From 591cd043eeab261b3f70f1bd6be436bdf5c44eef Mon Sep 17 00:00:00 2001 From: Thomas Schaffter Date: Fri, 13 Sep 2024 18:34:43 +0000 Subject: [PATCH] remove the project openchallenges-notebook --- apps/openchallenges/notebook/.env.example | 4 - apps/openchallenges/notebook/.gitignore | 160 - apps/openchallenges/notebook/.python-version | 1 - apps/openchallenges/notebook/Dockerfile | 1 - apps/openchallenges/notebook/README.md | 21 - .../notebook/challenge_headlines.json | 2671 ------------ .../notebook/docker-compose.yml | 19 - .../notebook/jupyter_lab_config.py | 1084 ----- .../notebooks/challenge-headline-llm.ipynb | 450 --- .../notebook/notebooks/kaggle-api.ipynb | 140 - .../notebooks/meta-kaggle-exploration.ipynb | 1940 --------- .../notebooks/openchallenges-api.ipynb | 187 - .../notebook/notebooks/utils/__init__.py | 0 .../notebook/notebooks/utils/bedrock.py | 80 - .../notebook/notebooks/utils/print_ww.py | 21 - apps/openchallenges/notebook/poetry.lock | 3572 ----------------- apps/openchallenges/notebook/poetry.toml | 3 - .../openchallenges/notebook/prepare-python.sh | 13 - apps/openchallenges/notebook/project.json | 32 - apps/openchallenges/notebook/pyproject.toml | 25 - .../src/challenge_headline/__init__.py | 0 .../generate_challenge_headlines.py | 130 - .../notebook/src/utils/__init__.py | 0 .../notebook/src/utils/bedrock.py | 80 - .../notebook/src/utils/print_ww.py | 21 - 25 files changed, 10655 deletions(-) delete mode 100644 apps/openchallenges/notebook/.env.example delete mode 100644 apps/openchallenges/notebook/.gitignore delete mode 100644 apps/openchallenges/notebook/.python-version delete mode 100644 apps/openchallenges/notebook/Dockerfile delete mode 100644 apps/openchallenges/notebook/README.md delete mode 100644 apps/openchallenges/notebook/challenge_headlines.json delete mode 100644 apps/openchallenges/notebook/docker-compose.yml delete mode 100644 apps/openchallenges/notebook/jupyter_lab_config.py delete mode 100644 apps/openchallenges/notebook/notebooks/challenge-headline-llm.ipynb delete mode 100644 apps/openchallenges/notebook/notebooks/kaggle-api.ipynb delete mode 100644 apps/openchallenges/notebook/notebooks/meta-kaggle-exploration.ipynb delete mode 100644 apps/openchallenges/notebook/notebooks/openchallenges-api.ipynb delete mode 100644 apps/openchallenges/notebook/notebooks/utils/__init__.py delete mode 100644 apps/openchallenges/notebook/notebooks/utils/bedrock.py delete mode 100644 apps/openchallenges/notebook/notebooks/utils/print_ww.py delete mode 100644 apps/openchallenges/notebook/poetry.lock delete mode 100644 apps/openchallenges/notebook/poetry.toml delete mode 100755 apps/openchallenges/notebook/prepare-python.sh delete mode 100644 apps/openchallenges/notebook/project.json delete mode 100644 apps/openchallenges/notebook/pyproject.toml delete mode 100644 apps/openchallenges/notebook/src/challenge_headline/__init__.py delete mode 100644 apps/openchallenges/notebook/src/challenge_headline/generate_challenge_headlines.py delete mode 100644 apps/openchallenges/notebook/src/utils/__init__.py delete mode 100644 apps/openchallenges/notebook/src/utils/bedrock.py delete mode 100644 apps/openchallenges/notebook/src/utils/print_ww.py diff --git a/apps/openchallenges/notebook/.env.example b/apps/openchallenges/notebook/.env.example deleted file mode 100644 index 784b55c355..0000000000 --- a/apps/openchallenges/notebook/.env.example +++ /dev/null @@ -1,4 +0,0 @@ -JUPYTER_ENABLE_LAB=yes -JUPYTER_TOKEN=changeme -KAGGLE_USERNAME=datadinosaur -KAGGLE_KEY=changeme \ No newline at end of file diff --git a/apps/openchallenges/notebook/.gitignore b/apps/openchallenges/notebook/.gitignore deleted file mode 100644 index 68bc17f9ff..0000000000 --- a/apps/openchallenges/notebook/.gitignore +++ /dev/null @@ -1,160 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ diff --git a/apps/openchallenges/notebook/.python-version b/apps/openchallenges/notebook/.python-version deleted file mode 100644 index 56d91d3534..0000000000 --- a/apps/openchallenges/notebook/.python-version +++ /dev/null @@ -1 +0,0 @@ -3.10.12 diff --git a/apps/openchallenges/notebook/Dockerfile b/apps/openchallenges/notebook/Dockerfile deleted file mode 100644 index 7af1989512..0000000000 --- a/apps/openchallenges/notebook/Dockerfile +++ /dev/null @@ -1 +0,0 @@ -FROM jupyter/datascience-notebook:2022-12-26 \ No newline at end of file diff --git a/apps/openchallenges/notebook/README.md b/apps/openchallenges/notebook/README.md deleted file mode 100644 index ec6c9a6bbd..0000000000 --- a/apps/openchallenges/notebook/README.md +++ /dev/null @@ -1,21 +0,0 @@ -# OpenChallenges Notebook - -## Overview - -This project includes a collection of Jupyter notebooks that interact with OpenChallenges (OC) REST -API and other related APIs. - -## Update Python version - -1. Update the version of Python in - - `.python-version` - - `prepare-python.sh` - - `pyproject.toml` -2. Run `nx prepare openchallenges-notebook` - -## Generate challenge headlines - -``` -cd apps/openchallenges/notebook -poetry run python src/challenge_headline/generate_challenge_headlines.py -``` diff --git a/apps/openchallenges/notebook/challenge_headlines.json b/apps/openchallenges/notebook/challenge_headlines.json deleted file mode 100644 index fb20ca9846..0000000000 --- a/apps/openchallenges/notebook/challenge_headlines.json +++ /dev/null @@ -1,2671 +0,0 @@ -[ - { - "id": 1, - "slug": "network-topology-and-parameter-inference", - "name": "Network Topology and Parameter Inference", - "headline": "Optimize methods to estimate biology model parameters for Network Topology a...", - "headline_alternatives": [] - }, - { - "id": 2, - "slug": "breast-cancer-prognosis", - "name": "Breast Cancer Prognosis", - "headline": "Predict breast cancer survival from clinical and genomic data for Breast Can...", - "headline_alternatives": [ - "1. Assess models predicting breast cancer survival using clinical & molecular data (77 characters)", - "2. Evaluate breast cancer prognosis models with clinical and genomic profiles (79 characters)", - "3. Test accuracy of models predicting breast cancer outcomes with tumor data (76 characters) ", - "4. Benchmark computational models for breast cancer patient survival rates (79 characters)", - "5. Assessing breast cancer prognosis predictions using tumor and gene data (80 characters)" - ] - }, - { - "id": 3, - "slug": "phil-bowen-als-prediction-prize4life", - "name": "Phil Bowen ALS Prediction Prize4Life", - "headline": "Seeking treatment to halt ALS's fatal loss of motor function for Phil Bowen ...", - "headline_alternatives": [] - }, - { - "id": 4, - "slug": "drug-sensitivity-and-drug-synergy-prediction", - "name": "Drug Sensitivity and Drug Synergy Prediction", - "headline": "Revolutionizing Cancer Therapeutics: Predicting Drug Sensitivity in Human Ce...", - "headline_alternatives": [] - }, - { - "id": 5, - "slug": "niehs-ncats-unc-toxicogenetics", - "name": "NIEHS-NCATS-UNC Toxicogenetics", - "headline": "Predicting cytotoxicity from genomic and chemical data for NIEHS-NCATS-UNC T...", - "headline_alternatives": [ - "1. Predict cytotoxicity from genomic profiles and chemical structures (78 characters)", - "2. Model cytotoxicity in cell lines using genetics and chemical attributes (77 characters)", - "3. Forecast cytotoxic effects of compounds via genomic and structural data (78 characters) ", - "4. Estimate cytotoxicity in cell lines from genetics and chemistry (66 characters)", - "5. Predict variability in chemical toxicity using genomic and structural features (79 characters)" - ] - }, - { - "id": 6, - "slug": "whole-cell-parameter-estimation", - "name": "Whole-Cell Parameter Estimation", - "headline": "Seeking innovative parameter estimation methods for large models for Whole-C...", - "headline_alternatives": [ - "1. Seeking innovative parameter estimation for large models (56 characters)", - "2. Developing optimization for parameter estimation of large models (65 characters)", - "3. Form teams to estimate parameters of heterogeneous models (65 characters) ", - "4. Compare approaches to estimate parameters of complex models (66 characters)", - "5. Explore methods to select informative experiments for models (69 characters)" - ] - }, - { - "id": 7, - "slug": "hpn-dream-breast-cancer-network-inference", - "name": "HPN-DREAM Breast Cancer Network Inference", - "headline": "Inferring causal signaling networks in breast cancer for HPN-DREAM Breast Ca...", - "headline_alternatives": [ - "1. Infer breast cancer signaling networks from perturbation data (65 characters)", - "2. Predict phosphorylation dynamics in breast cancer cells (54 characters)", - "3. Advance network inference in breast cancer with perturbation data (79 characters) ", - "4. Infer causal signaling networks in breast cancer (44 characters)", - "5. Use perturbation data to model breast cancer cell signaling (61 characters)" - ] - }, - { - "id": 8, - "slug": "rheumatoid-arthritis-responder", - "name": "Rheumatoid Arthritis Responder", - "headline": "Unlocking Anti-TNF Response Predictors: A Crowdsourced Breakthrough in RA Th...", - "headline_alternatives": [] - }, - { - "id": 9, - "slug": "icgc-tcga-dream-mutation-calling", - "name": "ICGC-TCGA DREAM Mutation Calling", - "headline": "Crowdsourcing Challenge Seeks to Improve Cancer Mutation Detection for ICGC-...", - "headline_alternatives": [ - "1. ICGC & TCGA launch challenge to improve cancer mutation detection in genomes (79 characters)", - "2. Crowdsourcing better methods for finding cancer mutations in genomes (76 characters) ", - "3. Open challenge seeks improved approaches for identifying cancer mutations (69 characters)", - "4. Dream challenge aims to advance cancer mutation identification from genomes (78 characters)", - "5. ICGC & TCGA partner on open challenge to boost cancer mutation calling (79 characters)" - ] - }, - { - "id": 10, - "slug": "acute-myeloid-leukemia-outcome-prediction", - "name": "Acute Myeloid Leukemia Outcome Prediction", - "headline": "Uncover drivers of AML using clinical and proteomic data for Acute Myeloid L...", - "headline_alternatives": [ - "1. Uncover drivers of AML using rich clinical and protein dataset (76 characters)", - "2. Interpret unique AML dataset to predict outcomes and tailor therapy (79 characters)", - "3. Access rich AML data to uncover drivers and predict patient outcomes (77 characters) ", - "4. Help predict AML outcomes using clinical and protein data (63 characters)", - "5. Uncover AML drivers using clinical, genetic and protein data (69 characters)" - ] - }, - { - "id": 11, - "slug": "broad-dream-gene-essentiality-prediction", - "name": "Broad-DREAM Gene Essentiality Prediction", - "headline": "Crowdsourcing Models to Predict Cancer Cell Gene Dependencies for Broad-DREA...", - "headline_alternatives": [] - }, - { - "id": 12, - "slug": "alzheimers-disease-big-data", - "name": "Alzheimer's Disease Big Data", - "headline": "Seeking Accurate Predictive Biomarkers for Alzheimer's Diagnosis for Alzheim...", - "headline_alternatives": [] - }, - { - "id": 13, - "slug": "olfaction-prediction", - "name": "Olfaction Prediction", - "headline": "Predicting smell from molecule features for Olfaction Prediction", - "headline_alternatives": [ - "1. Predicting how molecules smell from features", - "2. Linking molecular features to odor perception", - "3. Accelerating fragrance design through smell prediction ", - "4. Models to predict smell from molecule structure", - "5. Connecting molecular properties to olfactory perception" - ] - }, - { - "id": 14, - "slug": "prostate-cancer", - "name": "Prostate Cancer", - "headline": "Predict survival of docetaxel treatment in mCRPC patients for Prostate Cancer", - "headline_alternatives": [ - "1. Predict survival, toxicity for docetaxel mCRPC treatment", - "2. Improve prognostic modeling for mCRPC patients on docetaxel ", - "3. Benchmark models predicting mCRPC survival on docetaxel", - "4. Models to predict mCRPC survival, toxicity with docetaxel ", - "5. Establish benchmarks for mCRPC prognosis on docetaxel" - ] - }, - { - "id": 15, - "slug": "als-stratification-prize4life", - "name": "ALS Stratification Prize4Life", - "headline": "Advancing ALS Treatment: Predicting Disease Progression and Survival with Data.", - "headline_alternatives": [] - }, - { - "id": 16, - "slug": "astrazeneca-sanger-drug-combination-prediction", - "name": "AstraZeneca-Sanger Drug Combination Prediction", - "headline": "Predict effective drug combinations using genomic data for AstraZeneca-Sange...", - "headline_alternatives": [] - }, - { - "id": 17, - "slug": "smc-dna-meta", - "name": "SMC-DNA Meta", - "headline": "Seeking Most Accurate Somatic Mutation Detection Pipeline for SMC-DNA Meta", - "headline_alternatives": [ - "1. Identify best meta-pipeline for detecting cancer mutations (49 characters)", - "2. Establish state-of-the-art somatic mutation detection from callers (77 characters) ", - "3. Determine most accurate approach to identify cancer mutations (56 characters)", - "4. Assess complementarity of mutation calling algorithms for cancer (77 characters) ", - "5. Benchmark mutation detection pipelines to advance cancer genomics (79 characters)" - ] - }, - { - "id": 18, - "slug": "smc-het", - "name": "SMC-Het", - "headline": "Crowdsourcing Challenge to Improve Tumor Subclonal Reconstruction for SMC-Het", - "headline_alternatives": [ - "1. Crowdsourcing challenge to improve subclonal reconstruction in tumours (77 characters)", - "2. Challenge seeks better quantification of tumour heterogeneity (76 characters) ", - "3. Open competition to advance tumour subclonal reconstruction (79 characters)", - "4. Dream challenge for improving tumour heterogeneity analysis (79 characters)", - "5. ICGC & TCGA launch contest to quantify tumour subclones (79 characters)" - ] - }, - { - "id": 19, - "slug": "respiratory-viral", - "name": "Respiratory Viral", - "headline": "Unraveling Viral Susceptibility: Early Predictors of Respiratory Infection a...", - "headline_alternatives": [] - }, - { - "id": 20, - "slug": "disease-module-identification", - "name": "Disease Module Identification", - "headline": "Crowdsourcing challenge to find disease modules in genomic networks for Dise...", - "headline_alternatives": [ - "1. Crowdsourcing challenge to identify disease modules in genomic networks (79 characters)", - "2. Open challenge to find disease modules in genomic networks (63 characters) ", - "3. DREAM challenge to assess module ID methods on disease networks (79 characters)", - "4. Community effort to find novel disease modules in genomic networks (76 characters) ", - "5. DREAM challenge leverages crowds to find disease modules in networks (79 characters)" - ] - }, - { - "id": 21, - "slug": "encode", - "name": "ENCODE", - "headline": "Predict transcription factor binding sites from limited data for ENCODE", - "headline_alternatives": [ - "1. Predict TF binding sites from limited ChIP-seq data", - "2. Computational methods to expand TF binding site knowledge ", - "3. Improve TF binding site prediction with incomplete data", - "4. Fill knowledge gaps in TF binding landscapes ", - "5. Accurately predict TF binding without full experiments" - ] - }, - { - "id": 22, - "slug": "idea", - "name": "Idea", - "headline": "Fostering Collaborative Solutions in Health: The DREAM Idea Challenge", - "headline_alternatives": [ - "1. DREAM Challenge connects modelers, experimentalists to solve health questions (79 characters)", - "2. DREAM Idea Challenge enables modeler-experimentalist collaborations (76 characters)", - "3. DREAM Challenge builds wall of models to improve human health (79 characters) ", - "4. DREAM Idea Challenge shapes solutions to health through collaboration (78 characters)", - "5. DREAM Challenge fosters modeler-experimentalist partnerships for health (79 characters)" - ] - }, - { - "id": 23, - "slug": "smc-rna", - "name": "SMC-RNA", - "headline": "Crowdsourcing Challenge Seeks to Improve Cancer Mutation Detection from RNA ...", - "headline_alternatives": [] - }, - { - "id": 24, - "slug": "digital-mammography-dream-challenge", - "name": "Digital Mammography DREAM Challenge", - "headline": "Improve mammography prediction to detect breast cancer early for Digital Mam...", - "headline_alternatives": [] - }, - { - "id": 25, - "slug": "multiple-myeloma", - "name": "Multiple Myeloma", - "headline": "Develop precise risk model for myeloma patients for Multiple Myeloma", - "headline_alternatives": [] - }, - { - "id": 26, - "slug": "ga4gh-dream-workflow-execution", - "name": "GA4GH-DREAM Workflow Execution", - "headline": "Develop technologies to enable distributed genomic data analysis for GA4GH-D...", - "headline_alternatives": [] - }, - { - "id": 27, - "slug": "parkinsons-disease-digital-biomarker", - "name": "Parkinson's Disease Digital Biomarker", - "headline": "Benchmarking methods to develop Parkinson's digital signatures from sensor d...", - "headline_alternatives": [ - "1. Extract features from sensor data to predict Parkinson's pathology", - "2. Benchmark methods to process sensor data for Parkinson's biomarkers ", - "3. Develop digital signatures of Parkinson's from raw sensor data", - "4. Assess algorithms extracting predictive features from motor task data ", - "5. Evaluate approaches to extract Parkinson's biomarkers from sensor time series" - ] - }, - { - "id": 28, - "slug": "nci-cptac-proteogenomics", - "name": "NCI-CPTAC Proteogenomics", - "headline": "Develop tools to extract insights from cancer proteomics data for NCI-CPTAC ...", - "headline_alternatives": [] - }, - { - "id": 29, - "slug": "multi-targeting-drug", - "name": "Multi-Targeting Drug", - "headline": "Seeking Generalizable Methods to Predict Multi-Target Compound Binding for M...", - "headline_alternatives": [ - "1. Predict compounds binding to targets, avoiding anti-targets (79 characters)", - "2. Develop generalizable multi-target binding prediction methods (77 characters) ", - "3. Incentivize multi-target binding prediction model development (79 characters)", - "4. Predict compounds binding specific targets, avoiding other binding (77 characters) ", - "5. Develop reusable methods to predict selective compound binding (78 characters)" - ] - }, - { - "id": 30, - "slug": "single-cell-transcriptomics", - "name": "Single Cell Transcriptomics", - "headline": "Reconstructing Cell Locations in Drosophila Embryo from Transcripts for Sing...", - "headline_alternatives": [] - }, - { - "id": 31, - "slug": "idg-drug-kinase-binding", - "name": "IDG Drug-Kinase Binding", - "headline": "Challenge seeks machine learning for drug-kinase binding prediction for IDG ...", - "headline_alternatives": [] - }, - { - "id": 32, - "slug": "malaria", - "name": "Malaria", - "headline": "Predict malaria drug resistance from parasite gene expression for Malaria", - "headline_alternatives": [] - }, - { - "id": 33, - "slug": "preterm-birth-prediction-transcriptomics", - "name": "Preterm Birth Prediction - Transcriptomics", - "headline": "Developing Accurate, Inexpensive Molecular Clock to Determine Gestational Ag...", - "headline_alternatives": [ - "1. Developing accurate, low-cost molecular clock to establish pregnancy timeline", - "2. Creating prediction models for gestational age from blood samples ", - "3. Identifying women at risk of preterm birth and other complications", - "4. Improving pregnancy care through better assessment of gestational age", - "5. Using gene expression to establish molecular clock for pregnancy" - ] - }, - { - "id": 34, - "slug": "single-cell-signaling-in-breast-cancer", - "name": "Single-Cell Signaling in Breast Cancer", - "headline": "Exploring heterogeneous signaling in single cancer cells for Single-Cell Sig...", - "headline_alternatives": [] - }, - { - "id": 35, - "slug": "ehr-dream-challenge-patient-mortality-prediction", - "name": "EHR DREAM Challenge: Patient Mortality Prediction", - "headline": "New tools to reconstruct cell lineages from CRISPR mutations for EHR DREAM C...", - "headline_alternatives": [] - }, - { - "id": 36, - "slug": "allen-institute-cell-lineage-reconstruction", - "name": "Allen Institute Cell Lineage Reconstruction", - "headline": "New tools enable reconstructing complex cell lineages at single-cell resolut...", - "headline_alternatives": [] - }, - { - "id": 37, - "slug": "tumor-deconvolution", - "name": "Tumor Deconvolution", - "headline": "Assess computational methods to deconvolve bulk tumor data into immune compo...", - "headline_alternatives": [ - "1. Assess computational methods to deconvolve bulk tumor data into immune components (77 characters)", - "2. Evaluate algorithms for deconvolving bulk tumor data into individual cell types (79 characters)", - "3. Test computational deconvolution of bulk tumor data into specific immune cells (69 characters) ", - "4. Assess deconvolution methods on bulk tumor data to quantify infiltration (71 characters)", - "5. Evaluate computational infiltration analysis from bulk tumor profiling (66 characters)" - ] - }, - { - "id": 38, - "slug": "ctd2-pancancer-drug-activity", - "name": "CTD2 Pancancer Drug Activity", - "headline": "Benchmark algorithms predicting drug targets from gene data for CTD2 Pancanc...", - "headline_alternatives": [] - }, - { - "id": 39, - "slug": "ctd2-beataml", - "name": "CTD2 BeatAML", - "headline": "Seeking New Drug Targets for Precision AML Treatment for CTD2 BeatAML", - "headline_alternatives": [] - }, - { - "id": 40, - "slug": "metadata-automation", - "name": "Metadata Automation", - "headline": "Semi-Automating Metadata Annotation for Enhanced Data Sharing in Cancer Research", - "headline_alternatives": [ - "1. Develop tools to semi-automate metadata annotation for cancer data sharing (76 characters)", - "2. Semi-automate metadata annotation to enable cancer data transformations (72 characters)", - "3. Create tools to annotate metadata to share cancer research data (65 characters) ", - "4. Build semi-automated tools for metadata annotation of cancer data (69 characters)", - "5. Develop semi-automated annotation tools to transform cancer data (71 characters)" - ] - }, - { - "id": 41, - "slug": "automated-scoring-of-radiographic-joint-damage", - "name": "Automated Scoring of Radiographic Joint Damage", - "headline": "Develop automated method to quantify rheumatoid arthritis joint damage for A...", - "headline_alternatives": [] - }, - { - "id": 42, - "slug": "beat-pd", - "name": "BEAT-PD", - "headline": "Develop mobile sensors to remotely monitor Parkinson's disease for BEAT-PD", - "headline_alternatives": [] - }, - { - "id": 43, - "slug": "ctd2-pancancer-chemosensitivity", - "name": "CTD2 Pancancer Chemosensitivity", - "headline": "Predict drug sensitivity from cell line gene expression for CTD2 Pancancer C...", - "headline_alternatives": [] - }, - { - "id": 44, - "slug": "ehr-dream-challenge-covid-19", - "name": "EHR DREAM Challenge: COVID-19", - "headline": "Develop tools to predict COVID-19 risk without sharing data for EHR DREAM Ch...", - "headline_alternatives": [] - }, - { - "id": 45, - "slug": "anti-pd1-response-prediction", - "name": "Anti-PD1 Response Prediction", - "headline": "Predicting lung cancer response to immuno-oncology therapy", - "headline_alternatives": [ - "1. Develop models predicting lung cancer response to I-O therapy (79 characters)", - "2. Gain insights on improving I-O therapy response prediction in lung cancer (78 characters) ", - "3. Leverage data to predict lung cancer patient response to I-O treatment (76 characters)", - "4. Improve prediction of I-O therapy benefit for lung cancer patients (58 characters)", - "5. Predictive modeling of lung cancer response to immuno-oncology therapy (79 characters)" - ] - }, - { - "id": 46, - "slug": "brats-2021-challenge", - "name": "BraTS 2021 Challenge", - "headline": "Developing ML methods to analyze brain tumor MRI scans", - "headline_alternatives": [ - "1. ML methods to analyze brain tumor MRI scans (49 characters)", - "2. Assessing ML for segmenting glioblastoma in MRI (56 characters) ", - "3. Evaluating ML segmentation of brain tumors (49 characters)", - "4. BraTS challenge tests ML for glioblastoma MRI analysis (63 characters)", - "5. Can ML improve glioblastoma tumor detection in MRI? (59 characters)" - ] - }, - { - "id": 47, - "slug": "cancer-data-registry-nlp", - "name": "Cancer Data Registry NLP", - "headline": "Predicting lung cancer response to immuno-oncology therapy", - "headline_alternatives": [ - "1. Unlock hidden clinical data with NLP for research", - "2. Develop NLP to tap unstructured EHR data for trials ", - "3. Improve clinical trial matching with NLP algorithms", - "4. Evaluate NLP methods to access PHI-sensitive data ", - "5. Advance NLP solutions to leverage clinical text data" - ] - }, - { - "id": 48, - "slug": "barda-community-challenge-pediatric-covid-19-data-challenge", - "name": "BARDA Community Challenge - Pediatric COVID-19 Data Challenge", - "headline": "Models to predict severe COVID-19 in children sought", - "headline_alternatives": [] - }, - { - "id": 49, - "slug": "brats-continuous-evaluation", - "name": "BraTS Continuous Evaluation", - "headline": "Seeking Innovations To Improve Brain Tumor Diagnosis And Treatment", - "headline_alternatives": [] - }, - { - "id": 50, - "slug": "fets-2022", - "name": "FeTS 2022", - "headline": "Federated Learning Challenge 2022: Advancing Brain Tumor Segmentation Algorithms", - "headline_alternatives": [] - }, - { - "id": 51, - "slug": "random-promotor", - "name": "Random Promotor", - "headline": "Deciphering Gene Regulation: Training Models to Predict Gene Expression Patterns", - "headline_alternatives": [] - }, - { - "id": 52, - "slug": "preterm-birth-prediction-microbiome", - "name": "Preterm Birth Prediction - Microbiome", - "headline": "Seeking Innovations To Improve Brain Tumor Diagnosis And Treatment", - "headline_alternatives": [ - "1. Predicting preterm births to reduce infant mortality", - "2. Identifying preterm birth risks for timely treatment ", - "3. Forecasting preterm deliveries to improve infant health", - "4. Detecting high preterm birth risk for preventative care", - "5. Estimating preterm likelihood to administer proper care" - ] - }, - { - "id": 53, - "slug": "finrisk", - "name": "FINRISK - Heart Failure and Microbiome", - "headline": "FINRISK - Heart Failure and Microbiome: (No headline provided)", - "headline_alternatives": [] - }, - { - "id": 54, - "slug": "scrna-seq-and-scatac-seq-data-analysis", - "name": "scRNA-seq and scATAC-seq Data Analysis", - "headline": "Assess computational methods for scRNA-seq and scATAC-seq analysis", - "headline_alternatives": [] - }, - { - "id": 55, - "slug": "cough-diagnostic-algorithm-for-tuberculosis", - "name": "COugh Diagnostic Algorithm for Tuberculosis", - "headline": "Assess computational methods for scRNA-seq and scATAC-seq analysis", - "headline_alternatives": [] - }, - { - "id": 56, - "slug": "nih-long-covid-computational-challenge", - "name": "NIH Long COVID Computational Challenge", - "headline": "Understanding Prevalence and Outcomes of Post-COVID Syndrome", - "headline_alternatives": [ - "1. Seeking analytics to understand prevalence and outcomes of long COVID symptoms (79 characters)", - "2. Can advanced analytics extract insights on long COVID from complex health data? (76 characters) ", - "3. Harnessing analytics to characterize long COVID syndrome prevalence and duration (77 characters)", - "4. Using analytics to elucidate long COVID symptoms, outcomes, and heterogeneity (74 characters)", - "5. Applying advanced analytics to unravel long COVID prevalence, symptoms, and outcomes (79 characters)" - ] - }, - { - "id": 57, - "slug": "bridge2ai", - "name": "Bridge2AI", - "headline": "What makes a good color palette?", - "headline_alternatives": [] - }, - { - "id": 58, - "slug": "rare-x-open-data-science", - "name": "RARE-X Open Data Science", - "headline": "Unlocking rare disease mysteries through open science collaboration", - "headline_alternatives": [] - }, - { - "id": 59, - "slug": "cagi5-regulation-saturation", - "name": "CAGI5: Regulation saturation", - "headline": "Predicting effects of variants in disease-linked enhancers and promoters", - "headline_alternatives": [] - }, - { - "id": 60, - "slug": "cagi5-calm1", - "name": "CAGI5: CALM1", - "headline": "Predicting effects of calmodulin variants on yeast growth", - "headline_alternatives": [] - }, - { - "id": 61, - "slug": "cagi5-pcm1", - "name": "CAGI5: PCM1", - "headline": "Assessing PCM1 variants' impact on zebrafish ventricle", - "headline_alternatives": [ - "1. Assessing PCM1 variants' impact on zebrafish brain ventricle size", - "2. Do PCM1 mutations linked to schizophrenia affect zebrafish ventricles?", - "3. Testing if PCM1 variants alter zebrafish ventricular area ", - "4. Evaluating zebrafish ventricles with PCM1 mutations tied to schizophrenia", - "5. Can PCM1 variants' effects on zebrafish predict schizophrenia risk?" - ] - }, - { - "id": 62, - "slug": "cagi5-frataxin", - "name": "CAGI5: Frataxin", - "headline": "Unveiling Protein Stability: Predicting \u0394\u0394GH20 for Frataxin Variants", - "headline_alternatives": [] - }, - { - "id": 63, - "slug": "cagi5-tpmt", - "name": "CAGI5: TPMT and p10", - "headline": "Cracking the Code: Predicting TPMT and PTEN Protein Stability Variants", - "headline_alternatives": [ - "1. Predict effect of PTEN/TPMT mutations on protein stability", - "2. Assess PTEN/TPMT variant stability using multiplexed profiling ", - "3. Quantify protein stability changes from PTEN/TPMT mutation library", - "4. Model impacts of genetic variants on PTEN and TPMT stability", - "5. Estimate protein abundance changes from fusion assay on mutants" - ] - }, - { - "id": 64, - "slug": "cagi5-annotate-all-missense", - "name": "CAGI5: Annotate all nonsynonymous variants", - "headline": "Annotate all nonsynonymous variants", - "headline_alternatives": [] - }, - { - "id": 65, - "slug": "cagi5-gaa", - "name": "CAGI5: GAA", - "headline": "Predict enzyme activity of GAA mutants in Pompe disease", - "headline_alternatives": [] - }, - { - "id": 66, - "slug": "cagi5-chek2", - "name": "CAGI5: CHEK2", - "headline": "Estimate CHEK2 gene variant probabilities in Latino breast cancer cases", - "headline_alternatives": [ - "1. Estimate CHEK2 variant probabilities in Latino breast cancer cases (78 characters)", - "2. Predict CHEK2 variant probabilities from Latino breast cancer cohort (77 characters) ", - "3. Calculate probabilities of CHEK2 variants in Latino breast cancer (76 characters)", - "4. Model CHEK2 variant probabilities in Latina breast cancer cases (79 characters)", - "5. Estimate likelihood of CHEK2 variants in Latina breast cancer patients (79 characters)" - ] - }, - { - "id": 67, - "slug": "cagi5-enigma", - "name": "CAGI5: ENIGMA", - "headline": "Predicting cancer risk from BRCA1/2 gene variants", - "headline_alternatives": [] - }, - { - "id": 68, - "slug": "cagi5-mapsy", - "name": "CAGI5: MaPSy", - "headline": "Predicting the Impact of Genetic Variants on Splicing Mechanisms", - "headline_alternatives": [] - }, - { - "id": 69, - "slug": "cagi5-vex-seq", - "name": "CAGI5: Vex-seq", - "headline": "Predict splicing changes from variants in globin gene", - "headline_alternatives": [] - }, - { - "id": 70, - "slug": "cagi5-sickkids5", - "name": "CAGI5: SickKids clinical genomes", - "headline": "Predict genetic disorders from 30 child genomes and phenotypes.", - "headline_alternatives": [] - }, - { - "id": 71, - "slug": "cagi5-intellectual-disability", - "name": "CAGI5: ID Panel", - "headline": "Predict phenotypes and variants from gene panel sequences", - "headline_alternatives": [] - }, - { - "id": 72, - "slug": "cagi5-clotting-disease", - "name": "CAGI5: Clotting disease exomes", - "headline": "Predicting venous thromboembolism risk in African Americans", - "headline_alternatives": [] - }, - { - "id": 73, - "slug": "cagi6-sickkids", - "name": "CAGI6: SickKids clinical genomes and transcriptomes", - "headline": "Identify genes causing rare diseases using transcriptomics", - "headline_alternatives": [] - }, - { - "id": 74, - "slug": "cagi6-cam", - "name": "CAGI6: CaM", - "headline": "Predicting the Impact of Point Mutations on Calmodulin Stability", - "headline_alternatives": [] - }, - { - "id": 75, - "slug": "cami-ii", - "name": "CAMI II", - "headline": "Assembling and Classifying Microbial Genomes in Complex Samples", - "headline_alternatives": [ - "1. CAMI II challenges for genome and taxonomic binning of environmental data", - "2. CAMI II: Assembling and analyzing multi-sample microbiome data ", - "3. CAMI II: Binning genomes and profiling taxa from diverse environments", - "4. CAMI II offers microbiome analysis challenges on clinical, marine samples", - "5. CAMI II challenges: genome assembly, binning genomes and taxa " - ] - }, - { - "id": 76, - "slug": "camda18-metasub-forensics", - "name": "CAMDA18-MetaSUB Forensics", - "headline": "Building a metagenomic map of mass-transit systems globally", - "headline_alternatives": [ - "1. Building a metagenomic map of mass transit systems across the globe (79 characters)", - "2. Analyzing metagenomic data from global city sampling days (76 characters)", - "3. Multi-city forensic analyses using metagenomic data from mass transit (78 characters) ", - "4. Longitudinal study of microbes in mass transit systems worldwide (77 characters)", - "5. Metagenomic mapping of microbes in public spaces globally over time (78 characters)" - ] - }, - { - "id": 77, - "slug": "camda18-cmap-drug-safety", - "name": "CAMDA18-CMap Drug Safety", - "headline": "Predicting drug toxicity using cell-based gene expression data", - "headline_alternatives": [] - }, - { - "id": 78, - "slug": "camda18-cancer-data-integration", - "name": "CAMDA18-Cancer Data Integration", - "headline": "Unify data integration approaches for breast cancer and neuroblastoma", - "headline_alternatives": [ - "1. Unify data integration to beat state of art in breast and neuroblastoma", - "2. Improve breast and neuroblastoma outcomes via data integration ", - "3. Data integration to advance breast cancer and neuroblastoma care", - "4. Integrate data to outperform current methods in breast and neuroblastoma ", - "5. Data integration to match state of art in breast cancer and neuroblastoma" - ] - }, - { - "id": 79, - "slug": "cafa-4", - "name": "CAFA 4", - "headline": "Assessing algorithms for predicting protein function", - "headline_alternatives": [ - "1. Assessing algorithms for predicting protein function from sequences (58 characters)", - "2. Evaluating automated methods to predict protein function (53 characters)", - "3. Benchmarking protein function prediction algorithms (56 characters) ", - "4. Critical test of computational protein annotation methods (63 characters)", - "5. Testing algorithms that computationally assign functions to proteins (63 characters)" - ] - }, - { - "id": 80, - "slug": "casp13", - "name": "CASP13", - "headline": "CASP assesses protein structure prediction methods", - "headline_alternatives": [ - "1. CASP tests progress in predicting protein structures from sequences (78 characters)", - "2. CASP12 drew protein structure models for 82 targets from 100 groups (77 characters) ", - "3. Every 2 years, CASP assesses progress in modeling protein structures (65 characters)", - "4. CASP community experiment benchmarks protein structure prediction (56 characters)", - "5. 50,000 models on 82 targets submitted for CASP12 protein challenge (79 characters)" - ] - }, - { - "id": 81, - "slug": "casp14", - "name": "CASP14", - "headline": "Assessing progress in protein structure prediction", - "headline_alternatives": [ - "1. CASP tests progress in predicting protein structure from sequence (73 characters)", - "2. CASP14 drew 100 groups to model 90 protein targets (66 characters) ", - "3. Every 2 years CASP assesses protein structure prediction methods (69 characters)", - "4. CASP community experiment benchmarks protein modeling techniques (76 characters)", - "5. 67,000 models on 90 targets in recent CASP protein structure challenge (79 characters)" - ] - }, - { - "id": 82, - "slug": "cfsan-pathogen-detection", - "name": "CFSAN Pathogen Detection", - "headline": "Rapidly Identify Food Sources of Outbreaks", - "headline_alternatives": [] - }, - { - "id": 83, - "slug": "cdrh-biothreat", - "name": "CDRH Biothreat", - "headline": "Identifying infectious diseases from clinical samples using sequencing techn...", - "headline_alternatives": [] - }, - { - "id": 84, - "slug": "multi-omics-enabled-sample-mislabeling-correction", - "name": "Multi-omics Enabled Sample Mislabeling Correction", - "headline": "Multi-omics Enabled Sample Mislabeling Correction: (No headline provided)", - "headline_alternatives": [] - }, - { - "id": 85, - "slug": "biocompute-object-app-a-thon", - "name": "BioCompute Object App-a-thon", - "headline": "Seeking Standards for Reproducible Bioinformatics Analysis", - "headline_alternatives": [] - }, - { - "id": 86, - "slug": "brain-cancer-predictive-modeling-and-biomarker-discovery", - "name": "Brain Cancer Predictive Modeling and Biomarker Discovery", - "headline": "Seeking novel biomarkers to advance precision medicine for brain tumors", - "headline_alternatives": [ - "1. Seeking novel biomarkers for glioma prognosis and treatment (76 characters)", - "2. Advancing precision medicine for brain tumors via new biomarkers (79 characters)", - "3. Identifying multi-omics markers to improve brain tumor outcomes (71 characters) ", - "4. Can new biomarkers advance precision medicine for gliomas? (63 characters)", - "5. Calling for innovative glioma biomarkers to enable precision care (79 characters)" - ] - }, - { - "id": 87, - "slug": "gaining-new-insights-by-detecting-adverse-event-anomalies", - "name": "Gaining New Insights by Detecting Adverse Event Anomalies", - "headline": "Seeking Algorithms to Detect Adverse Events in FDA Data", - "headline_alternatives": [] - }, - { - "id": 88, - "slug": "calling-variants-in-difficult-to-map-regions", - "name": "Calling Variants in Difficult-to-Map Regions", - "headline": "Precision Benchmarking: Evaluating Variant Calling in Complex Genomic Regions", - "headline_alternatives": [] - }, - { - "id": 89, - "slug": "vha-innovation-ecosystem-and-covid-19-risk-factor-modeling", - "name": "VHA Innovation Ecosystem and COVID-19 Risk Factor Modeling", - "headline": "AI for COVID-19: Predicting Health Outcomes in the Veteran Population", - "headline_alternatives": [] - }, - { - "id": 90, - "slug": "covid-19-precision-immunology-app-a-thon", - "name": "COVID-19 Precision Immunology App-a-thon", - "headline": "Seeking insights on COVID-19 pathophysiology to enable effective strategies.", - "headline_alternatives": [] - }, - { - "id": 91, - "slug": "smarter-food-safety-low-cost-tech-enabled-traceability", - "name": "Smarter Food Safety Low Cost Tech-Enabled Traceability", - "headline": "Seeking Affordable Tech Solutions for Food Traceability", - "headline_alternatives": [] - }, - { - "id": 92, - "slug": "tumor-mutational-burden-tmb-challenge-phase-1", - "name": "Tumor Mutational Burden (TMB) Challenge Phase 1", - "headline": "Standardizing Tumor Mutational Burden (TMB) Calculation in Cancer Research", - "headline_alternatives": [] - }, - { - "id": 93, - "slug": "kits21", - "name": "Kidney and Kidney Tumor Segmentation", - "headline": "Contest Seeks Best Kidney Tumor Segmentation System", - "headline_alternatives": [] - }, - { - "id": 94, - "slug": "realnoisemri", - "name": "Real Noise MRI", - "headline": "Developing fast MRI techniques without fully sampled data", - "headline_alternatives": [ - "1. Develop fast MRI techniques without relying on fully sampled data", - "2. Improve MRI speed without using fully sampled images as ground truth ", - "3. Fast MRI acquisition through innovative methods not needing full data", - "4. Speed up MRI sans full k-space data as benchmark for undersampling", - "5. Faster MRI sans complete Fourier data as reference for undersampling" - ] - }, - { - "id": 95, - "slug": "deep-generative-model-challenge-for-da-in-surgery", - "name": "Deep Generative Model Challenge for DA in Surgery", - "headline": "Challenge aims to adapt algorithms from simulation to mitral valve surgery", - "headline_alternatives": [] - }, - { - "id": 96, - "slug": "aimdatathon", - "name": "AIM Datathon 2020", - "headline": "AIM Datathon 2020: \"Join the AI in Medicine (AIM) Datathon 2020\"", - "headline_alternatives": [] - }, - { - "id": 97, - "slug": "opc-recurrence", - "name": "Oropharynx Cancer (OPC) Radiomics Challenge :: Local Recurrence Prediction", - "headline": "Determine from CT data whether a tumor will be controlled by definitive radi...", - "headline_alternatives": [] - }, - { - "id": 98, - "slug": "oropharynx-radiomics-hpv", - "name": "Oropharynx Cancer (OPC) Radiomics Challenge :: Human Papilloma Virus (HPV) Status Prediction", - "headline": "Human Papilloma Virus (HPV) Status Prediction: \"Predict from CT data the HPV...", - "headline_alternatives": [] - }, - { - "id": 99, - "slug": "data-science-bowl-2017", - "name": "Data Science Bowl 2017", - "headline": "\"Can you improve lung cancer detection?\"", - "headline_alternatives": [] - }, - { - "id": 100, - "slug": "predict-impact-of-air-quality-on-death-rates", - "name": "Predict impact of air quality on mortality rates", - "headline": "\"Predict CVD and cancer caused mortality rates in England using air quality ...", - "headline_alternatives": [ - "1. Predict mortality in England from air quality data (34 characters)", - "2. Forecast CVD and cancer deaths using air monitoring (43 characters) ", - "3. Model England mortality rates with Copernicus air data (49 characters)", - "4. Estimate CVD and cancer mortality in England from air quality (56 characters)", - "5. Use Copernicus air monitoring to predict CVD and cancer deaths (59 characters)" - ] - }, - { - "id": 101, - "slug": "intel-mobileodt-cervical-cancer-screening", - "name": "Intel & MobileODT Cervical Cancer Screening", - "headline": "\"Which cancer treatment will be most effective?\"", - "headline_alternatives": [] - }, - { - "id": 102, - "slug": "msk-redefining-cancer-treatment", - "name": "Personalized Medicine-Redefining Cancer Treatment", - "headline": "Predict the effect of Genetic Variants to enable Personalized Medicine", - "headline_alternatives": [ - "1. Predicting Genetic Variants for Personalized Medicine (34 characters)", - "2. Enabling Personalized Medicine Through Genetic Analysis (43 characters)", - "3. Genetic Variant Effects Key to Personalized Care (34 characters) ", - "4. Unlocking Personalized Medicine with Genetic Predictions (43 characters)", - "5. Predicting Variants to Customize Medical Treatments (44 characters)" - ] - }, - { - "id": 103, - "slug": "mubravo", - "name": "Predicting Cancer Diagnosis", - "headline": "Bravo's machine learning competition!", - "headline_alternatives": [ - "1. ML competition hosted by Bravo", - "2. Bravo hosts machine learning contest ", - "3. Bravo challenges ML experts in new contest", - "4. Bravo seeks top ML solutions in competition", - "5. Bravo competition for best machine learning" - ] - }, - { - "id": 104, - "slug": "histopathologic-cancer-detection", - "name": "Histopathologic Cancer Detection", - "headline": "Identify metastatic tissue in histopathologic scans of lymph node sections", - "headline_alternatives": [] - }, - { - "id": 105, - "slug": "tjml1920-decision-trees", - "name": "TJML 2019-20 Breast Cancer Detection Competition", - "headline": "Use a decision tree to identify malignant breast cancer tumors", - "headline_alternatives": [] - }, - { - "id": 106, - "slug": "prostate-cancer-grade-assessment", - "name": "Prostate cANcer graDe Assessment (PANDA) Challenge", - "headline": "Prostate cancer diagnosis using the Gleason grading system", - "headline_alternatives": [ - "1. Gleason grading for prostate cancer diagnosis (34 characters)", - "2. Using Gleason system to grade prostate tumors (43 characters)", - "3. Grading prostate cancers with Gleason system (44 characters) ", - "4. Gleason grading to diagnose prostate cancer (41 characters)", - "5. Diagnose prostate cancer with Gleason grading (45 characters)" - ] - }, - { - "id": 107, - "slug": "breast-cancer", - "name": "Breast Cancer", - "headline": "Use cell nuclei categories to predict breast cancer tumor.", - "headline_alternatives": [] - }, - { - "id": 108, - "slug": "breast-cancer-detection", - "name": "Breast Cancer Detection", - "headline": "breast cancer detection", - "headline_alternatives": [] - }, - { - "id": 109, - "slug": "hrpred", - "name": "Prediction of High Risk Patients", - "headline": "Classification of high and low risk cancer patients", - "headline_alternatives": [] - }, - { - "id": 110, - "slug": "ml4moleng-cancer", - "name": "MIT ML4MolEng-Predicting Cancer Progression", - "headline": "MIT 3.100, 10.402, 20.301 In class ML competition (Spring 2021)", - "headline_alternatives": [ - "1. MIT classes compete in machine learning contest", - "2. MIT students battle in ML competition ", - "3. MIT classes face off in in-class ML contest", - "4. MIT classes hold ML competition during semester ", - "5. MIT students compete in machine learning challenge" - ] - }, - { - "id": 111, - "slug": "uw-madison-gi-tract-image-segmentation", - "name": "UW-Madison GI Tract Image Segmentation", - "headline": "Track healthy organs in medical scans to improve cancer treatment", - "headline_alternatives": [ - "1. Tracking organs in scans to boost cancer care", - "2. Scanning organs to enhance cancer treatment ", - "3. Organ tracking in scans improves cancer therapy", - "4. Monitoring organs via imaging aids cancer treatment", - "5. Organ imaging analysis boosts cancer treatments" - ] - }, - { - "id": 112, - "slug": "rsna-miccai-brain-tumor-radiogenomic-classification", - "name": "RSNA-MICCAI Brain Tumor Radiogenomic Classification", - "headline": "Predict the status of a genetic biomarker important for brain cancer treatment", - "headline_alternatives": [ - "1. BraTS challenge tests brain tumor segmentation methods (56 characters)", - "2. BraTS evaluates glioblastoma segmentation and classification (56 characters)", - "3. BraTS challenge focuses on brain tumor segmentation, prediction (59 characters) ", - "4. BraTS tests MRI-based brain tumor segmentation, MGMT prediction (65 characters)", - "5. BraTS challenge evaluates segmentation, classification of brain tumors (63 characters)" - ] - }, - { - "id": 113, - "slug": "breastcancer", - "name": "Breast Cancer - Beginners ML", - "headline": "Beginners hands-on experience with ML basics", - "headline_alternatives": [ - "1. Learn ML basics hands-on as a beginner (34 characters)", - "2. Hands-on ML for beginners to gain experience (43 characters) ", - "3. Beginners: Get hands-on with ML fundamentals (44 characters)", - "4. Hands-on ML basics for beginners' first experience (51 characters)", - "5. Beginners: Gain hands-on experience with ML basics (54 characters)" - ] - }, - { - "id": 114, - "slug": "ml-olympiad-health-and-education", - "name": "ML Olympiad -Let's Fight lung cancer", - "headline": "Use your ML expertise to help us step another step toward defeating cancer...", - "headline_alternatives": [ - "1. Join the fight against cancer with ML", - "2. Help defeat cancer with your ML skills ", - "3. ML experts: lend your skills to beat cancer", - "4. Can your ML help us defeat cancer?", - "5. Calling all ML experts - help us beat cancer" - ] - }, - { - "id": 115, - "slug": "cs98-22-dl-task1", - "name": "CS98X-22-DL-Task1", - "headline": "This competition is related to Task 1 in coursework-breast cancer classification", - "headline_alternatives": [ - "1. Breast cancer classification challenge via machine learning", - "2. Using AI to classify breast cancer from medical images ", - "3. Applying deep learning to breast cancer diagnosis", - "4. Automated breast cancer detection through image classification", - "5. Machine learning for breast cancer diagnosis from scans" - ] - }, - { - "id": 116, - "slug": "parasitedetection-iiitb2019", - "name": "Parasite detection", - "headline": "Detect if cell image has parasite or is uninfected", - "headline_alternatives": [] - }, - { - "id": 117, - "slug": "hpa-single-cell-image-classification", - "name": "Human Protein Atlas -Single Cell Classification", - "headline": "Find individual human cell differences in microscope images", - "headline_alternatives": [] - }, - { - "id": 118, - "slug": "stem-cell-predcition", - "name": "Stem Cell Predcition", - "headline": "Classify stem and non-stem cells using RNA-seq data", - "headline_alternatives": [] - }, - { - "id": 119, - "slug": "sartorius-cell-instance-segmentation", - "name": "Sartorius - Cell Instance Segmentation", - "headline": "Detect single neuronal cells in microscopy images", - "headline_alternatives": [ - "1. Segment neuronal cells in microscopy images to aid neuroresearch", - "2. Delineate distinct objects in cell images to quantify neurological data ", - "3. Detect and segment neuronal cells to enable neurobiology research", - "4. Identify neuronal cells in images to study neurological disorders", - "5. Segment cell images to measure effects of diseases and treatments" - ] - }, - { - "id": 120, - "slug": "pvelad", - "name": "Photovoltaic cell anomaly detection", - "headline": "Photovoltaic cell anomaly detection", - "headline_alternatives": [ - "1. Hebei U and Beihang U Host AI Competition", - "2. Hebei and Beihang Universities Host AI Challenge ", - "3. AIHebut and NAVE Groups Host AI Competition", - "4. Hebei U and Beihang U Host AI Challenge", - "5. Universities Host AI Competition for Research Groups" - ] - }, - { - "id": 121, - "slug": "blood-mnist", - "name": "Blood-MNIST", - "headline": "Classifying blood cell types using Weights and Biases", - "headline_alternatives": [ - "1. Classify Blood Cells with Weights and Biases (34 characters)", - "2. Using Weights and Biases for Blood Cell Classification (49 characters) ", - "3. Blood Cell Type Classification via Weights and Biases (56 characters)", - "4. Weights and Biases to Classify Blood Cell Types (51 characters) ", - "5. Classifying Blood Cells with W&B Machine Learning (56 characters)" - ] - }, - { - "id": 122, - "slug": "insilicomolhack", - "name": "MolHack", - "headline": "Apply deep learning to speedup drug validation", - "headline_alternatives": [ - "1. Deep learning to accelerate drug validation", - "2. Using deep learning for faster drug validation ", - "3. Applying deep learning to hasten drug validation", - "4. Deep learning speeds up drug validation process", - "5. Leveraging deep learning to expedite drug validation" - ] - }, - { - "id": 123, - "slug": "codata2019challenge", - "name": "Cell Response Classification", - "headline": "From recorded timeseries of many cells in a well, predict which drug treatme...", - "headline_alternatives": [] - }, - { - "id": 124, - "slug": "drug-solubility-challenge", - "name": "Drug solubility challenge", - "headline": "Crucial Role of Solubility in Drug Formulation for Optimal Efficacy", - "headline_alternatives": [ - "1. Improving drug solubility for optimal pharmacological response (54 characters)", - "2. Enhancing solubility to achieve desired drug concentrations (56 characters) ", - "3. Optimizing drug solubility for anticipated pharmacological effects (63 characters)", - "4. Solubility key to reaching target drug concentrations (49 characters)", - "5. Tuning solubility to get right drug levels for effects (56 characters)" - ] - }, - { - "id": 125, - "slug": "kinase-inhibition-challenge", - "name": "Kinase inhibition challenge", - "headline": "Unlocking the Therapeutic Potential of Protein Kinases: Big Data Insights", - "headline_alternatives": [] - }, - { - "id": 126, - "slug": "ai-drug-discovery", - "name": "AI Drug Discovery Workshop and Coding Challenge", - "headline": "Fostering Core AI Programming Proficiency for Drug Discovery Advancements", - "headline_alternatives": [ - "1. Honing AI skills for drug discovery (24 characters)", - "2. Building AI programming for drug R&D (32 characters)", - "3. Developing AI abilities for pharma innovation (39 characters) ", - "4. Advancing AI competencies in pharmaceuticals (43 characters)", - "5. Mastering AI coding fundamentals for new drugs (47 characters)" - ] - }, - { - "id": 127, - "slug": "protein-compound-affinity", - "name": "Structure-free protein-ligand affinity prediction - Task 1 Fitting", - "headline": "Developing new AI models for drug discovery, main portal (Task-1 fitting)", - "headline_alternatives": [ - "1. New AI models to advance drug discovery (34 characters)", - "2. Developing AI to accelerate pharmaceutical innovations (51 characters) ", - "3. Creating AI systems for faster drug development (44 characters)", - "4. AI models to enhance drug discovery process (41 characters)", - "5. Leveraging AI to discover new medicines faster (44 characters)" - ] - }, - { - "id": 128, - "slug": "cisc873-dm-f21-a5", - "name": "CISC873-DM-F21-A5", - "headline": "Anti-Cancer Drug Activity Prediction", - "headline_alternatives": [ - "1. Predicting Anti-Cancer Drug Potency with AI (34 characters)", - "2. AI to Forecast Anti-Cancer Drug Efficacy (32 characters) ", - "3. Using AI to Predict Anti-Cancer Drug Activity (40 characters)", - "4. AI Model Predicts Best Anti-Cancer Drugs (31 characters) ", - "5. AI Helps Identify Most Effective Anti-Cancer Drugs (45 characters)" - ] - }, - { - "id": 129, - "slug": "pro-lig-aff-task2-mse", - "name": "Structure-free protein-ligand affinity prediction - Task 2 Fitting", - "headline": "Developing new AI models for drug discovery (Task-2 fitting)", - "headline_alternatives": [] - }, - { - "id": 130, - "slug": "pro-lig-aff-task1-pearsonr", - "name": "Structure-free protein-ligand affinity prediction - Task 1 Ranking", - "headline": "Developing new AI models for drug discovery (Task-1 ranking)", - "headline_alternatives": [ - "1. New AI models to aid drug discovery (21 characters)", - "2. Applying AI to accelerate drug discovery (34 characters) ", - "3. Developing AI models to rank drug candidates (41 characters)", - "4. Using AI for faster, better drug discovery (38 characters)", - "5. AI models to improve drug candidate identification (49 characters)" - ] - }, - { - "id": 131, - "slug": "pro-lig-aff-task2-pearsonr", - "name": "Structure-free protein-ligand affinity prediction - Task 2 Ranking", - "headline": "Developing new AI models for drug discovery (Task-2 ranking)", - "headline_alternatives": [ - "1. Creating AI to Rank Drug Candidates (14 chars)", - "2. AI Models to Prioritize Drug Leads (24 chars)", - "3. AI for Ranking Potential New Drugs (25 chars) ", - "4. Develop AI to Identify Top Drug Candidates (33 chars)", - "5. Building AI to Rank Drug Discovery Results (34 chars)" - ] - }, - { - "id": 132, - "slug": "pro-lig-aff-task3-spearmanr", - "name": "Structure-free protein-ligand affinity prediction - Task 3 Ranking", - "headline": "Developing new AI models for drug discovery (Task-3 ranking)", - "headline_alternatives": [] - }, - { - "id": 133, - "slug": "hhp", - "name": "Heritage Health Prize", - "headline": "Identify patients who will be admitted to a hospital within the next year us...", - "headline_alternatives": [] - }, - { - "id": 134, - "slug": "pf2012", - "name": "Practice Fusion Analyze This! 2012 - Prediction Challenge", - "headline": "Delve into Electronic Health Records: Propose Innovative Predictive Modeling...", - "headline_alternatives": [ - "1. Predict disease risks from electronic health records (53 characters)", - "2. Mine electronic records for impactful modeling ideas (56 characters) ", - "3. Discover predictive signals in patient data (44 characters)", - "4. Electronic health records: a trove for predictive modeling (56 characters)", - "5. Ideate modeling competitions using patient records (49 characters)" - ] - }, - { - "id": 135, - "slug": "pf2012-at", - "name": "Practice Fusion Analyze This! 2012 - Open Challenge", - "headline": "Delve into Electronic Health Records: Propose Innovative Predictive Modeling...", - "headline_alternatives": [] - }, - { - "id": 136, - "slug": "seizure-detection", - "name": "UPenn and Mayo Clinic's Seizure Detection Challenge", - "headline": "Detect seizures in intracranial EEG recordings", - "headline_alternatives": [ - "1. Detecting seizures from brain recordings (34 characters)", - "2. Identifying seizures in EEG data (32 characters) ", - "3. Seizure detection using intracranial EEG (34 characters)", - "4. Finding seizures in brain activity recordings (39 characters)", - "5. Automatically detect seizures from EEG (31 characters)" - ] - }, - { - "id": 137, - "slug": "seizure-prediction", - "name": "American Epilepsy Society Seizure Prediction Challenge", - "headline": "Predict seizures in intracranial EEG recordings", - "headline_alternatives": [] - }, - { - "id": 138, - "slug": "deephealth-1", - "name": "Deep Health - alcohol", - "headline": "Find Correlations and patterns with Medical data", - "headline_alternatives": [ - "1. Uncover Insights in Medical Data", - "2. Discover Patterns in Health Records ", - "3. Medical Data Analysis to Find Links", - "4. Seeking Correlations in Patient Info", - "5. Analyze Medical Data for Connections" - ] - }, - { - "id": 139, - "slug": "deep-health-3", - "name": "Deep Health - Diabetes 2", - "headline": "Deep Health Hackathon: Predicting Future Diabetes Occurrences Challenge", - "headline_alternatives": [] - }, - { - "id": 140, - "slug": "d012554-2021", - "name": "D012554 - 2021", - "headline": "Classify the health of a fetus using CTG data", - "headline_alternatives": [ - "1. Predict fetal health from CTG readings (34 characters)", - "2. Classify fetal health using CTG data (32 characters) ", - "3. Assess fetal wellbeing with CTG analysis (38 characters)", - "4. Determine fetal condition from cardiotocography (56 characters)", - "5. Categorize fetus health through CTG data (39 characters)" - ] - }, - { - "id": 141, - "slug": "idao-2022-bootcamp-insomnia", - "name": "IDAO 2022. ML Bootcamp - Insomnia", - "headline": "Predict sleep disorder on given human health data", - "headline_alternatives": [ - "1. Predicting sleep disorders from health data (34 characters)", - "2. Using health data to forecast sleep disorders (43 characters)", - "3. Can health info predict sleep problems? (34 characters) ", - "4. Sleep disorder prediction via health analytics (43 characters)", - "5. Forecasting sleep disorders with human data (44 characters)" - ] - }, - { - "id": 142, - "slug": "tweet-mental-health-classification", - "name": "Tweet Mental Health Classification", - "headline": "Build Models to classify tweets to determine mental health", - "headline_alternatives": [ - "1. Build Models to Classify Tweets for Mental Health (56 characters)", - "2. Classifying Tweets to Determine Mental Health (49 characters) ", - "3. Modeling Tweets to Assess Mental Health Status (51 characters)", - "4. Develop Models for Mental Health Detection via Tweets (63 characters)", - "5. Using Tweet Classification to Gauge Mental Health (59 characters)" - ] - }, - { - "id": 143, - "slug": "ml-olympiad-good-health-and-well-being", - "name": "ML Olympiad - GOOD HEALTH AND WELL BEING", - "headline": "Use your ML expertise to classify if a patient has heart disease or not", - "headline_alternatives": [ - "1. ML to Diagnose Heart Disease (25 characters)", - "2. AI for Heart Disease Detection (32 characters) ", - "3. Classify Heart Disease with ML (34 characters)", - "4. Detecting Heart Disease with AI (38 characters)", - "5. ML Model Predicts Heart Disease (38 characters)" - ] - }, - { - "id": 144, - "slug": "rsna-breast-cancer-detection", - "name": "RSNA Screening Mammography Breast Cancer Detection", - "headline": "Find breast cancers in screening mammograms", - "headline_alternatives": [] - }, - { - "id": 145, - "slug": "biocreative-vii-text-mining-drug-and-chemical-protein-interactions-drugprot", - "name": "BioCreative VII: Text mining drug and chemical-protein interactions (DrugProt)", - "headline": "Develop systems to extract drug-gene relations from text", - "headline_alternatives": [ - "1. Develop systems to extract drug-gene relations from text (79 characters)", - "2. Automatically detect relations between drugs and proteins in text (78 characters) ", - "3. Build models to identify drug-protein interactions in literature (77 characters)", - "4. Extract and characterize drug-gene relations from biomedical text (78 characters)", - "5. Promote extraction of drug-protein relations to enable drug discovery (79 characters)" - ] - }, - { - "id": 146, - "slug": "extended-literature-ai-for-drug-induced-liver-injury", - "name": "Extended Literature AI for Drug Induced Liver Injury", - "headline": "Develop ML tools to analyze drug texts for liver injury data", - "headline_alternatives": [] - }, - { - "id": 147, - "slug": "anti-microbial-resistance-forensics", - "name": "Anti-Microbial Resistance Forensics", - "headline": "Classifying Bacteriophages to Understand Microbial Evolution", - "headline_alternatives": [ - "1. Develop new methods to classify and analyze bacteriophages' role in AMR spread (78 characters)", - "2. Understand how bacteriophages enable microbial evolution and antibiotic resistance (77 characters) ", - "3. Study correlation between phages and AMR to assess treatment potential (69 characters)", - "4. Improve phage classification amidst contradictory antibiotic replacement role (79 characters) ", - "5. Apply advanced algorithms to precisely describe bacteriophage capabilities (69 characters)" - ] - }, - { - "id": 148, - "slug": "disease-maps-to-modelling-covid-19", - "name": "Disease Maps to Modelling COVID-19", - "headline": "Disease Maps COVID-19 Challenge: Enhancing Drug Repurposing with Omic Data", - "headline_alternatives": [ - "1. Model COVID-19 mechanisms to find drug repurposing candidates (79 characters)", - "2. Leverage expert maps and data to expand COVID-19 knowledge (76 characters) ", - "3. Combine maps and data to understand COVID-19 and repurpose drugs (79 characters)", - "4. Use detailed maps to model COVID-19 and suggest drug repurposing (78 characters)", - "5. Expand biological knowledge of COVID-19 infection via modeling (76 characters)" - ] - }, - { - "id": 149, - "slug": "crowdsourced-evaluation-of-inchi-based-tautomer-identification", - "name": "Crowdsourced Evaluation of InChI-based Tautomer Identification", - "headline": "Crowdsourced Evaluation of InChI-Based Tautomer Identification Challenge", - "headline_alternatives": [] - }, - { - "id": 150, - "slug": "nctr-indel-calling-from-oncopanel-sequencing-challenge-phase-2", - "name": "NCTR Indel Calling from Oncopanel Sequencing Challenge Phase 2", - "headline": "NCTR Indel Calling from Oncopanel Sequencing Data Challenge", - "headline_alternatives": [ - "1. Develop standards for oncopanel sequencing quality control (77 characters)", - "2. Benchmark oncopanels with reference sample from FDA's SEQC2 project (79 characters) ", - "3. Assess analytical performance of oncopanels using SEQC2 reference sample (76 characters)", - "4. Establish quality metrics for clinical oncopanel sequencing (69 characters)", - "5. Standardize analysis protocols for oncopanel sequencing data (61 characters)" - ] - }, - { - "id": 151, - "slug": "nctr-indel-calling-from-oncopanel-sequencing-data-challenge-phase-1", - "name": "NCTR Indel Calling from Oncopanel Sequencing Data Challenge Phase 1", - "headline": "NCTR Indel Calling from Oncopanel Sequencing Data Challenge", - "headline_alternatives": [ - "1. Develop standards for oncopanel sequencing quality control (69 characters)", - "2. Benchmark oncopanels with reference sample from FDA's SEQC2 project (77 characters) ", - "3. Assess analytical performance of oncopanels using SEQC2 reference sample (76 characters)", - "4. Establish quality metrics for oncopanel sequencing in precision oncology (77 characters)", - "5. Create protocols for fit-for-purpose use of oncopanel NGS data (69 characters)" - ] - }, - { - "id": 152, - "slug": "vha-innovation-ecosystem-and-precisionfda-covid-19-risk-factor-modeling-challenge-phase-2", - "name": "VHA Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge Phase 2", - "headline": "The focus of Phase 2 was to validate the top performing models on two additi...", - "headline_alternatives": [] - }, - { - "id": 153, - "slug": "tumor-mutational-burden-tmb-challenge-phase-2", - "name": "Tumor Mutational Burden (TMB) Challenge Phase 2", - "headline": "The goal of the Friends of Cancer Research and precisionFDA Tumor Mutational...", - "headline_alternatives": [] - }, - { - "id": 154, - "slug": "predicting-gene-expression-using-millions-of-random-promoter-sequences", - "name": "Predicting Gene Expression Using Millions of Random Promoter Sequences", - "headline": "Decoding gene expression regulation to understand disease.", - "headline_alternatives": [] - }, - { - "id": 155, - "slug": "brats-2023", - "name": "BraTS 2023", - "headline": "Benchmarking brain tumor segmentation with expanded dataset.", - "headline_alternatives": [ - "1. BraTS challenge focuses on benchmarking brain tumor segmentation with expanded dataset. (79 characters)", - "2. BraTS challenge expands brain tumor dataset to advance segmentation. (56 characters) ", - "3. BraTS seeks to advance brain tumor segmentation with larger dataset. (59 characters)", - "4. BraTS challenge grows dataset to boost brain tumor segmentation. (59 characters)", - "5. BraTS challenge expands dataset to advance brain tumor delineation. (63 characters)" - ] - }, - { - "id": 156, - "slug": "cagi7", - "name": "CAGI7", - "headline": "The seventh round of CAGI.", - "headline_alternatives": [] - }, - { - "id": 157, - "slug": "casp15", - "name": "CASP15", - "headline": "Establish the state-of-art in modeling proteins and protein complexes.", - "headline_alternatives": [ - "1. CASP15 updates categories to advance deep learning for protein structures (76 characters)", - "2. CASP15 strengthens protein modeling with new categories, deep learning focus (79 characters) ", - "3. CASP15 evolves protein modeling categories to boost deep learning impact (76 characters)", - "4. CASP15 adds categories to drive deep learning advances in protein structures (79 characters)", - "5. CASP15 shifts categories to maximize deep learning insights for proteins (77 characters)" - ] - }, - { - "id": 158, - "slug": "synthrad2023", - "name": "SynthRAD2023", - "headline": "Synthesizing computed tomography for radiotherapy.", - "headline_alternatives": [ - "1. Platform to evaluate and compare sCT generation methods (79 characters)", - "2. Public benchmark for sCT generation algorithms (56 characters) ", - "3. First platform to evaluate sCT generation methods (59 characters)", - "4. Challenge to compare sCT generation algorithms (49 characters)", - "5. Public metrics to evaluate sCT generation methods (59 characters)" - ] - }, - { - "id": 159, - "slug": "synthetic-data-for-instrument-segmentation-in-surgery-syn-iss", - "name": "Synthetic Data for Instrument Segmentation in Surgery (Syn-ISS)", - "headline": "Challenging Machine Learning in Surgical Instrument Segmentation with Synthe...", - "headline_alternatives": [] - }, - { - "id": 160, - "slug": "pitvis", - "name": "PitVis", - "headline": "Surgical workflow and instrument recognition in endonasal surgery.", - "headline_alternatives": [ - "1. Guiding surgeons to remove pituitary tumors in tight spaces", - "2. Assisting surgeons with computer guidance for pituitary surgery ", - "3. Improving outcomes in complex pituitary tumor removal", - "4. Computer-assisted intervention for challenging pituitary surgeries", - "5. Enhancing surgical technique for pituitary tumor treatment" - ] - }, - { - "id": 161, - "slug": "mvseg2023", - "name": "MVSEG2023", - "headline": "Automatically segment mitral valve leaflets from single frame 3D trans-esoph...", - "headline_alternatives": [] - }, - { - "id": 162, - "slug": "crossmoda23", - "name": "crossMoDA23", - "headline": "This challenge proposes is the third edition of the first medical imaging be...", - "headline_alternatives": [] - }, - { - "id": 163, - "slug": "icr-identify-age-related-conditions", - "name": "ICR - Identifying Age-Related Conditions", - "headline": "Use Machine Learning to detect conditions with measurements of anonymous cha...", - "headline_alternatives": [] - }, - { - "id": 164, - "slug": "cafa-5-protein-function-prediction", - "name": "CAFA 5: Protein Function Prediction", - "headline": "Predict the biological function of a protein.", - "headline_alternatives": [] - }, - { - "id": 165, - "slug": "rsna-2023-abdominal-trauma-detection", - "name": "RSNA 2023 Abdominal Trauma Detection", - "headline": "Detect and classify traumatic abdominal injuries.", - "headline_alternatives": [] - }, - { - "id": 166, - "slug": "hubmap-hacking-the-human-vasculature", - "name": "HuBMAP: Hacking the Human Vasculature", - "headline": "Segment instances of microvascular structures from healthy human kidney tiss...", - "headline_alternatives": [ - "1. Segment microvascular structures in kidney histology images (56 characters)", - "2. Model to identify capillaries, arterioles, venules in tissues (56 characters)", - "3. Automate segmentation of blood vessels in kidney slides (51 characters) ", - "4. Improve understanding of microvasculature in tissues (51 characters)", - "5. Segment capillaries, arterioles, venules in kidney images (59 characters)" - ] - }, - { - "id": 167, - "slug": "amp-parkinsons-disease-progression-prediction", - "name": "AMP(R)-Parkinson's Disease Progression Prediction", - "headline": "Use protein and peptide data measurements from Parkinson's Disease patients ...", - "headline_alternatives": [] - }, - { - "id": 168, - "slug": "open-problems-multimodal", - "name": "Open Problems -Multimodal Single-Cell Integration", - "headline": "Predict how DNA, RNA & protein measurements co-vary in single cells.", - "headline_alternatives": [] - }, - { - "id": 169, - "slug": "multi-atlas-labeling-beyond-the-cranial-vault", - "name": "Multi-Atlas Labeling Beyond the Cranial Vault", - "headline": "Innovative Multi-Atlas Labeling for Soft Tissue Segmentation on Clinical CT", - "headline_alternatives": [] - }, - { - "id": 170, - "slug": "hubmap-organ-segmentation", - "name": "HuBMAP + HPA: Hacking the Human Body", - "headline": "Segment multi-organ functional tissue units.", - "headline_alternatives": [ - "1. Segment functional tissue units in human organs to understand cell relationships (79 characters)", - "2. Identify tissue units across organs to reveal cell organization insights (79 characters)", - "3. Segment tissue images to map cell relationships and advance research (78 characters) ", - "4. Map functional tissue units across organs to unlock cell organization (77 characters)", - "5. Segment tissue images to construct atlas revealing cell relationships (78 characters)" - ] - }, - { - "id": 171, - "slug": "hubmap-kidney-segmentation", - "name": "HuBMAP: Hacking the Kidney", - "headline": "Identify glomeruli in human kidney tissue images.", - "headline_alternatives": [ - "1. Map kidney at single cell level, detect functional tissue units", - "2. Identify functional tissue units in human kidney to understand cell relationships ", - "3. Build tools to map kidney and identify cell blocks affecting health", - "4. Detect functional tissue units in kidney to advance cell atlas ", - "5. Identify cell blocks in kidney images to understand impact on human health" - ] - }, - { - "id": 172, - "slug": "ventilator-pressure-prediction", - "name": "Google Brain: Ventilator Pressure Prediction", - "headline": "Simulate a ventilator connected to a sedated patient's lung.", - "headline_alternatives": [] - }, - { - "id": 173, - "slug": "stanford-covid-vaccine", - "name": "OpenVaccine - COVID-19 mRNA Vaccine Degradation Prediction", - "headline": "Urgent need to bring the COVID-19 vaccine to mass production.", - "headline_alternatives": [ - "1. Predict RNA degradation rates to aid COVID vaccine design (79 characters)", - "2. Model RNA degradation for COVID vaccine mRNAs (52 characters) ", - "3. Leverage data science for RNA degradation prediction (56 characters)", - "4. Develop models of RNA degradation rates (34 characters)", - "5. Predict mRNA vaccine degradation with machine learning (56 characters)" - ] - }, - { - "id": 174, - "slug": "openvaccine", - "name": "OpenVaccine", - "headline": "A research initiative aimed at developing innovative design principles for R...", - "headline_alternatives": [ - "1. Seeking more stable mRNA vaccines to enable worldwide access", - "2. Can you design a more stable mRNA vaccine? ", - "3. Help create fridge-stable mRNA vaccines for global use", - "4. Improve mRNA vaccine stability for worldwide immunization", - "5. Enhance mRNA vaccines: 2x-10x more stable formulations wanted" - ] - }, - { - "id": 175, - "slug": "opentb", - "name": "OpenTB", - "headline": "We aim to gain fundamental insights into the ribosome's RNA sequence-folding.", - "headline_alternatives": [] - }, - { - "id": 176, - "slug": "opencrispr", - "name": "OpenCRISPR", - "headline": "Can you improve the algorithm that classifies drugs based on their biologica...", - "headline_alternatives": [ - "1. Develop small molecule switches to control CRISPR gene editing (76 characters)", - "2. Create on/off switches for CRISPR to enable safe gene editing (77 characters) ", - "3. Enable turning CRISPR on/off with small molecules for safe use (76 characters)", - "4. Design small molecule switches to control CRISPR activity (63 characters)", - "5. Develop safe, controllable CRISPR gene editing using small molecules (78 characters)" - ] - }, - { - "id": 177, - "slug": "openknot", - "name": "OpenKnot", - "headline": "CellSignal - Disentangling biological signal from experimental noise in cell...", - "headline_alternatives": [ - "1. Understanding folding pathways and dynamics of RNA pseudoknots (76 characters)", - "2. Elucidating structure and function of crucial RNA pseudoknots (59 characters) ", - "3. Investigating role of RNA pseudoknots in gene regulation (52 characters)", - "4. Analyzing viral replication mechanisms of RNA pseudoknots (56 characters)", - "5. Exploring enzymatic activity and catalysis by RNA pseudoknots (59 characters)" - ] - }, - { - "id": 178, - "slug": "openaso", - "name": "OpenASO", - "headline": "Event detection from wearable sensor data.", - "headline_alternatives": [] - }, - { - "id": 179, - "slug": "openribosome", - "name": "OpenRibosome", - "headline": "AI competition seeks cancer diagnosis and treatment solutions.", - "headline_alternatives": [] - }, - { - "id": 180, - "slug": "lish-moa", - "name": "Mechanisms of Action (MoA) Prediction", - "headline": "Segmenting Cerebral Arteries from 3D Angiography Images.", - "headline_alternatives": [] - }, - { - "id": 181, - "slug": "recursion-cellular-image-classification", - "name": "Recursion Cellular Image Classification", - "headline": "Challenge compares Circle of Willis classification methods.", - "headline_alternatives": [] - }, - { - "id": 182, - "slug": "tlvmc-parkinsons-freezing-gait-prediction", - "name": "Parkinson's Freezing of Gait Prediction", - "headline": "The US Food and Drug Administration (FDA) calls on stakeholders, including t...", - "headline_alternatives": [ - "1. Detect freezing of gait in Parkinson's using wearable sensor data (77 characters)", - "2. Machine learning to detect freezing of gait from sensor data (63 characters) ", - "3. Improve understanding of freezing in Parkinson's with ML (63 characters)", - "4. ML model to detect freezing episodes in Parkinson's (63 characters)", - "5. Wearable sensors to monitor freezing of gait in Parkinson's (79 characters)" - ] - }, - { - "id": 183, - "slug": "chaimeleon", - "name": "CHAIMELEON Open Challenges", - "headline": "The Veterans Health Administration Innovation Ecosystem, the Digital Health ...", - "headline_alternatives": [ - "1. CHAIMELEON challenges train AI to answer cancer questions (56 characters)", - "2. Competition refines AI for cancer diagnosis and treatment (54 characters) ", - "3. CHAIMELEON: AI collaboration to advance cancer care (56 characters)", - "4. Developing trustworthy AI for cancer prognosis and outcomes (66 characters)", - "5. Advancing cancer AI - CHAIMELEON open competition (49 characters)" - ] - }, - { - "id": 184, - "slug": "topcow23", - "name": "Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA", - "headline": "Predicting High Risk Breast Cancer - a Nightingale OS & AHLI data challenge.", - "headline_alternatives": [] - }, - { - "id": 185, - "slug": "circle-of-willis-intracranial-artery-classification-and-quantification-challenge-2023", - "name": "Circle of Willis Intracranial Artery Classification and Quantification Challenge 2023", - "headline": "Predicting High Risk Breast Cancer - a Nightingale OS & AHLI data challenge.", - "headline_alternatives": [ - "1. Challenge compares methods for CoW classification and quantification (77 characters)", - "2. CoW challenge: Classify configuration, quantify diameters and angles (79 characters)", - "3. CoW challenge: Compare methods for classification and measurement (65 characters) ", - "4. Challenge to evaluate CoW configuration classification and measurement (79 characters)", - "5. Comparing methods for CoW configuration classification and measurement (79 characters)" - ] - }, - { - "id": 186, - "slug": "making-sense-of-electronic-health-record-ehr-race-and-ethnicity-data", - "name": "Making Sense of Electronic Health Record (EHR) Race and Ethnicity Data", - "headline": "Predicting the connectivity and properties of in-silico networks.", - "headline_alternatives": [] - }, - { - "id": 187, - "slug": "the-veterans-cardiac-health-and-ai-model-predictions-v-champs", - "name": "The Veterans Cardiac Health and AI Model Predictions (V-CHAMPS)", - "headline": "The goal of the in silico challenges is the reverse engineering of gene netw...", - "headline_alternatives": [] - }, - { - "id": 188, - "slug": "predicting-high-risk-breast-cancer-phase-1", - "name": "Predicting High Risk Breast Cancer - Phase 1", - "headline": "The goal of the in silico network challenge is to reverse engineer gene regu...", - "headline_alternatives": [ - "1. Developing algorithms to predict harmful breast cancers, reduce overtreatment (76 characters)", - "2. Reducing invasive breast cancer procedures via AI analysis of biopsies (73 characters)", - "3. Algorithms to identify harmful breast cancers, limit unnecessary treatment (78 characters) ", - "4. Using AI to analyze biopsies, identify aggressive breast cancers (63 characters)", - "5. Harnessing AI to reduce invasive procedures for harmless breast cancers (77 characters)" - ] - }, - { - "id": 189, - "slug": "predicting-high-risk-breast-cancer-phase-2", - "name": "Predicting High Risk Breast Cancer - Phase 2", - "headline": "The goal of this Network Inference Challenge is to reverse engineer gene reg...", - "headline_alternatives": [ - "1. AI to identify harmful breast cancers, reduce unnecessary procedures (79 characters)", - "2. Algorithms to predict metastatic potential, limit overtreatment (78 characters) ", - "3. Using AI on biopsies to distinguish harmful vs harmless cancers (76 characters)", - "4. Reducing invasive breast cancer procedures via AI analysis (71 characters) ", - "5. Can AI identify truly dangerous breast cancers? (43 characters)" - ] - }, - { - "id": 190, - "slug": "dream-2-in-silico-network-inference", - "name": "DREAM 2 - In Silico Network Inference", - "headline": "Identify dates in clinical notes.", - "headline_alternatives": [] - }, - { - "id": 191, - "slug": "dream-3-in-silico-network-challenge", - "name": "DREAM 3 - In Silico Network Challenge", - "headline": "Identify person names in clinical notes.", - "headline_alternatives": [ - "1. Reverse engineer gene networks from steady state & time series data (76 characters)", - "2. Predict directed unsigned network topology from in silico gene datasets (79 characters) ", - "3. Reconstruct gene networks through reverse engineering of in silico data (77 characters)", - "4. Uncover gene network topology from steady state & time series gene data (79 characters)", - "5. Infer directed gene network models from simulated steady state & time series data (79 characters)" - ] - }, - { - "id": 192, - "slug": "dream-4-in-silico-network-challenge", - "name": "DREAM 4 - In Silico Network Challenge", - "headline": "Identify location information in clinical notes.", - "headline_alternatives": [ - "1. Reverse engineer gene networks from simulated data (34 characters)", - "2. Infer gene regulation networks from in silico data (43 characters) ", - "3. Reconstruct gene networks from steady-state and time-series data (59 characters)", - "4. Uncover network structure from simulated gene expression data (59 characters)", - "5. Predict network response to novel perturbations from provided data (65 characters)" - ] - }, - { - "id": 193, - "slug": "dream-5-network-inference-challenge", - "name": "DREAM 5 - Network Inference Challenge", - "headline": "Identify contact information in clinical notes.", - "headline_alternatives": [] - }, - { - "id": 194, - "slug": "nlp-sandbox-date-annotation", - "name": "NLP Sandbox Date Annotation", - "headline": "Identify identifiers in clinical notes.", - "headline_alternatives": [ - "1. Extract dates from clinical notes using NLP", - "2. Identify dates in clinical notes with a date annotator ", - "3. Build a date extractor for clinical notes", - "4. Develop an NLP model to find dates in notes", - "5. Annotate dates in clinical text using Sandbox" - ] - }, - { - "id": 195, - "slug": "nlp-sandbox-person-name-annotation", - "name": "NLP Sandbox Person Name Annotation", - "headline": "Predict BCL6 transcriptomic targets from biological data.", - "headline_alternatives": [ - "1. Predict person names in clinical notes (22 characters)", - "2. Annotate person names in clinical text (27 characters) ", - "3. Identify person names from clinical notes (32 characters)", - "4. Extract person names from clinical documents (37 characters)", - "5. Find and label person names in medical records (44 characters)" - ] - }, - { - "id": 196, - "slug": "nlp-sandbox-location-annotation", - "name": "NLP Sandbox Location Annotation", - "headline": "Predict a protein-protein interaction network of 47 proteins.", - "headline_alternatives": [ - "1. Predict locations in clinical notes (16 characters)", - "2. Annotate locations from clinical notes (24 characters) ", - "3. Extract locations from clinical text (23 characters)", - "4. Identify locations mentioned in notes (26 characters)", - "5. Generate location annotations for notes (34 characters)" - ] - }, - { - "id": 197, - "slug": "nlp-sandbox-contact-annotation", - "name": "NLP Sandbox Contact Annotation", - "headline": "Reconstruct genome-scale networks from microarray data.", - "headline_alternatives": [ - "1. Predict contact annotations in clinical notes (34 characters)", - "2. Annotate contacts found in clinical notes (38 characters) ", - "3. Identify contacts in clinical notes with NLP (43 characters)", - "4. Contact annotation of clinical notes using NLP (50 characters) ", - "5. Predict contacts in clinical notes with an NLP annotator (56 characters)" - ] - }, - { - "id": 198, - "slug": "nlp-sandbox-id-annotation", - "name": "NLP Sandbox ID Annotation", - "headline": "Inferring five-gene networks from synthetic data.", - "headline_alternatives": [ - "1. Predict patient IDs in clinical notes (34 characters)", - "2. Annotate clinical notes with patient IDs (39 characters) ", - "3. Identify patient IDs in unstructured clinical text (45 characters)", - "4. Extract patient identifiers from clinical narratives (56 characters)", - "5. Automatically tag patient IDs in doctor's notes (56 characters)" - ] - }, - { - "id": 199, - "slug": "dream-2-bcl6-transcriptomic-target-prediction", - "name": "DREAM 2 - BCL6 Transcriptomic Target Prediction", - "headline": "Inferring signaling cascade dynamics from flow cytometry data.", - "headline_alternatives": [] - }, - { - "id": 200, - "slug": "dream-2-protein-protein-interaction-network-inference", - "name": "DREAM 2 - Protein-Protein Interaction Network Inference", - "headline": "Predicting gene expression from gene datasets.", - "headline_alternatives": [] - }, - { - "id": 201, - "slug": "dream-2-genome-scale-network-inference", - "name": "DREAM 2 - Genome-Scale Network Inference", - "headline": "Cell-type specific high-throughput experimental data.", - "headline_alternatives": [] - }, - { - "id": 202, - "slug": "dream-2-synthetic-five-gene-network-inference", - "name": "DREAM 2 - Synthetic Five-Gene Network Inference", - "headline": "Predict missing protein concentrations from a large corpus of measurements.", - "headline_alternatives": [] - }, - { - "id": 203, - "slug": "dream-3-signaling-cascade-identification", - "name": "DREAM 3 - Signaling Cascade Identification", - "headline": "Predict binding specificity of peptide-antibody interactions.", - "headline_alternatives": [ - "1. Inferring signaling cascade dynamics from flow cytometry data (76 characters)", - "2. Analyzing signaling cascade topology with incomplete data (77 characters)", - "3. Exploring signaling cascade interactions with flow cytometry (77 characters) ", - "4. Inferring signaling cascade dynamics and topology (69 characters)", - "5. Analyzing signaling cascade properties from flow data (79 characters)" - ] - }, - { - "id": 204, - "slug": "dream-3-gene-expression-prediction", - "name": "DREAM 3 - Gene Expression Prediction", - "headline": "Predict binding intensities for transcription factors from motifs.", - "headline_alternatives": [] - }, - { - "id": 205, - "slug": "dream-4-predictive-signaling-network-modelling", - "name": "DREAM 4 - Predictive Signaling Network Modelling", - "headline": "Predict the binding specificity of peptide-antibody interactions.", - "headline_alternatives": [ - "1. Create cell-type model of signaling using HepG2 data", - "2. Leverage prior knowledge to build HepG2 signaling model ", - "3. Build interpretable HepG2 signaling network consistent with data", - "4. Can existing signaling knowledge explain HepG2 protein activity?", - "5. Evaluate agreement between signaling knowledge and HepG2 data" - ] - }, - { - "id": 206, - "slug": "dream-3-signaling-response-prediction", - "name": "DREAM 3 - Signaling Response Prediction", - "headline": "Predict gene expression levels from promoter sequences in eukaryotes.", - "headline_alternatives": [ - "1. Predicting cell signaling response in liver cells with phospho-protein and cytokine data (79 characters)", - "2. Modeling phospho-protein and cytokine dynamics in normal and cancer liver cells (77 characters) ", - "3. Analyzing stimulation effects on signaling in hepatocytes using multi-omics data (79 characters)", - "4. Mapping signaling networks in liver cells using phospho-protein and cytokine data (76 characters)", - "5. Integrating intracellular and extracellular data to understand liver cell signaling (79 characters)" - ] - }, - { - "id": 207, - "slug": "dream-4-peptide-recognition-domain-prd-specificity-prediction", - "name": "DREAM 4 - Peptide Recognition Domain (PRD) Specificity Prediction", - "headline": "Predict disease phenotypes and infer gene networks from systems genetics data.", - "headline_alternatives": [] - }, - { - "id": 208, - "slug": "dream-5-transcription-factor-dna-motif-recognition-challenge", - "name": "DREAM 5 - Transcription-Factor, DNA-Motif Recognition Challenge", - "headline": "Challenge to estimate model parameters.", - "headline_alternatives": [ - "1. Predict signal intensities for transcription factors binding DNA", - "2. Model transcription factor binding to genomic DNA sequences", - "3. Infer transcription factor binding motifs from genomic sequences", - "4. Quantify transcription factor binding specificities", - "5. Estimate transcription factor affinities from sequence motifs" - ] - }, - { - "id": 209, - "slug": "dream-5-epitope-antibody-recognition-ear-challenge", - "name": "DREAM 5 - Epitope-Antibody Recognition (EAR) Challenge", - "headline": "The goal of this challenge is to diagnose Acute Myeloid Leukemia from patien...", - "headline_alternatives": [] - }, - { - "id": 210, - "slug": "dream-gene-expression-prediction-challenge", - "name": "DREAM Gene Expression Prediction Challenge", - "headline": "Assess accuracy of mRNA-seq alternative splicing reconstruction.", - "headline_alternatives": [] - }, - { - "id": 211, - "slug": "dream-5-systems-genetics-challenge", - "name": "DREAM 5 - Systems Genetics Challenge", - "headline": "A machine learning contest for gene network inference from single-cell pertu...", - "headline_alternatives": [] - }, - { - "id": 212, - "slug": "dream-6-estimation-of-model-parameters-challenge", - "name": "DREAM 6 - Estimation of Model Parameters Challenge", - "headline": "The challenge related to computational geometry and topology for ICLR 2022.", - "headline_alternatives": [ - "1. Estimate parameters and predict outcomes for gene regulation models (79 characters)", - "2. Apply optimization to accurately parameterize gene network models (79 characters)", - "3. Select informative experiments to parameterize gene regulation models (78 characters) ", - "4. Develop methods to predict gene network model perturbations (74 characters)", - "5. Optimize estimation of kinetic parameters in gene networks (77 characters)" - ] - }, - { - "id": 213, - "slug": "dream-6-flowcap2-molecular-classification-of-acute-myeloid-leukemia-challenge", - "name": "DREAM 6 - FlowCAP2 Molecular Classification of Acute Myeloid Leukemia Challenge", - "headline": "Automating Identification of Cell Populations in Flow Cytometry Data", - "headline_alternatives": [] - }, - { - "id": 214, - "slug": "dream-6-alternative-splicing-challenge", - "name": "DREAM 6 - Alternative Splicing Challenge", - "headline": "Compare mRNA-seq methods on primate and rhino transcripts", - "headline_alternatives": [ - "1. Assess accuracy of mRNA-seq transcript reconstruction vs long reads", - "2. Compare mRNA-seq to long reads for transcriptome analysis in stem cells ", - "3. Evaluate mRNA-seq transcript reconstruction on rhino, mandrill cells", - "4. mRNA-seq vs long reads for transcriptomes of stem cells and fibroblasts", - "5. Test mRNA-seq transcript reconstruction against long reads across species" - ] - }, - { - "id": 215, - "slug": "causalbench-challenge", - "name": "CausalBench Challenge", - "headline": "Deriving gene-gene networks to improve causal disease insights", - "headline_alternatives": [ - "1. Mapping gene interactions to advance drug discovery (34 characters)", - "2. Deriving gene networks from single-cell data (32 characters) ", - "3. Advancing gene network inference for drug discovery (43 characters)", - "4. Improving causal insights into biology via gene networks (49 characters)", - "5. Machine learning to map gene interactions for drug discovery (56 characters)" - ] - }, - { - "id": 216, - "slug": "iclr-computational-geometry-and-topology-challenge-2022", - "name": "ICLR Computational Geometry & Topology Challenge 2022", - "headline": "Fostering Geometric Learning: Crowdsourced Algorithms for Reproducible Deep ...", - "headline_alternatives": [] - }, - { - "id": 217, - "slug": "iclr-computational-geometry-and-topology-challenge-2021", - "name": "ICLR Computational Geometry & Topology Challenge 2021", - "headline": "Advancing computational geometry and topology with Python", - "headline_alternatives": [] - }, - { - "id": 218, - "slug": "genedisco-challenge", - "name": "GeneDisco Challenge", - "headline": "Exploring experimental design with active learning for genetics", - "headline_alternatives": [] - }, - { - "id": 219, - "slug": "hidden-treasures-warm-up", - "name": "Hidden Treasures: Warm Up", - "headline": "Assess genome sequencing software accuracy with unknown variants", - "headline_alternatives": [] - }, - { - "id": 220, - "slug": "data-management-and-graph-extraction-for-large-models-in-the-biomedical-space", - "name": "Data management and graph extraction for large models in the biomedical space", - "headline": "CMU Libraries & DNAnexus Data Management Hackathon: Advancing Biomedical Kno...", - "headline_alternatives": [] - }, - { - "id": 221, - "slug": "cagi2-asthma-twins", - "name": "CAGI2: Asthma discordant monozygotic twins", - "headline": "Identify genetic differences between asthmatic and healthy twins", - "headline_alternatives": [ - "1. Identify genomic differences between asthmatic and healthy twins with DNA and RNA data (79 characters)", - "2. Find genetic causes for asthma discordance in identical twins (77 characters) ", - "3. Use genomic and transcriptomic data to understand asthma in twins (65 characters)", - "4. Asthma discordance in twins: genetic factors from genome and RNA sequencing (79 characters)", - "5. Asthma genetics: genome and transcriptome comparison of asthmatic vs healthy twins (78 characters)" - ] - }, - { - "id": 222, - "slug": "cagi4-bipolar", - "name": "CAGI4: Bipolar disorder", - "headline": "Predicting bipolar disorder from exome data", - "headline_alternatives": [ - "1. Predicting bipolar disorder from exome data (34 characters)", - "2. Identifying bipolar disorder using exome sequencing (49 characters) ", - "3. Detecting bipolar disorder with exome data (41 characters)", - "4. Classifying bipolar disorder from exomes (41 characters)", - "5. Diagnosing bipolar disorder through exome analysis (55 characters)" - ] - }, - { - "id": 223, - "slug": "cagi3-brca", - "name": "CAGI3: BRCA1 & BRCA2", - "headline": "Assess hereditary cancer risk via BRCA gene analysis", - "headline_alternatives": [ - "1. Develop open access BRCA1/2 mutation test to assess cancer risk (56 characters)", - "2. Create non-proprietary hereditary breast/ovarian cancer test (54 characters) ", - "3. New open access test for BRCA mutations and cancer risk (51 characters)", - "4. Open source alternative to proprietary BRCA1/2 cancer test (56 characters)", - "5. Non-proprietary test to identify BRCA mutations linked to cancer (63 characters)" - ] - }, - { - "id": 224, - "slug": "cagi2-breast-cancer-pkg", - "name": "CAGI2: Breast cancer pharmacogenomics", - "headline": "Exploring CHEK2 as a candidate gene for cancer susceptibility", - "headline_alternatives": [ - "1. Studying CHEK2 as a cancer susceptibility gene (50 characters)", - "2. Examining CHEK2's role in DNA repair and cell cycle regulation (59 characters)", - "3. Investigating CHEK2 interactions with BRCA1/TP53 in cancer risk (66 characters) ", - "4. Can CHEK2 mutations explain increased cancer susceptibility? (63 characters)", - "5. Evaluating CHEK2's function in genome integrity maintenance (63 characters)" - ] - }, - { - "id": 225, - "slug": "cagi4-2eqtl", - "name": "CAGI4: eQTL causal SNPs", - "headline": "Identify regulatory variants causing gene expression differences", - "headline_alternatives": [] - }, - { - "id": 226, - "slug": "cagi1-cbs", - "name": "CAGI1: CBS", - "headline": "Seeking to understand CBS enzyme function in cysteine production", - "headline_alternatives": [ - "1. Enzyme for cysteine production requires B6 and heme (50 characters)", - "2. CBS deficiency causes rare sulfur amino acid disorder (56 characters) ", - "3. Studying cofactor dependence of cysteine synthase enzyme (56 characters)", - "4. Understanding CBS enzyme function in cysteine biosynthesis (63 characters) ", - "5. Modeling effects of CBS mutations in cysteine metabolism (63 characters)" - ] - }, - { - "id": 227, - "slug": "cagi2-cbs", - "name": "CAGI2: CBS", - "headline": "Developing treatment for homocystinuria caused by CBS deficiency", - "headline_alternatives": [ - "1. Developing treatment for homocystinuria caused by CBS enzyme deficiency (79 characters)", - "2. Targeting CBS enzyme requiring B6 & heme to treat homocystinuria (77 characters) ", - "3. Treating recessive disease homocystinuria by restoring CBS function (76 characters)", - "4. Restoring vitamin-dependent CBS enzyme to treat homocystinuria (74 characters) ", - "5. Fixing defective CBS enzyme needing B6 & heme to treat homocystinuria (79 characters)" - ] - }, - { - "id": 228, - "slug": "cagi1-chek2", - "name": "CAGI1: CHEK2", - "headline": "Variants in the ATM & CHEK2 genes are associated with breast cancer.", - "headline_alternatives": [] - }, - { - "id": 229, - "slug": "cagi3-fch", - "name": "CAGI3: FCH", - "headline": "Seeking to understand genetic basis of common hyperlipidemia disorder", - "headline_alternatives": [ - "1. Identify genetic basis of common hyperlipidemia disorder FCH (76 characters)", - "2. Discover genes linked to elevated cholesterol in FCH patients (79 characters) ", - "3. Uncover genetic factors contributing to high triglycerides, LDL in FCH (78 characters)", - "4. Find genetic variants associated with increased CAD risk in FCH (76 characters)", - "5. Determine genetic causes of variable lipid levels in familial hyperlipidemia (79 characters)" - ] - }, - { - "id": 230, - "slug": "cagi3-ha", - "name": "CAGI3: HA", - "headline": "Raising HDL levels to reduce heart disease risk", - "headline_alternatives": [ - "1. Raising HDL levels to reduce heart disease risk in hypoalphalipoproteinemia (78 characters)", - "2. Treating low HDL & APOA1 levels in hypoalphalipoproteinemia patients (77 characters) ", - "3. Developing new therapies to increase HDL in hypoalphalipoproteinemia (79 characters)", - "4. Targeting low HDL as a risk factor for heart disease in hypoalphalipoproteinemia (79 characters) ", - "5. Improving HDL & APOA1 to lower coronary disease risk in hypoalphalipoproteinemia (78 characters)" - ] - }, - { - "id": 231, - "slug": "cagi2-croshn-s", - "name": "CAGI2: Crohn's disease", - "headline": "Seeking genes linked to Crohn's, an inflammatory bowel disease", - "headline_alternatives": [ - "1. Seeking genetic causes of Crohn's, an inflammatory bowel disease (78 characters)", - "2. Understanding the genetics behind Crohn's disease and GI inflammation (76 characters) ", - "3. Investigating the complex genetics of Crohn's disease (44 characters)", - "4. Identifying genetic factors in Crohn's disease pathology (56 characters) ", - "5. Elucidating the genetics of chronic inflammation in Crohn's disease (63 characters)" - ] - }, - { - "id": 232, - "slug": "cagi3-crohn-s", - "name": "CAGI3: Crohn's disease", - "headline": "Understanding the genetics behind Crohn's disease", - "headline_alternatives": [] - }, - { - "id": 233, - "slug": "cagi4-chron-s-exome", - "name": "CAGI4: Crohn's exomes", - "headline": "Seeking to understand genetic basis of Crohn's bowel disease", - "headline_alternatives": [ - "1. Seeking genetic causes of Crohn's disease, an inflammatory bowel disorder (77 characters)", - "2. Understanding the genetics behind Crohn's disease, a chronic inflammatory bowel disease (76 characters) ", - "3. Investigating the complex genetics of Crohn's disease, an inflammatory bowel disorder (75 characters)", - "4. Studying the genetics of Crohn's, an inflammatory bowel disease with chronic inflammation (79 characters) ", - "5. Exploring the genetic factors in Crohn's disease, a relapsing inflammatory bowel disorder (79 characters)" - ] - }, - { - "id": 234, - "slug": "cagi4-hopkins", - "name": "CAGI4: Hopkins clinical panel", - "headline": "Exonic sequences of 83 genes linked to 14 diseases analyzed", - "headline_alternatives": [ - "1. Hopkins challenge: Classify exonic sequences into 14 disease types", - "2. Hopkins DNA challenge: Categorize 83 gene exonic sequences ", - "3. Hopkins exome challenge: Assign sequences to one of 14 diseases", - "4. Classify 83 Hopkins gene exonic sequences into diseases", - "5. Categorize Hopkins exonic sequences into one of 14 diseases" - ] - }, - { - "id": 235, - "slug": "cagi2-mouse-exomes", - "name": "CAGI2: Mouse exomes", - "headline": "Predict causative variants from exome sequencing data.", - "headline_alternatives": [] - }, - { - "id": 236, - "slug": "cagi3-mrn-mre11", - "name": "CAGI3: MRE11", - "headline": "Genomes are subject to constant threat by damaging agents that generate DNA", - "headline_alternatives": [] - }, - { - "id": 237, - "slug": "cagi4-naglu", - "name": "CAGI4: NAGLU", - "headline": "Predicting enzymatic activity of NAGLU mutants", - "headline_alternatives": [] - }, - { - "id": 238, - "slug": "cagi4-npm-alk", - "name": "CAGI4: NPM: ALK", - "headline": "Predicting kinase activity of NPM-ALK fusion mutants", - "headline_alternatives": [ - "1. Predict kinase activity and binding of NPM-ALK mutants (78 characters)", - "2. Assess impact of mutations on NPM-ALK kinase and binding (77 characters) ", - "3. Model effects of NPM-ALK mutations on activity and binding (79 characters)", - "4. Quantify NPM-ALK mutant kinase activity and Hsp90 binding (79 characters)", - "5. Forecast NPM-ALK mutant kinase function from sequence changes (79 characters)" - ] - }, - { - "id": 239, - "slug": "cagi3-mrn-nbs1", - "name": "CAGI3: NBS1", - "headline": "Predicting Pathogenicity of Rare MRE11 and NBS1 Variants", - "headline_alternatives": [] - }, - { - "id": 240, - "slug": "cagi3-p16", - "name": "CAGI3: p16", - "headline": "Assessing p16 protein variants' effects on cell growth", - "headline_alternatives": [] - }, - { - "id": 241, - "slug": "cagi2-p53", - "name": "CAGI2: p53 reactivation", - "headline": "Predictors are asked to submit predictions on the effect of the cancer rescue...", - "headline_alternatives": [] - }, - { - "id": 242, - "slug": "cagi1-pgp", - "name": "CAGI1: PGP", - "headline": "CAGI Challenges Utilizing Public Genomic and Phenotypic Data Resources", - "headline_alternatives": [] - }, - { - "id": 243, - "slug": "cagi2-pgp", - "name": "CAGI2: PGP", - "headline": "CAGI Challenges Utilizing Public Genomic and Phenotypic Data Resources", - "headline_alternatives": [] - }, - { - "id": 244, - "slug": "cagi3-pgp", - "name": "CAGI3: PGP", - "headline": "CAGI Challenges Utilizing Public Genomic and Phenotypic Data Resources", - "headline_alternatives": [ - "1. Participants share full data to enable CAGI analysis (52 characters)", - "2. CAGI challenges based on participants' shared sequence data (56 characters) ", - "3. Participants publicly share data for CAGI challenge analysis (55 characters)", - "4. CAGI challenges utilize participants' shared sequence data (57 characters)", - "5. Participants make data public for CAGI challenge analysis (59 characters)" - ] - }, - { - "id": 245, - "slug": "cagi4-pgp", - "name": "CAGI4: PGP", - "headline": "CAGI Challenges Utilizing Public Genomic and Phenotypic Data Resources", - "headline_alternatives": [] - }, - { - "id": 246, - "slug": "cagi4-pyruvate-kinase", - "name": "CAGI4: Pyruvate kinase", - "headline": "Predicting mutation impacts on pyruvate kinase activity and regulation", - "headline_alternatives": [] - }, - { - "id": 247, - "slug": "cagi2-rad50", - "name": "CAGI2: RAD50", - "headline": "Assessing RAD50 variants for breast cancer risk", - "headline_alternatives": [ - "1. Identifying RAD50 variants associated with breast cancer risk (54 characters)", - "2. Assessing RAD50 variants for breast cancer susceptibility (59 characters)", - "3. Evaluating RAD50 gene variants in breast cancer cases and controls (71 characters) ", - "4. Determining if RAD50 variants confer intermediate breast cancer risk (79 characters)", - "5. Can RAD50 gene variants help predict breast cancer susceptibility? (76 characters)" - ] - }, - { - "id": 248, - "slug": "cagi2-risksnps", - "name": "CAGI2: riskSNPs", - "headline": "Exploring molecular mechanisms linking SNPs to disease risk", - "headline_alternatives": [] - }, - { - "id": 249, - "slug": "cagi3-risksnps", - "name": "CAGI3: riskSNPs", - "headline": "Exploring molecular mechanisms linking SNPs to disease risk", - "headline_alternatives": [] - }, - { - "id": 250, - "slug": "cagi2-nav1-5", - "name": "CAGI2: SCN5A", - "headline": "Predictors are asked to submit predictions on the effect of the mutants on t...", - "headline_alternatives": [] - }, - { - "id": 251, - "slug": "cagi2-mr-1", - "name": "CAGI2: Shewanella oneidensis strain MR-1", - "headline": "Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...", - "headline_alternatives": [] - }, - { - "id": 252, - "slug": "cagi3-mr-1", - "name": "CAGI3: Shewanella oneidensis strain MR-1", - "headline": "Shewanella oneidensis strain MR-1 (formerly known as S. putrefaciens) is a m...", - "headline_alternatives": [] - }, - { - "id": 253, - "slug": "cagi4-sickkids", - "name": "CAGI4: SickKids", - "headline": "The challenge presented here is to use computational methods to match each g...", - "headline_alternatives": [ - "1. Predict phenotypes from genome sequences of kids with disorders (79 characters)", - "2. Match genome sequences to clinical descriptions for 25 children (77 characters) ", - "3. Identify variants predicting genetic disorders from sequences (76 characters)", - "4. Infer phenotypes and disease risk from genomic data (74 characters)", - "5. Predict traits and disease predispositions from genomes (78 characters)" - ] - }, - { - "id": 254, - "slug": "cagi4-sumo-ligase", - "name": "CAGI4: SUMO ligase", - "headline": "Participants are asked to submit predictions of the effect of the variants o...", - "headline_alternatives": [] - }, - { - "id": 255, - "slug": "cagi3-splicing", - "name": "CAGI3: TP53 splicing", - "headline": "With the provided data, determine which disease-causing mutations in the TP5...", - "headline_alternatives": [] - }, - { - "id": 256, - "slug": "cagi4-warfarin", - "name": "CAGI4: Warfarin exomes", - "headline": "With the provided exome data and clinical covariates, predict the therapeuti...", - "headline_alternatives": [ - "1. Improve warfarin dosing to reduce adverse events", - "2. Seek better warfarin dosing for fewer patient hospitalizations", - "3. Find optimal warfarin doses despite high variability ", - "4. Reduce warfarin's adverse events via personalized dosing", - "5. Develop improved warfarin dosing given high usage" - ] - }, - { - "id": 257, - "slug": "cagi6-calmodulin", - "name": "CAGI6: Calmodulin", - "headline": "Participants were asked to submit predictions for the competitive growth sco...", - "headline_alternatives": [] - }, - { - "id": 258, - "slug": "cagi2-splicing", - "name": "CAGI2: splicing", - "headline": "Predictors are asked to compare exons from wild type and disease-associated ...", - "headline_alternatives": [ - "1. Developing methods to improve accuracy of pre-mRNA splicing", - "2. Improving understanding of spliceosome assembly for splicing regulation ", - "3. Elucidating mechanisms regulating spliceosome assembly on pre-mRNAs", - "4. Characterizing spliceosome dynamics during pre-mRNA splicing", - "5. Analyzing spliceosome assembly on nascent pre-mRNA introns" - ] - }, - { - "id": 259, - "slug": "cagi6-lc-arsa", - "name": "CAGI6: ARSA", - "headline": "Predicting the effect of naturally occurring missense mutations on enzymatic...", - "headline_alternatives": [] - }, - { - "id": 260, - "slug": "predict-hits-for-the-wdr-domain-of-lrrk2", - "name": "CACHE1: Predict Hits for The WDR Domain of LRRK2", - "headline": "Finding ligands targeting the central cavity of the WD-40 repeat (WDR) domai...", - "headline_alternatives": [ - "1. Find hits targeting LRRK2 WD40 domain for Parkinson's (56 characters)", - "2. Discover compounds binding LRRK2 WDR to treat Parkinson's (64 characters) ", - "3. Seek ligands for LRRK2 WD40 to modulate Parkinson's risk (65 characters)", - "4. Target LRRK2 WD40 repeats to impact Parkinson's pathogenesis (79 characters) ", - "5. Identify small molecules binding LRRK2 WDR domain for Parkinson's (76 characters)" - ] - }, - { - "id": 261, - "slug": "finding-ligands-targeting-the-conserved-rna-binding-site-of-sars-cov-2-nsp13", - "name": "CACHE2: Finding Ligands Targeting The Conserved RNA Binding Site of SARS-CoV-2 NSP13", - "headline": "Finding ligands targeting the conserved RNA binding site of SARS-CoV-2 NSP13...", - "headline_alternatives": [] - }, - { - "id": 262, - "slug": "finding-ligands-targeting-the-macrodomain-of-sars-cov-2-nsp3", - "name": "CACHE3: Finding ligands targeting the macrodomain of SARS-CoV-2 Nsp3", - "headline": "Studying the macrodomain of Severe acute respiratory syndrome coronavirus 2 ...", - "headline_alternatives": [] - }, - { - "id": 263, - "slug": "finding-ligands-targeting-the-tkb-domain-of-cblb", - "name": "CACHE4: Finding ligands targeting the TKB domain of CBLB", - "headline": "Investigating the TKB domain of CBLB, a protein involved in cancer and immun...", - "headline_alternatives": [ - "1. Predict novel compounds binding CBLB TKB domain (76 characters)", - "2. Discover new chemical templates binding CBLB protein (69 characters)", - "3. Find compounds binding CBLB below 30 micromolar KD (63 characters) ", - "4. Model binding of novel ligands to CBLB TKB domain (65 characters)", - "5. Predict tight-binding compounds for closed CBLB conformation (79 characters)" - ] - }, - { - "id": 264, - "slug": "rare-disease-ai-hackathon", - "name": "Rare Disease AI Hackathon", - "headline": "Researchers and medical experts are invited to collaborate on our patient ca...", - "headline_alternatives": [] - }, - { - "id": 265, - "slug": "cometh-benchmark", - "name": "COMETH Benchmark", - "headline": "Quantify tumor heterogeneity\u2014how many cell types are present and in which pr...", - "headline_alternatives": [] - }, - { - "id": 266, - "slug": "the-miccai-2014-machine-learning-challenge", - "name": "The MICCAI 2014 Machine Learning Challenge", - "headline": "Predicting Binary and Continuous Phenotypes from Structural Brain MRI Data i...", - "headline_alternatives": [] - }, - { - "id": 267, - "slug": "cagi6-annotate-all-missense", - "name": "CAGI6: Annotate All Missense", - "headline": "Predictors are asked to predict the functional effect of every coding single...", - "headline_alternatives": [] - }, - { - "id": 268, - "slug": "cagi6-hmbs", - "name": "CAGI6: HMBS", - "headline": "Participants are asked to submit predictions of the fitness score for each o...", - "headline_alternatives": [] - }, - { - "id": 269, - "slug": "cagi6-id-panel", - "name": "CAGI6: Intellectual Disability Panel", - "headline": "In this challenge, predictors are asked to analyze the sequence data for the...", - "headline_alternatives": [] - }, - { - "id": 270, - "slug": "cagi6-mapk1", - "name": "CAGI6: MAPK1", - "headline": "For each variant, participants are asked to predict the \u0394\u0394G(H2O) value for t...", - "headline_alternatives": [] - }, - { - "id": 271, - "slug": "cagi6-mapk3", - "name": "CAGI6: MAPK3", - "headline": "For each variant, participants are asked to predict the \u0394\u0394G(H2O) value for t...", - "headline_alternatives": [] - }, - { - "id": 272, - "slug": "cagi6-mthfr", - "name": "CAGI6: MTHFR", - "headline": "Participants are asked to submit predictions of the fitness score for each m...", - "headline_alternatives": [] - }, - { - "id": 273, - "slug": "cagi6-prs", - "name": "CAGI6: Polygenic Risk Scores", - "headline": "Participants will be expected to provide a fully trained prediction model th...", - "headline_alternatives": [] - }, - { - "id": 274, - "slug": "cagi6-rgp", - "name": "CAGI6: Rare Genomes Project", - "headline": "The prediction challenge involves approximately 30 families. The prediction ...", - "headline_alternatives": [] - }, - { - "id": 275, - "slug": "cagi6-invitae", - "name": "CAGI6: Sherloc clinical classification", - "headline": "Over 122,000 coding (missense, silent, frameshift, stop gained, in-frame cod...", - "headline_alternatives": [ - "1. Predict pathogenicity of 122,000 variants for submission to ClinVar by Invitae (79 characters)", - "2. Classify 122,000 variants by predicted pathogenicity before submission to ClinVar (79 characters) ", - "3. Assess clinical utility of 122,000 variant interpretations before ClinVar submission (78 characters)", - "4. Interpret pathogenicity of 122,000 variants spanning clinical effects for ClinVar (79 characters)", - "5. Predict effects of 122,000 uncharacterized variants for Invitae's ClinVar submission (80 characters)" - ] - }, - { - "id": 276, - "slug": "cagi6-splicing-vus", - "name": "CAGI6: Splicing VUS", - "headline": "Predict whether the experimentally validated variants of unknown significanc...", - "headline_alternatives": [] - }, - { - "id": 277, - "slug": "cagi6-stk11", - "name": "CAGI6: STK11", - "headline": "Participants are asked to submit predictions on the impact of the variants l...", - "headline_alternatives": [] - }, - { - "id": 278, - "slug": "qbi-hackathon", - "name": "QBI hackathon", - "headline": "A 48-hour event connecting the Bay Area developer community with scientists ...", - "headline_alternatives": [] - }, - { - "id": 279, - "slug": "niddk-central-repository-data-centric-challenge", - "name": "NIDDK Central Repository Data-Centric Challenge", - "headline": "Enhancing NIDDK datasets for future Artificial Intelligence (AI) application...", - "headline_alternatives": [] - }, - { - "id": 280, - "slug": "stanford-ribonanza-rna-folding", - "name": "Stanford Ribonanza RNA Folding", - "headline": "Pioneering RNA Science: A Path to Programmable Medicine and Scientific Break...", - "headline_alternatives": [ - "1. Harnessing RNA to cure disease and fight climate change", - "2. Understanding RNA structure to enable programmable medicine ", - "3. Data science key to unraveling RNA for new medicines", - "4. Decoding RNA structure for disease cures and biotech advances", - "5. RNA manipulation through data science for medicine and biotech" - ] - }, - { - "id": 281, - "slug": "uls23", - "name": "Universal Lesion Segmentation '23 Challenge", - "headline": "Revolutionizing Lesion Segmentation: Advancements, Challenges, and a Univers...", - "headline_alternatives": [] - } -] diff --git a/apps/openchallenges/notebook/docker-compose.yml b/apps/openchallenges/notebook/docker-compose.yml deleted file mode 100644 index 8807bd2d04..0000000000 --- a/apps/openchallenges/notebook/docker-compose.yml +++ /dev/null @@ -1,19 +0,0 @@ -version: '3.8' - -services: - openchallenges-notebook: - image: ghcr.io/sage-bionetworks/openchallenges-notebook:local - container_name: openchallenges-notebook - restart: always - env_file: - - .env - volumes: - - ./notebooks:/home/jovyan/notebooks - networks: - - openchallenges - ports: - - '8888:8888' - -networks: - openchallenges: - name: openchallenges diff --git a/apps/openchallenges/notebook/jupyter_lab_config.py b/apps/openchallenges/notebook/jupyter_lab_config.py deleted file mode 100644 index ea6efc5b3f..0000000000 --- a/apps/openchallenges/notebook/jupyter_lab_config.py +++ /dev/null @@ -1,1084 +0,0 @@ -# Configuration file for lab. - -c = get_config() # noqa - -# ------------------------------------------------------------------------------ -# Application(SingletonConfigurable) configuration -# ------------------------------------------------------------------------------ -## This is an application. - -## The date format used by logging formatters for %(asctime)s -# Default: '%Y-%m-%d %H:%M:%S' -# c.Application.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# Default: '[%(name)s]%(highlevel)s %(message)s' -# c.Application.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL'] -# Default: 30 -# c.Application.log_level = 30 - -## Configure additional log handlers. -# -# The default stderr logs handler is configured by the log_level, log_datefmt -# and log_format settings. -# -# This configuration can be used to configure additional handlers (e.g. to -# output the log to a file) or for finer control over the default handlers. -# -# If provided this should be a logging configuration dictionary, for more -# information see: -# https://docs.python.org/3/library/logging.config.html#logging-config- -# dictschema -# -# This dictionary is merged with the base logging configuration which defines -# the following: -# -# * A logging formatter intended for interactive use called -# ``console``. -# * A logging handler that writes to stderr called -# ``console`` which uses the formatter ``console``. -# * A logger with the name of this application set to ``DEBUG`` -# level. -# -# This example adds a new handler that writes to a file: -# -# .. code-block:: python -# -# c.Application.logging_config = { -# 'handlers': { -# 'file': { -# 'class': 'logging.FileHandler', -# 'level': 'DEBUG', -# 'filename': '', -# } -# }, -# 'loggers': { -# '': { -# 'level': 'DEBUG', -# # NOTE: if you don't list the default "console" -# # handler here then it will be disabled -# 'handlers': ['console', 'file'], -# }, -# } -# } -# Default: {} -# c.Application.logging_config = {} - -## Instead of starting the Application, dump configuration to stdout -# Default: False -# c.Application.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# Default: False -# c.Application.show_config_json = False - -# ------------------------------------------------------------------------------ -# JupyterApp(Application) configuration -# ------------------------------------------------------------------------------ -## Base class for Jupyter applications - -## Answer yes to any prompts. -# Default: False -# c.JupyterApp.answer_yes = False - -## Full path of a config file. -# Default: '' -# c.JupyterApp.config_file = '' - -## Specify a config file to load. -# Default: '' -# c.JupyterApp.config_file_name = '' - -## Generate default config file. -# Default: False -# c.JupyterApp.generate_config = False - -## The date format used by logging formatters for %(asctime)s -# See also: Application.log_datefmt -# c.JupyterApp.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# See also: Application.log_format -# c.JupyterApp.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# See also: Application.log_level -# c.JupyterApp.log_level = 30 - -## -# See also: Application.logging_config -# c.JupyterApp.logging_config = {} - -## Instead of starting the Application, dump configuration to stdout -# See also: Application.show_config -# c.JupyterApp.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# See also: Application.show_config_json -# c.JupyterApp.show_config_json = False - -# ------------------------------------------------------------------------------ -# ExtensionApp(JupyterApp) configuration -# ------------------------------------------------------------------------------ -## Base class for configurable Jupyter Server Extension Applications. -# -# ExtensionApp subclasses can be initialized two ways: -# 1. Extension is listed as a jpserver_extension, and ServerApp calls -# its load_jupyter_server_extension classmethod. This is the -# classic way of loading a server extension. -# 2. Extension is launched directly by calling its `launch_instance` -# class method. This method can be set as a entry_point in -# the extensions setup.py - -## Answer yes to any prompts. -# See also: JupyterApp.answer_yes -# c.ExtensionApp.answer_yes = False - -## Full path of a config file. -# See also: JupyterApp.config_file -# c.ExtensionApp.config_file = '' - -## Specify a config file to load. -# See also: JupyterApp.config_file_name -# c.ExtensionApp.config_file_name = '' - -# Default: '' -# c.ExtensionApp.default_url = '' - -## Generate default config file. -# See also: JupyterApp.generate_config -# c.ExtensionApp.generate_config = False - -## Handlers appended to the server. -# Default: [] -# c.ExtensionApp.handlers = [] - -## The date format used by logging formatters for %(asctime)s -# See also: Application.log_datefmt -# c.ExtensionApp.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# See also: Application.log_format -# c.ExtensionApp.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# See also: Application.log_level -# c.ExtensionApp.log_level = 30 - -## -# See also: Application.logging_config -# c.ExtensionApp.logging_config = {} - -## Whether to open in a browser after starting. -# The specific browser used is platform dependent and -# determined by the python standard library `webbrowser` -# module, unless it is overridden using the --browser -# (ServerApp.browser) configuration option. -# Default: False -# c.ExtensionApp.open_browser = False - -## Settings that will passed to the server. -# Default: {} -# c.ExtensionApp.settings = {} - -## Instead of starting the Application, dump configuration to stdout -# See also: Application.show_config -# c.ExtensionApp.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# See also: Application.show_config_json -# c.ExtensionApp.show_config_json = False - -## paths to search for serving static files. -# -# This allows adding javascript/css to be available from the notebook server machine, -# or overriding individual files in the IPython -# Default: [] -# c.ExtensionApp.static_paths = [] - -## Url where the static assets for the extension are served. -# Default: '' -# c.ExtensionApp.static_url_prefix = '' - -## Paths to search for serving jinja templates. -# -# Can be used to override templates from notebook.templates. -# Default: [] -# c.ExtensionApp.template_paths = [] - -# ------------------------------------------------------------------------------ -# LabServerApp(ExtensionApp) configuration -# ------------------------------------------------------------------------------ -## A Lab Server Application that runs out-of-the-box - -## "A list of comma-separated URIs to get the allowed extensions list -# -# .. versionchanged:: 2.0.0 -# `LabServerApp.whitetlist_uris` renamed to `allowed_extensions_uris` -# Default: '' -# c.LabServerApp.allowed_extensions_uris = '' - -## Answer yes to any prompts. -# See also: JupyterApp.answer_yes -# c.LabServerApp.answer_yes = False - -## The application settings directory. -# Default: '' -# c.LabServerApp.app_settings_dir = '' - -## The url path for the application. -# Default: '/lab' -# c.LabServerApp.app_url = '/lab' - -## Deprecated, use `LabServerApp.blocked_extensions_uris` -# Default: '' -# c.LabServerApp.blacklist_uris = '' - -## A list of comma-separated URIs to get the blocked extensions list -# -# .. versionchanged:: 2.0.0 -# `LabServerApp.blacklist_uris` renamed to `blocked_extensions_uris` -# Default: '' -# c.LabServerApp.blocked_extensions_uris = '' - -## Whether to cache files on the server. This should be `True` except in dev -# mode. -# Default: True -# c.LabServerApp.cache_files = True - -## Full path of a config file. -# See also: JupyterApp.config_file -# c.LabServerApp.config_file = '' - -## Specify a config file to load. -# See also: JupyterApp.config_file_name -# c.LabServerApp.config_file_name = '' - -## Extra paths to look for federated JupyterLab extensions -# Default: [] -# c.LabServerApp.extra_labextensions_path = [] - -## Generate default config file. -# See also: JupyterApp.generate_config -# c.LabServerApp.generate_config = False - -## Handlers appended to the server. -# See also: ExtensionApp.handlers -# c.LabServerApp.handlers = [] - -## Options to pass to the jinja2 environment for this -# Default: {} -# c.LabServerApp.jinja2_options = {} - -## The standard paths to look in for federated JupyterLab extensions -# Default: [] -# c.LabServerApp.labextensions_path = [] - -## The url for federated JupyterLab extensions -# Default: '' -# c.LabServerApp.labextensions_url = '' - -## The interval delay in seconds to refresh the lists -# Default: 3600 -# c.LabServerApp.listings_refresh_seconds = 3600 - -## The optional kwargs to use for the listings HTTP requests as -# described on https://2.python-requests.org/en/v2.7.0/api/#requests.request -# Default: {} -# c.LabServerApp.listings_request_options = {} - -## The listings url. -# Default: '' -# c.LabServerApp.listings_url = '' - -## The date format used by logging formatters for %(asctime)s -# See also: Application.log_datefmt -# c.LabServerApp.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# See also: Application.log_format -# c.LabServerApp.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# See also: Application.log_level -# c.LabServerApp.log_level = 30 - -## -# See also: Application.logging_config -# c.LabServerApp.logging_config = {} - -## Whether a notebook should start a kernel automatically. -# Default: True -# c.LabServerApp.notebook_starts_kernel = True - -## Whether to open in a browser after starting. -# See also: ExtensionApp.open_browser -# c.LabServerApp.open_browser = False - -## The optional location of the settings schemas directory. If given, a handler -# will be added for settings. -# Default: '' -# c.LabServerApp.schemas_dir = '' - -## Settings that will passed to the server. -# See also: ExtensionApp.settings -# c.LabServerApp.settings = {} - -## The url path of the settings handler. -# Default: '' -# c.LabServerApp.settings_url = '' - -## Instead of starting the Application, dump configuration to stdout -# See also: Application.show_config -# c.LabServerApp.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# See also: Application.show_config_json -# c.LabServerApp.show_config_json = False - -## The optional location of local static files. If given, a static file handler -# will be added. -# Default: '' -# c.LabServerApp.static_dir = '' - -## paths to search for serving static files. -# See also: ExtensionApp.static_paths -# c.LabServerApp.static_paths = [] - -## Url where the static assets for the extension are served. -# See also: ExtensionApp.static_url_prefix -# c.LabServerApp.static_url_prefix = '' - -## Paths to search for serving jinja templates. -# See also: ExtensionApp.template_paths -# c.LabServerApp.template_paths = [] - -## The application templates directory. -# Default: '' -# c.LabServerApp.templates_dir = '' - -## The optional location of the themes directory. If given, a handler will be -# added for themes. -# Default: '' -# c.LabServerApp.themes_dir = '' - -## The theme url. -# Default: '' -# c.LabServerApp.themes_url = '' - -## The url path of the translations handler. -# Default: '' -# c.LabServerApp.translations_api_url = '' - -## The url path of the tree handler. -# Default: '' -# c.LabServerApp.tree_url = '' - -## The optional location of the user settings directory. -# Default: '' -# c.LabServerApp.user_settings_dir = '' - -## Deprecated, use `LabServerApp.allowed_extensions_uris` -# Default: '' -# c.LabServerApp.whitelist_uris = '' - -## The url path of the workspaces API. -# Default: '' -# c.LabServerApp.workspaces_api_url = '' - -## The optional location of the saved workspaces directory. If given, a handler -# will be added for workspaces. -# Default: '' -# c.LabServerApp.workspaces_dir = '' - -# ------------------------------------------------------------------------------ -# LabApp(LabServerApp) configuration -# ------------------------------------------------------------------------------ -## -# See also: LabServerApp.allowed_extensions_uris -# c.LabApp.allowed_extensions_uris = '' - -## Answer yes to any prompts. -# See also: JupyterApp.answer_yes -# c.LabApp.answer_yes = False - -## The app directory to launch JupyterLab from. -# Default: None -# c.LabApp.app_dir = None - -## The application settings directory. -# Default: '' -# c.LabApp.app_settings_dir = '' - -## The url path for the application. -# Default: '/lab' -# c.LabApp.app_url = '/lab' - -## Deprecated, use `LabServerApp.blocked_extensions_uris` -# See also: LabServerApp.blacklist_uris -# c.LabApp.blacklist_uris = '' - -## -# See also: LabServerApp.blocked_extensions_uris -# c.LabApp.blocked_extensions_uris = '' - -## Whether to cache files on the server. This should be `True` except in dev -# mode. -# Default: True -# c.LabApp.cache_files = True - -## Whether to enable collaborative mode (experimental). -# Default: False -# c.LabApp.collaborative = False - -## Full path of a config file. -# See also: JupyterApp.config_file -# c.LabApp.config_file = '' - -## Specify a config file to load. -# See also: JupyterApp.config_file_name -# c.LabApp.config_file_name = '' - -## Whether to start the app in core mode. In this mode, JupyterLab -# will run using the JavaScript assets that are within the installed -# JupyterLab Python package. In core mode, third party extensions are disabled. -# The `--dev-mode` flag is an alias to this to be used when the Python package -# itself is installed in development mode (`pip install -e .`). -# Default: False -# c.LabApp.core_mode = False - -## The default URL to redirect to from `/` -# Default: '/lab' -# c.LabApp.default_url = '/lab' - -## Whether to start the app in dev mode. Uses the unpublished local -# JavaScript packages in the `dev_mode` folder. In this case JupyterLab will -# show a red stripe at the top of the page. It can only be used if JupyterLab -# is installed as `pip install -e .`. -# Default: False -# c.LabApp.dev_mode = False - -## Whether to expose the global app instance to browser via window.jupyterlab -# Default: False -# c.LabApp.expose_app_in_browser = False - -## Whether to load prebuilt extensions in dev mode. This may be -# useful to run and test prebuilt extensions in development installs of -# JupyterLab. APIs in a JupyterLab development install may be -# incompatible with published packages, so prebuilt extensions compiled -# against published packages may not work correctly. -# Default: False -# c.LabApp.extensions_in_dev_mode = False - -## Extra paths to look for federated JupyterLab extensions -# Default: [] -# c.LabApp.extra_labextensions_path = [] - -## Generate default config file. -# See also: JupyterApp.generate_config -# c.LabApp.generate_config = False - -## Handlers appended to the server. -# See also: ExtensionApp.handlers -# c.LabApp.handlers = [] - -## Options to pass to the jinja2 environment for this -# Default: {} -# c.LabApp.jinja2_options = {} - -## The standard paths to look in for federated JupyterLab extensions -# Default: [] -# c.LabApp.labextensions_path = [] - -## The url for federated JupyterLab extensions -# Default: '' -# c.LabApp.labextensions_url = '' - -## The interval delay in seconds to refresh the lists -# See also: LabServerApp.listings_refresh_seconds -# c.LabApp.listings_refresh_seconds = 3600 - -## The optional kwargs to use for the listings HTTP requests as -# described on https://2.python-requests.org/en/v2.7.0/api/#requests.request -# See also: LabServerApp.listings_request_options -# c.LabApp.listings_request_options = {} - -## The listings url. -# Default: '' -# c.LabApp.listings_url = '' - -## The date format used by logging formatters for %(asctime)s -# See also: Application.log_datefmt -# c.LabApp.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# See also: Application.log_format -# c.LabApp.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# See also: Application.log_level -# c.LabApp.log_level = 30 - -## -# See also: Application.logging_config -# c.LabApp.logging_config = {} - -## Whether a notebook should start a kernel automatically. -# Default: True -# c.LabApp.notebook_starts_kernel = True - -## Whether to open in a browser after starting. -# See also: ExtensionApp.open_browser -# c.LabApp.open_browser = False - -## The override url for static lab assets, typically a CDN. -# Default: '' -# c.LabApp.override_static_url = '' - -## The override url for static lab theme assets, typically a CDN. -# Default: '' -# c.LabApp.override_theme_url = '' - -## The optional location of the settings schemas directory. If given, a handler -# will be added for settings. -# Default: '' -# c.LabApp.schemas_dir = '' - -## Settings that will passed to the server. -# See also: ExtensionApp.settings -# c.LabApp.settings = {} - -## The url path of the settings handler. -# Default: '' -# c.LabApp.settings_url = '' - -## Instead of starting the Application, dump configuration to stdout -# See also: Application.show_config -# c.LabApp.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# See also: Application.show_config_json -# c.LabApp.show_config_json = False - -## Splice source packages into app directory. -# Default: False -# c.LabApp.splice_source = False - -## The optional location of local static files. If given, a static file handler -# will be added. -# Default: '' -# c.LabApp.static_dir = '' - -## paths to search for serving static files. -# See also: ExtensionApp.static_paths -# c.LabApp.static_paths = [] - -## Url where the static assets for the extension are served. -# See also: ExtensionApp.static_url_prefix -# c.LabApp.static_url_prefix = '' - -## Paths to search for serving jinja templates. -# See also: ExtensionApp.template_paths -# c.LabApp.template_paths = [] - -## The application templates directory. -# Default: '' -# c.LabApp.templates_dir = '' - -## The optional location of the themes directory. If given, a handler will be -# added for themes. -# Default: '' -# c.LabApp.themes_dir = '' - -## The theme url. -# Default: '' -# c.LabApp.themes_url = '' - -## The url path of the translations handler. -# Default: '' -# c.LabApp.translations_api_url = '' - -## The url path of the tree handler. -# Default: '' -# c.LabApp.tree_url = '' - -## The directory for user settings. -# Default: '/home/vscode/.jupyter/lab/user-settings' -# c.LabApp.user_settings_dir = '/home/vscode/.jupyter/lab/user-settings' - -## Whether to serve the app in watch mode -# Default: False -# c.LabApp.watch = False - -## Deprecated, use `LabServerApp.allowed_extensions_uris` -# See also: LabServerApp.whitelist_uris -# c.LabApp.whitelist_uris = '' - -## The url path of the workspaces API. -# Default: '' -# c.LabApp.workspaces_api_url = '' - -## The directory for workspaces -# Default: '/home/vscode/.jupyter/lab/workspaces' -# c.LabApp.workspaces_dir = '/home/vscode/.jupyter/lab/workspaces' - -# ------------------------------------------------------------------------------ -# ServerApp(JupyterApp) configuration -# ------------------------------------------------------------------------------ -## Set the Access-Control-Allow-Credentials: true header -# Default: False -# c.ServerApp.allow_credentials = False - -## Set the Access-Control-Allow-Origin header -# -# Use '*' to allow any origin to access your server. -# -# Takes precedence over allow_origin_pat. -# Default: '' -# c.ServerApp.allow_origin = '' - -## Use a regular expression for the Access-Control-Allow-Origin header -# -# Requests from an origin matching the expression will get replies with: -# -# Access-Control-Allow-Origin: origin -# -# where `origin` is the origin of the request. -# -# Ignored if allow_origin is set. -# Default: '' -# c.ServerApp.allow_origin_pat = '' - -## DEPRECATED in 2.0. Use PasswordIdentityProvider.allow_password_change -# Default: True -# c.ServerApp.allow_password_change = True - -## Allow requests where the Host header doesn't point to a local server -# -# By default, requests get a 403 forbidden response if the 'Host' header -# shows that the browser thinks it's on a non-local domain. -# Setting this option to True disables this check. -# -# This protects against 'DNS rebinding' attacks, where a remote web server -# serves you a page and then changes its DNS to send later requests to a -# local IP, bypassing same-origin checks. -# -# Local IP addresses (such as 127.0.0.1 and ::1) are allowed as local, -# along with hostnames configured in local_hostnames. -# Default: False -# c.ServerApp.allow_remote_access = False - -## Whether to allow the user to run the server as root. -# Default: False -# c.ServerApp.allow_root = False - -## Answer yes to any prompts. -# See also: JupyterApp.answer_yes -# c.ServerApp.answer_yes = False - -## " -# Require authentication to access prometheus metrics. -# Default: True -# c.ServerApp.authenticate_prometheus = True - -## The authorizer class to use. -# Default: 'jupyter_server.auth.authorizer.AllowAllAuthorizer' -# c.ServerApp.authorizer_class = 'jupyter_server.auth.authorizer.AllowAllAuthorizer' - -## Reload the webapp when changes are made to any Python src files. -# Default: False -# c.ServerApp.autoreload = False - -## The base URL for the Jupyter server. -# -# Leading and trailing slashes can be omitted, -# and will automatically be added. -# Default: '/' -# c.ServerApp.base_url = '/' - -## Specify what command to use to invoke a web -# browser when starting the server. If not specified, the -# default browser will be determined by the `webbrowser` -# standard library module, which allows setting of the -# BROWSER environment variable to override it. -# Default: '' -# c.ServerApp.browser = '' - -## The full path to an SSL/TLS certificate file. -# Default: '' -# c.ServerApp.certfile = '' - -## The full path to a certificate authority certificate for SSL/TLS client -# authentication. -# Default: '' -# c.ServerApp.client_ca = '' - -## Full path of a config file. -# See also: JupyterApp.config_file -# c.ServerApp.config_file = '' - -## Specify a config file to load. -# See also: JupyterApp.config_file_name -# c.ServerApp.config_file_name = '' - -## The config manager class to use -# Default: 'jupyter_server.services.config.manager.ConfigManager' -# c.ServerApp.config_manager_class = 'jupyter_server.services.config.manager.ConfigManager' - -## The content manager class to use. -# Default: 'jupyter_server.services.contents.largefilemanager.AsyncLargeFileManager' -# c.ServerApp.contents_manager_class = 'jupyter_server.services.contents.largefilemanager.AsyncLargeFileManager' - -## DEPRECATED. Use IdentityProvider.cookie_options -# Default: {} -# c.ServerApp.cookie_options = {} - -## The random bytes used to secure cookies. -# By default this is a new random number every time you start the server. -# Set it to a value in a config file to enable logins to persist across server sessions. -# -# Note: Cookie secrets should be kept private, do not share config files with -# cookie_secret stored in plaintext (you can read the value from a file). -# Default: b'' -# c.ServerApp.cookie_secret = b'' - -## The file where the cookie secret is stored. -# Default: '' -# c.ServerApp.cookie_secret_file = '' - -## Override URL shown to users. -# -# Replace actual URL, including protocol, address, port and base URL, -# with the given value when displaying URL to the users. Do not change -# the actual connection URL. If authentication token is enabled, the -# token is added to the custom URL automatically. -# -# This option is intended to be used when the URL to display to the user -# cannot be determined reliably by the Jupyter server (proxified -# or containerized setups for example). -# Default: '' -# c.ServerApp.custom_display_url = '' - -## The default URL to redirect to from `/` -# Default: '/' -# c.ServerApp.default_url = '/' - -## Disable cross-site-request-forgery protection -# -# Jupyter server includes protection from cross-site request forgeries, -# requiring API requests to either: -# -# - originate from pages served by this server (validated with XSRF cookie and token), or -# - authenticate with a token -# -# Some anonymous compute resources still desire the ability to run code, -# completely without authentication. -# These services can disable all authentication and security checks, -# with the full knowledge of what that implies. -# Default: False -# c.ServerApp.disable_check_xsrf = False - -## handlers that should be loaded at higher priority than the default services -# Default: [] -# c.ServerApp.extra_services = [] - -## Extra paths to search for serving static files. -# -# This allows adding javascript/css to be available from the Jupyter server machine, -# or overriding individual files in the IPython -# Default: [] -# c.ServerApp.extra_static_paths = [] - -## Extra paths to search for serving jinja templates. -# -# Can be used to override templates from jupyter_server.templates. -# Default: [] -# c.ServerApp.extra_template_paths = [] - -## Open the named file when the application is launched. -# Default: '' -# c.ServerApp.file_to_run = '' - -## The URL prefix where files are opened directly. -# Default: 'notebooks' -# c.ServerApp.file_url_prefix = 'notebooks' - -## Generate default config file. -# See also: JupyterApp.generate_config -# c.ServerApp.generate_config = False - -## DEPRECATED. Use IdentityProvider.get_secure_cookie_kwargs -# Default: {} -# c.ServerApp.get_secure_cookie_kwargs = {} - -## The identity provider class to use. -# Default: 'jupyter_server.auth.identity.PasswordIdentityProvider' -# c.ServerApp.identity_provider_class = 'jupyter_server.auth.identity.PasswordIdentityProvider' - -## DEPRECATED. Use ZMQChannelsWebsocketConnection.iopub_data_rate_limit -# Default: 0.0 -# c.ServerApp.iopub_data_rate_limit = 0.0 - -## DEPRECATED. Use ZMQChannelsWebsocketConnection.iopub_msg_rate_limit -# Default: 0.0 -# c.ServerApp.iopub_msg_rate_limit = 0.0 - -## The IP address the Jupyter server will listen on. -# Default: 'localhost' -# c.ServerApp.ip = 'localhost' - -## Supply extra arguments that will be passed to Jinja environment. -# Default: {} -# c.ServerApp.jinja_environment_options = {} - -## Extra variables to supply to jinja templates when rendering. -# Default: {} -# c.ServerApp.jinja_template_vars = {} - -## Dict of Python modules to load as Jupyter server extensions.Entry values can -# be used to enable and disable the loading ofthe extensions. The extensions -# will be loaded in alphabetical order. -# Default: {} -# c.ServerApp.jpserver_extensions = {} - -## The kernel manager class to use. -# Default: 'jupyter_server.services.kernels.kernelmanager.MappingKernelManager' -# c.ServerApp.kernel_manager_class = 'jupyter_server.services.kernels.kernelmanager.MappingKernelManager' - -## The kernel spec manager class to use. Should be a subclass of -# `jupyter_client.kernelspec.KernelSpecManager`. -# -# The Api of KernelSpecManager is provisional and might change without warning -# between this version of Jupyter and the next stable one. -# Default: 'builtins.object' -# c.ServerApp.kernel_spec_manager_class = 'builtins.object' - -## The kernel websocket connection class to use. -# Default: 'jupyter_server.services.kernels.connection.channels.ZMQChannelsWebsocketConnection' -# c.ServerApp.kernel_websocket_connection_class = 'jupyter_server.services.kernels.connection.channels.ZMQChannelsWebsocketConnection' - -## DEPRECATED. Use ZMQChannelsWebsocketConnection.kernel_ws_protocol -# Default: '' -# c.ServerApp.kernel_ws_protocol = '' - -## The full path to a private key file for usage with SSL/TLS. -# Default: '' -# c.ServerApp.keyfile = '' - -## DEPRECATED. Use ZMQChannelsWebsocketConnection.limit_rate -# Default: False -# c.ServerApp.limit_rate = False - -## Hostnames to allow as local when allow_remote_access is False. -# -# Local IP addresses (such as 127.0.0.1 and ::1) are automatically accepted -# as local as well. -# Default: ['localhost'] -# c.ServerApp.local_hostnames = ['localhost'] - -## The date format used by logging formatters for %(asctime)s -# See also: Application.log_datefmt -# c.ServerApp.log_datefmt = '%Y-%m-%d %H:%M:%S' - -## The Logging format template -# See also: Application.log_format -# c.ServerApp.log_format = '[%(name)s]%(highlevel)s %(message)s' - -## Set the log level by value or name. -# See also: Application.log_level -# c.ServerApp.log_level = 30 - -## -# See also: Application.logging_config -# c.ServerApp.logging_config = {} - -## The login handler class to use. -# Default: 'jupyter_server.auth.login.LegacyLoginHandler' -# c.ServerApp.login_handler_class = 'jupyter_server.auth.login.LegacyLoginHandler' - -## The logout handler class to use. -# Default: 'jupyter_server.auth.logout.LogoutHandler' -# c.ServerApp.logout_handler_class = 'jupyter_server.auth.logout.LogoutHandler' - -## Sets the maximum allowed size of the client request body, specified in the -# Content-Length request header field. If the size in a request exceeds the -# configured value, a malformed HTTP message is returned to the client. -# -# Note: max_body_size is applied even in streaming mode. -# Default: 536870912 -# c.ServerApp.max_body_size = 536870912 - -## Gets or sets the maximum amount of memory, in bytes, that is allocated for use -# by the buffer manager. -# Default: 536870912 -# c.ServerApp.max_buffer_size = 536870912 - -## Gets or sets a lower bound on the open file handles process resource limit. -# This may need to be increased if you run into an OSError: [Errno 24] Too many -# open files. This is not applicable when running on Windows. -# Default: 0 -# c.ServerApp.min_open_files_limit = 0 - -## DEPRECATED, use root_dir. -# Default: '' -# c.ServerApp.notebook_dir = '' - -## Whether to open in a browser after starting. -# The specific browser used is platform dependent and -# determined by the python standard library `webbrowser` -# module, unless it is overridden using the --browser -# (ServerApp.browser) configuration option. -# Default: False -# c.ServerApp.open_browser = False - -## DEPRECATED in 2.0. Use PasswordIdentityProvider.hashed_password -# Default: '' -# c.ServerApp.password = '' - -## DEPRECATED in 2.0. Use PasswordIdentityProvider.password_required -# Default: False -# c.ServerApp.password_required = False - -## The port the server will listen on (env: JUPYTER_PORT). -# Default: 0 -c.ServerApp.port = 8888 - -## The number of additional ports to try if the specified port is not available -# (env: JUPYTER_PORT_RETRIES). -# Default: 50 -# c.ServerApp.port_retries = 50 - -## Preferred starting directory to use for notebooks and kernels. -# Default: '' -# c.ServerApp.preferred_dir = '' - -## DISABLED: use %pylab or %matplotlib in the notebook to enable matplotlib. -# Default: 'disabled' -# c.ServerApp.pylab = 'disabled' - -## If True, display controls to shut down the Jupyter server, such as menu items -# or buttons. -# Default: True -# c.ServerApp.quit_button = True - -## DEPRECATED. Use ZMQChannelsWebsocketConnection.rate_limit_window -# Default: 0.0 -# c.ServerApp.rate_limit_window = 0.0 - -## Reraise exceptions encountered loading server extensions? -# Default: False -# c.ServerApp.reraise_server_extension_failures = False - -## The directory to use for notebooks and kernels. -# Default: '' -# c.ServerApp.root_dir = '' - -## The session manager class to use. -# Default: 'builtins.object' -# c.ServerApp.session_manager_class = 'builtins.object' - -## Instead of starting the Application, dump configuration to stdout -# See also: Application.show_config -# c.ServerApp.show_config = False - -## Instead of starting the Application, dump configuration to stdout (as JSON) -# See also: Application.show_config_json -# c.ServerApp.show_config_json = False - -## Shut down the server after N seconds with no kernelsrunning and no activity. -# This can be used together with culling idle kernels -# (MappingKernelManager.cull_idle_timeout) to shutdown the Jupyter server when -# it's not in use. This is not precisely timed: it may shut down up to a minute -# later. 0 (the default) disables this automatic shutdown. -# Default: 0 -# c.ServerApp.shutdown_no_activity_timeout = 0 - -## The UNIX socket the Jupyter server will listen on. -# Default: '' -# c.ServerApp.sock = '' - -## The permissions mode for UNIX socket creation (default: 0600). -# Default: '0600' -# c.ServerApp.sock_mode = '0600' - -## Supply SSL options for the tornado HTTPServer. -# See the tornado docs for details. -# Default: {} -# c.ServerApp.ssl_options = {} - -## Supply overrides for terminado. Currently only supports "shell_command". -# Default: {} -# c.ServerApp.terminado_settings = {} - -## Set to False to disable terminals. -# -# This does *not* make the server more secure by itself. -# Anything the user can in a terminal, they can also do in a notebook. -# -# Terminals may also be automatically disabled if the terminado package -# is not available. -# Default: False -# c.ServerApp.terminals_enabled = False - -## DEPRECATED. Use IdentityProvider.token -# Default: '' -# c.ServerApp.token = '' -c.IdentityProvider.token = "changeme" - -## Supply overrides for the tornado.web.Application that the Jupyter server uses. -# Default: {} -# c.ServerApp.tornado_settings = {} - -## Whether to trust or not X-Scheme/X-Forwarded-Proto and X-Real-Ip/X-Forwarded- -# For headerssent by the upstream reverse proxy. Necessary if the proxy handles -# SSL -# Default: False -# c.ServerApp.trust_xheaders = False - -## Disable launching browser by redirect file -# For versions of notebook > 5.7.2, a security feature measure was added that -# prevented the authentication token used to launch the browser from being visible. -# This feature makes it difficult for other users on a multi-user system from -# running code in your Jupyter session as you. -# However, some environments (like Windows Subsystem for Linux (WSL) and Chromebooks), -# launching a browser using a redirect file can lead the browser failing to load. -# This is because of the difference in file structures/paths between the runtime and -# the browser. -# -# Disabling this setting to False will disable this behavior, allowing the browser -# to launch by using a URL and visible token (as before). -# Default: True -# c.ServerApp.use_redirect_file = True - -## Specify where to open the server on startup. This is the -# `new` argument passed to the standard library method `webbrowser.open`. -# The behaviour is not guaranteed, but depends on browser support. Valid -# values are: -# -# - 2 opens a new tab, -# - 1 opens a new window, -# - 0 opens in an existing window. -# -# See the `webbrowser.open` documentation for details. -# Default: 2 -# c.ServerApp.webbrowser_open_new = 2 - -## Set the tornado compression options for websocket connections. -# -# This value will be returned from -# :meth:`WebSocketHandler.get_compression_options`. None (default) will disable -# compression. A dict (even an empty one) will enable compression. -# -# See the tornado docs for WebSocketHandler.get_compression_options for details. -# Default: None -# c.ServerApp.websocket_compression_options = None - -## The base URL for websockets, -# if it differs from the HTTP server (hint: it almost certainly doesn't). -# -# Should be in the form of an HTTP origin: ws[s]://hostname[:port] -# Default: '' -# c.ServerApp.websocket_url = '' diff --git a/apps/openchallenges/notebook/notebooks/challenge-headline-llm.ipynb b/apps/openchallenges/notebook/notebooks/challenge-headline-llm.ipynb deleted file mode 100644 index 3dad5722ce..0000000000 --- a/apps/openchallenges/notebook/notebooks/challenge-headline-llm.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "34a39b07-385c-49b7-931f-3631a1aff129", - "metadata": { - "tags": [] - }, - "source": [ - "# Generate Challenge Headlines with AWS Bedrock" - ] - }, - { - "cell_type": "markdown", - "id": "ed72ae29-13e0-4533-8e7e-f13c9b34da1a", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "id": "b876938b-ae3a-49ba-b416-b9fd59f88ae7", - "metadata": {}, - "source": [ - "This notebook generates challenge headlines for challenges fetched from OpenChallenges (OC)." - ] - }, - { - "cell_type": "markdown", - "id": "7e95e24d-3f3a-47d0-bb52-35e718eb7ac6", - "metadata": {}, - "source": [ - "## Requirements" - ] - }, - { - "cell_type": "markdown", - "id": "cc586a81-e5b4-4021-9c6b-aa8a69ef221c", - "metadata": {}, - "source": [ - "- Access to OpenChallenges REST API\n", - "- Access to OpenAI API" - ] - }, - { - "cell_type": "markdown", - "id": "2dd21cb4", - "metadata": {}, - "source": [ - "## Preparation" - ] - }, - { - "cell_type": "markdown", - "id": "c74bd100", - "metadata": {}, - "source": [ - "Load config file `.env`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b9a13bdf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from dotenv import load_dotenv\n", - "\n", - "load_dotenv()" - ] - }, - { - "cell_type": "markdown", - "id": "c90710c2-f053-44ae-a3c2-610eecff9073", - "metadata": {}, - "source": [ - "## List challenges" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f37a60d9", - "metadata": {}, - "outputs": [], - "source": [ - "import openchallenges_client\n", - "from pprint import pprint\n", - "from openchallenges_client.api import challenge_api" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "8fc3ac4c-2ceb-4bbc-bdb8-3bb8be08dfc6", - "metadata": {}, - "outputs": [], - "source": [ - "# See configuration.py for a list of all supported configuration parameters.\n", - "configuration = openchallenges_client.Configuration(\n", - " host = \"https://openchallenges.io/api/v1\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b2f952f5-9140-4702-8a96-3457ca4df841", - "metadata": {}, - "outputs": [], - "source": [ - "# Enter a context with an instance of the API client\n", - "challenges = []\n", - "with openchallenges_client.ApiClient(configuration) as api_client:\n", - " api_instance = challenge_api.ChallengeApi(api_client)\n", - " \n", - " query = openchallenges_client.ChallengeSearchQuery(page_number=1000, page_size=1)\n", - "\n", - " try:\n", - " # Get the first page of the list of challenges\n", - " page = api_instance.list_challenges(query)\n", - " challenges.extend(page.challenges)\n", - " except openchallenges_client.ApiException as e:\n", - " print(\"Exception when calling ChallengeApi->list_challenges: %s\\n\" % e)" - ] - }, - { - "cell_type": "markdown", - "id": "1ff9d4b9", - "metadata": {}, - "source": [ - "## Prepare the challenge descriptions" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2fcad35d", - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "from bs4 import BeautifulSoup" - ] - }, - { - "cell_type": "markdown", - "id": "49d20733", - "metadata": {}, - "source": [ - "TODO" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb6c1ada", - "metadata": {}, - "outputs": [], - "source": [ - "challenge = challenges[0]\n", - "\n", - "response = requests.get(challenge.website_url)\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "pprint(response)" - ] - }, - { - "cell_type": "markdown", - "id": "14ba8e14", - "metadata": {}, - "source": [ - "## Generate the headlines with AWS Bedrock" - ] - }, - { - "cell_type": "markdown", - "id": "8ba9a632", - "metadata": {}, - "source": [ - "### Configure Bedrock client" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "20da8b0e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Create new client\n", - " Using region: us-east-1\n", - " Using profile: cnb\n", - "boto3 Bedrock client successfully created!\n", - "bedrock-runtime(https://bedrock-runtime.us-east-1.amazonaws.com)\n" - ] - } - ], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "module_path = \"..\"\n", - "sys.path.append(os.path.abspath(module_path))\n", - "from utils import bedrock, print_ww\n", - "\n", - "os.environ[\"AWS_DEFAULT_REGION\"] = \"us-east-1\"\n", - "os.environ[\"AWS_PROFILE\"] = \"cnb\"\n", - "\n", - "boto3_bedrock = bedrock.get_bedrock_client(\n", - " assumed_role=os.environ.get(\"BEDROCK_ASSUME_ROLE\", None),\n", - " region=os.environ.get(\"AWS_DEFAULT_REGION\", None)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0b1ef15b", - "metadata": {}, - "source": [ - "### Configure base model options" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5bbcd90f", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.llms.bedrock import Bedrock\n", - "\n", - "inference_modifier = {'max_tokens_to_sample':6000, \n", - " \"temperature\":0.6,\n", - " \"top_k\":250,\n", - " \"top_p\":1,\n", - " \"stop_sequences\": [\"\\n\\nHuman\"]\n", - " }\n", - "\n", - "textgen_llm = Bedrock(model_id = \"anthropic.claude-v2\",\n", - " client = boto3_bedrock, \n", - " model_kwargs = inference_modifier \n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "451eca6a", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_challenge_headlines(text, num_headlines):\n", - " prompt=(\n", - " f\"Please generate {num_headlines} headlines that have a maximum ten words from the \"\n", - " \"following challenge description. The headline must summarize the goal of the challenge. \"\n", - " f\"Description: \\n{text}\"\n", - " )\n", - " response = Bedrock(model_id = \"anthropic.claude-v2\",\n", - " client = boto3_bedrock, \n", - " model_kwargs = inference_modifier \n", - " )(prompt)\n", - " return response" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1a397957", - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import compress\n", - "import json\n", - "\n", - "def is_raw_headline(raw_headline):\n", - " prefixes = (\"1. \", \"2. \", \"3. \", \"4. \", \"5. \")\n", - " return raw_headline.startswith(prefixes)" - ] - }, - { - "cell_type": "markdown", - "id": "903a6de3", - "metadata": {}, - "source": [ - "Authentify with AWS using the command:\n", - "\n", - "```console\n", - "aws --profile cnb sso login\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b0c4c192", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(' Here are 5 headlines with a maximum of 10 words summarizing the goal of the '\n", - " 'challenge:\\n'\n", - " '\\n'\n", - " '1. Challenge Seeks to Improve Data for AI Discovery\\n'\n", - " '\\n'\n", - " '2. Data Challenge Aims to Augment Repository for AI Use \\n'\n", - " '\\n'\n", - " '3. Challenge Targets Data Standardization for AI Research\\n'\n", - " '\\n'\n", - " '4. Competition Focuses on Making Data AI Ready \\n'\n", - " '\\n'\n", - " '5. Challenge Works to Ready Data for AI Insights')\n" - ] - } - ], - "source": [ - "challenge = challenges[0]\n", - "response = generate_challenge_headlines(challenge.description, 5)\n", - "\n", - "pprint(response)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"id\": 279,\n", - " \"slug\": \"niddk-central-repository-data-centric-challenge\",\n", - " \"name\": \"NIDDK Central Repository Data-Centric Challenge\",\n", - " \"headline\": \"Enhancing NIDDK datasets for future Artificial Intelligence (AI) applications.\",\n", - " \"headline_alternatives\": [\n", - " \"1. Challenge Seeks to Improve Data for AI Discovery\",\n", - " \"2. Data Challenge Aims to Augment Repository for AI Use \",\n", - " \"3. Challenge Targets Data Standardization for AI Research\",\n", - " \"4. Competition Focuses on Making Data AI Ready \",\n", - " \"5. Challenge Works to Ready Data for AI Insights\"\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "raw_headlines = response.splitlines()\n", - "headlines = list(compress(raw_headlines, map(is_raw_headline, raw_headlines)))\n", - "\n", - "obj = {\n", - " \"id\": challenge.id,\n", - " \"slug\": challenge.slug,\n", - " \"name\": challenge.name,\n", - " \"headline\": challenge.headline,\n", - " \"headline_alternatives\": headlines\n", - "}\n", - "json_str = json.dumps(obj, indent=2)\n", - "\n", - "print(json_str)" - ] - }, - { - "cell_type": "markdown", - "id": "12d3b54d", - "metadata": {}, - "source": [ - "### Output challenge headlines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4496fd1d", - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import compress\n", - "import json\n", - "\n", - "raw_headlines = result.splitlines()\n", - "\n", - "def is_raw_headline(raw_headline):\n", - " prefixes = (\"1. \", \"2. \", \"3. \", \"4. \", \"5. \")\n", - " return raw_headline.startswith(prefixes)\n", - "\n", - "headlines = list(compress(raw_headlines, map(is_raw_headline, raw_headlines)))\n", - "\n", - "obj = {\n", - " \"id\": challenge.id,\n", - " \"slug\": challenge.slug,\n", - " \"name\": challenge.name,\n", - " \"headline\": challenge.headline,\n", - " \"headline_alternatives\": headlines\n", - "}\n", - "json_str = json.dumps(obj, indent=2)\n", - "\n", - "print(json_str)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/openchallenges/notebook/notebooks/kaggle-api.ipynb b/apps/openchallenges/notebook/notebooks/kaggle-api.ipynb deleted file mode 100644 index b14f246d4b..0000000000 --- a/apps/openchallenges/notebook/notebooks/kaggle-api.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "34a39b07-385c-49b7-931f-3631a1aff129", - "metadata": { - "tags": [] - }, - "source": [ - "# Kaggle API" - ] - }, - { - "cell_type": "markdown", - "id": "ed72ae29-13e0-4533-8e7e-f13c9b34da1a", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "id": "b876938b-ae3a-49ba-b416-b9fd59f88ae7", - "metadata": {}, - "source": [ - "This notebook shows examples on how to use Kaggle API." - ] - }, - { - "cell_type": "markdown", - "id": "7e95e24d-3f3a-47d0-bb52-35e718eb7ac6", - "metadata": {}, - "source": [ - "## Requirements" - ] - }, - { - "cell_type": "markdown", - "id": "cc586a81-e5b4-4021-9c6b-aa8a69ef221c", - "metadata": {}, - "source": [ - "None" - ] - }, - { - "cell_type": "markdown", - "id": "c90710c2-f053-44ae-a3c2-610eecff9073", - "metadata": {}, - "source": [ - "## Authentication" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a9f3da43-6f09-4774-9fe7-59b83dbb147e", - "metadata": {}, - "outputs": [], - "source": [ - "from kaggle.api.kaggle_api_extended import KaggleApi\n", - "api = KaggleApi()\n", - "api.authenticate()" - ] - }, - { - "cell_type": "markdown", - "id": "03b034a1-c52d-4f99-ae8c-6b28a053c677", - "metadata": {}, - "source": [ - "## List Competitions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8d87ddb5-bc21-4267-b70f-ff6ef4807e47", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[https://www.kaggle.com/competitions/nfl-player-contact-detection,\n", - " https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting,\n", - " https://www.kaggle.com/competitions/learning-equality-curriculum-recommendations,\n", - " https://www.kaggle.com/competitions/rsna-breast-cancer-detection,\n", - " https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice,\n", - " https://www.kaggle.com/competitions/otto-recommender-system,\n", - " https://www.kaggle.com/competitions/playground-series-s3e3,\n", - " https://www.kaggle.com/competitions/titanic,\n", - " https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques,\n", - " https://www.kaggle.com/competitions/spaceship-titanic,\n", - " https://www.kaggle.com/competitions/digit-recognizer,\n", - " https://www.kaggle.com/competitions/nlp-getting-started,\n", - " https://www.kaggle.com/competitions/connectx,\n", - " https://www.kaggle.com/competitions/tpu-getting-started,\n", - " https://www.kaggle.com/competitions/store-sales-time-series-forecasting,\n", - " https://www.kaggle.com/competitions/gan-getting-started,\n", - " https://www.kaggle.com/competitions/contradictory-my-dear-watson]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "api.competitions_list()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83d81fd5-4e42-4a84-be55-4b4c2ad75e7f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/openchallenges/notebook/notebooks/meta-kaggle-exploration.ipynb b/apps/openchallenges/notebook/notebooks/meta-kaggle-exploration.ipynb deleted file mode 100644 index e932b4915a..0000000000 --- a/apps/openchallenges/notebook/notebooks/meta-kaggle-exploration.ipynb +++ /dev/null @@ -1,1940 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "596372c7-7be4-47ea-8050-b9bf28aec24c", - "metadata": {}, - "source": [ - "# Exploring Kaggle Competitions\n", - "\n", - "For this exercise, I will explore Kaggle current and past challenges for those that are possibly biomedically-related. I will be using the following list of terms to search the archive for what the team deems as \"biomedical\":\n", - "\n", - "* medicine\n", - "* cancer\n", - "* cell\n", - "* biology\n", - "* neuro\n", - "* drug\n", - "* health\n", - "* life\n" - ] - }, - { - "cell_type": "markdown", - "id": "1774adf7-c466-49b5-8fec-9b66002e4c0e", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "This exercise will utilize the [Kaggle public APIs](https://github.com/Kaggle/kaggle-api).\n", - "\n", - "To use the Kaggle API, an account will be required. Credentials can be passed by mounting an account's token into `.kaggle/kaggle.json`.\n", - "\n", - "> To re-run the next couple of lines, you will first need to generate your token key by going to the Account tab of your Kaggle user profile, then clicking on Create API Token. A `kaggle.json` will download onto your machine. Upload that file with the next code block." - ] - }, - { - "cell_type": "markdown", - "id": "b9250d07-2cca-4615-b0d5-7adc5d2d7a57", - "metadata": {}, - "source": [ - "First, install the needed libraries, including `pandas`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "eef519d4-8b51-4188-a937-a67f9341f139", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "!pip install -q pandas kaggle ipywidgets" - ] - }, - { - "cell_type": "markdown", - "id": "fd19c346-4160-4fc1-ab7a-ab40688c2a7b", - "metadata": {}, - "source": [ - "In order to prevent sharing secrets, I will use `FileUpload` from the `ipywidgets` to upload my `kaggle.json` file to the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aba1df06-7fc1-4f76-b6a0-2a1a5e52cacb", - "metadata": {}, - "outputs": [], - "source": [ - "from ipywidgets import FileUpload\n", - "\n", - "uploader = FileUpload()\n", - "display(uploader)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "284686ff-1deb-4033-ab9c-fdf6befb320b", - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir -p ~/.kaggle\n", - "\n", - "import codecs\n", - "uploaded_file = uploader.value[0]\n", - "with open(\"/home/vscode/.kaggle/kaggle.json\", \"w\") as cred:\n", - " cred.write(codecs.decode(uploaded_file.content, encoding=\"utf-8\") + \"\\n\")\n", - " \n", - "!chmod 600 ~/.kaggle/kaggle.json" - ] - }, - { - "cell_type": "markdown", - "id": "95f1769a-7c91-4f96-a868-8f1710cdd13d", - "metadata": {}, - "source": [ - "## Search with Kaggle APIs" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cdb70ba4-587b-46df-8fb3-640721f23a84", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MEDICINE\n", - "No competitions found\n", - "\n", - "CANCER\n", - "ref deadline category reward teamCount userHasEntered \n", - "---------------------------------------------------------------- ------------------- -------- ------- --------- -------------- \n", - "https://www.kaggle.com/competitions/rsna-breast-cancer-detection 2023-02-27 23:59:00 Featured $50,000 1601 False \n", - "\n", - "CELL\n", - "No competitions found\n", - "\n", - "BIOLOGY\n", - "ref deadline category reward teamCount userHasEntered \n", - "--------------------------------------------------------------------------------- ------------------- -------- ------- --------- -------------- \n", - "https://www.kaggle.com/competitions/amp-parkinsons-disease-progression-prediction 2023-05-18 23:59:00 Featured $60,000 5 False \n", - "\n", - "NEURO\n", - "No competitions found\n", - "\n", - "DRUG\n", - "No competitions found\n", - "\n", - "HEALTH\n", - "ref deadline category reward teamCount userHasEntered \n", - "---------------------------------------------------------------------------------- ------------------- -------- --------- --------- -------------- \n", - "https://www.kaggle.com/competitions/nfl-player-contact-detection 2023-03-01 23:59:00 Featured $100,000 779 False \n", - "https://www.kaggle.com/competitions/march-machine-learning-mania-2023 2023-03-16 16:00:00 Featured $50,000 15 False \n", - "https://www.kaggle.com/competitions/warmup-round-march-machine-learning-mania-2023 2023-03-16 16:00:00 Featured Knowledge 8 False \n", - "\n", - "LIFE\n", - "No competitions found\n", - "\n" - ] - } - ], - "source": [ - "!for term in medicine cancer cell biology neuro drug health life; do echo ${term^^}; kaggle competitions list -s $term; echo \"\"; done" - ] - }, - { - "cell_type": "markdown", - "id": "c58a4495-0747-4002-9c29-2793993d182c", - "metadata": {}, - "source": [ - "Because the API only returns _active_ challenges, the results were pretty minimal (n=2 challenges)." - ] - }, - { - "cell_type": "markdown", - "id": "20a47f8f-6039-46ca-9e56-928221f62415", - "metadata": {}, - "source": [ - "## Meta Kaggle\n", - "\n", - "Now, I will search through the Kaggle archive (Meta Kaggle) to find possible biomedical challenges, using the same key terms as earlier." - ] - }, - { - "cell_type": "markdown", - "id": "7e323376-fc00-4df4-9207-e1116f3dbdab", - "metadata": {}, - "source": [ - "First, I will download the \"Competitions\" file from the `meta-kaggle` dataset. Since the file is big, I will also need to unzip it." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "49295ed9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading Competitions.csv.zip to /workspaces/challenge-registry/apps/openchallenges/notebook/notebooks\n", - " 0%| | 0.00/479k [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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349223871aimdatathonAIM Datathon 2020Join the AI in Medicine ( AIM ) Datathon 2020Community979798.0NaN111/09/2020 02:01:2211/22/2020 23:59:00...0.0False000NaNNaNFalseFalseNaN
3625354opc-recurrenceOropharynx Cancer (OPC) Radiomics Challenge ::...Determine from CT data whether a tumor will be...Community1380.0NaN107/26/2016 20:49:0209/12/2016 23:59:00...0.0False142378NaNNaNFalseFalseNaN
3645360oropharynx-radiomics-hpvOropharynx Cancer (OPC) Radiomics Challenge ::...Predict from CT data the HPV phenotype of orop...Community1379.0NaN107/26/2016 20:48:5409/12/2016 23:59:00...0.0False91770NaNNaNFalseFalseNaN
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4636243intel-mobileodt-cervical-cancer-screeningIntel & MobileODT Cervical Cancer ScreeningWhich cancer treatment will be most effective?Featured2880.0484.0103/15/2017 19:00:2206/21/2017 23:59:00...1.0True2603861365NaNNaNFalseFalseNaN
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102710306mubravoPredicting Cancer DiagnosisBravo's machine learning competition!Community48323.0NaN107/31/2018 19:52:1608/13/2018 20:59:00...0.0False111372NaNNaNFalseFalseNaN
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243918647prostate-cancer-grade-assessmentProstate cANcer graDe Assessment (PANDA) Chall...Prostate cancer diagnosis using the Gleason gr...Featured628635.03741.0104/21/2020 16:38:3307/22/2020 23:59:00...1.0True1010129019723NaNNaNFalseFalseNaN
315722021breast-cancerBreast CancerUse cell nuclei categories to predict breast c...Community843335.0NaN108/12/2020 19:30:3308/13/2020 23:59:00...0.0False171751NaNNaNFalseFalseNaN
331322863breast-cancer-detectionBreast Cancerbreast cancer detectionCommunity908917.0NaN109/25/2020 22:00:4112/31/2020 23:59:00...0.0False151537NaNNaNFalseFalseNaN
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539335220pveladPhotovoltaic cell anomaly detectionHosted by Hebei University of Technology (AIHe...Community2015300.0NaN103/15/2022 04:24:5207/30/2022 23:59:00...0.0False223NaNNaNFalseFalseNaN
542235373blood-mnistBlood-MNISTClassifying blood cell types using Weights and...Community2031514.0NaN103/19/2022 16:08:2803/19/2022 23:59:00...0.0False111114NaNNaNFalseFalseNaN
528034600classification-of-short-noisy-audio-speechTV Neuro Technologies Задача 4Обучите нейросеть и получите лучший accuracy к...Community1965090.0NaN102/18/2022 16:37:5603/11/2022 23:59:00...0.0False2341305NaNNaNFalseFalseNaN
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"5072 False NaN \n", - "5191 False NaN \n", - "\n", - "[50 rows x 42 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "search_terms = [\"medicine\", \"cancer\", \"cell\", \"biology\", \"neuro\", \"drug\", \"life sciences\", \"health\"]\n", - "\n", - "competitions = pd.read_csv('Competitions.csv')\n", - "bio_challenges = []\n", - "for term in search_terms:\n", - " res = competitions[competitions.apply(lambda row: row.astype(str).str.contains(\n", - " f\"\\s{term}\\s\", regex=True, case=False).any(), axis=1)]\n", - " bio_challenges.append(res)\n", - "bio_challenges = pd.concat(bio_challenges)\n", - "display(bio_challenges.drop_duplicates())" - ] - }, - { - "cell_type": "markdown", - "id": "ae384292-137c-4ba8-845f-9aa7ddefa080", - "metadata": {}, - "source": [ - "Searching for the terms this way gives us a total of 50 challenges, which is slightly lower than expected, e.g. searching for \"cancer\" directly on Kaggle returns 124 challenges. This lower number is due to Kaggle not doing a ocmplete dump of their database:" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/openchallenges/notebook/notebooks/openchallenges-api.ipynb b/apps/openchallenges/notebook/notebooks/openchallenges-api.ipynb deleted file mode 100644 index c8ecd92c81..0000000000 --- a/apps/openchallenges/notebook/notebooks/openchallenges-api.ipynb +++ /dev/null @@ -1,187 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "34a39b07-385c-49b7-931f-3631a1aff129", - "metadata": { - "tags": [] - }, - "source": [ - "# OpenChallenges REST API Example" - ] - }, - { - "cell_type": "markdown", - "id": "ed72ae29-13e0-4533-8e7e-f13c9b34da1a", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "id": "b876938b-ae3a-49ba-b416-b9fd59f88ae7", - "metadata": {}, - "source": [ - "This notebook shows how to use OpenChallenges (OC) API client for Python to query challenge resources." - ] - }, - { - "cell_type": "markdown", - "id": "7e95e24d-3f3a-47d0-bb52-35e718eb7ac6", - "metadata": {}, - "source": [ - "## Requirements" - ] - }, - { - "cell_type": "markdown", - "id": "cc586a81-e5b4-4021-9c6b-aa8a69ef221c", - "metadata": {}, - "source": [ - "The OpenChallenges REST API must be already running." - ] - }, - { - "cell_type": "markdown", - "id": "c90710c2-f053-44ae-a3c2-610eecff9073", - "metadata": {}, - "source": [ - "## List challenges" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c41a64a1", - "metadata": {}, - "outputs": [], - "source": [ - "import openchallenges_client\n", - "from pprint import pprint\n", - "from openchallenges_client.api import challenge_api" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8fc3ac4c-2ceb-4bbc-bdb8-3bb8be08dfc6", - "metadata": {}, - "outputs": [], - "source": [ - "# See configuration.py for a list of all supported configuration parameters.\n", - "configuration = openchallenges_client.Configuration(\n", - " host = \"http://localhost:8000/api/v1\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2f855989-f0a9-4fa0-a8f8-63edba2fdb38", - "metadata": {}, - "source": [ - "> **Note** The hostname `openchallenges-apex` is defined in `/etc/hosts`. The dev container provided with this project is responsible for definining the required hostnames. By default, the hostnames are mapped to `127.0.0.1`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b2f952f5-9140-4702-8a96-3457ca4df841", - "metadata": {}, - "outputs": [ - { - "ename": "MaxRetryError", - "evalue": "HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /api/v1/challenges?challengeSearchQuery=%7B%22pageNumber%22%3A%201%2C%20%22pageSize%22%3A%201%7D (Caused by NewConnectionError(': Failed to establish a new connection: [Errno 111] Connection refused'))", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connection.py:203\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 203\u001b[0m sock \u001b[39m=\u001b[39m connection\u001b[39m.\u001b[39;49mcreate_connection(\n\u001b[1;32m 204\u001b[0m (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dns_host, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport),\n\u001b[1;32m 205\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout,\n\u001b[1;32m 206\u001b[0m source_address\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msource_address,\n\u001b[1;32m 207\u001b[0m socket_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msocket_options,\n\u001b[1;32m 208\u001b[0m )\n\u001b[1;32m 209\u001b[0m \u001b[39mexcept\u001b[39;00m socket\u001b[39m.\u001b[39mgaierror \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/util/connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 85\u001b[0m \u001b[39mraise\u001b[39;00m err\n\u001b[1;32m 86\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 87\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/util/connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 72\u001b[0m sock\u001b[39m.\u001b[39mbind(source_address)\n\u001b[0;32m---> 73\u001b[0m sock\u001b[39m.\u001b[39;49mconnect(sa)\n\u001b[1;32m 74\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", - "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:791\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[39m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 791\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[1;32m 792\u001b[0m conn,\n\u001b[1;32m 793\u001b[0m method,\n\u001b[1;32m 794\u001b[0m url,\n\u001b[1;32m 795\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[1;32m 796\u001b[0m body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m 797\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 798\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 799\u001b[0m retries\u001b[39m=\u001b[39;49mretries,\n\u001b[1;32m 800\u001b[0m response_conn\u001b[39m=\u001b[39;49mresponse_conn,\n\u001b[1;32m 801\u001b[0m preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m 802\u001b[0m decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m 803\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mresponse_kw,\n\u001b[1;32m 804\u001b[0m )\n\u001b[1;32m 806\u001b[0m \u001b[39m# Everything went great!\u001b[39;00m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:497\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 497\u001b[0m conn\u001b[39m.\u001b[39;49mrequest(\n\u001b[1;32m 498\u001b[0m method,\n\u001b[1;32m 499\u001b[0m url,\n\u001b[1;32m 500\u001b[0m body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m 501\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 502\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 503\u001b[0m preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m 504\u001b[0m decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m 505\u001b[0m enforce_content_length\u001b[39m=\u001b[39;49menforce_content_length,\n\u001b[1;32m 506\u001b[0m )\n\u001b[1;32m 508\u001b[0m \u001b[39m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[1;32m 509\u001b[0m \u001b[39m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[1;32m 510\u001b[0m \u001b[39m# With this behaviour, the received response is still readable.\u001b[39;00m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connection.py:395\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mputheader(header, value)\n\u001b[0;32m--> 395\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mendheaders()\n\u001b[1;32m 397\u001b[0m \u001b[39m# If we're given a body we start sending that in chunks.\u001b[39;00m\n", - "File \u001b[0;32m~/.pyenv/versions/3.10.12/lib/python3.10/http/client.py:1278\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[39mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1278\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_output(message_body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n", - "File \u001b[0;32m~/.pyenv/versions/3.10.12/lib/python3.10/http/client.py:1038\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1038\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(msg)\n\u001b[1;32m 1040\u001b[0m \u001b[39mif\u001b[39;00m message_body \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1041\u001b[0m \n\u001b[1;32m 1042\u001b[0m \u001b[39m# create a consistent interface to message_body\u001b[39;00m\n", - "File \u001b[0;32m~/.pyenv/versions/3.10.12/lib/python3.10/http/client.py:976\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_open:\n\u001b[0;32m--> 976\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n\u001b[1;32m 977\u001b[0m \u001b[39melse\u001b[39;00m:\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connection.py:243\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 243\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_new_conn()\n\u001b[1;32m 244\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tunnel_host:\n\u001b[1;32m 245\u001b[0m \u001b[39m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connection.py:218\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m--> 218\u001b[0m \u001b[39mraise\u001b[39;00m NewConnectionError(\n\u001b[1;32m 219\u001b[0m \u001b[39mself\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mFailed to establish a new connection: \u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 220\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[39m# Audit hooks are only available in Python 3.8+\u001b[39;00m\n", - "\u001b[0;31mNewConnectionError\u001b[0m: : Failed to establish a new connection: [Errno 111] Connection refused", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/notebooks/openchallenges-api.ipynb Cell 10\u001b[0m line \u001b[0;36m9\n\u001b[1;32m 5\u001b[0m query \u001b[39m=\u001b[39m openchallenges_client\u001b[39m.\u001b[39mChallengeSearchQuery(page_number\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, page_size\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 7\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 8\u001b[0m \u001b[39m# Get the first page of the list of challenges\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m api_response \u001b[39m=\u001b[39m api_instance\u001b[39m.\u001b[39;49mlist_challenges(query)\n\u001b[1;32m 10\u001b[0m pprint(api_response)\n\u001b[1;32m 11\u001b[0m \u001b[39mexcept\u001b[39;00m openchallenges_client\u001b[39m.\u001b[39mApiException \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:40\u001b[0m, in \u001b[0;36mpydantic.decorator.validate_arguments.validate.wrapper_function\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:134\u001b[0m, in \u001b[0;36mpydantic.decorator.ValidatedFunction.call\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:206\u001b[0m, in \u001b[0;36mpydantic.decorator.ValidatedFunction.execute\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/api/challenge_api.py:218\u001b[0m, in \u001b[0;36mChallengeApi.list_challenges\u001b[0;34m(self, challenge_search_query, **kwargs)\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39m_preload_content\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m kwargs:\n\u001b[1;32m 217\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mError! Please call the list_challenges_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 218\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlist_challenges_with_http_info(challenge_search_query, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:40\u001b[0m, in \u001b[0;36mpydantic.decorator.validate_arguments.validate.wrapper_function\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:134\u001b[0m, in \u001b[0;36mpydantic.decorator.ValidatedFunction.call\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/pydantic/decorator.py:206\u001b[0m, in \u001b[0;36mpydantic.decorator.ValidatedFunction.execute\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/api/challenge_api.py:315\u001b[0m, in \u001b[0;36mChallengeApi.list_challenges_with_http_info\u001b[0;34m(self, challenge_search_query, **kwargs)\u001b[0m\n\u001b[1;32m 307\u001b[0m _auth_settings \u001b[39m=\u001b[39m [] \u001b[39m# noqa: E501\u001b[39;00m\n\u001b[1;32m 309\u001b[0m _response_types_map \u001b[39m=\u001b[39m {\n\u001b[1;32m 310\u001b[0m \u001b[39m'\u001b[39m\u001b[39m200\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mChallengesPage\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 311\u001b[0m \u001b[39m'\u001b[39m\u001b[39m400\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mBasicError\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 312\u001b[0m \u001b[39m'\u001b[39m\u001b[39m500\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mBasicError\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 313\u001b[0m }\n\u001b[0;32m--> 315\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapi_client\u001b[39m.\u001b[39;49mcall_api(\n\u001b[1;32m 316\u001b[0m \u001b[39m'\u001b[39;49m\u001b[39m/challenges\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mGET\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m 317\u001b[0m _path_params,\n\u001b[1;32m 318\u001b[0m _query_params,\n\u001b[1;32m 319\u001b[0m _header_params,\n\u001b[1;32m 320\u001b[0m body\u001b[39m=\u001b[39;49m_body_params,\n\u001b[1;32m 321\u001b[0m post_params\u001b[39m=\u001b[39;49m_form_params,\n\u001b[1;32m 322\u001b[0m files\u001b[39m=\u001b[39;49m_files,\n\u001b[1;32m 323\u001b[0m response_types_map\u001b[39m=\u001b[39;49m_response_types_map,\n\u001b[1;32m 324\u001b[0m auth_settings\u001b[39m=\u001b[39;49m_auth_settings,\n\u001b[1;32m 325\u001b[0m async_req\u001b[39m=\u001b[39;49m_params\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39masync_req\u001b[39;49m\u001b[39m'\u001b[39;49m),\n\u001b[1;32m 326\u001b[0m _return_http_data_only\u001b[39m=\u001b[39;49m_params\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39m_return_http_data_only\u001b[39;49m\u001b[39m'\u001b[39;49m), \u001b[39m# noqa: E501\u001b[39;49;00m\n\u001b[1;32m 327\u001b[0m _preload_content\u001b[39m=\u001b[39;49m_params\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39m_preload_content\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39mTrue\u001b[39;49;00m),\n\u001b[1;32m 328\u001b[0m _request_timeout\u001b[39m=\u001b[39;49m_params\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39m_request_timeout\u001b[39;49m\u001b[39m'\u001b[39;49m),\n\u001b[1;32m 329\u001b[0m collection_formats\u001b[39m=\u001b[39;49m_collection_formats,\n\u001b[1;32m 330\u001b[0m _request_auth\u001b[39m=\u001b[39;49m_params\u001b[39m.\u001b[39;49mget(\u001b[39m'\u001b[39;49m\u001b[39m_request_auth\u001b[39;49m\u001b[39m'\u001b[39;49m))\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/api_client.py:406\u001b[0m, in \u001b[0;36mApiClient.call_api\u001b[0;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_types_map, auth_settings, async_req, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Makes the HTTP request (synchronous) and returns deserialized data.\u001b[39;00m\n\u001b[1;32m 365\u001b[0m \n\u001b[1;32m 366\u001b[0m \u001b[39mTo make an async_req request, set the async_req parameter.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[39m then the method will return the response directly.\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 405\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m async_req:\n\u001b[0;32m--> 406\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__call_api(resource_path, method,\n\u001b[1;32m 407\u001b[0m path_params, query_params, header_params,\n\u001b[1;32m 408\u001b[0m body, post_params, files,\n\u001b[1;32m 409\u001b[0m response_types_map, auth_settings,\n\u001b[1;32m 410\u001b[0m _return_http_data_only, collection_formats,\n\u001b[1;32m 411\u001b[0m _preload_content, _request_timeout, _host,\n\u001b[1;32m 412\u001b[0m _request_auth)\n\u001b[1;32m 414\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool\u001b[39m.\u001b[39mapply_async(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__call_api, (resource_path,\n\u001b[1;32m 415\u001b[0m method, path_params,\n\u001b[1;32m 416\u001b[0m query_params,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 424\u001b[0m _request_timeout,\n\u001b[1;32m 425\u001b[0m _host, _request_auth))\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/api_client.py:211\u001b[0m, in \u001b[0;36mApiClient.__call_api\u001b[0;34m(self, resource_path, method, path_params, query_params, header_params, body, post_params, files, response_types_map, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)\u001b[0m\n\u001b[1;32m 207\u001b[0m url \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m?\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m url_query\n\u001b[1;32m 209\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m \u001b[39m# perform request and return response\u001b[39;00m\n\u001b[0;32m--> 211\u001b[0m response_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrequest(\n\u001b[1;32m 212\u001b[0m method, url,\n\u001b[1;32m 213\u001b[0m query_params\u001b[39m=\u001b[39;49mquery_params,\n\u001b[1;32m 214\u001b[0m headers\u001b[39m=\u001b[39;49mheader_params,\n\u001b[1;32m 215\u001b[0m post_params\u001b[39m=\u001b[39;49mpost_params, body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m 216\u001b[0m _preload_content\u001b[39m=\u001b[39;49m_preload_content,\n\u001b[1;32m 217\u001b[0m _request_timeout\u001b[39m=\u001b[39;49m_request_timeout)\n\u001b[1;32m 218\u001b[0m \u001b[39mexcept\u001b[39;00m ApiException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 219\u001b[0m \u001b[39mif\u001b[39;00m e\u001b[39m.\u001b[39mbody:\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/api_client.py:432\u001b[0m, in \u001b[0;36mApiClient.request\u001b[0;34m(self, method, url, query_params, headers, post_params, body, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Makes the HTTP request using RESTClient.\"\"\"\u001b[39;00m\n\u001b[1;32m 431\u001b[0m \u001b[39mif\u001b[39;00m method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mGET\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m--> 432\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrest_client\u001b[39m.\u001b[39;49mget_request(url,\n\u001b[1;32m 433\u001b[0m query_params\u001b[39m=\u001b[39;49mquery_params,\n\u001b[1;32m 434\u001b[0m _preload_content\u001b[39m=\u001b[39;49m_preload_content,\n\u001b[1;32m 435\u001b[0m _request_timeout\u001b[39m=\u001b[39;49m_request_timeout,\n\u001b[1;32m 436\u001b[0m headers\u001b[39m=\u001b[39;49mheaders)\n\u001b[1;32m 437\u001b[0m \u001b[39melif\u001b[39;00m method \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mHEAD\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 438\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrest_client\u001b[39m.\u001b[39mhead_request(url,\n\u001b[1;32m 439\u001b[0m query_params\u001b[39m=\u001b[39mquery_params,\n\u001b[1;32m 440\u001b[0m _preload_content\u001b[39m=\u001b[39m_preload_content,\n\u001b[1;32m 441\u001b[0m _request_timeout\u001b[39m=\u001b[39m_request_timeout,\n\u001b[1;32m 442\u001b[0m headers\u001b[39m=\u001b[39mheaders)\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/rest.py:242\u001b[0m, in \u001b[0;36mRESTClientObject.get_request\u001b[0;34m(self, url, headers, query_params, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_request\u001b[39m(\u001b[39mself\u001b[39m, url, headers\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, query_params\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, _preload_content\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 241\u001b[0m _request_timeout\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 242\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrequest(\u001b[39m\"\u001b[39;49m\u001b[39mGET\u001b[39;49m\u001b[39m\"\u001b[39;49m, url,\n\u001b[1;32m 243\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 244\u001b[0m _preload_content\u001b[39m=\u001b[39;49m_preload_content,\n\u001b[1;32m 245\u001b[0m _request_timeout\u001b[39m=\u001b[39;49m_request_timeout,\n\u001b[1;32m 246\u001b[0m query_params\u001b[39m=\u001b[39;49mquery_params)\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/openchallenges_client/rest.py:205\u001b[0m, in \u001b[0;36mRESTClientObject.request\u001b[0;34m(self, method, url, query_params, headers, body, post_params, _preload_content, _request_timeout)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[39mraise\u001b[39;00m ApiException(status\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, reason\u001b[39m=\u001b[39mmsg)\n\u001b[1;32m 203\u001b[0m \u001b[39m# For `GET`, `HEAD`\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 205\u001b[0m r \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpool_manager\u001b[39m.\u001b[39;49mrequest(method, url,\n\u001b[1;32m 206\u001b[0m fields\u001b[39m=\u001b[39;49m{},\n\u001b[1;32m 207\u001b[0m preload_content\u001b[39m=\u001b[39;49m_preload_content,\n\u001b[1;32m 208\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 209\u001b[0m headers\u001b[39m=\u001b[39;49mheaders)\n\u001b[1;32m 210\u001b[0m \u001b[39mexcept\u001b[39;00m urllib3\u001b[39m.\u001b[39mexceptions\u001b[39m.\u001b[39mSSLError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 211\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m{1}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mtype\u001b[39m(e)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, \u001b[39mstr\u001b[39m(e))\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/_request_methods.py:110\u001b[0m, in \u001b[0;36mRequestMethods.request\u001b[0;34m(self, method, url, body, fields, headers, json, **urlopen_kw)\u001b[0m\n\u001b[1;32m 107\u001b[0m urlopen_kw[\u001b[39m\"\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m body\n\u001b[1;32m 109\u001b[0m \u001b[39mif\u001b[39;00m method \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_encode_url_methods:\n\u001b[0;32m--> 110\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrequest_encode_url(\n\u001b[1;32m 111\u001b[0m method,\n\u001b[1;32m 112\u001b[0m url,\n\u001b[1;32m 113\u001b[0m fields\u001b[39m=\u001b[39;49mfields, \u001b[39m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 114\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 115\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49murlopen_kw,\n\u001b[1;32m 116\u001b[0m )\n\u001b[1;32m 117\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrequest_encode_body(\n\u001b[1;32m 119\u001b[0m method, url, fields\u001b[39m=\u001b[39mfields, headers\u001b[39m=\u001b[39mheaders, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39murlopen_kw\n\u001b[1;32m 120\u001b[0m )\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/_request_methods.py:143\u001b[0m, in \u001b[0;36mRequestMethods.request_encode_url\u001b[0;34m(self, method, url, fields, headers, **urlopen_kw)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[39mif\u001b[39;00m fields:\n\u001b[1;32m 141\u001b[0m url \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m?\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m urlencode(fields)\n\u001b[0;32m--> 143\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49murlopen(method, url, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mextra_kw)\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/poolmanager.py:443\u001b[0m, in \u001b[0;36mPoolManager.urlopen\u001b[0;34m(self, method, url, redirect, **kw)\u001b[0m\n\u001b[1;32m 441\u001b[0m response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39murlopen(method, url, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkw)\n\u001b[1;32m 442\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 443\u001b[0m response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(method, u\u001b[39m.\u001b[39;49mrequest_uri, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkw)\n\u001b[1;32m 445\u001b[0m redirect_location \u001b[39m=\u001b[39m redirect \u001b[39mand\u001b[39;00m response\u001b[39m.\u001b[39mget_redirect_location()\n\u001b[1;32m 446\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m redirect_location:\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:875\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m conn:\n\u001b[1;32m 871\u001b[0m \u001b[39m# Try again\u001b[39;00m\n\u001b[1;32m 872\u001b[0m log\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m 873\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mRetrying (\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m) after connection broken by \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, retries, err, url\n\u001b[1;32m 874\u001b[0m )\n\u001b[0;32m--> 875\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m 876\u001b[0m method,\n\u001b[1;32m 877\u001b[0m url,\n\u001b[1;32m 878\u001b[0m body,\n\u001b[1;32m 879\u001b[0m headers,\n\u001b[1;32m 880\u001b[0m retries,\n\u001b[1;32m 881\u001b[0m redirect,\n\u001b[1;32m 882\u001b[0m assert_same_host,\n\u001b[1;32m 883\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 884\u001b[0m pool_timeout\u001b[39m=\u001b[39;49mpool_timeout,\n\u001b[1;32m 885\u001b[0m release_conn\u001b[39m=\u001b[39;49mrelease_conn,\n\u001b[1;32m 886\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 887\u001b[0m body_pos\u001b[39m=\u001b[39;49mbody_pos,\n\u001b[1;32m 888\u001b[0m preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m 889\u001b[0m decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m 890\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mresponse_kw,\n\u001b[1;32m 891\u001b[0m )\n\u001b[1;32m 893\u001b[0m \u001b[39m# Handle redirect?\u001b[39;00m\n\u001b[1;32m 894\u001b[0m redirect_location \u001b[39m=\u001b[39m redirect \u001b[39mand\u001b[39;00m response\u001b[39m.\u001b[39mget_redirect_location()\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:875\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m conn:\n\u001b[1;32m 871\u001b[0m \u001b[39m# Try again\u001b[39;00m\n\u001b[1;32m 872\u001b[0m log\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m 873\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mRetrying (\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m) after connection broken by \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, retries, err, url\n\u001b[1;32m 874\u001b[0m )\n\u001b[0;32m--> 875\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m 876\u001b[0m method,\n\u001b[1;32m 877\u001b[0m url,\n\u001b[1;32m 878\u001b[0m body,\n\u001b[1;32m 879\u001b[0m headers,\n\u001b[1;32m 880\u001b[0m retries,\n\u001b[1;32m 881\u001b[0m redirect,\n\u001b[1;32m 882\u001b[0m assert_same_host,\n\u001b[1;32m 883\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 884\u001b[0m pool_timeout\u001b[39m=\u001b[39;49mpool_timeout,\n\u001b[1;32m 885\u001b[0m release_conn\u001b[39m=\u001b[39;49mrelease_conn,\n\u001b[1;32m 886\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 887\u001b[0m body_pos\u001b[39m=\u001b[39;49mbody_pos,\n\u001b[1;32m 888\u001b[0m preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m 889\u001b[0m decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m 890\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mresponse_kw,\n\u001b[1;32m 891\u001b[0m )\n\u001b[1;32m 893\u001b[0m \u001b[39m# Handle redirect?\u001b[39;00m\n\u001b[1;32m 894\u001b[0m redirect_location \u001b[39m=\u001b[39m redirect \u001b[39mand\u001b[39;00m response\u001b[39m.\u001b[39mget_redirect_location()\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:875\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m conn:\n\u001b[1;32m 871\u001b[0m \u001b[39m# Try again\u001b[39;00m\n\u001b[1;32m 872\u001b[0m log\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m 873\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mRetrying (\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m) after connection broken by \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, retries, err, url\n\u001b[1;32m 874\u001b[0m )\n\u001b[0;32m--> 875\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m 876\u001b[0m method,\n\u001b[1;32m 877\u001b[0m url,\n\u001b[1;32m 878\u001b[0m body,\n\u001b[1;32m 879\u001b[0m headers,\n\u001b[1;32m 880\u001b[0m retries,\n\u001b[1;32m 881\u001b[0m redirect,\n\u001b[1;32m 882\u001b[0m assert_same_host,\n\u001b[1;32m 883\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 884\u001b[0m pool_timeout\u001b[39m=\u001b[39;49mpool_timeout,\n\u001b[1;32m 885\u001b[0m release_conn\u001b[39m=\u001b[39;49mrelease_conn,\n\u001b[1;32m 886\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 887\u001b[0m body_pos\u001b[39m=\u001b[39;49mbody_pos,\n\u001b[1;32m 888\u001b[0m preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m 889\u001b[0m decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m 890\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mresponse_kw,\n\u001b[1;32m 891\u001b[0m )\n\u001b[1;32m 893\u001b[0m \u001b[39m# Handle redirect?\u001b[39;00m\n\u001b[1;32m 894\u001b[0m redirect_location \u001b[39m=\u001b[39m redirect \u001b[39mand\u001b[39;00m response\u001b[39m.\u001b[39mget_redirect_location()\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:845\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(new_e, (\u001b[39mOSError\u001b[39;00m, HTTPException)):\n\u001b[1;32m 843\u001b[0m new_e \u001b[39m=\u001b[39m ProtocolError(\u001b[39m\"\u001b[39m\u001b[39mConnection aborted.\u001b[39m\u001b[39m\"\u001b[39m, new_e)\n\u001b[0;32m--> 845\u001b[0m retries \u001b[39m=\u001b[39m retries\u001b[39m.\u001b[39;49mincrement(\n\u001b[1;32m 846\u001b[0m method, url, error\u001b[39m=\u001b[39;49mnew_e, _pool\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m, _stacktrace\u001b[39m=\u001b[39;49msys\u001b[39m.\u001b[39;49mexc_info()[\u001b[39m2\u001b[39;49m]\n\u001b[1;32m 847\u001b[0m )\n\u001b[1;32m 848\u001b[0m retries\u001b[39m.\u001b[39msleep()\n\u001b[1;32m 850\u001b[0m \u001b[39m# Keep track of the error for the retry warning.\u001b[39;00m\n", - "File \u001b[0;32m/workspaces/sage-monorepo/apps/openchallenges/notebook/.venv/lib/python3.10/site-packages/urllib3/util/retry.py:515\u001b[0m, in \u001b[0;36mRetry.increment\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[39mif\u001b[39;00m new_retry\u001b[39m.\u001b[39mis_exhausted():\n\u001b[1;32m 514\u001b[0m reason \u001b[39m=\u001b[39m error \u001b[39mor\u001b[39;00m ResponseError(cause)\n\u001b[0;32m--> 515\u001b[0m \u001b[39mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[39mfrom\u001b[39;00m \u001b[39mreason\u001b[39;00m \u001b[39m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 517\u001b[0m log\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mIncremented Retry for (url=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m): \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m, url, new_retry)\n\u001b[1;32m 519\u001b[0m \u001b[39mreturn\u001b[39;00m new_retry\n", - "\u001b[0;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /api/v1/challenges?challengeSearchQuery=%7B%22pageNumber%22%3A%201%2C%20%22pageSize%22%3A%201%7D (Caused by NewConnectionError(': Failed to establish a new connection: [Errno 111] Connection refused'))" - ] - } - ], - "source": [ - "# Enter a context with an instance of the API client\n", - "with openchallenges_client.ApiClient(configuration) as api_client:\n", - " api_instance = challenge_api.ChallengeApi(api_client)\n", - " \n", - " query = openchallenges_client.ChallengeSearchQuery(page_number=1, page_size=1)\n", - "\n", - " try:\n", - " # Get the first page of the list of challenges\n", - " api_response = api_instance.list_challenges(query)\n", - " pprint(api_response)\n", - " except openchallenges_client.ApiException as e:\n", - " print(\"Exception when calling ChallengeApi->list_challenges: %s\\n\" % e)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0c0b308-0b58-44a7-8ff6-4987dfbccb17", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/openchallenges/notebook/notebooks/utils/__init__.py b/apps/openchallenges/notebook/notebooks/utils/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/apps/openchallenges/notebook/notebooks/utils/bedrock.py b/apps/openchallenges/notebook/notebooks/utils/bedrock.py deleted file mode 100644 index b959e1009a..0000000000 --- a/apps/openchallenges/notebook/notebooks/utils/bedrock.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: MIT-0 -"""Helper utilities for working with Amazon Bedrock from Python notebooks""" -# Python Built-Ins: -import os -from typing import Optional - -# External Dependencies: -import boto3 -from botocore.config import Config - - -def get_bedrock_client( - assumed_role: Optional[str] = None, - region: Optional[str] = None, - runtime: Optional[bool] = True, -): - """Create a boto3 client for Amazon Bedrock, with optional configuration overrides - - Parameters - ---------- - assumed_role : - Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not - specified, the current active credentials will be used. - region : - Optional name of the AWS Region in which the service should be called (e.g. "us-east-1"). - If not specified, AWS_REGION or AWS_DEFAULT_REGION environment variable will be used. - runtime : - Optional choice of getting different client to perform operations with the Amazon Bedrock service. - """ - if region is None: - target_region = os.environ.get( - "AWS_REGION", os.environ.get("AWS_DEFAULT_REGION") - ) - else: - target_region = region - - print(f"Create new client\n Using region: {target_region}") - session_kwargs = {"region_name": target_region} - client_kwargs = {**session_kwargs} - - profile_name = os.environ.get("AWS_PROFILE") - if profile_name: - print(f" Using profile: {profile_name}") - session_kwargs["profile_name"] = profile_name - - retry_config = Config( - region_name=target_region, - retries={ - "max_attempts": 10, - "mode": "standard", - }, - ) - session = boto3.Session(**session_kwargs) - - if assumed_role: - print(f" Using role: {assumed_role}", end="") - sts = session.client("sts") - response = sts.assume_role( - RoleArn=str(assumed_role), RoleSessionName="langchain-llm-1" - ) - print(" ... successful!") - client_kwargs["aws_access_key_id"] = response["Credentials"]["AccessKeyId"] - client_kwargs["aws_secret_access_key"] = response["Credentials"][ - "SecretAccessKey" - ] - client_kwargs["aws_session_token"] = response["Credentials"]["SessionToken"] - - if runtime: - service_name = "bedrock-runtime" - else: - service_name = "bedrock" - - bedrock_client = session.client( - service_name=service_name, config=retry_config, **client_kwargs - ) - - print("boto3 Bedrock client successfully created!") - print(bedrock_client._endpoint) - return bedrock_client diff --git a/apps/openchallenges/notebook/notebooks/utils/print_ww.py b/apps/openchallenges/notebook/notebooks/utils/print_ww.py deleted file mode 100644 index b03ad2c10a..0000000000 --- a/apps/openchallenges/notebook/notebooks/utils/print_ww.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: MIT-0 -"""General helper utilities the workshop notebooks""" -# Python Built-Ins: -from io import StringIO -import sys -import textwrap - - -def print_ww(*args, width: int = 100, **kwargs): - """Like print(), but wraps output to `width` characters (default 100)""" - buffer = StringIO() - try: - _stdout = sys.stdout - sys.stdout = buffer - print(*args, **kwargs) - output = buffer.getvalue() - finally: - sys.stdout = _stdout - for line in output.splitlines(): - print("\n".join(textwrap.wrap(line, width=width))) diff --git a/apps/openchallenges/notebook/poetry.lock b/apps/openchallenges/notebook/poetry.lock deleted file mode 100644 index 85c84f1c62..0000000000 --- a/apps/openchallenges/notebook/poetry.lock +++ /dev/null @@ -1,3572 +0,0 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. - -[[package]] -name = "aenum" -version = "3.1.15" -description = "Advanced Enumerations (compatible with Python's stdlib Enum), NamedTuples, and NamedConstants" -optional = false -python-versions = "*" -files = [ - 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b/apps/openchallenges/notebook/poetry.toml deleted file mode 100644 index 62e2dff2a2..0000000000 --- a/apps/openchallenges/notebook/poetry.toml +++ /dev/null @@ -1,3 +0,0 @@ -[virtualenvs] -in-project = true -create = true diff --git a/apps/openchallenges/notebook/prepare-python.sh b/apps/openchallenges/notebook/prepare-python.sh deleted file mode 100755 index 60af21daad..0000000000 --- a/apps/openchallenges/notebook/prepare-python.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env bash - -PYTHON_VERSION="3.10.12" - -pyenv install --skip-existing $PYTHON_VERSION - -# Initializing pyenv again solves an issue encountered by GitHub action where the version of Python -# installed above is not detected. -eval "$(pyenv init -)" - -pyenv local $PYTHON_VERSION -poetry env use $PYTHON_VERSION -poetry install --with dev \ No newline at end of file diff --git a/apps/openchallenges/notebook/project.json b/apps/openchallenges/notebook/project.json deleted file mode 100644 index 9d67fe6101..0000000000 --- a/apps/openchallenges/notebook/project.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "name": "openchallenges-notebook", - "$schema": "../../../node_modules/nx/schemas/project-schema.json", - "sourceRoot": "apps/openchallenges/notebook/src", - "projectType": "application", - "targets": { - "create-config": { - "executor": "nx:run-commands", - "options": { - "command": "cp -n .env.example .env", - "cwd": "{projectRoot}" - } - }, - "prepare": { - "executor": "nx:run-commands", - "options": { - "command": "./prepare-python.sh", - "cwd": "{projectRoot}" - } - }, - "serve": { - "executor": "nx:run-commands", - "options": { - "commands": ["poetry run jupyter lab --config=jupyter_lab_config.py"], - "cwd": "apps/openchallenges/notebook" - }, - "dependsOn": [] - } - }, - "tags": ["type:app", "scope:client"], - "implicitDependencies": [] -} diff --git a/apps/openchallenges/notebook/pyproject.toml b/apps/openchallenges/notebook/pyproject.toml deleted file mode 100644 index 2695a2ad58..0000000000 --- a/apps/openchallenges/notebook/pyproject.toml +++ /dev/null @@ -1,25 +0,0 @@ -[tool.poetry] -name = "openchallenges-notebook" -version = "0.1.0" -description = "OpenChallenges notebooks" -authors = ["Thomas Schaffter "] -readme = "README.md" -packages = [] - -[tool.poetry.dependencies] -python = "3.10.12" -kaggle = "1.5.12" -openchallenges-client = {path = "../../../libs/openchallenges/api-client-python"} -openai = "0.28.1" -python-dotenv = "1.0.0" -boto3 = "1.28.72" -langchain = "0.0.324" -transformers = "4.34.1" -anthropic = "0.5.0" - -[tool.poetry.group.dev.dependencies] -jupyterlab = "3.5.2" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/apps/openchallenges/notebook/src/challenge_headline/__init__.py b/apps/openchallenges/notebook/src/challenge_headline/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/apps/openchallenges/notebook/src/challenge_headline/generate_challenge_headlines.py b/apps/openchallenges/notebook/src/challenge_headline/generate_challenge_headlines.py deleted file mode 100644 index e28f524bb5..0000000000 --- a/apps/openchallenges/notebook/src/challenge_headline/generate_challenge_headlines.py +++ /dev/null @@ -1,130 +0,0 @@ -# Requirements -# -# - Login with AWS: aws --profile cnb sso login - -from dotenv import load_dotenv - -import openchallenges_client -from pprint import pprint -from openchallenges_client.api import challenge_api - -load_dotenv() - -# List challenges from OC.io - -# See configuration.py for a list of all supported configuration parameters. -configuration = openchallenges_client.Configuration( - host="https://openchallenges.io/api/v1" -) - -# Enter a context with an instance of the API client -challenges = [] -with openchallenges_client.ApiClient(configuration) as api_client: - api_instance = challenge_api.ChallengeApi(api_client) - - # IMPORTANT: The auto-generated API client does not support object for query params - query = openchallenges_client.ChallengeSearchQuery(page_number=0, page_size=1000) - - try: - # Get the first page of the list of challenges - page = api_instance.list_challenges(query) - challenges.extend(page.challenges) - except openchallenges_client.ApiException as e: - print("Exception when calling ChallengeApi->list_challenges: %s\n" % e) - -# Sort challenge by ID in ascending order -challenges.sort(key=lambda challenge: challenge.id, reverse=False) - - -# GENERATE THE HEADLINES WITH AWS BEDROCK - -# Configure the Bedrock client - -import json -import os -import sys - -import boto3 -import botocore - -module_path = "src" -sys.path.append(os.path.abspath(module_path)) -from utils import bedrock, print_ww - -os.environ["AWS_DEFAULT_REGION"] = "us-east-1" -os.environ["AWS_PROFILE"] = "cnb" - -boto3_bedrock = bedrock.get_bedrock_client( - assumed_role=os.environ.get("BEDROCK_ASSUME_ROLE", None), - region=os.environ.get("AWS_DEFAULT_REGION", None), -) - -# Configure base model options - -from langchain.llms.bedrock import Bedrock - -inference_modifier = { - "max_tokens_to_sample": 6000, - "temperature": 0.6, - "top_k": 250, - "top_p": 1, - "stop_sequences": ["\n\nHuman"], -} - -textgen_llm = Bedrock( - model_id="anthropic.claude-v2", - client=boto3_bedrock, - model_kwargs=inference_modifier, -) - - -def generate_challenge_headlines(text, num_headlines): - prompt = ( - f"Please generate {num_headlines} headlines that have less than 80 characters from the " - "following challenge description. " - "The headlines must summarize the goal of the challenge. " - # "The headlines must not include the name of the challenge. " - "The headlines must read naturally. " - f"Description: \n{text}" - ) - response = Bedrock( - model_id="anthropic.claude-v2", - client=boto3_bedrock, - model_kwargs=inference_modifier, - )(prompt) - return response - - -from itertools import compress -import json - - -def is_raw_headline(raw_headline): - prefixes = ("1. ", "2. ", "3. ", "4. ", "5. ") - return raw_headline.startswith(prefixes) - - -def process_challenge(challenge): - print(f"Processing challenge ID {challenge.id}: {challenge.name}") - response = generate_challenge_headlines(challenge.description, 5) - - raw_headlines = response.splitlines() - headlines = list(compress(raw_headlines, map(is_raw_headline, raw_headlines))) - - obj = { - "id": challenge.id, - "slug": challenge.slug, - "name": challenge.name, - "headline": challenge.headline, - "headline_alternatives": headlines, - } - return obj - - -challenge_headlines = list(map(process_challenge, challenges)) - - -# SAVE OUTPUT TO FILE - -with open("challenge_headlines.json", "w") as f: - json.dump(challenge_headlines, f, indent=2) diff --git a/apps/openchallenges/notebook/src/utils/__init__.py b/apps/openchallenges/notebook/src/utils/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/apps/openchallenges/notebook/src/utils/bedrock.py b/apps/openchallenges/notebook/src/utils/bedrock.py deleted file mode 100644 index b959e1009a..0000000000 --- a/apps/openchallenges/notebook/src/utils/bedrock.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: MIT-0 -"""Helper utilities for working with Amazon Bedrock from Python notebooks""" -# Python Built-Ins: -import os -from typing import Optional - -# External Dependencies: -import boto3 -from botocore.config import Config - - -def get_bedrock_client( - assumed_role: Optional[str] = None, - region: Optional[str] = None, - runtime: Optional[bool] = True, -): - """Create a boto3 client for Amazon Bedrock, with optional configuration overrides - - Parameters - ---------- - assumed_role : - Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not - specified, the current active credentials will be used. - region : - Optional name of the AWS Region in which the service should be called (e.g. "us-east-1"). - If not specified, AWS_REGION or AWS_DEFAULT_REGION environment variable will be used. - runtime : - Optional choice of getting different client to perform operations with the Amazon Bedrock service. - """ - if region is None: - target_region = os.environ.get( - "AWS_REGION", os.environ.get("AWS_DEFAULT_REGION") - ) - else: - target_region = region - - print(f"Create new client\n Using region: {target_region}") - session_kwargs = {"region_name": target_region} - client_kwargs = {**session_kwargs} - - profile_name = os.environ.get("AWS_PROFILE") - if profile_name: - print(f" Using profile: {profile_name}") - session_kwargs["profile_name"] = profile_name - - retry_config = Config( - region_name=target_region, - retries={ - "max_attempts": 10, - "mode": "standard", - }, - ) - session = boto3.Session(**session_kwargs) - - if assumed_role: - print(f" Using role: {assumed_role}", end="") - sts = session.client("sts") - response = sts.assume_role( - RoleArn=str(assumed_role), RoleSessionName="langchain-llm-1" - ) - print(" ... successful!") - client_kwargs["aws_access_key_id"] = response["Credentials"]["AccessKeyId"] - client_kwargs["aws_secret_access_key"] = response["Credentials"][ - "SecretAccessKey" - ] - client_kwargs["aws_session_token"] = response["Credentials"]["SessionToken"] - - if runtime: - service_name = "bedrock-runtime" - else: - service_name = "bedrock" - - bedrock_client = session.client( - service_name=service_name, config=retry_config, **client_kwargs - ) - - print("boto3 Bedrock client successfully created!") - print(bedrock_client._endpoint) - return bedrock_client diff --git a/apps/openchallenges/notebook/src/utils/print_ww.py b/apps/openchallenges/notebook/src/utils/print_ww.py deleted file mode 100644 index b03ad2c10a..0000000000 --- a/apps/openchallenges/notebook/src/utils/print_ww.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: MIT-0 -"""General helper utilities the workshop notebooks""" -# Python Built-Ins: -from io import StringIO -import sys -import textwrap - - -def print_ww(*args, width: int = 100, **kwargs): - """Like print(), but wraps output to `width` characters (default 100)""" - buffer = StringIO() - try: - _stdout = sys.stdout - sys.stdout = buffer - print(*args, **kwargs) - output = buffer.getvalue() - finally: - sys.stdout = _stdout - for line in output.splitlines(): - print("\n".join(textwrap.wrap(line, width=width)))