From cc76120a9ae238256d4d084f97003c878fa1cecf Mon Sep 17 00:00:00 2001 From: Wei Ji <23487320+weiji14@users.noreply.github.com> Date: Fri, 13 Oct 2023 17:31:24 +1300 Subject: [PATCH] :busts_in_silhouette: Compare data loading between kvikIO and Zarr engine (#7) * :necktie: Get mean time by averaging over ten epochs Benchmark results between the Zarr and kvikIO engine were too close for one epoch, so looping over 10 epochs and reporting the average instead. Not printing the MSE Loss anymore to declutter the console output. * :heavy_plus_sign: Add ipywidgets Jupyter Interactive Widgets! Repo at https://github.com/jupyter-widgets/ipywidgets * :recycle: Use tqdm.auto to also work in notebooks Will be reusing some of this code in a Jupyter Notebook, so refactoring to use tqdm.auto instead of standard tqdm. * :loud_sound: Report median time and standard deviation across epochs Save the time taken to complete each epoch, and compute the median, mean and standard deviation across all epochs. Needed because the time to process one epoch can vary by a few seconds across the ten epochs depending on various factors (e.g. caching), so computing the average time as total_time / num_epochs can lead to misleading results. Also updated main README.md to say be more specific about the reported total/median/mean/std benchmark times and the size of the ERA5 subset dataset. * :busts_in_silhouette: Compare data loading between kvikIO and Zarr engine Reporting the actual numbers on which is faster - kvikIO or Zarr! Reusing some code from 1_benchmark_kvikIOzarr.py, but now the total/median/mean/std times can be displayed. Final cell calculates the speedup of kvikIO to be ~20% over Zarr, but note that this speedup can actually fluctuate depending on lots of factors (have seen values from 10%-30% over multiple runs). * :heavy_plus_sign: Add seaborn Statistical data visualization in Python! * :dizzy: Plot bar graph showing time taken between kvikio and zarr engine A bar plot (with error bars) to visually compare kvikio (with GPUDirect Storage) against the zarr (no GPUDirect Storage) xarray backend engines in terms of data loading speed. Speedup results still fluctuates between runs, but are mostly around the 20% mark. Also did some slight refactoring to use pandas instead of numpy for the mean/median/std calculations. Using ddof=1 for the standard deviation. * :bug: Change barplot estimator to median instead of mean Seaborn plots the mean value by default, but changing to median instead. The kvikIO engine is now reported as 35% faster than the Zarr engine. * :speech_balloon: Report as percentage less time, not speedup Speed is equal to Distance (or epochs) over time. It makes more sense to report 'less time' (absolute measure) instead of 'faster speed' (inverse measure), so fixing the formulation. Previous calculation of speedup may actually have been incorrect? --- 1_benchmark_kvikIOzarr.py | 41 +- 2_compare_results.ipynb | 639 ++++++++++++++++++++++++++ README.md | 27 +- conda-lock.yml | 942 ++++++++++++++++++++++++-------------- environment.yml | 4 +- 5 files changed, 1278 insertions(+), 375 deletions(-) create mode 100644 2_compare_results.ipynb diff --git a/1_benchmark_kvikIOzarr.py b/1_benchmark_kvikIOzarr.py index bf9ae1d..bfffe54 100644 --- a/1_benchmark_kvikIOzarr.py +++ b/1_benchmark_kvikIOzarr.py @@ -10,12 +10,13 @@ import cupy import lightning as L +import numpy as np import torch import torchdata import torchdata.dataloader2 -import tqdm import xarray as xr import zen3geo +from tqdm.auto import tqdm, trange # %% @@ -167,16 +168,30 @@ def train_dataloader(self) -> torchdata.dataloader2.DataLoader2: datamodule.setup() train_dataloader = datamodule.train_dataloader() - # Start timing - tic = time.perf_counter() - # Training loop - for i, batch in tqdm.tqdm(iterable=enumerate(train_dataloader), total=23): - input, target, metadata = batch - # Compute Mean Squared Error loss between t=0 and t=1, just for fun - loss: torch.Tensor = torch.functional.F.mse_loss(input=input, target=target) - print(f"Batch {i}, MSE Loss: {loss}") - - # Stop timing - toc = time.perf_counter() - print(f"Total: {toc - tic:0.4f} seconds") + num_epochs: int = 10 + epoch_timings: list = [] + for epoch in trange(num_epochs): + # Start timing + tic: float = time.perf_counter() + + # Mini-batch processing + for i, batch in tqdm(iterable=enumerate(train_dataloader), total=23): + input, target, metadata = batch + # Compute Mean Squared Error loss between t=0 and t=1, just for fun + loss: torch.Tensor = torch.functional.F.mse_loss(input=input, target=target) + # print(f"Batch {i}, MSE Loss: {loss}") + + # Stop timing + toc: float = time.perf_counter() + epoch_timings.append(toc - tic) + + total_time: float = np.sum(a=epoch_timings) + median_time: float = np.median(a=epoch_timings) + mean_time: float = np.mean(a=epoch_timings) + std_time: float = np.std(a=epoch_timings, ddof=1) + print( + f"Total: {total_time:0.4f} seconds, " + f"Median: {median_time:0.4f} seconds/epoch, " + f"Mean: {mean_time:0.4f} ± {std_time:0.4f} seconds/epoch" + ) diff --git a/2_compare_results.ipynb b/2_compare_results.ipynb new file mode 100644 index 0000000..dd2fb42 --- /dev/null +++ b/2_compare_results.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "89613f27-a847-4fbb-b3c1-d7dea25ecbd4", + "metadata": {}, + "source": [ + "# Comparing data loading speeds with `kvikIO` and `Zarr` engine\n", + "\n", + "Benchmark details:\n", + "- Loading a 1 year subset of the WeatherBench2 (~18.2GB)\n", + "- Calculate median time to load entire dataset in 23 mini-batches (of batch size 32),\n", + " across 10 epochs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b553b3d2-71ac-44e7-b6e4-da564bfdaf5e", + "metadata": {}, + "outputs": [], + "source": [ + "import importlib\n", + "import time\n", + "\n", + "import lightning as L\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import torch\n", + "from tqdm.auto import tqdm, trange\n", + "\n", + "module = importlib.import_module(name=\"1_benchmark_kvikIOzarr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53799aa7-3da5-49d3-a152-cc07cf82fedd", + "metadata": {}, + "outputs": [], + "source": [ + "# Empty dataframe to store results\n", + "df = pd.DataFrame()\n", + "# Optimize torch performance\n", + "torch.set_float32_matmul_precision(precision=\"medium\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4b36c1a7-8dd3-439a-9689-533f1eaa5960", + "metadata": {}, + "outputs": [], + "source": [ + "# Training loop data loading function\n", + "def training_loop(train_dataloader) -> list[float]:\n", + " epoch_timings: list = []\n", + " for epoch in trange(10):\n", + " # Start timing\n", + " tic: float = time.perf_counter()\n", + "\n", + " # Mini-batch processing\n", + " for i, batch in tqdm(iterable=enumerate(train_dataloader), total=23):\n", + " input, target, metadata = batch\n", + " # Compute Mean Squared Error loss between t=0 and t=1, just for fun\n", + " loss: torch.Tensor = torch.functional.F.mse_loss(input=input, target=target)\n", + " # print(f\"Batch {i}, MSE Loss: {loss}\")\n", + "\n", + " # Stop timing\n", + " toc: float = time.perf_counter()\n", + " epoch_timings.append(toc - tic)\n", + " return epoch_timings" + ] + }, + { + "cell_type": "markdown", + "id": "01793895-d093-4098-b793-fd333ed0e31c", + "metadata": {}, + "source": [ + "## kvikIO engine" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "867ada60-17cf-4fec-84c3-55ef6a727f79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data using kvikio engine\n" + ] + } + ], + "source": [ + "# Setup data\n", + "datamodule: L.LightningDataModule = module.WeatherBench2DataModule(engine=\"kvikio\")\n", + "datamodule.setup()\n", + "train_dataloader = datamodule.train_dataloader()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f1209931-6bf7-4543-bc94-59e7382bb2ae", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "375fcb11738d4528ae53edb29451cc59", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_theme(context=\"talk\", palette=[\"#7400ff\", \"#e01073\"])\n", + "ax = sns.barplot(data=df, estimator=\"median\")\n", + "for container in ax.containers:\n", + " ax.bar_label(container=container, fontsize=11, fmt=\"%.1fs\", label_type=\"center\")\n", + "ax.set_ylabel(ylabel=\"Median data load time per epoch\\n ◀ seconds, lower is better\")\n", + "ax.set_xlabel(\n", + " xlabel=\" (with GDS) (without GDS) \\n\\n xarray backend engine\"\n", + ")\n", + "ax.set_title(label=\"Reading ERA5 data with/without GPUDirect Storage\")\n", + "fig = ax.get_figure()\n", + "fig.savefig(fname=\"figures/compare_kvikio_zarr.svg\", bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e91222bf-8619-4388-9f7d-5cd9609849b0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "foss4g2023oceania", + "language": "python", + "name": "foss4g2023oceania" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index d963eb1..d73ed07 100644 --- a/README.md +++ b/README.md @@ -112,18 +112,33 @@ To create a subset of the WeatherBench2 Zarr dataset, run: python 0_weatherbench2zarr.py -This will save a one year subset of the WeatherBench2 ERA5 dataset to your -local disk. It will include data at pressure level 500hPa, with the variables -'geopotential', 'u_component_of_wind', and 'v_component_of_wind' only. +This will save a one year subset of the WeatherBench2 ERA5 dataset at 6 hourly +resolution to your local disk (total size is about 18.2GB). It will include +data at pressure level 500hPa, with the variables 'geopotential', +'u_component_of_wind', and 'v_component_of_wind' only. To run the benchmark experiment loading with the kvikIO engine, run: python 1_benchmark_kvikIOzarr.py This will print out a progress bar showing the ERA5 data being loaded in -mini-batches (simulating a neural network training loop), and a total count -for the time taken to finish. One 'epoch' should take under 15 seconds on an -Ampere generation (e.g. RTX A2000) NVIDIA GPU. +mini-batches (simulating a neural network training loop). One 'epoch' should +take under 15 seconds on an Ampere generation (e.g. RTX A2000) NVIDIA GPU. A +total of ten epochs will be ran, and the total time taken will be reported, as +well as the median/mean/standard deviation time taken per epoch. + +To compare the benchmark results between the `kvikio` and `zarr` engines, do +the following: + +1. Run `jupyter lab` to launch a JupyterLab session +2. In your browser, open the `2_compare_results.ipynb` notebook in JupyterLab +3. Run all the cells in the notebook + +The time to load the ERA5 subset data using the `kvikio` and `zarr` engines +will be printed out. There will also be a summary report of the relative +time difference between the CPU-based `zarr` and GPU-based `kvikio` engine, and +bar plots of the absolute time taken for each backend engine. + # References diff --git a/conda-lock.yml b/conda-lock.yml index 63a13c1..3871bb1 100644 --- a/conda-lock.yml +++ b/conda-lock.yml @@ -13,7 +13,7 @@ version: 1 metadata: content_hash: - linux-64: fe13a3f405e4bd51f9717d572b3c7654bc42420cd40117093dc7c261013272f4 + linux-64: b4eeffa650351aa699069121cde4a69479ddeef3c5ef7d99fe41bb8e12eaf351 channels: - url: conda-forge used_env_vars: [] @@ -112,6 +112,17 @@ package: sha256: f6cc89d887555912d6c61b295d398cff9ec982a3417d38025c45d5dd9b9e79cd category: main optional: false +- name: libboost-headers + version: 1.82.0 + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/linux-64/libboost-headers-1.82.0-ha770c72_6.conda + hash: + md5: a943dcb8fd22cf23ce901ac84f6538c2 + sha256: c996950b85808115ea833e577a0af2969dbb0378c299560c2b945401a7770823 + category: main + optional: false - 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cupy~=12.2.0 - gcsfs~=2023.9.2 - gsutil~=5.26 - - jupyterlab~=4.0.6 + - ipywidgets~=8.1.1 + - jupyterlab~=4.0.7 - rapidsai::kvikio~=23.06.00 - lightning~=2.0.9.post0 - pip~=23.2.1 - pynvml~=11.5.0 - python~=3.10 + - seaborn~=0.13.0 - torchdata~=0.6.1 - xarray~=2023.9.0 - xbatcher~=0.3.0