mirror of
https://github.com/DifferentiableUniverseInitiative/JaxPM.git
synced 2025-04-17 00:20:55 +00:00
157 lines
4.1 KiB
Text
157 lines
4.1 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "22803ddc",
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"metadata": {
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"colab_type": "text",
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"id": "view-in-github"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/DifferentiableUniverseInitiative/JaxPM/blob/main/notebooks/Introduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "9Jy5BL1XiK1s",
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"metadata": {
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"id": "9Jy5BL1XiK1s"
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},
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"outputs": [],
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"source": [
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"!pip install --quiet git+https://github.com/DifferentiableUniverseInitiative/JaxPM.git\n",
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"!pip install diffrax"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c5f42bbe",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "c5f42bbe",
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"outputId": "a7841b28-5f20-4856-bd1d-f8a3572095b5"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"!salloc --account=tkc@a100 -C a100 --gres=gpu:8 --ntasks-per-node=8 --time=00:30:00 --cpus-per-task=8 --hint=nomultithread --qos=qos_gpu-dev --nodes=2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7ebdfc00",
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"metadata": {},
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"outputs": [],
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"source": [
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"!squeue -u $USER"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c014316c",
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"metadata": {},
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"outputs": [],
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"source": [
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"export JOB_ID=123456"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b7eabac5",
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"metadata": {},
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"outputs": [],
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"source": [
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"!srun --jobid=$JOB_ID -n 16 python 03-MultiHost_PM.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "472dd4bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"data = np.load(\"multihost_pm.npz\")\n",
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"initial_conditions = data['initial_conditions']\n",
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"lpt_displacements = data['lpt_displacements']\n",
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"ode_solutions = data['ode_solutions']\n",
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"solver_stats = data['solver_stats']\n",
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"print(f\"Solver stats: {solver_stats}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e012ce8",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 323
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},
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"id": "4e012ce8",
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"outputId": "75390318-8072-481f-ffb9-ec09cd71cb1d"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"shape of grid_mesh: (256, 256, 256)\n"
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]
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}
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],
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"source": [
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"from visualize import plot_fields\n",
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"\n",
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"fields = {\"Initial Conditions\" : initial_conditions , \"LPT Field\" : cic_paint(jnp.zeros(mesh_shape) ,lpt_particles)}\n",
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"for i , field in enumerate(ode_particles):\n",
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" fields[f\"field_{i}\"] = cic_paint(jnp.zeros(mesh_shape) , field)\n",
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"plot_fields(fields)"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"include_colab_link": true,
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"name": "Introduction.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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