{ "cells": [ { "cell_type": "markdown", "id": "899624e8ac3a5979", "metadata": {}, "source": [ "# Baseline\n", "\n", "This guide is a good beginning to familiarize with basic `mml` usage. The goal is to run some baseline models for an existing task. We will use the widely known `MNIST` dataset for demonstration purposes.\n", "\n", "## Step 1: Prepare data\n", "\n", "`MNIST` is already available in `mml` through the `mml-tasks` plugin, which might be installed through `pip` easily.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "4896f0ce2d0385ea", "metadata": { "ExecuteTime": { "end_time": "2024-11-22T14:14:38.123607Z", "start_time": "2024-11-22T14:14:37.118505Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name: mml-tasks\n", "Version: 0.6.0\n", "Summary: This is the MML tasks plugin, providing dataset and task implementation for a bunch of datasets.\n", "Home-page: https://git.dkfz.de/imsy/ise/mml\n", "Author: Patrick Godau\n", "Author-email: patrick.godau@dkfz-heidelberg.de\n", "License: MIT\n", "Location: /home/scholzpa/miniconda3/envs/mml-dev/lib/python3.10/site-packages\n", "Editable project location: /home/scholzpa/Documents/development/github/mml/plugins/tasks\n", "Requires: mml-core\n", "Required-by: \n" ] } ], "source": [ "!pip show mml-tasks" ] }, { "cell_type": "code", "execution_count": 4, "id": "eb0ef16dadb7b3f2", "metadata": { "ExecuteTime": { "end_time": "2024-11-22T14:15:09.023643Z", "start_time": "2024-11-22T14:15:01.134140Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ 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"\u001B[34m|\u001B[0m\u001B[96m idle_action_recognition \u001B[34m|\u001B[0m\u001B[96m idle_action \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m bean_plant_disease_classification \u001B[34m|\u001B[0m\u001B[96m ibean \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m MIT \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m caltech101_object_classification \u001B[34m|\u001B[0m\u001B[96m caltech101 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cifar10_object_classification \u001B[34m|\u001B[0m\u001B[96m cifar10 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cifar100_object_classification \u001B[34m|\u001B[0m\u001B[96m cifar100 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m sklin2_skin_lesions \u001B[34m|\u001B[0m\u001B[96m sklin2 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m barretts_esophagus_diagnosis \u001B[34m|\u001B[0m\u001B[96m barretts_esophagus \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_wrist \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_shoulder \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_humerus \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_hand \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_forearm \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_finger \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mura_xr_elbow \u001B[34m|\u001B[0m\u001B[96m mura \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m eye_condition_classification \u001B[34m|\u001B[0m\u001B[96m cataract \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m brain_tumor_classification \u001B[34m|\u001B[0m\u001B[96m brain_tumor \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m derm7pt_skin_lesions \u001B[34m|\u001B[0m\u001B[96m derm7pt \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m covid-19-chest-ct-image-augmentation_raw \u001B[34m|\u001B[0m\u001B[96m covid_chest_ct \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m DATABASE_CONTENTS_LICENSE_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m covid-19-chest-ct-image-augmentation_Aug \u001B[34m|\u001B[0m\u001B[96m covid_chest_ct \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m DATABASE_CONTENTS_LICENSE_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m covid-19-chest-ct-image-augmentation_CGAN \u001B[34m|\u001B[0m\u001B[96m covid_chest_ct \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m DATABASE_CONTENTS_LICENSE_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m covid-19-chest-ct-image-augmentation_Aug&CGAN \u001B[34m|\u001B[0m\u001B[96m covid_chest_ct \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m DATABASE_CONTENTS_LICENSE_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m deep_drid_quality \u001B[34m|\u001B[0m\u001B[96m deep_drid \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m deep_drid_dr_level \u001B[34m|\u001B[0m\u001B[96m deep_drid \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m deep_drid_clarity \u001B[34m|\u001B[0m\u001B[96m deep_drid \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m deep_drid_field \u001B[34m|\u001B[0m\u001B[96m deep_drid \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m deep_drid_artifact \u001B[34m|\u001B[0m\u001B[96m deep_drid \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m caltech256_object_classification \u001B[34m|\u001B[0m\u001B[96m caltech256 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m emnist_digit_classification \u001B[34m|\u001B[0m\u001B[96m emnist \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m kvasir_capsule_anatomy \u001B[34m|\u001B[0m\u001B[96m kvasir_capsule \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m kvasir_capsule_content \u001B[34m|\u001B[0m\u001B[96m kvasir_capsule \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m kvasir_capsule_pathologies \u001B[34m|\u001B[0m\u001B[96m kvasir_capsule \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m suncolondb-classification \u001B[34m|\u001B[0m\u001B[96m SUNdatabase \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m False \u001B[34m|\u001B[0m\u001B[96m CUSTOM \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m brain_tumor_type_classification \u001B[34m|\u001B[0m\u001B[96m brain_tumor_type \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m breast_cancer_classification_v2 \u001B[34m|\u001B[0m\u001B[96m breast_ultrasound \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_0_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m stanford_dogs_image_categorization \u001B[34m|\u001B[0m\u001B[96m stanford_dogs \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m hyperkvasir_anatomical-landmarks \u001B[34m|\u001B[0m\u001B[96m hyperkvasir \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m hyperkvasir_pathological-findings \u001B[34m|\u001B[0m\u001B[96m hyperkvasir \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m hyperkvasir_quality-of-mucosal-views \u001B[34m|\u001B[0m\u001B[96m hyperkvasir \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m hyperkvasir_therapeutic-interventions \u001B[34m|\u001B[0m\u001B[96m hyperkvasir \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m nerthus_bowel_cleansing_quality \u001B[34m|\u001B[0m\u001B[96m nerthus \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cervix_type_classification \u001B[34m|\u001B[0m\u001B[96m cervical_screening \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m False \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m laryngeal_tissues \u001B[34m|\u001B[0m\u001B[96m laryngeal \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m laryngeal_tissues_original_folds \u001B[34m|\u001B[0m\u001B[96m laryngeal \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m pneumonia_classification \u001B[34m|\u001B[0m\u001B[96m pneumonia \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m lapgyn4_anatomical_structures \u001B[34m|\u001B[0m\u001B[96m lapgyn4 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m lapgyn4_surgical_actions \u001B[34m|\u001B[0m\u001B[96m lapgyn4 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m lapgyn4_instrument_count \u001B[34m|\u001B[0m\u001B[96m lapgyn4 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m lapgyn4_anatomical_actions \u001B[34m|\u001B[0m\u001B[96m lapgyn4 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m shenzen_chest_xray_tuberculosis \u001B[34m|\u001B[0m\u001B[96m shenzhen_xray_tb \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholect45_triplet \u001B[34m|\u001B[0m\u001B[96m cholect45 \u001B[34m|\u001B[0m\u001B[96m multilabel_classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholect45_instrument \u001B[34m|\u001B[0m\u001B[96m cholect45 \u001B[34m|\u001B[0m\u001B[96m multilabel_classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholect45_verb \u001B[34m|\u001B[0m\u001B[96m cholect45 \u001B[34m|\u001B[0m\u001B[96m multilabel_classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholect45_target \u001B[34m|\u001B[0m\u001B[96m cholect45 \u001B[34m|\u001B[0m\u001B[96m multilabel_classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholect45_triplet_soft \u001B[34m|\u001B[0m\u001B[96m cholect45 \u001B[34m|\u001B[0m\u001B[96m multilabel_classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m svhn \u001B[34m|\u001B[0m\u001B[96m svhn \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_grasper_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_bipolar_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_hook_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_scissors_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_clipper_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_irrigator_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m cholec80_specimenbag_presence \u001B[34m|\u001B[0m\u001B[96m cholec80 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_SA_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_enlarged_cardiomediastinum \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_cardiomegaly \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_lung_opacity \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_lung_lesion \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_edema \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_consolidation \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_pneumonia \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_atelectasis \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_pneumothorax \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_pleural_effusion \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_pleural_other \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_fracture \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m chexpert_support_devices \u001B[34m|\u001B[0m\u001B[96m chexpert \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m identify_nbi_infframes \u001B[34m|\u001B[0m\u001B[96m nbi_infframes \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m identify_nbi_infframes_original_folds \u001B[34m|\u001B[0m\u001B[96m nbi_infframes \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m False \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m covid_xray_classification \u001B[34m|\u001B[0m\u001B[96m covid_xray \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_0_1_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m aptos19_blindness_detection \u001B[34m|\u001B[0m\u001B[96m aptos_blindness \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m isic20_melanoma_classification \u001B[34m|\u001B[0m\u001B[96m isic20 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mnist_digit_classification \u001B[34m|\u001B[0m\u001B[96m mnist \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m crawled_covid_ct_classification \u001B[34m|\u001B[0m\u001B[96m covid_ct_crawled \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m mednode_melanoma_classification \u001B[34m|\u001B[0m\u001B[96m mednode \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m ph2-melanocytic-lesions-segmentation \u001B[34m|\u001B[0m\u001B[96m PH2 \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m ph2-melanocytic-lesions-classification \u001B[34m|\u001B[0m\u001B[96m PH2 \u001B[34m|\u001B[0m\u001B[96m classification \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m pascal_voc_challenge_2012 \u001B[34m|\u001B[0m\u001B[96m VOC12 \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m endovissub18_robotic_instrument_seg \u001B[34m|\u001B[0m\u001B[96m endovis18_rob_instr \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m glenda_endometriosis_segmentation \u001B[34m|\u001B[0m\u001B[96m glenda \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m hyperkvasir_polyp_segmentation \u001B[34m|\u001B[0m\u001B[96m hyperkvasir_seg \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m motion-based-segmentation \u001B[34m|\u001B[0m\u001B[96m motion-based-rec \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m crowdsourced-endoscopic-instrument-segmentation-crowd-only \u001B[34m|\u001B[0m\u001B[96m crowdsourced-EIS \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m endometrial_implants \u001B[34m|\u001B[0m\u001B[96m enid \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m CC_BY_NC_4_0 \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m image2image-raw \u001B[34m|\u001B[0m\u001B[96m image2image \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m image2image-rand \u001B[34m|\u001B[0m\u001B[96m image2image \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", "\u001B[34m|\u001B[0m\u001B[96m image2image-cholec80 \u001B[34m|\u001B[0m\u001B[96m image2image \u001B[34m|\u001B[0m\u001B[96m semantic_segmentation \u001B[34m|\u001B[0m\u001B[96m True \u001B[34m|\u001B[0m\u001B[96m UNKNOWN \u001B[34m|\u001B[0m\u001B[96m\n", 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"Overall 96 raw tasks available, from 48 datasets. Filter matches 95 raw tasks with additional 248 variants derived thereof.\n", "\u001B[0m" ] } ], "source": [ "# available datasets\n", "!mml-tasks" ] }, { "cell_type": "code", "execution_count": 5, "id": "f77519e1c16b242f", "metadata": { "ExecuteTime": { "end_time": "2024-11-22T14:16:29.406429Z", "start_time": "2024-11-22T14:16:22.942232Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\u001B[36m2025-05-02 22:11:23,262\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - Started MML 1.0.4 on Python 3.10.17 with mode CREATE.\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,263\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - Plugins loaded: ['mml-tasks']\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,595\u001B[0m][\u001B[34mmml.core.scripts.schedulers.create_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Skipping creation of task mnist_digit_classification because there already seems to be a RAW version of that.\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,596\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - MML init time was 0.0h 0.0m 0.33s.\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,600\u001B[0m][\u001B[34mmml.core.scripts.schedulers.create_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Starting task creation!\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,600\u001B[0m][\u001B[34mmml.core.data_loading.file_manager\u001B[0m][\u001B[32mINFO\u001B[0m] - A total of 0 paths have been created during this run.\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,601\u001B[0m][\u001B[34mmml.core.scripts.schedulers.base_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Successfully finished all experiments!\u001B[0m\n", "[\u001B[36m2025-05-02 22:11:23,601\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - MML run time was 0.0h 0.0m 0.00s.\u001B[0m\n" ] } ], "source": [ "# install data for MNIST (data already installed here, might take a couple of minutes depending on hardware and ethernet connection)\n", "!mml create task_list=[mnist_digit_classification]" ] }, { "cell_type": "code", "execution_count": 4, "id": "bfece1f893e49e8f", "metadata": { "ExecuteTime": { "end_time": "2024-11-22T14:21:05.831035Z", "start_time": "2024-11-22T14:18:15.345112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/scholzpa/miniconda3/envs/mml/lib/python3.8/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\r\n", " @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)\r\n", "[\u001B[36m2024-11-22 15:18:20,420\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - Started MML 0.14.2 on Python 3.8.12 with mode PP.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,420\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - Plugins loaded: ['mml-tasks', 'mml-dimensionality', 'mml-similarity', 'mml-tags', 'mml-sql', 'mml-drive', 'mml-lsf', 'mml-suggest', 'mml-prevalences', 'mml-tf']\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,649\u001B[0m][\u001B[34mmml.core.scripts.schedulers.base_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Executing after init hook: check_lsf_workers\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,650\u001B[0m][\u001B[34mmml_lsf.workers\u001B[0m][\u001B[32mINFO\u001B[0m] - LSF cluster plugin detected local system, no changes made to the number of workers.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,657\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - MML init time was 0.0h 0.0m 0.24s.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,659\u001B[0m][\u001B[34mmml.core.scripts.schedulers.base_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Preparing experiment ...\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,660\u001B[0m][\u001B[34mmml.core.scripts.schedulers.base_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Starting experiment!\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,660\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Starting preprocessing data for task \u001B[33m\u001B[46m\u001B[1mmnist_digit_classification\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:20,660\u001B[0m][\u001B[34mpy.warnings\u001B[0m][\u001B[33mWARNING\u001B[0m] - /home/scholzpa/Documents/development/gitlab/mml/src/mml/core/scripts/schedulers/preprocess_scheduler.py:101: UserWarning: THIS BEHAVIOUR CHANGED: Test data is now also to be preprocessed!\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:21,009\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Preprocessing split: full_train\u001B[0m\r\n", "100%|███████████████████████████████████| 60000/60000 [00:23<00:00, 2564.80it/s]\r\n", "[\u001B[36m2024-11-22 15:18:44,669\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Existing full_train files found:\r\n", "image 0\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:44,670\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Preprocessing split: test\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:44,670\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - No samples in split: test\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:44,670\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Preprocessing split: unlabelled\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:44,670\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - No samples in split: unlabelled\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:45,041\u001B[0m][\u001B[34mmml.core.data_loading.file_manager\u001B[0m][\u001B[32mINFO\u001B[0m] - Writing task description at /home/scholzpa/Pictures/datasets/mml_data/PREPROCESSED/size224/DSET_mnist/temp.json.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:18:45,122\u001B[0m][\u001B[34mmml.core.data_preparation.utils\u001B[0m][\u001B[32mINFO\u001B[0m] - Calculating mean, std and size. This may take a couple of minutes.\u001B[0m\r\n", "Gathering sizes: 100%|█████████████████| 60000/60000 [00:05<00:00, 11661.43it/s]\r\n", "Gathering mean and std: 100%|█████████████████| 600/600 [02:12<00:00, 4.53it/s]\r\n", "[\u001B[36m2024-11-22 15:21:03,916\u001B[0m][\u001B[34mmml.core.data_loading.file_manager\u001B[0m][\u001B[32mINFO\u001B[0m] - Writing task description at /home/scholzpa/Pictures/datasets/mml_data/PREPROCESSED/size224/DSET_mnist/TASK_mnist_digit_classification.json.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:03,998\u001B[0m][\u001B[34mmml.core.data_preparation.task_creator\u001B[0m][\u001B[32mINFO\u001B[0m] - Testing the loading of /home/scholzpa/Pictures/datasets/mml_data/PREPROCESSED/size224/DSET_mnist/TASK_mnist_digit_classification.json...\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:04,601\u001B[0m][\u001B[34mmml.core.data_preparation.task_creator\u001B[0m][\u001B[32mINFO\u001B[0m] - Testing of /home/scholzpa/Pictures/datasets/mml_data/PREPROCESSED/size224/DSET_mnist/TASK_mnist_digit_classification.json finished, dataset loading time was 0.60 seconds, sample loading time was 0.00 seconds.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:04,615\u001B[0m][\u001B[34mmml.core.scripts.schedulers.preprocess_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Finished preprocessing the data for task \u001B[33m\u001B[46m\u001B[1mmnist_digit_classification\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:04,630\u001B[0m][\u001B[34mmml.core.data_loading.file_manager\u001B[0m][\u001B[32mINFO\u001B[0m] - A total of 0 paths have been created during this run.\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:04,630\u001B[0m][\u001B[34mmml.core.scripts.schedulers.base_scheduler\u001B[0m][\u001B[32mINFO\u001B[0m] - Successfully finished all experiments!\u001B[0m\r\n", "[\u001B[36m2024-11-22 15:21:04,630\u001B[0m][\u001B[34mmml\u001B[0m][\u001B[32mINFO\u001B[0m] - MML run time was 0.0h 2.0m 43.97s.\u001B[0m\r\n" ] } ], "source": [ "# preprocess task\n", "!mml pp task_list=[mnist_digit_classification] preprocessing=size224" ] }, { "cell_type": "markdown", "id": "9fa178d147376fee", "metadata": {}, "source": [ "## Step 2: Use defaults for training" ] }, { "cell_type": "code", "execution_count": null, "id": "33891aeb2bbf72bf", "metadata": {}, "outputs": [], "source": [ "# to run a single training\n", "!mml train pivot.name=mnist_digit_classification preprocessing=size224 proj=demo_baseline\n", "# afterwards inspect the training via running tensorboard and inspecting through the browser\n", "!tensorboard --logdir /path/to/mml/results/demo_baseline" ] }, { "cell_type": "markdown", "id": "76000e8998ee5df5", "metadata": {}, "source": [ "![image not found](../../../../docs/source/_static/tensorboard_example.png \"Tensorboard view\")" ] }, { "cell_type": "markdown", "id": "49c76e16e67254d1", "metadata": {}, "source": [ "## Step 3: Optimizing hyperparameters\n", "\n", "We will perform a simple grid search with a couple of architectures & augmentation strategies." ] }, { "cell_type": "code", "execution_count": null, "id": "6556cc458ea30636", "metadata": {}, "outputs": [], "source": [ "# to run a hyperparameter search (here: grid search with 3 models x 3 augmentation strategies),\n", "# to speed things up: no cross-validation, only 1 epoch, deactivated learning rate tuning\n", "!mml train pivot.name=mnist_digit_classification preprocessing=size224 arch.name=resnet18,tiny_vit_21m_224.dist_in22k_ft_in1k,tf_efficientnet_b0.ns_jft_in1k augmentations=basic,randaugment,load_imagenet_aa proj=demo_grid trainer.max_epochs=1 tune.lr=false sampling.batch_size=100 mode.cv=false mode.nested=False +hpo/sampler=grid --multirun" ] }, { "cell_type": "markdown", "id": "d1c658d74afcc506", "metadata": {}, "source": [ "at the end it prints:\n", "\n", "> [HYDRA] Best parameters: {'arch.name': 'tiny_vit_21m_224.dist_in22k_ft_in1k', 'augmentations': 'randaugment'}\n", "> [HYDRA] Best value: 0.0211497992277145" ] }, { "cell_type": "code", "execution_count": null, "id": "c67617588ea39032", "metadata": {}, "outputs": [], "source": [ "# use the best hyperparameters in a full training run (the use_best_params is the name of our search, a folder inside the proj folder, inside \"hpo\", i.e. MML_RESULTS_PATH/demo_grid/hpo/2024-12-03_12-28-46_362374)\n", "!mml train proj=demo_grid use_best_params=2024-12-03_12-28-46_362374 pivot.name=mnist_digit_classification" ] }, { "cell_type": "markdown", "id": "2ff83f854dbba771", "metadata": {}, "source": [ "[mml][INFO] - -------------------------------------------------------------------------------------\n", "[mml][INFO] - Loaded hpo results from study 2024-12-03_12-28-46_362374 and merged 2 params into config.\n", "[mml][INFO] - > arch.name=tiny_vit_21m_224.dist_in22k_ft_in1k\n", "[mml][INFO] - > augmentations=randaugment\n", "[mml][INFO] - -------------------------------------------------------------------------------------\n", "[mml][INFO] - Started MML 0.14.2 on Python 3.10.9 with mode TRAIN.\n", "..." ] }, { "cell_type": "markdown", "id": "f3abca00-12e0-4993-9709-5cbb5d8b9d0f", "metadata": {}, "source": [ "For more complex optimization setups (e.g. multi node optimization) one may use the `mml-sql` plugin. See the plugin documentation for installation and setup instructions. " ] }, { "cell_type": "code", "execution_count": null, "id": "62bf1503-ca31-4d8c-a86b-5c7bfa614d89", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "mml-dev", "language": "python", "name": "mml-dev" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.17" } }, "nbformat": 4, "nbformat_minor": 5 }