • device="cpu" (use CPU)
  • device="gpu" (use default GPU)
  • device="gpu:0" (use first GPU)
  • device="gpu:1" (use second GPU)

Combine GPU acceleration with the cuml and sklearnex estimator engines. The XGBoost, LightGBM and CatBoost models come with their own GPU engine. Setting device="gpu" is sufficient to accelerate them with GPU, regardless of the engine parameter.

Warning

ATOM does not support multi-GPU training. If there is more than one GPU on the machine and the device parameter does not specify which one to use, the first one is used by default.

Example

Colab

Train a model on a GPU yourself using Google Colab. Just click on the badge above and run the notebook! Make sure to choose the GPU runtime type.

Estimator acceleration

The estimator engine can be specified through the engine parameter, e.g. engine="sklearnex" or engine={"data": "pyarrow","estimator": "sklearnex"} to combine it with a data engine. ATOM integrates the following estimator engines: sklearn, sklearnex and cuml. Read here how to run the estimators on GPU instead of CPU.

Warning

Estimators accelerated with sklearnex or cuML sometimes use slightly different hyperparameters than their sklearn counterparts.

sklearn

This is the default option, which uses the standard estimators from sklearn. Sklearn does not support training on GPU.

sklearnex

The Intel® Extension for Scikit-learn package (or sklearnex, for brevity) accelerates sklearn models and transformers, keeping full conformance with sklearn's API. Sklearnex is a free software AI accelerator that offers a way to make sklearn code 10–100 times faster. The software acceleration is achieved through the use of vector instructions, IA hardware-specific memory optimizations, threading, and optimizations for all upcoming Intel platforms at launch time. See here an example using the sklearnex engine.

Warning

sklearnex estimators don't support sparse datasets nor multioutput tasks.

Tip

Intel® processors provide better performance than other CPUs.

Prerequisites

  • Operating System:
    • Linux (Ubuntu, Fedora, etc...)
    • Windows 8.1+
    • macOS (no GPU support)
  • CPU:
    • Processor must have x86 architecture.
    • Processor must support at least one of SSE2, AVX, AVX2, AVX512 instruction sets.
    • ARM* architecture is not supported.
  • GPU:
    • All Intel® integrated and discrete GPUs.
    • Intel® GPU drivers.
  • Libraries:
    • sklearnex>=2023.2.1 (automatically installed with atom when the processor has x86 architecture)
    • dpcpp_cpp_rt>=2023.2 (only for GPU acceleration)

Supported estimators

cuML

cuML is the machine learning library of the RAPIDS project. cuML enables you to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents.

ATOM uses pandas pandas (which in turn uses numpy) as its data backend. This means that the data must move from CPU to GPU memory before it can be used by a cuML estimator. To avoid this overhead, consider using cudf-pandas, which accelerates pandas' workflows on GPU, with automatic CPU fallback if needed. Remember to load cudf.pandas before importing ATOM.

Warning

  • cuML estimators don't support multioutput tasks.
  • Install cuML using pip install --extra-index-url=https://pypi.nvidia.com cuml-cu11 or pip install --extra-index-url=https://pypi.nvidia.com cuml-cu12 depending on your CUDA version. Read more about RAPIDS' installation here.

Tip

Only transformers and predictors are converted to the requested engine. To use a metric from cuML, insert it directly in the run method:

from atom import ATOMClassifier
from cuml.metrics import accuracy_score
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100, random_state=1)

atom = ATOMClassifier(X, y, engine={"estimator": "cuml"}, verbose=2)
atom.run("LR", metric=accuracy_score)

Prerequisites

  • Operating System:
    • Ubuntu 18.04/20.04 or CentOS 7/8 with gcc/++ 9.0+
    • Windows 10+ with WSL2 (see here a tutorial)
  • GPU:
  • Drivers:
    • CUDA & NVIDIA Drivers of versions 11.0, 11.2, 11.4 or 11.5
  • Libraries:

Supported estimators

Parallel execution

Another way to accelerate your pipelines is executing processes in parallel. Use the backend parameter to select one of several parallelization backends.

  • loky: Used by default, can induce some communication and memory overhead when exchanging input and output data with the worker Python processes. On some rare systems (such as Pyiodide), the loky backend may not be available.
  • multiprocessing: Previous process-based backend based on multiprocessing.Pool. Less robust than loky.
  • threading: Very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects. It's mostly useful when the execution bottleneck is a compiled extension that explicitly releases the GIL (for instance a Cython loop wrapped in a "with nogil" block or an expensive call to a library such as numpy).
  • ray: Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads. Read more about Ray here. See here an example use case.
  • dask: Dask is a flexible parallel computing library for analytics. Read more about Dask here.

The parallelization backend is applied in the following cases:

Note

The njobs parameter sets the number of cores for the individual models as well as for parallel training. You won't gain much training two models in parallel with 2 cores, when the models also parallelize computations internally. Instead, use parallel training for models that can't parallelize their training (their constructor doesn't have the n_jobs parameter).