From 0da83add0c9035ad02e88e14e136e056484e7287 Mon Sep 17 00:00:00 2001 From: Taurean Dyer Date: Fri, 18 Oct 2019 05:48:41 +0000 Subject: [PATCH] numbered notebook folders and edited readme --- .../basics/Dask_Hello_World.ipynb | 0 .../basics/Getting_Started_with_Dask.ipynb | 0 .../basics/Getting_Started_with_cuDF.ipynb | 0 .../basics/hello_streamz.ipynb | 0 .../01_Introduction_to_RAPIDS.ipynb | 0 .../02_Introduction_to_cuDF.ipynb | 0 .../03_Introduction_to_Dask.ipynb | 0 ...uction_to_Dask_using_cuDF_DataFrames.ipynb | 0 ..._Introduction_to_Supervised_Learning.ipynb | 0 .../07_Introduction_to_XGBoost.ipynb | 0 .../08_Introduction_to_Dask_XGBoost.ipynb | 0 ...oduction_to_Dimensionality_Reduction.ipynb | 0 .../10_Introduction_to_Clustering.ipynb | 0 .../intro_tutorials/README.md | 0 .../census/census_education2income_demo.ipynb | 0 .../E2E/synthetic_3D/Dockerfile | 0 .../E2E/synthetic_3D/README.md | 0 .../E2E/synthetic_3D/dataset.png | Bin .../E2E/synthetic_3D/fig_helpers.py | 0 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+15,13 @@ Please use the [BUILD.md](BUILD.md) to check the pre-requisite packages and inst Please see our [guide for contributing to notebooks-contrib](CONTRIBUTING.md). ## Exploring the Repo +These folders are number to ease your progression in mastering RAPIDS +- `1_getting_started_notebooks` - “how to start using RAPIDS”. Contains notebooks showing "hello worlds", getting started with RAPIDS libraries, and tutorials around RAPIDS concepts. +- `2_intermediate_notebooks` - “how to accomplish your workflows with RAPIDS”. Contains notebooks showing algorthim and workflow examples, benchmarking tools, and some complete end-to-end (E2E) workflows. +- `3_advanced_notebooks` - "how to master RAPIDS". Contains notebooks showing kernel customization and advanced end-to-end workflows. -- `getting_started_notebooks` - “how to start using RAPIDS”. Contains notebooks showing "hello worlds", getting started with RAPIDS libraries, and tutorials around RAPIDS concepts. -- `intermediate_notebooks` - “how to accomplish your workflows with RAPIDS”. Contains notebooks showing algorthim and workflow examples, benchmarking tools, and some complete end-to-end (E2E) workflows. -- `advanced_notebooks` - "how to master RAPIDS". Contains notebooks showing kernel customization and advanced end-to-end workflows. -- `colab_notebooks` - contains colab versions of popular notebooks to quickly try out in browser +These folders have notebooks are associated with external content, such as presentations, blogs, or are used for workshops. They are of mixed skill levels and teach you tips and tricks or interesting workflow examples +- `colab_notebooks` - contains colab-enabled versions of popular notebooks for users to quickly try out in browser. Used primarily in workshops. - `blog notebooks` - contains shared notebooks mentioned and used in blogs that showcase RAPIDS workflows and capabilities - `conference notebooks` - contains notebooks used in conferences, such as GTC - `competition notebooks` - contains notebooks used in competitions, such as Kaggle @@ -36,46 +38,46 @@ Below is a listing of the notebooks in this repository. Each row will tell you - Design is for a `Single GPU`(SG) or `Mutliple GPUs`(MG) in **GPU** (don't worry, you can still run the multi-GPU notebooks with a single GPU) - Data can be found in **Datasts Used** -## Getting Started Notebooks: +## 1_Getting Started Notebooks: | Folder | Notebook Title | Description | GPU | Dataset Used |-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----| -| basics | [Getting_Started_with_cuDF](getting_started_notebooks/basics/Getting_Started_with_cuDF.ipynb) | This notebook shows how to get started with GPU DataFrames (single GPU onle) using cuDF in RAPIDS. | SG | Self Generated | -| basics | [Dask_Hello_World](getting_started_notebooks/basics/Dask_Hello_World.ipynb) | This notebook shows how to quickly setup Dask and run a "Hello World" example. | MG | Self Generated | -| basics | [Getting_Started_with_Dask](getting_started_notebooks/basics/Getting_Started_with_Dask.ipynb) | This notebook shows how to get started with multi-GPU DataFrames using Dask and cuDF in RAPIDS. | MG | Self Generated | -| basics | [hello_streamz](getting_started_notebooks/basics/hello_streamz.ipynb) | This notebook demonstrates use of cuDF to perform streaming word-count using a small portion of the Streamz API. | SG | Self Generated | -| intro_tutorials | [01_Introduction_to_RAPIDS](getting_started_notebooks/intro_tutorials/01_Introduction_to_RAPIDS.ipynb) | This notebook shows at a high level what each of the packages in RAPIDS are as well as what they do. | MG | Self Generated | -| intro_tutorials | [02_Introduction_to_cuDF](getting_started_notebooks/intro_tutorials/02_Introduction_to_cuDF.ipynb) | This notebook shows how to work with cuDF DataFrames in RAPIDS. | SG | Self Generated | -| intro_tutorials | [03_Introduction_to_Dask](getting_started_notebooks/intro_tutorials/03_Introduction_to_Dask.ipynb) | This notebook shows how to work with Dask using basic Python primitives like integers and strings. | MG | Self Generated | -| intro_tutorials | [04_Introduction_to_Dask_using_cuDF_DataFrames](getting_started_notebooks/intro_tutorials/04_Introduction_to_Dask_using_cuDF_DataFrames.ipynb) | This notebook shows how to work with cuDF DataFrames using Dask. | MG | Self Generated | -| intro_tutorials | [06_Introduction_to_Supervised_Learning](getting_started_notebooks/intro_tutorials/06_Introduction_to_Supervised_Learning.ipynb) | This notebook shows how to do GPU accelerated Supervised Learning in RAPIDS. | SG | Self Generated | -| intro_tutorials | [07_Introduction_to_XGBoost](getting_started_notebooks/intro_tutorials/07_Introduction_to_XGBoost.ipynb) | This notebook shows how to work with GPU accelerated XGBoost in RAPIDS. | SG | Self Generated | -| intro_tutorials | [08_Introduction_to_Dask_XGBoost](getting_started_notebooks/intro_tutorials/08_Introduction_to_Dask_XGBoost.ipynb) | This notebook shows how to work with Dask XGBoost in RAPIDS. | MG | Self Generated | -| intro_tutorials | [09_Introduction_to_Dimensionality_Reduction](getting_started_notebooks/intro_tutorials/09_Introduction_to_Dimensionality_Reduction.ipynb) | This notebook shows how to do GPU accelerated Dimensionality Reduction in RAPIDS. | SG | Self Generated | -| intro_tutorials | [10_Introduction_to_Clustering](getting_started_notebooks/intro_tutorials/10_Introduction_to_Clustering.ipynb) | This notebook shows how to do GPU accelerated Clustering in RAPIDS. | SG | Self Generated | - -## Intermediate Notebooks: +| basics | [Getting_Started_with_cuDF](1_getting_started_notebooks/basics/Getting_Started_with_cuDF.ipynb) | This notebook shows how to get started with GPU DataFrames (single GPU onle) using cuDF in RAPIDS. | SG | Self Generated | +| basics | [Dask_Hello_World](1_getting_started_notebooks/basics/Dask_Hello_World.ipynb) | This notebook shows how to quickly setup Dask and run a "Hello World" example. | MG | Self Generated | +| basics | [Getting_Started_with_Dask](1_getting_started_notebooks/basics/Getting_Started_with_Dask.ipynb) | This notebook shows how to get started with multi-GPU DataFrames using Dask and cuDF in RAPIDS. | MG | Self Generated | +| basics | [hello_streamz](1_getting_started_notebooks/basics/hello_streamz.ipynb) | This notebook demonstrates use of cuDF to perform streaming word-count using a small portion of the Streamz API. | SG | Self Generated | +| intro_tutorials | [01_Introduction_to_RAPIDS](1_getting_started_notebooks/intro_tutorials/01_Introduction_to_RAPIDS.ipynb) | This notebook shows at a high level what each of the packages in RAPIDS are as well as what they do. | MG | Self Generated | +| intro_tutorials | [02_Introduction_to_cuDF](1_getting_started_notebooks/intro_tutorials/02_Introduction_to_cuDF.ipynb) | This notebook shows how to work with cuDF DataFrames in RAPIDS. | SG | Self Generated | +| intro_tutorials | [03_Introduction_to_Dask](1_getting_started_notebooks/intro_tutorials/03_Introduction_to_Dask.ipynb) | This notebook shows how to work with Dask using basic Python primitives like integers and strings. | MG | Self Generated | +| intro_tutorials | [04_Introduction_to_Dask_using_cuDF_DataFrames](1_getting_started_notebooks/intro_tutorials/04_Introduction_to_Dask_using_cuDF_DataFrames.ipynb) | This notebook shows how to work with cuDF DataFrames using Dask. | MG | Self Generated | +| intro_tutorials | [06_Introduction_to_Supervised_Learning](1_getting_started_notebooks/intro_tutorials/06_Introduction_to_Supervised_Learning.ipynb) | This notebook shows how to do GPU accelerated Supervised Learning in RAPIDS. | SG | Self Generated | +| intro_tutorials | [07_Introduction_to_XGBoost](1_getting_started_notebooks/intro_tutorials/07_Introduction_to_XGBoost.ipynb) | This notebook shows how to work with GPU accelerated XGBoost in RAPIDS. | SG | Self Generated | +| intro_tutorials | [08_Introduction_to_Dask_XGBoost](1_getting_started_notebooks/intro_tutorials/08_Introduction_to_Dask_XGBoost.ipynb) | This notebook shows how to work with Dask XGBoost in RAPIDS. | MG | Self Generated | +| intro_tutorials | [09_Introduction_to_Dimensionality_Reduction](1_getting_started_notebooks/intro_tutorials/09_Introduction_to_Dimensionality_Reduction.ipynb) | This notebook shows how to do GPU accelerated Dimensionality Reduction in RAPIDS. | SG | Self Generated | +| intro_tutorials | [10_Introduction_to_Clustering](1_getting_started_notebooks/intro_tutorials/10_Introduction_to_Clustering.ipynb) | This notebook shows how to do GPU accelerated Clustering in RAPIDS. | SG | Self Generated | + +## 2_Intermediate Notebooks: | Folder | Notebook Title | Description | GPU | Dataset Used |-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----| -| examples | [linear_regression_demo.ipynb](intermediate_notebooks/examples/linear_regression_demo.ipynb) |In this notebook we will show how to use linear regression and its GPU accelerated implementation present in RAPIDS. | SG | [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| -| examples | [ridge_regression_demo](intermediate_notebooks/examples/ridge_regression_demo.ipynb) | Demonstration of using both NetworkX and cuGraph to compute the the number of Triangles in our test dataset. | SG | [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| -| examples | [umap_demo_full](intermediate_notebooks/examples/umap_demo_full.ipynb) | In this notebook we will show how to use UMAP and its GPU accelerated implementation present in RAPIDS. | SG | [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist)| -| examples | [rf_demo](intermediate_notebooks/examples/rf_demo.ipynb) | Demonstration of using both cuml and sklearn to train a RandomForestClassifier on the Higgs dataset. | SG | [Higgs Boson](https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz) -| examples | [cuDatashader](intermediate_notebooks/examples/cuDatashader.ipynb) | Demonstration of using our GPU acclerated version of DataShader | SG | [NYC Jan 2015 Taxi Data](https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2015-01.csv) | -| examples | [weather](intermediate_notebooks/examples/weather.ipynb) | Demonstration of using Dask and cuDF to process and analyze weather history | MG | [NOAA Annual Weather Data](ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/) | -| E2E-> taxi | [NYCTaxi](intermediate_notebooks/E2E/taxi/NYCTaxi-E2E.ipynb) | Demonstrates multi-node ETL for cleanup of raw data into cleaned train and test dataframes. Shows how to run multi-node XGBoost training with dask-xgboost. **Please Note: requires Google Dataproc to run!** [Blog](https://medium.com/rapids-ai/scale-out-rapids-on-google-cloud-dataproc-8a873233258f) | MG | [Google Dataproc Hosted NYC Taxi Data](https://console.cloud.google.com/storage/browser/anaconda-public-data/nyc-taxi/csv/?pli=1) | -| E2E-> synthetic_3D | [rapids_ml_workflow_demo](intermediate_notebooks/E2E/synthetic_3D/rapids_ml_workflow_demo.ipynb) | A 3D visual showcase of a machine learning workflow with RAPIDS (load data, transform/normalize, train XGBoost model, evaluate accuracy, use model for inference). Along the way we compare the performance gains of RAPIDS [GPU] vs sklearn/pandas methods [CPU]. | SG | SciKit-Learn's demo datasets | -| E2E-> census | [census_education2income_demo](intermediate_notebooks/E2E/census/census_education2income_demo.ipynb) | In this notebook we use 50 years of census data to see how education affects income. | SG | [Custom IPUMS Data pull](https://rapidsai-data.s3.us-east-2.amazonaws.com/datasets/ipums_education2income_1970-2010.csv.gz) -| benchmarks | [cuml_benchmarks](intermediate_notebooks/benchmarks/cuml_benchmarks.ipynb) | The purpose of this notebook is to extensively benchmark all of the single GPU cuML algorithms against their skLearn counterparts, while also providing the ability to find and verify upper bounds. **Note: Best on large memory GPUs** | SG | Self Generated | -| benchmarks | [cuml_benchmarks_quick](intermediate_notebooks/benchmarks/cuml_benchmarks_quick.ipynb) | The purpose of this notebook is to quickly benchmark all of the single GPU cuML algorithms against their skLearn counterparts, while also providing the ability to find and verify upper bounds. **Note: Best for small memory GPUs** | SG | Self Generated and [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| -| benchmarks | [rapids_decomposition](intermediate_notebooks/benchmarks/rapids_decomposition.ipynb) | This notebook benchmarks and visualize RAPIDS decomposition methods against each other. You have the opportunity to self-compare it to CPU speeds and methods | SG | SciKit-Learn's demo datasets | -| benchmarks-> cugraph_benchmarks | [louvain_benchmark](intermediate_notebooks/benchmarks/cugraph_benchmarks/louvain_benchmark.ipynb) | This notebook benchmarks performance improvement of running the Louvain clustering algorithm within cuGraph against NetworkX. | SG | SciKit-Learn's demo datasets | -| benchmarks-> cugraph_benchmarks | [pagerank_benchmark](intermediate_notebooks/benchmarks/cugraph_benchmarks/pagerank_benchmark.ipynb) | This notebook benchmarks performance improvement of running PageRank within cuGraph against NetworkX. - -## Advanced Notebooks: +| examples | [linear_regression_demo.ipynb](2_intermediate_notebooks/examples/linear_regression_demo.ipynb) |In this notebook we will show how to use linear regression and its GPU accelerated implementation present in RAPIDS. | SG | [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| +| examples | [ridge_regression_demo](2_intermediate_notebooks/examples/ridge_regression_demo.ipynb) | Demonstration of using both NetworkX and cuGraph to compute the the number of Triangles in our test dataset. | SG | [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| +| examples | [umap_demo_full](2_intermediate_notebooks/examples/umap_demo_full.ipynb) | In this notebook we will show how to use UMAP and its GPU accelerated implementation present in RAPIDS. | SG | [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist)| +| examples | [rf_demo](2_intermediate_notebooks/examples/rf_demo.ipynb) | Demonstration of using both cuml and sklearn to train a RandomForestClassifier on the Higgs dataset. | SG | [Higgs Boson](https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz) +| examples | [cuDatashader](2_intermediate_notebooks/examples/cuDatashader.ipynb) | Demonstration of using our GPU acclerated version of DataShader | SG | [NYC Jan 2015 Taxi Data](https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2015-01.csv) | +| examples | [weather](2_intermediate_notebooks/examples/weather.ipynb) | Demonstration of using Dask and cuDF to process and analyze weather history | MG | [NOAA Annual Weather Data](ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/) | +| E2E-> taxi | [NYCTaxi](2_intermediate_notebooks/E2E/taxi/NYCTaxi-E2E.ipynb) | Demonstrates multi-node ETL for cleanup of raw data into cleaned train and test dataframes. Shows how to run multi-node XGBoost training with dask-xgboost. **Please Note: requires Google Dataproc to run!** [Blog](https://medium.com/rapids-ai/scale-out-rapids-on-google-cloud-dataproc-8a873233258f) | MG | [Google Dataproc Hosted NYC Taxi Data](https://console.cloud.google.com/storage/browser/anaconda-public-data/nyc-taxi/csv/?pli=1) | +| E2E-> synthetic_3D | [rapids_ml_workflow_demo](2_intermediate_notebooks/E2E/synthetic_3D/rapids_ml_workflow_demo.ipynb) | A 3D visual showcase of a machine learning workflow with RAPIDS (load data, transform/normalize, train XGBoost model, evaluate accuracy, use model for inference). Along the way we compare the performance gains of RAPIDS [GPU] vs sklearn/pandas methods [CPU]. | SG | SciKit-Learn's demo datasets | +| E2E-> census | [census_education2income_demo](2_intermediate_notebooks/E2E/census/census_education2income_demo.ipynb) | In this notebook we use 50 years of census data to see how education affects income. | SG | [Custom IPUMS Data pull](https://rapidsai-data.s3.us-east-2.amazonaws.com/datasets/ipums_education2income_1970-2010.csv.gz) +| benchmarks | [cuml_benchmarks](2_intermediate_notebooks/benchmarks/cuml_benchmarks.ipynb) | The purpose of this notebook is to extensively benchmark all of the single GPU cuML algorithms against their skLearn counterparts, while also providing the ability to find and verify upper bounds. **Note: Best on large memory GPUs** | SG | Self Generated | +| benchmarks | [cuml_benchmarks_quick](2_intermediate_notebooks/benchmarks/cuml_benchmarks_quick.ipynb) | The purpose of this notebook is to quickly benchmark all of the single GPU cuML algorithms against their skLearn counterparts, while also providing the ability to find and verify upper bounds. **Note: Best for small memory GPUs** | SG | Self Generated and [Preprocessed Mortgage Data](https://s3.us-east-2.amazonaws.com/rapidsai-data/datasets/mortgage/mortgage.npy.gz)| +| benchmarks | [rapids_decomposition](2_intermediate_notebooks/benchmarks/rapids_decomposition.ipynb) | This notebook benchmarks and visualize RAPIDS decomposition methods against each other. You have the opportunity to self-compare it to CPU speeds and methods | SG | SciKit-Learn's demo datasets | +| benchmarks-> cugraph_benchmarks | [louvain_benchmark](2_intermediate_notebooks/benchmarks/cugraph_benchmarks/louvain_benchmark.ipynb) | This notebook benchmarks performance improvement of running the Louvain clustering algorithm within cuGraph against NetworkX. | SG | SciKit-Learn's demo datasets | +| benchmarks-> cugraph_benchmarks | [pagerank_benchmark](2_intermediate_notebooks/benchmarks/cugraph_benchmarks/pagerank_benchmark.ipynb) | This notebook benchmarks performance improvement of running PageRank within cuGraph against NetworkX. + +## 3_Advanced Notebooks: | Folder | Notebook Title | Description | GPU | Dataset Used |-----------|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----|----| -| tutorials | [rapids_customized_kernels](advanced_notebooks/tutorials/rapids_customized_kernels.ipynb) | This notebook shows how create customized kernels using CUDA to make your workflow in RAPIDS even faster. | SG | Self Generated | +| tutorials | [rapids_customized_kernels](3_advanced_notebooks/tutorials/rapids_customized_kernels.ipynb) | This notebook shows how create customized kernels using CUDA to make your workflow in RAPIDS even faster. | SG | Self Generated | ## Blog Notebooks: | Folder | Notebook Title | Description | GPU | Dataset Used