{
"cells": [
{
"cell_type": "markdown",
"id": "30c7f92a",
"metadata": {},
"source": [
"# Running simulations through the cloud \n",
"\n",
"This notebook is a tutorial of the API used for submitting simulations to our servers.\n"
]
},
{
"cell_type": "code",
"id": "e3db6278",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:01:54.140100Z",
"iopub.status.busy": "2023-08-19T02:01:54.139501Z",
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"end_time": "2025-10-29T14:10:43.465631Z",
"start_time": "2025-10-29T14:10:41.995324Z"
}
},
"source": [
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"\n",
"# set logging level to ERROR because we'll only running simulations to demonstrate the web API, we don't care about the results.\n",
"td.config.logging_level = \"ERROR\""
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m15:10:42 CET\u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: Using canonical configuration directory at \u001B[0m\n",
"\u001B[2;36m \u001B[0m\u001B[32m'/home/marco/.config/tidy3d'\u001B[0m\u001B[31m. Found legacy directory at \u001B[0m\n",
"\u001B[2;36m \u001B[0m\u001B[32m'~/.tidy3d'\u001B[0m\u001B[31m, which will be ignored. Remove it manually or run \u001B[0m\n",
"\u001B[2;36m \u001B[0m\u001B[32m'tidy3d config migrate --delete-legacy'\u001B[0m\u001B[31m to clean up. \u001B[0m\n"
],
"text/html": [
"15:10:42 CET WARNING: Using canonical configuration directory at \n",
" '/home/marco/.config/tidy3d'. Found legacy directory at \n",
" '~/.tidy3d', which will be ignored. Remove it manually or run \n",
" 'tidy3d config migrate --delete-legacy' to clean up. \n",
"\n"
]
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{
"cell_type": "markdown",
"id": "33ed2fc9",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Let's set up a simple simulation to get started."
]
},
{
"cell_type": "code",
"id": "0f48e0d0",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:01:55.544332Z",
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"source": [
"# whether to print output in web functions, note: if False (default) they will all run silently\n",
"verbose = True\n",
"\n",
"# 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(geometry=td.Box(center=[0, 0, 0], size=[1.5, 1.5, 1.5]), medium=dielectric)\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",
")"
],
"outputs": [],
"execution_count": 2
},
{
"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",
"id": "00aa7bf9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:01:55.568962Z",
"iopub.status.busy": "2023-08-19T02:01:55.568697Z",
"iopub.status.idle": "2023-08-19T02:02:20.757644Z",
"shell.execute_reply": "2023-08-19T02:02:20.756982Z"
},
"ExecuteTime": {
"end_time": "2025-10-29T14:15:06.327931Z",
"start_time": "2025-10-29T14:10:43.523307Z"
}
},
"source": [
"# upload the simulation to our servers\n",
"task_id = web.upload(sim, task_name=\"webAPI\", verbose=verbose)\n",
"\n",
"# start the simulation running\n",
"web.start(task_id)\n",
"\n",
"# monitor the simulation, don't move on to next line until completed.\n",
"web.monitor(task_id, verbose=verbose)\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\", verbose=verbose)"
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m15:10:45 CET\u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'webAPI'\u001B[0m with resource_id \n",
"\u001B[2;36m \u001B[0m\u001B[32m'fdve-8a9d34dd-4033-4cd9-9315-184865d379f2'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m. \n"
],
"text/html": [
"15:10:45 CET Created task 'webAPI' with resource_id \n",
" 'fdve-8a9d34dd-4033-4cd9-9315-184865d379f2' and task_type 'FDTD'. \n",
"\n"
]
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"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at \n",
"\u001B[2;36m \u001B[0m\u001B]8;id=811009;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=292393;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=811009;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=246506;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=811009;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32m-8a9d34dd-403\u001B[0m\u001B]8;;\u001B\\\n",
"\u001B[2;36m \u001B[0m\u001B]8;id=811009;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[32m3-4cd9-9315-184865d379f2'\u001B[0m\u001B]8;;\u001B\\. \n"
],
"text/html": [
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-403\n",
" 3-4cd9-9315-184865d379f2'. \n",
"\n"
]
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"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mTask folder: \u001B]8;id=154002;https://tidy3d.simulation.cloud/folders/folder-df61810d-cad6-4474-8ea9-e4f00d5dfcb0\u001B\\\u001B[32m'default'\u001B[0m\u001B]8;;\u001B\\. \n"
],
"text/html": [
" Task folder: 'default'. \n",
"\n"
]
},
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{
"data": {
"text/plain": [
"Output()"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "2460efac0b7e4fa7b883c8e2e441dcb1"
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"text/plain": [],
"text/html": [
"\n"
]
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"metadata": {},
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{
"data": {
"text/plain": [
"\u001B[2;36m15:10:48 CET\u001B[0m\u001B[2;36m \u001B[0mEstimated FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Minimum cost depends on task \n",
"\u001B[2;36m \u001B[0mexecution details. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed \n",
"\u001B[2;36m \u001B[0mFlexCredit cost after a simulation run. \n"
],
"text/html": [
"15:10:48 CET Estimated FlexCredit cost: 0.025. Minimum cost depends on task \n",
" execution details. Use 'web.real_cost(task_id)' to get the billed \n",
" FlexCredit cost after a simulation run. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
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{
"data": {
"text/plain": [
"\u001B[2;36m15:10:50 CET\u001B[0m\u001B[2;36m \u001B[0mstatus = queued \n"
],
"text/html": [
"15:10:50 CET status = queued \n",
"\n"
]
},
"metadata": {},
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}
},
{
"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or \n",
"\u001B[2;36m \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI. \n",
"\u001B[2;36m \u001B[0mTerminating the Python script will not stop the job running on the \n",
"\u001B[2;36m \u001B[0mcloud. \n"
],
"text/html": [
" To cancel the simulation, use 'web.abort(task_id)' or \n",
" 'web.delete(task_id)' or abort/delete the task in the web UI. \n",
" Terminating the Python script will not stop the job running on the \n",
" cloud. \n",
"\n"
]
},
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"data": {
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"Output()"
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"version_minor": 0,
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"data": {
"text/plain": [
"\u001B[2;36m15:14:57 CET\u001B[0m\u001B[2;36m \u001B[0mstatus = success \n"
],
"text/html": [
"15:14:57 CET status = success \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [],
"text/html": [
"\n"
]
},
"metadata": {},
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"display_id": null
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},
{
"data": {
"text/plain": [
"\u001B[2;36m15:14:59 CET\u001B[0m\u001B[2;36m \u001B[0mstarting up solver \n"
],
"text/html": [
"15:14:59 CET starting up solver \n",
"\n"
]
},
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"jetTransient": {
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{
"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mrunning solver \n"
],
"text/html": [
" running solver \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
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"data": {
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"Output()"
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"text/html": [
"\n"
]
},
"metadata": {},
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},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:00 CET\u001B[0m\u001B[2;36m \u001B[0mstatus = success \n"
],
"text/html": [
"15:15:00 CET status = success \n",
"\n"
]
},
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"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mView simulation result at \n",
"\u001B[2;36m \u001B[0m\u001B]8;id=673546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=12410;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=673546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=351622;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=673546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34m-8a9d34dd-403\u001B[0m\u001B]8;;\u001B\\\n",
"\u001B[2;36m \u001B[0m\u001B]8;id=673546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-4033-4cd9-9315-184865d379f2\u001B\\\u001B[4;34m3-4cd9-9315-184865d379f2'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m \n"
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"text/html": [
" View simulation result at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-8a9d34dd-403\n",
" 3-4cd9-9315-184865d379f2'. \n",
"\n"
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"text/html": [
"\n"
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"data": {
"text/plain": [
"\u001B[2;36m15:15:06 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from data/sim.hdf5 \n"
],
"text/html": [
"15:15:06 CET Loading simulation from data/sim.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
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}
}
],
"execution_count": 3
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{
"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/latest/api/_autosummary/tidy3d.web.api.run.run.html) 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/latest/api/_autosummary/tidy3d.web.api.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/latest/api/_autosummary/tidy3d.web.api.webapi.load.html).\n"
]
},
{
"cell_type": "markdown",
"id": "01806b3b",
"metadata": {},
"source": [
"## Job Container\n",
"\n",
"The [web.Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) interface provides a more convenient way to manage single simulations, mainly because it eliminates the need for keeping track of the `task_id` and original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
"\n",
"We can get the cost estimate of running the task before actually running it. This prevents us from accidentally running large jobs that we set up by mistake. The estimated cost is the maximum cost corresponding to running all the time steps."
]
},
{
"cell_type": "code",
"id": "148d4559",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:02:21.209875Z",
"iopub.status.busy": "2023-08-19T02:02:21.209738Z",
"iopub.status.idle": "2023-08-19T02:02:27.321806Z",
"shell.execute_reply": "2023-08-19T02:02:27.319557Z"
},
"ExecuteTime": {
"end_time": "2025-10-29T14:15:11.889453Z",
"start_time": "2025-10-29T14:15:06.338483Z"
}
},
"source": [
"# initializes job, puts task on server (but doesn't run it)\n",
"job = web.Job(simulation=sim, task_name=\"job\", verbose=verbose)\n",
"\n",
"# estimate the maximum cost\n",
"estimated_cost = web.estimate_cost(job.task_id)"
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'job'\u001B[0m with resource_id \n",
"\u001B[2;36m \u001B[0m\u001B[32m'fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m. \n"
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"text/html": [
" Created task 'job' with resource_id \n",
" 'fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6' and task_type 'FDTD'. \n",
"\n"
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{
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"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at \n",
"\u001B[2;36m \u001B[0m\u001B]8;id=589602;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=350894;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=589602;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=335527;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=589602;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32m-d6c5317b-6b0\u001B[0m\u001B]8;;\u001B\\\n",
"\u001B[2;36m \u001B[0m\u001B]8;id=589602;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b06-4d8c-8b0b-0cbf102494d6\u001B\\\u001B[32m6-4d8c-8b0b-0cbf102494d6'\u001B[0m\u001B]8;;\u001B\\. \n"
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"text/html": [
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d6c5317b-6b0\n",
" 6-4d8c-8b0b-0cbf102494d6'. \n",
"\n"
]
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"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mTask folder: \u001B]8;id=284749;https://tidy3d.simulation.cloud/folders/folder-df61810d-cad6-4474-8ea9-e4f00d5dfcb0\u001B\\\u001B[32m'default'\u001B[0m\u001B]8;;\u001B\\. \n"
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"text/html": [
" Task folder: 'default'. \n",
"\n"
]
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"data": {
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"Output()"
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"\n"
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"data": {
"text/plain": [
"\u001B[2;36m15:15:09 CET\u001B[0m\u001B[2;36m \u001B[0mEstimated FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Minimum cost depends on task \n",
"\u001B[2;36m \u001B[0mexecution details. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed \n",
"\u001B[2;36m \u001B[0mFlexCredit cost after a simulation run. \n"
],
"text/html": [
"15:15:09 CET Estimated FlexCredit cost: 0.025. Minimum cost depends on task \n",
" execution details. Use 'web.real_cost(task_id)' to get the billed \n",
" FlexCredit cost after a simulation run. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:11 CET\u001B[0m\u001B[2;36m \u001B[0mEstimated FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Minimum cost depends on task \n",
"\u001B[2;36m \u001B[0mexecution details. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed \n",
"\u001B[2;36m \u001B[0mFlexCredit cost after a simulation run. \n"
],
"text/html": [
"15:15:11 CET Estimated FlexCredit cost: 0.025. Minimum cost depends on task \n",
" execution details. Use 'web.real_cost(task_id)' to get the billed \n",
" FlexCredit cost after a simulation run. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 4
},
{
"cell_type": "markdown",
"id": "fc1465a5",
"metadata": {},
"source": [
"While [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.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."
]
},
{
"cell_type": "code",
"id": "e9628d1e",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:02:27.336611Z",
"iopub.status.busy": "2023-08-19T02:02:27.336386Z",
"iopub.status.idle": "2023-08-19T02:03:00.112828Z",
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"end_time": "2025-10-29T14:15:18.452896Z",
"start_time": "2025-10-29T14:15:11.937187Z"
}
},
"source": [
"# start job, monitor, and load results\n",
"sim_data = job.run(path=\"data/sim.hdf5\")"
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:15 CET\u001B[0m\u001B[2;36m \u001B[0mstatus = success \n"
],
"text/html": [
"15:15:15 CET status = success \n",
"\n"
]
},
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"data": {
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"Output()"
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"\n"
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{
"data": {
"text/plain": [
"\u001B[2;36m15:15:18 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from data/sim.hdf5 \n"
],
"text/html": [
"15:15:18 CET Loading simulation from data/sim.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 5
},
{
"cell_type": "markdown",
"id": "ebceab07",
"metadata": {},
"source": [
"Another convenient thing about [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.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`."
]
},
{
"cell_type": "code",
"id": "55808199",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:03:00.716819Z",
"iopub.status.busy": "2023-08-19T02:03:00.716560Z",
"iopub.status.idle": "2023-08-19T02:03:01.208721Z",
"shell.execute_reply": "2023-08-19T02:03:01.207857Z"
},
"ExecuteTime": {
"end_time": "2025-10-29T14:15:21.618584Z",
"start_time": "2025-10-29T14:15:18.457607Z"
}
},
"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\")"
],
"outputs": [
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fde051e3429741db86fea073e1b76795"
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"jetTransient": {
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{
"data": {
"text/plain": [],
"text/html": [
"\n"
]
},
"metadata": {},
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"jetTransient": {
"display_id": null
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},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:21 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from data/sim.hdf5 \n"
],
"text/html": [
"15:15:21 CET Loading simulation from data/sim.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 6
},
{
"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/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) object.\n",
"The batch object is like a [Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html), but stores task metadata for a series of simulations."
]
},
{
"cell_type": "code",
"id": "b40efc4f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:03:01.220541Z",
"iopub.status.busy": "2023-08-19T02:03:01.220388Z",
"iopub.status.idle": "2023-08-19T02:03:36.168303Z",
"shell.execute_reply": "2023-08-19T02:03:36.167357Z"
},
"ExecuteTime": {
"end_time": "2025-10-29T14:15:53.383391Z",
"start_time": "2025-10-29T14:15:21.624702Z"
}
},
"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, verbose=verbose)\n",
"\n",
"# run the batch and store all of the data in the `data/` dir.\n",
"batch_results = batch.run(path_dir=\"data\")"
],
"outputs": [
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "5ad4735940514044b1530e88ce152f20"
}
},
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"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [],
"text/html": [
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:26 CET\u001B[0m\u001B[2;36m \u001B[0mStarted working on Batch containing \u001B[1;36m3\u001B[0m tasks. \n"
],
"text/html": [
"15:15:26 CET Started working on Batch containing 3 tasks. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:33 CET\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.075\u001B[0m for the whole batch. \n"
],
"text/html": [
"15:15:33 CET Maximum FlexCredit cost: 0.075 for the whole batch. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mUse \u001B[32m'Batch.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get the billed FlexCredit cost after \n",
"\u001B[2;36m \u001B[0mcompletion. \n"
],
"text/html": [
" Use 'Batch.real_cost()' to get the billed FlexCredit cost after \n",
" completion. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"Output()"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ab5df97f5af148d9b080c4e892dcf9ef"
}
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:48 CET\u001B[0m\u001B[2;36m \u001B[0mBatch complete. \n"
],
"text/html": [
"15:15:48 CET Batch complete. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [],
"text/html": [
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 7
},
{
"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/latest/api/_autosummary/tidy3d.SimulationData.html) but rather a [BatchData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.BatchData.html). The data within this [BatchData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.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/latest/api/_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/latest/api/_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."
]
},
{
"cell_type": "code",
"id": "e855fe08",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:03:36.530933Z",
"iopub.status.busy": "2023-08-19T02:03:36.530695Z",
"iopub.status.idle": "2023-08-19T02:03:38.581550Z",
"shell.execute_reply": "2023-08-19T02:03:38.580933Z"
},
"tags": [],
"ExecuteTime": {
"end_time": "2025-10-29T14:16:00.136519Z",
"start_time": "2025-10-29T14:15:53.389178Z"
}
},
"source": [
"# grab the sum of intensities in the simulation one by one (to save memory)\n",
"intensities = {}\n",
"for task_name, sim_data in batch_results.items():\n",
" intensity = sim_data.get_intensity(\"field\").sel(f=freq0)\n",
" sum_intensity = float(intensity.sum((\"x\", \"y\")).values[0])\n",
" intensities[task_name] = sum_intensity\n",
"\n",
"print(intensities)"
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:55 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from \n",
"\u001B[2;36m \u001B[0mdata/fdve-\u001B[93mef73aa96-65f9-4793-9b1d-26ea9b39c6b2\u001B[0m.hdf5 \n"
],
"text/html": [
"15:15:55 CET Loading simulation from \n",
" data/fdve-ef73aa96-65f9-4793-9b1d-26ea9b39c6b2.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:15:56 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from \n",
"\u001B[2;36m \u001B[0mdata/fdve-\u001B[93mc229e1b2-0bb8-44df-a570-df73ab419ba8\u001B[0m.hdf5 \n"
],
"text/html": [
"15:15:56 CET Loading simulation from \n",
" data/fdve-c229e1b2-0bb8-44df-a570-df73ab419ba8.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"\u001B[2;36m15:16:00 CET\u001B[0m\u001B[2;36m \u001B[0mLoading simulation from \n",
"\u001B[2;36m \u001B[0mdata/fdve-\u001B[93m2cd0c62f-c207-4191-b057-f2a1c753aed1\u001B[0m.hdf5 \n"
],
"text/html": [
"15:16:00 CET Loading simulation from \n",
" data/fdve-2cd0c62f-c207-4191-b057-f2a1c753aed1.hdf5 \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'sim_0': 6473211.0, 'sim_1': 6473211.0, 'sim_2': 6473211.0}\n"
]
}
],
"execution_count": 8
},
{
"cell_type": "markdown",
"id": "b1e19538-d559-4ea2-b555-0df0135417ff",
"metadata": {},
"source": [
"## Simulation Batching\n",
"\n",
"Finally, one perform batch processing of several simulations in a single function call.\n",
"\n",
"For this purpose, [web.run](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.run.run.html) function can also be used for any nested combinations of dictionaries, lists or tuples.\n",
"\n",
"Here is the previous example repeated using this feature."
]
},
{
"cell_type": "code",
"id": "0a56613f-78f0-4aad-bb4f-826193a809b0",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-19T02:03:38.612831Z",
"iopub.status.busy": "2023-08-19T02:03:38.612656Z",
"iopub.status.idle": "2023-08-19T02:04:17.604868Z",
"shell.execute_reply": "2023-08-19T02:04:17.604263Z"
},
"ExecuteTime": {
"end_time": "2025-10-29T14:16:20.476211Z",
"start_time": "2025-10-29T14:16:00.142436Z"
}
},
"source": [
"batch_results_dict = web.run(sims, verbose=verbose)\n",
"# sim_list = list(sims.values())\n",
"# batch_results_list = web.run(sim_list, verbose=verbose) # this would return a plain list of SimulationData"
],
"outputs": [
{
"data": {
"text/plain": [
"\u001B[2;36m15:16:01 CET\u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'fdtd_2025-10-29_15-16-00'\u001B[0m with resource_id \n",
"\u001B[2;36m \u001B[0m\u001B[32m'fdve-0e9a1dac-0a3c-431d-af51-3e63bc484aa2'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m. \n"
],
"text/html": [
"15:16:01 CET Created task 'fdtd_2025-10-29_15-16-00' with resource_id \n",
" 'fdve-0e9a1dac-0a3c-431d-af51-3e63bc484aa2' and task_type 'FDTD'. \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
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"\u001B[2;36m \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at \n",
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"intensities = {}\n",
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" intensity = sim_data.get_intensity(\"field\").sel(f=freq0)\n",
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" intensities[task_name] = sum_intensity\n",
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"After going through this tutorial, you have learned how to run simulations with Tidy3D via the web API. If you are new to Tidy3D or the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials and [example library](https://www.flexcompute.com/tidy3d/examples/) before starting your own simulation adventure. "
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