{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using GPUs to accelerate training and inference\n", "In this notebook, we will see the advantage of using GPUs to do training and inference.\n", "\n", "**Note:** The results in this notebook are only relevant if you are running on a machine with a GPU." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-08-02 14:07:29.561400: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-08-02 14:07:30.152340: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "/home/so/miniconda3/envs/gpsat/lib/python3.8/site-packages/gpflow/experimental/utils.py:42: UserWarning: You're calling gpflow.experimental.check_shapes.decorator.check_shapes which is considered *experimental*. Expect: breaking changes, poor documentation, and bugs.\n", " warn(\n", "/home/so/miniconda3/envs/gpsat/lib/python3.8/site-packages/gpflow/experimental/utils.py:42: UserWarning: You're calling gpflow.experimental.check_shapes.inheritance.inherit_check_shapes which is considered *experimental*. Expect: breaking changes, poor documentation, and bugs.\n", " warn(\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from GPSat.models.sklearn_models import sklearnGPRModel\n", "from GPSat.models.gpflow_models import GPflowGPRModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the experiment, we use the same model as before but sampling more data points." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set random seed\n", "np.random.seed(0)\n", "\n", "# Generate data\n", "N = 2500\n", "L = 5\n", "noise_std = 0.05\n", "\n", "X_grid = np.linspace(-L, L, 100)\n", "X = np.random.uniform(-L, L, (N,))\n", "f = lambda x: np.cos(x)\n", "epsilon = noise_std * np.random.randn(N)\n", "\n", "y = f(X) + epsilon\n", "f_truth = f(X_grid) # Ground truth\n", "\n", "# Plot\n", "plt.plot(X_grid, f_truth, 'k', zorder=1, label='Ground truth')\n", "plt.scatter(X, y, color='C3', alpha=0.6, zorder=2, s=0.5, label='Noisy observations')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we model this data using ``sklearnGPRModel``. Scikit-learn does not have GPU support so this will do all the training and prediciton on CPU, even if it detects GPUs on the machine." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found GPU\n", "'__init__': 1.206 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2023-08-02 14:07:34.661093: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:0 with 6618 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:17:00.0, compute capability: 7.5\n", "2023-08-02 14:07:34.661674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:1 with 5973 MB memory: -> device: 1, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:65:00.0, compute capability: 7.5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "'optimise_parameters': 42.258 seconds\n", "'predict': 0.012 seconds\n" ] } ], "source": [ "# ---------------\n", "# Training on CPU\n", "# ---------------\n", "\n", "# Initialise sklearn GPR model\n", "sklearn_gpr = sklearnGPRModel(coords=X, obs=y, kernel='RBF', likelihood_variance=noise_std**2, verbose=False)\n", "\n", "# Train model\n", "_ = sklearn_gpr.optimise_parameters()\n", "\n", "# Predict on test points\n", "pred_dict_sklearn = sklearn_gpr.predict(X_grid[:,None])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's use ``GPflowGPRModel`` to model the same data, which is based on the python package ``GPflow``, itself a tensorflow based package for modelling with GPs. This automatically does the training and prediction on GPUs, if available." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found GPU\n", "setting lengthscales to: [1.]\n", "'__init__': 0.195 seconds\n", "setting parameter likelihood_variance to be untrainable\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2023-08-02 14:11:00.478252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:0 with 6618 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:17:00.0, compute capability: 7.5\n", "2023-08-02 14:11:00.478796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:1 with 5973 MB memory: -> device: 1, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:65:00.0, compute capability: 7.5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "'optimise_parameters': 5.645 seconds\n", "'predict': 0.176 seconds\n" ] } ], "source": [ "# ---------------\n", "# Training on GPU\n", "# ---------------\n", "\n", "# Initialise GPflow GPR model\n", "gpflow_gpr = GPflowGPRModel(coords=X, obs=y, kernel='RBF', noise_variance=noise_std**2, verbose=False)\n", "\n", "# Train model\n", "_ = gpflow_gpr.optimise_parameters(fixed_params=['likelihood_variance'])\n", "\n", "# Predict on test points\n", "pred_dict_gpflow = gpflow_gpr.predict(X_grid[:,None])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** In ``GPflowGPRModel``, the likelihood variance is initialised with the argument ``noise_variance`` instead of ``likelihood_variance`` as in ``sklearnGPRModel``. Note also that since likelihood variance is a trainable parameter in ``GPflowGPRModel``, we pass an additional argument ``fixed_params = ['likelihood_variance']`` to the ``optimise_parameters()`` method to freeze the assigned likelhood variance value. For more details, see the API references for both models.\n", "\n", "We see that training time is much shorter on a GPU than on CPU, which is where most of the computation takes place in the enitre GP workflow. Hence, when we have a large dataset, it is advantageous to use GPUs over CPUs.\n", "\n", "We can also check that the results of the two predictions are near identical:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Extract mean and variance for the sklearn prediction\n", "f_mean_sklearn = pred_dict_sklearn['f*']\n", "f_var_sklearn = pred_dict_sklearn['f*_var']\n", "f_std_sklearn = np.sqrt(f_var_sklearn)\n", "\n", "# Extract mean and variance for the gpflow prediction\n", "f_mean_gpflow = pred_dict_gpflow['f*']\n", "f_var_gpflow = pred_dict_gpflow['f*_var']\n", "f_std_gpflow = np.sqrt(f_var_gpflow)\n", "\n", "# Plot results\n", "plt.plot(X_grid, f_truth, 'k', zorder=0, label='Ground truth')\n", "plt.plot(X_grid, f_mean_sklearn, color='C0', zorder=1, label='sklearn Prediction')\n", "plt.fill_between(X_grid, f_mean_sklearn-1.96*f_std_sklearn, f_mean_sklearn+1.96*f_std_sklearn, color='C0', alpha=0.6)\n", "plt.plot(X_grid, f_mean_gpflow, color='C1', zorder=1, label='gpflow Prediction')\n", "plt.fill_between(X_grid, f_mean_gpflow-1.96*f_std_gpflow, f_mean_gpflow+1.96*f_std_gpflow, color='C1', alpha=0.2)\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.17 ('gpsat2')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "42c89ee418f45ab16d4cd7d85b9f5fd46783f67990f590db7ef8d9e48f3f848d" } } }, "nbformat": 4, "nbformat_minor": 2 }