Merge branch 'master' of github.com:monash-emu/AuTuMN

autumn/projects/sm_covid2/common_school/runner_tools.py

@@ -40,6 +40,7 @@
 
 
 DEFAULT_RUN_CONFIG = {
+    "N_CORES": 8,
     "N_CHAINS": 8,
     "N_OPTI_SEARCHES": 16,
     "OPTI_BUDGET": 10000,
@@ -326,6 +327,7 @@ def get_quantile_outputs(outputs_df, diff_outputs_df, quantiles=[.025, .25, .5,
 def run_full_analysis(
     iso3, 
     analysis="main", 
+    n_cores=8,
     opti_params={'n_searches': 8, 'search_iterations': 5000},
     mcmc_params={'draws': 30000, 'tune': 5000, 'chains': 8, 'method': 'DEMetropolisZ'},
     full_run_params={'samples': 1000, 'burn_in': 20000},
@@ -358,12 +360,12 @@ def opti_func(sample_dict):
         best_p, _ = optimise_model_fit(bcm, num_workers=n_opti_workers, search_iterations=opti_params['search_iterations'], suggested_start=sample_dict)
         return best_p
 
-    best_params = map_parallel(opti_func, sample_as_dicts, n_workers=2 * mcmc_params['chains'] / n_opti_workers)  # oversubscribing
+    best_params = map_parallel(opti_func, sample_as_dicts, n_workers=2 * n_cores / n_opti_workers)  # oversubscribing
     # Store optimal solutions
     with open(out_path / "best_params.yml", "w") as f:
         yaml.dump(best_params, f)
 
-    # Keep only n_best_retained best solutions
+    # Keep only n_chains best solutions
     loglikelihoods = [bcm.loglikelihood(**p) for p in best_params]
     ll_cutoff = sorted(loglikelihoods, reverse=True)[mcmc_params['chains'] - 1]
 
@@ -404,7 +406,7 @@ def opti_func(sample_dict):
     n_repeat_seed = 1
     init_vals = [[best_p] * n_repeat_seed for i, best_p in enumerate(retained_best_params)]     
     init_vals = [p_dict for sublist in init_vals for p_dict in sublist]  
-    idata = sample_with_pymc(bcm, initvals=init_vals, draws=mcmc_params['draws'], tune=mcmc_params['tune'], cores=mcmc_params['chains'], chains=mcmc_params['chains'], method=mcmc_params['method'])
+    idata = sample_with_pymc(bcm, initvals=init_vals, draws=mcmc_params['draws'], tune=mcmc_params['tune'], cores=n_cores, chains=mcmc_params['chains'], method=mcmc_params['method'])
     idata.to_netcdf(out_path / "idata.nc")
     make_post_mc_plots(idata, full_run_params['burn_in'], output_folder)
     if logger:

notebooks/user/rragonnet/project_specific/School_Closure/remote/remote_task_store.ipynb

@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autumn.infrastructure.remote import springboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rts = springboard.task.RemoteTaskStore(\"projects/school_project\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_name = \"2023-09-11T1023-test_multirun_main_LHS16_opt10000_mc5000n30000\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_paths = rts.glob(f\"*/{run_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mt_list = [rts.get_managed_task(run_path) for run_path in run_paths]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for mt in mt_list:\n",
+    "    print(mt.get_status())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "summer2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/user/rragonnet/project_specific/School_Closure/remote/sb_full_analysis.ipynb

@@ -2,10 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f5408fea",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\rrag0004\\.conda\\envs\\summer2\\lib\\site-packages\\summer\\runner\\vectorized_runner.py:363: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+      "  def get_strain_infection_values(\n",
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    }
+   ],
    "source": [
     "from autumn.infrastructure.remote import springboard\n",
     "from autumn.projects.sm_covid2.common_school.runner_tools import (\n",
@@ -36,24 +46,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c4aa031b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'N_CHAINS': 8,\n",
+       " 'N_OPTI_SEARCHES': 16,\n",
+       " 'OPTI_BUDGET': 10000,\n",
+       " 'METROPOLIS_TUNE': 5000,\n",
+       " 'METROPOLIS_DRAWS': 30000,\n",
+       " 'METROPOLIS_METHOD': 'DEMetropolisZ',\n",
+       " 'FULL_RUNS_SAMPLES': 1000,\n",
+       " 'BURN_IN': 20000}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "DEFAULT_RUN_CONFIG"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a5f49d54",
    "metadata": {},
    "outputs": [],
    "source": [
     "ISO3 = \"FRA\"\n",
     "ANALYSIS = \"main\"\n",
     "\n",
+    "N_CORES = DEFAULT_RUN_CONFIG['N_CORES']\n",
+    "\n",
     "N_CHAINS = DEFAULT_RUN_CONFIG['N_CHAINS']\n",
     "N_OPTI_SEARCHES = DEFAULT_RUN_CONFIG['N_OPTI_SEARCHES']\n",
     "OPTI_BUDGET = DEFAULT_RUN_CONFIG['OPTI_BUDGET']\n",
@@ -77,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "86180b5e",
    "metadata": {},
    "outputs": [],
@@ -88,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "4810f137",
    "metadata": {},
    "outputs": [],
@@ -103,6 +133,7 @@
     "    idata, uncertainty_df, diff_quantiles_df = run_full_analysis(\n",
     "        iso3,\n",
     "        analysis=analysis, \n",
+    "        n_cores=N_CORES,\n",
     "        opti_params={'n_searches': N_OPTI_SEARCHES, 'search_iterations': OPTI_BUDGET},\n",
     "        mcmc_params={'draws': METROPOLIS_DRAWS, 'tune': METROPOLIS_TUNE, 'chains': N_CHAINS, 'method': METROPOLIS_METHOD},\n",
     "        full_run_params={'samples': FULL_RUNS_SAMPLES, 'burn_in': BURN_IN},\n",
@@ -115,12 +146,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "1a1207a8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mspec = springboard.EC2MachineSpec(N_CHAINS, 4, \"compute\")\n",
+    "mspec = springboard.EC2MachineSpec(N_CORES, 4, \"compute\")\n",
     "task_kwargs = {\n",
     "    \"iso3\": ISO3,\n",
     "    \"analysis\": ANALYSIS\n",
@@ -130,12 +161,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e12adbec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'projects/school_project/FRA/2023-09-11T1117-test_4cpu_main_LHS16_opt10000_mc5000n30000'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "analysis_title = \"single_start\"\n",
+    "analysis_title = \"test_4cpu\"\n",
     "config_str = f\"_{ANALYSIS}_LHS{N_OPTI_SEARCHES}_opt{OPTI_BUDGET}_mc{METROPOLIS_TUNE}n{METROPOLIS_DRAWS}\"\n",
     "\n",
     "run_path = springboard.launch.get_autumn_project_run_path(\"school_project\", ISO3, analysis_title + config_str)\n",
@@ -144,20 +186,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "16657a60",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "SSH connection still waiting, retrying 1/5\n"
+     ]
+    }
+   ],
    "source": [
     "runner = springboard.launch.launch_synced_autumn_task(tspec, mspec, run_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "b416f02f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SUCCESS'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "runner.s3.get_status()"
    ]
@@ -195,13 +256,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "a8d31fe3",
    "metadata": {},
    "outputs": [],
    "source": [
     "download_analysis(run_path)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82728672",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {