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20 changed files with 10389 additions and 0 deletions
725
tabular_classification/digits.ipynb
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725
tabular_classification/digits.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Import libraries and read data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"logging.getLogger(\"lightning.pytorch.utilities.rank_zero\").setLevel(logging.WARNING)\n",
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"logging.getLogger(\"lightning.pytorch.accelerators.cuda\").setLevel(logging.WARNING)\n",
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"from time import time\n",
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"from pandas import DataFrame\n",
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"from sklearn.datasets import load_digits\n",
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"from sklearn.model_selection import train_test_split\n",
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"from torch import cuda\n",
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"from lightning import Trainer\n",
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"from lightning.pytorch.loggers import CSVLogger\n",
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"from ideal_init.lightning_model import NNClassifier, merge_logs\n",
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"\n",
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"# Define training parameters\n",
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"BATCH_SIZE = 256\n",
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"LEARNING_RATE = 1e-1\n",
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"HIDDEN_SIZES = (10,)\n",
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"DIRECTORY = \"./\"\n",
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"EPOCHS = 10\n",
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"DATASET_NAME = \"Handwritten digits\"\n",
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"\n",
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"def experiment_data():\n",
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" X, y = load_digits(return_X_y=True)\n",
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" # Split data: 70% train, 10% validation, 20% test\n",
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" X, X_test, y, y_test = train_test_split(X, y, test_size=0.2)\n",
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" X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.125)\n",
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" return X_train, y_train, X_val, y_val, X_test, y_test"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Run experiments and measure performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# keep record of the results of all the iterations\n",
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"values=[\"IDEAL_acc_before_train\", \"IDEAL_acc_after_train\", \"IDEAL_init_time\", \"IDEAL_train_time\", \"He_acc_before_train\", \"He_acc_after_train\", \"He_init_time\", \"He_train_time\"]\n",
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"results = {value: [] for value in values}\n",
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"\n",
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"# repeat experiment multiple times\n",
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"NUM_EXPERIMENTS = 10\n",
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"for experiment in range(NUM_EXPERIMENTS):\n",
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"\n",
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" print(f\"Running experiment {experiment+1}\")\n",
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" X_train, y_train, X_val, y_val, X_test, y_test = experiment_data()\n",
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"\n",
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" # Create models\n",
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" init_model = NNClassifier(X_train, y_train, X_val, y_val, X_test, y_test, initialize=True, hidden_sizes=HIDDEN_SIZES, learning_rate=LEARNING_RATE, batch_size=BATCH_SIZE)\n",
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" no_init_model = NNClassifier(X_train, y_train, X_val, y_val, X_test, y_test, initialize=False, hidden_sizes=HIDDEN_SIZES, learning_rate=LEARNING_RATE, batch_size=BATCH_SIZE)\n",
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"\n",
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" # Create trainers\n",
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" init_trainer = Trainer(default_root_dir=DIRECTORY, accelerator=\"auto\", devices=\"auto\", max_epochs=EPOCHS, logger=CSVLogger(save_dir=DIRECTORY), enable_checkpointing=False, enable_progress_bar=False, enable_model_summary=False, val_check_interval=1, log_every_n_steps=1, limit_val_batches=1, precision=64)\n",
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" no_init_trainer = Trainer(default_root_dir=DIRECTORY, accelerator=\"auto\", devices=\"auto\", max_epochs=EPOCHS, logger=CSVLogger(save_dir=DIRECTORY), enable_checkpointing=False, enable_progress_bar=False, enable_model_summary=False, val_check_interval=1, log_every_n_steps=1, limit_val_batches=1, precision=64)\n",
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"\n",
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" # Test models before training\n",
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" results[\"IDEAL_acc_before_train\"].append(init_trainer.test(init_model, verbose=False)[0][\"test_metric\"])\n",
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" results[\"He_acc_before_train\"].append(no_init_trainer.test(no_init_model, verbose=False)[0][\"test_metric\"])\n",
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"\n",
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" # Train models and plot training comparison\n",
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" init_time = time()\n",
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" init_trainer.validate(init_model)\n",
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" init_trainer.fit(init_model)\n",
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" init_time = time() - init_time\n",
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" init_model_logs = f\"{init_trainer.logger.log_dir}/metrics.csv\"\n",
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"\n",
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" no_init_time = time()\n",
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" no_init_trainer.validate(no_init_model)\n",
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" no_init_trainer.fit(no_init_model)\n",
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" no_init_time = time() - no_init_time\n",
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" no_init_model_logs = f\"{no_init_trainer.logger.log_dir}/metrics.csv\"\n",
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"\n",
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" logs = merge_logs(init_model_logs, no_init_model_logs)\n",
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" logs[\"dataset\"] = DATASET_NAME\n",
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"\n",
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" # Test models after training\n",
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" results[\"IDEAL_acc_after_train\"].append(init_trainer.test(init_model, verbose=False)[0][\"test_metric\"])\n",
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" results[\"He_acc_after_train\"].append(no_init_trainer.test(no_init_model, verbose=False)[0][\"test_metric\"])\n",
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"\n",
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" # Init and train times\n",
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" results[\"IDEAL_init_time\"].append(init_model.init_time)\n",
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" results[\"IDEAL_train_time\"].append(init_time)\n",
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" results[\"He_init_time\"].append(no_init_model.init_time)\n",
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" results[\"He_train_time\"].append(no_init_time)\n",
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"\n",
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" # store logs\n",
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" with open(\"results.csv\", \"a\", encoding=\"utf8\") as results_file:\n",
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" logs.to_csv(results_file, header=False, index=False, quoting=1)\n",
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"\n",
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" #clear cache\n",
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" del init_model, no_init_model\n",
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" del init_trainer, no_init_trainer\n",
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" del X_train, y_train, X_val, y_val, X_test, y_test\n",
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" del logs\n",
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" cuda.empty_cache()\n",
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"\n",
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"results = DataFrame(results)\n",
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"mean = results.mean()\n",
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"error = results.sem()*1.96\n",
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"print(\"Mean values:\")\n",
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"print(mean.astype(str).apply(lambda x: x[:6]))\n",
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"print(\"Confidence error:\")\n",
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"print(error.astype(str).apply(lambda x: x[:6]))"
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]
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}
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],
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"metadata": {
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||||
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|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 128,
|
||||
"name": "ml.g4dn.8xlarge",
|
||||
"vcpuNum": 32
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 33,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 4,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 192,
|
||||
"name": "ml.g4dn.12xlarge",
|
||||
"vcpuNum": 48
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 34,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 256,
|
||||
"name": "ml.g4dn.16xlarge",
|
||||
"vcpuNum": 64
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 35,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 61,
|
||||
"name": "ml.p3.2xlarge",
|
||||
"vcpuNum": 8
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 36,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 4,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 244,
|
||||
"name": "ml.p3.8xlarge",
|
||||
"vcpuNum": 32
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 37,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 488,
|
||||
"name": "ml.p3.16xlarge",
|
||||
"vcpuNum": 64
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 38,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 768,
|
||||
"name": "ml.p3dn.24xlarge",
|
||||
"vcpuNum": 96
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 39,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 16,
|
||||
"name": "ml.r5.large",
|
||||
"vcpuNum": 2
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 40,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 32,
|
||||
"name": "ml.r5.xlarge",
|
||||
"vcpuNum": 4
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 41,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 64,
|
||||
"name": "ml.r5.2xlarge",
|
||||
"vcpuNum": 8
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 42,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 128,
|
||||
"name": "ml.r5.4xlarge",
|
||||
"vcpuNum": 16
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 43,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 256,
|
||||
"name": "ml.r5.8xlarge",
|
||||
"vcpuNum": 32
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 44,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 384,
|
||||
"name": "ml.r5.12xlarge",
|
||||
"vcpuNum": 48
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 45,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 512,
|
||||
"name": "ml.r5.16xlarge",
|
||||
"vcpuNum": 64
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 46,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Memory Optimized",
|
||||
"gpuNum": 0,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 768,
|
||||
"name": "ml.r5.24xlarge",
|
||||
"vcpuNum": 96
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 47,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 16,
|
||||
"name": "ml.g5.xlarge",
|
||||
"vcpuNum": 4
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 48,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 32,
|
||||
"name": "ml.g5.2xlarge",
|
||||
"vcpuNum": 8
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 49,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 64,
|
||||
"name": "ml.g5.4xlarge",
|
||||
"vcpuNum": 16
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 50,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 128,
|
||||
"name": "ml.g5.8xlarge",
|
||||
"vcpuNum": 32
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 51,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 1,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 256,
|
||||
"name": "ml.g5.16xlarge",
|
||||
"vcpuNum": 64
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 52,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 4,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 192,
|
||||
"name": "ml.g5.12xlarge",
|
||||
"vcpuNum": 48
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 53,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 4,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 384,
|
||||
"name": "ml.g5.24xlarge",
|
||||
"vcpuNum": 96
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 54,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 768,
|
||||
"name": "ml.g5.48xlarge",
|
||||
"vcpuNum": 192
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 55,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 1152,
|
||||
"name": "ml.p4d.24xlarge",
|
||||
"vcpuNum": 96
|
||||
},
|
||||
{
|
||||
"_defaultOrder": 56,
|
||||
"_isFastLaunch": false,
|
||||
"category": "Accelerated computing",
|
||||
"gpuNum": 8,
|
||||
"hideHardwareSpecs": false,
|
||||
"memoryGiB": 1152,
|
||||
"name": "ml.p4de.24xlarge",
|
||||
"vcpuNum": 96
|
||||
}
|
||||
],
|
||||
"instance_type": "ml.g4dn.xlarge",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.10.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue