From 58300e6bb2cc482619f10aecc2f65e9dbc735472 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Mon, 23 Sep 2019 14:00:55 +0200
Subject: [PATCH] use adaptive-scheduler
---
phase_diagram.ipynb | 85 +++++++++++++++++++++++++++------------------
1 file changed, 51 insertions(+), 34 deletions(-)
diff --git a/phase_diagram.ipynb b/phase_diagram.ipynb
index 009d5c9..1f0e82f 100644
--- a/phase_diagram.ipynb
+++ b/phase_diagram.ipynb
@@ -6,17 +6,10 @@
"metadata": {},
"outputs": [],
"source": [
+ "%%writefile learners_file.py\n",
+ "\n",
+ "import phase_diagram\n",
"import adaptive\n",
- "adaptive.notebook_extension()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import phase_diagram as funcs\n",
"\n",
"lead_pars = dict(\n",
" a=10, r1=50, r2=70, coverage_angle=135, angle=45, with_shell=True, which_lead=\"\"\n",
@@ -43,36 +36,40 @@
" r1=None,\n",
" sigma=None,\n",
" x0=None,\n",
- " **funcs.constants.__dict__\n",
+ " **phase_diagram.constants.__dict__\n",
")\n",
"\n",
"\n",
"def pf(xy, params=params, lead_pars=lead_pars):\n",
- " import phase_diagram as funcs\n",
+ " import phase_diagram \n",
"\n",
" params[\"B_x\"], params[\"mu_lead\"] = xy\n",
- " lead = funcs.make_lead(**lead_pars).finalized()\n",
- " return funcs.calculate_pfaffian(lead, params)\n",
+ " lead = phase_diagram.make_lead(**lead_pars).finalized()\n",
+ " return phase_diagram.calculate_pfaffian(lead, params)\n",
"\n",
"\n",
"def smallest_gap(xy, params=params, lead_pars=lead_pars):\n",
- " import phase_diagram as funcs\n",
+ " import phase_diagram\n",
"\n",
" params[\"B_x\"], params[\"mu_lead\"] = xy\n",
- " lead = funcs.make_lead(**lead_pars).finalized()\n",
- " pf = funcs.calculate_pfaffian(lead, params)\n",
- " gap = funcs.gap_from_modes(lead, params)\n",
- " return pf * gap"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "learner = adaptive.Learner2D(smallest_gap, bounds=[(0, 2), (0, 35)])\n",
- "# learner.load('phase_diagram.pickle')"
+ " lead = phase_diagram.make_lead(**lead_pars).finalized()\n",
+ " pf = phase_diagram.calculate_pfaffian(lead, params)\n",
+ " gap = phase_diagram.gap_from_modes(lead, params)\n",
+ " return pf * gap\n",
+ "\n",
+ "fname = 'phase_diagram_gap.pickle'\n",
+ "f = smallest_gap\n",
+ "\n",
+ "# fname = 'phase_diagram.pickle'\n",
+ "# f = pf\n",
+ "\n",
+ "loss = adaptive.learner.learnerND.curvature_loss_function()\n",
+ "learner = adaptive.LearnerND(smallest_gap, bounds=[(0, 2), (0, 35)], loss_per_simplex=loss)\n",
+ "\n",
+ "# learner.load(fname)\n",
+ "\n",
+ "learners = [learner]\n",
+ "fnames = [fname]"
]
},
{
@@ -81,7 +78,10 @@
"metadata": {},
"outputs": [],
"source": [
- "runner = adaptive.Runner(learner, goal=lambda l: l.npoints > 60000)"
+ "import adaptive\n",
+ "adaptive.notebook_extension()\n",
+ "runner = adaptive.Runner(learner, goal=lambda l: l.npoints > 60000)\n",
+ "runner.live_info()"
]
},
{
@@ -90,7 +90,8 @@
"metadata": {},
"outputs": [],
"source": [
- "runner.live_info()"
+ "%%output size=100\n",
+ "learner.plot(tri_alpha=0.4, n=None)"
]
},
{
@@ -99,8 +100,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%%output size=100\n",
- "learner.plot(tri_alpha=0.4, n=None)"
+ "learner.save(fname)"
]
},
{
@@ -109,7 +109,24 @@
"metadata": {},
"outputs": [],
"source": [
- "learner.save('phase_diagram.pickle')"
+ "import adaptive_scheduler\n",
+ "\n",
+ "def goal(learner):\n",
+ " return learner.npoints > 100_000\n",
+ "\n",
+ "scheduler = adaptive_scheduler.scheduler.PBS(\n",
+ " cores=13*10,\n",
+ " cores_per_node=10,\n",
+ ") # every learner get this many cores\n",
+ "\n",
+ "run_manager = adaptive_scheduler.server_support.RunManager(\n",
+ " scheduler=scheduler,\n",
+ " learners_file=\"learners_file.py\",\n",
+ " goal=goal,\n",
+ " log_interval=30,\n",
+ " save_interval=300,\n",
+ ")\n",
+ "run_manager.start()\n"
]
},
{
--
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