From 4824b08dae4de2cbc24064f8feb396ae4f395de2 Mon Sep 17 00:00:00 2001
From: Tim Bauerle <tim.bauerle@rwth-aachen.de>
Date: Tue, 27 Aug 2024 15:04:16 +0200
Subject: [PATCH] Updated result generation notebook
---
results/result_generation.ipynb | 95 ++++++++++++++++-----------------
1 file changed, 47 insertions(+), 48 deletions(-)
diff --git a/results/result_generation.ipynb b/results/result_generation.ipynb
index 5c3ed11..fe4e16d 100644
--- a/results/result_generation.ipynb
+++ b/results/result_generation.ipynb
@@ -13,31 +13,56 @@
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
"source": [
- "# Downloading the dataframes (takes time)"
+ "df_base_dir = \"./dataframes\"\n",
+ "projects_with_model = [\n",
+ " (\"5_devices_unlimited_new\", \"resnet110\"),\n",
+ " (\"50_devices_unlimited_new\", \"resnet110\"),\n",
+ " (\"controller_comparison\", \"resnet110\"),\n",
+ " (\"controller_comparison_het_bat\", \"resnet110\"),\n",
+ " (\"controller_comparison_homogeneous\", \"resnet110\")\n",
+ "]"
],
"metadata": {
"collapsed": false
},
- "id": "3e72d0fe76a55e56"
+ "id": "5b81c8c9ba4b483d"
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
- "df_base_dir = \"./dataframes\"\n",
- "projects_with_model = [\n",
- " (\"5_devices_unlimited_new\", \"resnet110\"),\n",
- " (\"50_devices_unlimited_new\", \"resnet110\"),\n",
- " (\"controller_comparison\", \"resnet110\")\n",
- "]"
+ "strategy_autoencoder = {\n",
+ " \"psl_sequential__\": False,\n",
+ " \"fed___\": False,\n",
+ " \"split___\": False,\n",
+ " \"swarm_sequential__\": False,\n",
+ " \"swarm_max_battery__\": False,\n",
+ " \"swarm_smart__\": False,\n",
+ " \"psl_sequential_static_at_resnet_decoderpth\": True,\n",
+ " \"psl_sequential__resnet_decoderpth\": True,\n",
+ " \"psl_sequential_static_at_\": False,\n",
+ "}\n",
+ "strategies = list(strategy_autoencoder.keys())"
],
"metadata": {
"collapsed": false
},
- "id": "5b81c8c9ba4b483d"
+ "id": "26e70757d4650fc1"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Downloading the dataframes (takes time)"
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "3e72d0fe76a55e56"
},
{
"cell_type": "code",
@@ -45,29 +70,7 @@
"outputs": [],
"source": [
"for project_name, _ in projects_with_model:\n",
- " save_dataframes(project_name=project_name, strategies=[\n",
- " #\"swarm_seq\",\n",
- " #\"fed\",\n",
- " #\"swarm_max\",\n",
- " #\"swarm_rand\",\n",
- " #\"swarm_smart\",\n",
- " #\"split\",\n",
- " #\"psl_rand_\",\n",
- " #\"psl_sequential_\",\n",
- " #\"psl_max_batteries_\",\n",
- " #\"swarm_rand_\",\n",
- " #\"swarm_sequential_\",\n",
- " #\"swarm_max_batteries_\",\n",
- " \"psl_sequential__\",\n",
- " \"fed___\",\n",
- " \"split___\",\n",
- " \"swarm_sequential__\",\n",
- " \"swarm_max_battery__\",\n",
- " \"swarm_smart__\",\n",
- " \"psl_sequential_static_at_resnet_decoderpth\",\n",
- " \"psl_sequential__resnet_decoderpth\",\n",
- " \"psl_sequential_static_at_\",\n",
- " ])"
+ " save_dataframes(project_name=project_name, strategies=strategies)"
],
"metadata": {
"collapsed": false
@@ -90,27 +93,23 @@
"outputs": [],
"source": [
"# Required for total number of FLOPs computation\n",
+ "# plain = forward FLOPs without AE = 1/2 backward FLOPs with or without AE (number of backward FLOPs equal with and without AE as AE is skipped during BP)\n",
+ "# ae = forward FLOPs with AE\n",
"model_flops = {\n",
- " \"resnet20\": 41498880,\n",
- " \"resnet20_ae\": 45758720,\n",
- " \"resnet110\": 258136320,\n",
- " \"resnet110_ae\": 262396160,\n",
- " \"tcn\": 27240000,\n",
- " \"simple_conv\": 16621560\n",
+ " \"resnet20\": {\"plain\": 41498880, \"ae\": 45758720},\n",
+ " \"resnet110\": {\"plain\": 258136320, \"ae\": 262396160},\n",
+ " \"tcn\": {\"plain\": 27240000, \"ae\": 27240000}, # no AE implemented yet\n",
+ " \"simple_conv\": {\"plain\": 16621560, \"ae\": 16621560}, # no AE implemented yet\n",
"}\n",
"\n",
"client_model_flops = {\n",
- " \"resnet20\": 15171584,\n",
- " \"resnet20_ae\": 19005440,\n",
- " \"resnet110\": 88408064,\n",
- " \"resnet110_ae\": 92241920,\n",
+ " \"resnet20\": {\"plain\": 15171584, \"ae\": 19005440},\n",
+ " \"resnet110\": {\"plain\": 88408064, \"ae\": 92241920},\n",
"}\n",
"\n",
"server_model_flops = {\n",
- " \"resnet20\": 26327296,\n",
- " \"resnet20_ae\": 26753280,\n",
- " \"resnet110\": 169728256,\n",
- " \"resnet110_ae\": 170154240,\n",
+ " \"resnet20\": {\"plain\": 26327296, \"ae\": 26753280},\n",
+ " \"resnet110\": {\"plain\": 169728256, \"ae\": 170154240},\n",
"}\n",
"experiment_batch_size = 64"
],
@@ -144,7 +143,7 @@
" dataframes = load_dataframes(proj_name, df_base_dir)\n",
" print(\" generating metrics\")\n",
" generate_metric_files(dataframes, proj_name, model_flops[model_name], client_model_flops[model_name],\n",
- " # TODO distinguish AE\n",
+ " strategy_autoencoder_mapping=strategy_autoencoder,\n",
" base_path=metrics_base_path, batch_size=experiment_batch_size)\n",
" print(\" generating plots\")\n",
" generate_plots(dataframes, proj_name)"
--
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