{
"cells": [
{
"cell_type": "markdown",
"id": "30c7f92a",
"metadata": {},
"source": [
"# Web API\n",
"\n",
"This notebook is a tutorial of the API used for submitting simulations to our servers."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e3db6278",
"metadata": {
"execution": {
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"shell.execute_reply": "2023-02-03T02:11:44.418197Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"[20:11:44] WARNING This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on __init__.py:103\n",
" PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on, \n",
" please use 'pip install tidy3d' instead. \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[20:11:44]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on \u001b]8;id=882539;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=269219;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n",
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"\u001b[2;36m \u001b[0m please use \u001b[32m'pip install tidy3d'\u001b[0m instead. \u001b[2m \u001b[0m\n"
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"metadata": {},
"output_type": "display_data"
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"data": {
"text/html": [
" INFO Using client version: 1.9.0rc1 __init__.py:121\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1 \u001b]8;id=181335;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=30720;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n"
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],
"source": [
"import tidy3d as td"
]
},
{
"cell_type": "markdown",
"id": "33ed2fc9",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Let's set up a simple simulation to get started."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0f48e0d0",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# set up parameters of simulation\n",
"dl = 0.05\n",
"pml = td.PML()\n",
"sim_size = [4, 4, 4]\n",
"freq0 = 3e14\n",
"fwidth = 1e13\n",
"run_time = 1 / fwidth\n",
"\n",
"# create structure\n",
"dielectric = td.Medium.from_nk(n=2, k=0, freq=freq0)\n",
"square = td.Structure(\n",
" geometry=td.Box(center=[0, 0, 0], size=[1.5, 1.5, 1.5]), medium=dielectric\n",
")\n",
"\n",
"# create source\n",
"source = td.UniformCurrentSource(\n",
" center=(-1.5, 0, 0),\n",
" size=(0, 0.4, 0.4),\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" polarization=\"Ex\",\n",
")\n",
"\n",
"# create monitor\n",
"monitor = td.FieldMonitor(\n",
" fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
" center=(0, 0, 0),\n",
" size=(td.inf, td.inf, 0),\n",
" freqs=[freq0],\n",
" name=\"field\",\n",
")\n",
"\n",
"# Initialize simulation\n",
"sim = td.Simulation(\n",
" size=sim_size,\n",
" grid_spec=td.GridSpec.uniform(dl),\n",
" structures=[square],\n",
" sources=[source],\n",
" monitors=[monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(boundary=pml),\n",
")\n"
]
},
{
"cell_type": "markdown",
"id": "31a360a5",
"metadata": {},
"source": [
"## Running simulation manually\n",
"\n",
"For the most control, you can run the simulation through the Tidy3D web API.\n",
"Each simulation running on the server is identified by a `task_id`, which must be specified in the web API.\n",
"Let's walk through submitting a simulation this way."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "00aa7bf9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-03T02:11:44.425264Z",
"iopub.status.busy": "2023-02-03T02:11:44.425169Z",
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"shell.execute_reply": "2023-02-03T02:12:10.149610Z"
}
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"outputs": [
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"\n"
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],
"source": [
"import tidy3d.web as web\n",
"\n",
"# upload the simulation to our servers\n",
"task_id = web.upload(sim, task_name=\"webAPI\")\n",
"\n",
"# start the simulation running\n",
"web.start(task_id)\n",
"\n",
"# monitor the simulation, dont move on to next line until completed.\n",
"web.monitor(task_id)\n",
"\n",
"# download the results and load into a simulation data object for plotting, post processing etc.\n",
"sim_data = web.load(task_id, path=\"data/sim.hdf5\")\n"
]
},
{
"cell_type": "markdown",
"id": "78cb3ca3",
"metadata": {},
"source": [
"While we broke down each of the individual steps above, one can also perform the entire process in one line by calling the [web.run()](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.webapi.run.html#tidy3d.web.webapi.run) function as follows.\n",
"\n",
"```python\n",
"sim_data = web.run(sim, task_name='webAPI', path='data/sim.hdf5')\n",
"```\n",
"\n",
"(We won't run it again in this notebook to save time).\n",
"\n",
"Sometimes this is more convenient, but other times it can be helpful to have the steps broken down one by one, for example if the simulation is long, you may want to [web.start](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.webapi.start.html) and then exit the session and load the results at a later time using [web.load](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.webapi.load.html#tidy3d.web.webapi.load).\n"
]
},
{
"cell_type": "markdown",
"id": "01806b3b",
"metadata": {},
"source": [
"## Job Container\n",
"\n",
"The [web.Job](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Job.html) interface provides a more convenient way to manage single simuations, mainly because it eliminates the need for keeping track of the `task_id` and original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.Simulation.html).\n",
"\n",
"While [Job](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Job.html) has methods with names identical to the web API functions above, which give some more granular control, it is often most convenient to call [.run()] so we will do that now."
]
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"source": [
"# set the logging level to warning to reduce downloading messages.\n",
"td.config.logging_level = \"warning\"\n",
"\n",
"# initializes job, puts task on server (but doesnt run it)\n",
"job = web.Job(simulation=sim, task_name=\"job\")\n",
"\n",
"# start job, monitor, and load results\n",
"sim_data = job.run(path=\"data/sim.hdf5\")\n"
]
},
{
"cell_type": "markdown",
"id": "ebceab07",
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"source": [
"Another convenient thing about [Job](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Job.html) objects is that they can be saved and loaded just like other Tidy3d components.\n",
"\n",
"This is convenient if you want to save and load up the results of a job that has already finished, without needing to know the `task_id`."
]
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"source": [
"# saves the job metadata to a single file\n",
"job.to_file(\"data/job.json\")\n",
"\n",
"# can exit session, break here, or continue in new session.\n",
"\n",
"# load the job metadata from file\n",
"job_loaded = web.Job.from_file(\"data/job.json\")\n",
"\n",
"# download the data from the server and load it into a SimulationData object.\n",
"sim_data = job_loaded.load(path=\"data/sim.hdf5\")\n"
]
},
{
"cell_type": "markdown",
"id": "47ef716b",
"metadata": {},
"source": [
"## Batch Processing\n",
"\n",
"Commonly one needs to submit a batch of Simulations.\n",
"One way to approach this using the web API is to upload, start, monitor, and load a series of tasks one by one, but this is clumsy and you can lose your data if the session gets interrupted.\n",
"\n",
"A better way is to use the built-in [Batch](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Batch.html) object.\n",
"The batch object is like a [Job](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.Job.html), but stores task metadata for a series of simulations."
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"source": [
"# make a dictionary of {task name : simulation} for demonstration\n",
"sims = {f\"sim_{i}\": sim for i in range(3)}\n",
"\n",
"# initialize a batch and run them all\n",
"batch = web.Batch(simulations=sims)\n",
"\n",
"# run the batch and store all of the data in the `data/` dir.\n",
"batch_results = batch.run(path_dir=\"data\")\n"
]
},
{
"cell_type": "markdown",
"id": "0ae64033",
"metadata": {},
"source": [
"When the batch is completed, the output is not a [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.SimulationData.html) but rather a [BatchData](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.BatchData.html). The data within this [BatchData](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.web.container.BatchData.html) object can either be indexed directly `batch_results[task_name]` or can be looped through `batch_results.items()` to get the [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.SimulationData.html) for each task.\n",
"\n",
"This was chosen to reduce the memory strain from loading all [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/_autosummary/tidy3d.SimulationData.html) objects at once.\n",
"\n",
"Alternatively, the batch can be looped through (several times) using the `.items()` method, similar to a dictionary."
]
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"[20:13:30] WARNING Simulation final field decay value of 0.33 is greater than the simulation shutoff webapi.py:421\n",
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