Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Overview

Google Cloud Vertex AI Samples

License

Welcome to the Google Cloud Vertex AI sample repository.

Overview

The repository contains notebooks and community content that demonstrate how to develop and manage ML workflows using Google Cloud Vertex AI.

Repository structure

├── community-content - Sample code and tutorials contributed by the community
├── notebooks
│   ├── community - Notebooks contributed by the community
│   ├── official - Notebooks demonstrating use of each Vertex AI service
│   │   ├── automl
│   │   ├── custom
│   │   ├── ...

Contributing

Contributions welcome! See the Contributing Guide.

Getting help

Please use the issues page to provide feedback or submit a bug report.

Disclaimer

This is not an officially supported Google product. The code in this repository is for demonstrative purposes only.

Feedback

Please feel free to fill out our survey to give us feedback on the repo and its content.

Comments
  • Hierarchical forecasting notebook

    Hierarchical forecasting notebook

    This is a hierarchical forecasting notebook demonstrating the use of the new hierarchical forecasting parameters for AutoML Forecasting.

    If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under the Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by ivanmkc 76
  • Adding the official version for SDK-FB-prophet-online-forecasting-notebook

    Adding the official version for SDK-FB-prophet-online-forecasting-notebook

    Checklist for moving the notebook to the main repo:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by krishr2d2 35
  • add two tower

    add two tower

    If you are opening a PR for Notebooks under the official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks
    • [x] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    If you are opening a PR for Notebooks under the community folder:

    • [ ] This notebook has been added to the CODEOWNERS file under the # Community Notebooks section, pointing to the author or the author's team.

    If you are opening a PR for Community Content, and it will NOT be used on cloud.google.com/docs:

    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the # Community Content section, pointing to the author or the author's team.

    If you are opening a PR for Community Content, and it will be used on cloud.google.com/docs:

    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] Use the notebook template for all the notebooks in your content directory.
    • [ ] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify each notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the content has been reviewed by a tech writer, and they have approved it.
    • [ ] This content has been added (/moved) to the CODEOWNERS file under the # Official Community Content section (/from the # Community Content section), pointing to the author or the author's team.
    • [ ] Each notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by shenzhimo2 34
  • Adding official version for Fraud detection notebook

    Adding official version for Fraud detection notebook

    This PR contains a notebook which shows you how to build, deploy, and analyze predictions from a simple random forest model using tools like scikit-learn, Vertex AI, and the What-IF Tool (WIT) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by sudarshan-SpringML 33
  • Create explore_data_in_bigquery_with_workbench.ipynb

    Create explore_data_in_bigquery_with_workbench.ipynb

    Adding in notebook for exploratory data analysis as part of "Data to AI" effort. See this Colab for what this notebook looks like after it is run: https://colab.research.google.com/drive/1JeNeMtj2A_5P5vo9wxSkrwHQM5JQSoAu. Submitting it with outputs shown since a lot of this about interactive visualization, which can inspire folks to use/read the notebook beyond just the code.

    REQUIRED: Fill out the below checklists or remove if irrelevant

    1. If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:
    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [x] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under the Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    opened by alokpattani 19
  • Inardini  bqml components pipeline official blog

    Inardini bqml components pipeline official blog

    If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [x] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by inardini 19
  • Added notebook demonstrating Swivel

    Added notebook demonstrating Swivel

    If you are opening a PR for Notebooks under the official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks
    • [x] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    If you are opening a PR for Notebooks under the community folder:

    • [ ] This notebook has been added to the CODEOWNERS file under the # Community Notebooks section, pointing to the author or the author's team.

    If you are opening a PR for Community Content, and it will NOT be used on cloud.google.com/docs:

    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the # Community Content section, pointing to the author or the author's team.

    If you are opening a PR for Community Content, and it will be used on cloud.google.com/docs:

    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] Use the notebook template for all the notebooks in your content directory.
    • [ ] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify each notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the content has been reviewed by a tech writer, and they have approved it.
    • [ ] This content has been added (/moved) to the CODEOWNERS file under the # Official Community Content section (/from the # Community Content section), pointing to the author or the author's team.
    • [ ] Each notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by yinghsienwu 18
  • Refresh of notebook - MLOps stage 1 : data management: get started with Dataflow

    Refresh of notebook - MLOps stage 1 : data management: get started with Dataflow

    This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with Dataflow.

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [x] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [x] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by manuelamunategui 16
  • Added changes such as timestamp, removed colab references, added markup text mentioning usage of flag -q in a command for the file SDK_Custom_Container_Prediction

    Added changes such as timestamp, removed colab references, added markup text mentioning usage of flag -q in a command for the file SDK_Custom_Container_Prediction

    Added changes such as timestamp, removed colab references, added markup text mentioning usage of flag -q in a command for the file SDK_Custom_Container_Prediction

    If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [x] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [x] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by Tharun-SpringML 14
  • minor changes on Sdk automl tabular regression online bq1

    minor changes on Sdk automl tabular regression online bq1

    If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:

    • [x] Use the notebook template as a starting point.
    • [x] Follow the style and grammar rules outlined in the above notebook template.
    • [x] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [ ] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    If you are opening a PR for Community Notebooks under the notebooks/community folder:

    • [ ] This notebook has been added to the CODEOWNERS file under the # Community Notebooks section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.

    If you are opening a PR for Community Content under the community-content folder:

    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the # Community Content section, pointing to the author or the author's team.
    • [x] Passes all the required formatting and linting checks. You can locally test with these instructions.
    opened by ashakella 14
  • Made minor changes to SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container notebook

    Made minor changes to SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container notebook

    Made minor changes to SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container notebook

    • [X] Use the notebook template as a starting point.
    • [X] Follow the style and grammar rules outlined in the above notebook template.
    • [X] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [X] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [X] This notebook has been added to the CODEOWNERS file under # Official Notebooks section, pointing to the author or the author's team.
    • [X] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.
    opened by sudarshan-SpringML 13
  • fastai example - community contribution (unable to push)

    fastai example - community contribution (unable to push)

    Expected Behavior

    I expect to see in the contribution guide whether to open a PR or an issue to add a community example.

    Actual Behavior

    I cannot push my changes under community to another branch.

    Here's the example I'd like to add https://github.com/OmaymaS/fastai_torch_models/tree/main/fastai_training_job

    opened by OmaymaS 0
  • feat: Add E2E notebook featuring Vertex Feature Store, Training and Prediction

    feat: Add E2E notebook featuring Vertex Feature Store, Training and Prediction

    Add E2E notebook featuring Vertex Feature Store, Training and Prediction. This notebook is developed by our third vender, so we are committing to the community folder.

    REQUIRED: Fill out the below checklists or remove if irrelevant

    1. If you are opening a PR for Community Notebooks under the notebooks/community folder:
    • [x] This notebook has been added to the CODEOWNERS file under the Community Notebooks section, pointing to the author or the author's team.

    • [x] Passes all the required formatting and linting checks. You can locally test with these instructions.

    opened by junkourata 1
  • Workaround for shapely

    Workaround for shapely

    Without this workaround, the command "from google.cloud import aiplatform as vertex_ai" fails due to the following issue:

    https://github.com/googleapis/python-aiplatform/issues/1852

    REQUIRED: Add a summary of your PR here, typically including why the change is needed and what was changed. Include any design alternatives for discussion purposes.


    --- YOUR PR SUMMARY GOES HERE ---


    REQUIRED: Fill out the below checklists or remove if irrelevant

    1. If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:
    • [ ] Use the notebook template as a starting point.
    • [ ] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under the Official Notebooks section, pointing to the author or the author's team.
    • [ ] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    1. If you are opening a PR for Community Notebooks under the notebooks/community folder:
    • [ ] This notebook has been added to the CODEOWNERS file under the Community Notebooks section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.

    1. If you are opening a PR for Community Content under the community-content folder:
    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the Community Content section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.
    opened by nwipfli 1
  • chore(deps): update dependency nbqa to v1.6.0

    chore(deps): update dependency nbqa to v1.6.0

    Mend Renovate

    This PR contains the following updates:

    | Package | Change | Age | Adoption | Passing | Confidence | |---|---|---|---|---|---| | nbqa | ==1.5.3 -> ==1.6.0 | age | adoption | passing | confidence |


    Release Notes

    nbQA-dev/nbQA

    v1.6.0

    Compare Source


    Configuration

    📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

    🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

    Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

    🔕 Ignore: Close this PR and you won't be reminded about this update again.


    • [ ] If you want to rebase/retry this PR, check this box

    This PR has been generated by Mend Renovate. View repository job log here.

    opened by renovate-bot 0
  • migrate

    migrate

    REQUIRED: Add a summary of your PR here, typically including why the change is needed and what was changed. Include any design alternatives for discussion purposes.


    migrating


    REQUIRED: Fill out the below checklists or remove if irrelevant

    1. If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:
    • [ ] Use the notebook template as a starting point.
    • [ ] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under the Official Notebooks section, pointing to the author or the author's team.
    • [ ] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    1. If you are opening a PR for Community Notebooks under the notebooks/community folder:
    • [ ] This notebook has been added to the CODEOWNERS file under the Community Notebooks section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.

    1. If you are opening a PR for Community Content under the community-content folder:
    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the Community Content section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.
    opened by andrewferlitsch 2
  • migrate

    migrate

    REQUIRED: Add a summary of your PR here, typically including why the change is needed and what was changed. Include any design alternatives for discussion purposes.


    migrating


    REQUIRED: Fill out the below checklists or remove if irrelevant

    1. If you are opening a PR for Official Notebooks under the notebooks/official folder, follow this mandatory checklist:
    • [ ] Use the notebook template as a starting point.
    • [ ] Follow the style and grammar rules outlined in the above notebook template.
    • [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes.
    • [ ] Passes all the required automated checks. You can locally test for formatting and linting with these instructions.
    • [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it.
    • [ ] This notebook has been added to the CODEOWNERS file under the Official Notebooks section, pointing to the author or the author's team.
    • [ ] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.

    1. If you are opening a PR for Community Notebooks under the notebooks/community folder:
    • [ ] This notebook has been added to the CODEOWNERS file under the Community Notebooks section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.

    1. If you are opening a PR for Community Content under the community-content folder:
    • [ ] Make sure your main Content Directory Name is descriptive, informative, and includes some of the key products and attributes of your content, so that it is differentiable from other content
    • [ ] The main content directory has been added to the CODEOWNERS file under the Community Content section, pointing to the author or the author's team.
    • [ ] Passes all the required formatting and linting checks. You can locally test with these instructions.
    opened by andrewferlitsch 1
Demonstration of the Model Training as a CI/CD System in Vertex AI

Model Training as a CI/CD System This project demonstrates the machine model training as a CI/CD system in GCP platform. You will see more detailed wo

Chansung Park 19 Dec 28, 2022
Image Captioning on google cloud platform based on iot

Image-Captioning-on-google-cloud-platform-based-on-iot - Image Captioning on google cloud platform based on iot

Shweta_kumawat 1 Jan 20, 2022
REGTR: End-to-end Point Cloud Correspondences with Transformers

REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme

Zi Jian Yew 108 Dec 17, 2022
A PyTorch library and evaluation platform for end-to-end compression research

CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: c

InterDigital 680 Jan 6, 2023
The end-to-end platform for building voice products at scale

Picovoice Made in Vancouver, Canada by Picovoice Picovoice is the end-to-end platform for building voice products on your terms. Unlike Alexa and Goog

Picovoice 318 Jan 7, 2023
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the

null 34 May 24, 2022
An end-to-end machine learning library to directly optimize AUC loss

LibAUC An end-to-end machine learning library for AUC optimization. Why LibAUC? Deep AUC Maximization (DAM) is a paradigm for learning a deep neural n

Andrew 75 Dec 12, 2022
End-to-end machine learning project for rices detection

Basmatinet Welcome to this project folks ! Whether you like it or not this project is all about riiiiice or riz in french. It is also about Deep Learn

Béranger 47 Jun 18, 2022
Nb workflows - A workflow platform which allows you to run parameterized notebooks programmatically

NB Workflows Description If SQL is a lingua franca for querying data, Jupyter sh

Xavier Petit 6 Aug 18, 2022
This repository contains the entire code for our work "Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding"

Two-Timescale-DNN Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding This repository contains the entire code for our work

QiyuHu 3 Mar 7, 2022
Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

null 75 Nov 24, 2022
MPRNet-Cloud-removal: Progressive cloud removal

MPRNet-Cloud-removal Progressive cloud removal Requirements 1.Pytorch >= 1.0 2.Python 3 3.NVIDIA GPU + CUDA 9.0 4.Tensorboard Installation 1.Clone the

Semi 95 Dec 18, 2022
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine

null 419 Jan 3, 2023
Research code for CVPR 2021 paper "End-to-End Human Pose and Mesh Reconstruction with Transformers"

MeshTransformer ✨ This is our research code of End-to-End Human Pose and Mesh Reconstruction with Transformers. MEsh TRansfOrmer is a simple yet effec

Microsoft 473 Dec 31, 2022
Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)

End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta

Andrew Luo 41 Dec 9, 2022
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B

null 58 Dec 21, 2022
Code & Models for 3DETR - an End-to-end transformer model for 3D object detection

3DETR: An End-to-End Transformer Model for 3D Object Detection PyTorch implementation and models for 3DETR. 3DETR (3D DEtection TRansformer) is a simp

Facebook Research 487 Dec 31, 2022