From 0cfcab7040817af98c3bac374a7c3d0d476a067a Mon Sep 17 00:00:00 2001 From: Winston A Robson Date: Thu, 14 May 2020 02:36:13 +0000 Subject: [PATCH] Update install scripts, +BSQL Notebooks --- .../basics/blazingsql/README.md | 27 ++++++++++++------- .../examples/blazingsql/README.md | 27 ++++++++++++------- 2 files changed, 35 insertions(+), 19 deletions(-) diff --git a/getting_started_notebooks/basics/blazingsql/README.md b/getting_started_notebooks/basics/blazingsql/README.md index d8d7dda0..de3f0f7d 100644 --- a/getting_started_notebooks/basics/blazingsql/README.md +++ b/getting_started_notebooks/basics/blazingsql/README.md @@ -6,30 +6,39 @@ Demo Python notebooks using BlazingSQL with the RAPIDS AI ecoystem. | Getting Started | How to set up and get started with BlazingSQL and the RAPIDS AI suite |[![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BlazingDB/bsql-demos/blob/master/colab_notebooks/blazingsql_demo.ipynb)| | Federated Query | In a single query, join an Apache Parquet file, a CSV file, and a GPU DataFrame (GDF) in GPU memory. |[![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BlazingDB/bsql-demos/blob/master/colab_notebooks/federated_query_demo.ipynb)| +### BlazingSQL Notebooks +Run demos free in BlazingSQL Notebooks, a web-based Jupyter Notebook that lets you quickly run BlazingSQL + RAPIDS AI. We will walk you through each demo, but feel free to modify each demo for your own needs. + +| Notebook Title | Description | Try Now | +| -------------- | ----------- | ------- | +| Welcome Notebook | An introduction to BlazingSQL Notebooks and the GPU Data Science Ecosystem. | Launch on BlazingSQL Notebooks | +| The DataFrame | Learn how to use BlazingSQL and cuDF to create GPU DataFrames with SQL and Pandas-like APIs. | Launch on BlazingSQL Notebooks | +| Data Visualization | Plug in your favorite Python visualization packages, or use GPU accelerated visualization tools to render millions of rows in a flash. | Launch on BlazingSQL Notebooks | +| Machine Learning | Learn about cuML, mirrored after the Scikit-Learn API, it offers GPU accelerated machine learning on GPU DataFrames. | Launch on BlazingSQL Notebooks | + + ## Getting Started with BlazingSQL You can install BlazingSQL simply by running the [python script](https://github.com/rapidsai/notebooks-contrib/tree/branch-0.12/utils/sql_check.py) `sql_check.py` found in the `notebooks-contrib/utils/` directory. -#### Stable (v0.11) +#### Stable (v0.13) -You can find the latest install scripts [in our docs here](https://docs.blazingdb.com/docs/install-via-conda) or just below. +You can find the latest install scripts [in our docs here](https://docs.blazingdb.com/docs/install-via-conda), [in our main GitHub repo](https://github.com/blazingdb/blazingsql#install-using-conda) or just below. ```bash -# for CUDA 9.2 & Python 3.7 -conda install -c blazingsql/label/cuda9.2 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 cudatoolkit=9.2 +# for CUDA 10.0 & Python 3.6 +conda install -c blazingsql/label/cuda10.0 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.6 -# for CUDA 10.0 & Python 3.7 -conda install -c blazingsql/label/cuda10.0 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 cudatoolkit=10.0 +# for CUDA 10.2 & Python 3.7 +conda install -c blazingsql/label/cuda10.2 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 ``` #### Nightly ```bash -conda install -c blazingsql-nightly/label/cuda10.0 -c blazingsql-nightly -c rapidsai-nightly -c conda-forge -c defaults blazingsql +conda install -c blazingsql-nightly/label/cuda10.0 -c blazingsql-nightly -c rapidsai-nightly -c nvidia -c conda-forge -c defaults blazingsql python=3.7 ``` -Note: BlazingSQL-Nightly is supported only on Linux, with CUDA 9.2 or 10 and Python 3.6 or 3.7. - ## Troubleshooting ### On RAPIDS Docker diff --git a/intermediate_notebooks/examples/blazingsql/README.md b/intermediate_notebooks/examples/blazingsql/README.md index 209d0fbe..50a43706 100644 --- a/intermediate_notebooks/examples/blazingsql/README.md +++ b/intermediate_notebooks/examples/blazingsql/README.md @@ -3,34 +3,41 @@ Demo Python notebooks using BlazingSQL with the RAPIDS AI ecoystem. | Notebook Title | Description |Launch in Colab| |----------------|----------------|----------------| -| Netflow | Query 73M+ rows of network security data (netflow) with BlazingSQL and then pass to Graphistry to visualize and interact with the data. |[![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BlazingDB/bsql-demos/blob/master/colab_notebooks/graphistry_netflow_demo.ipynb)| | Taxi | Train a linear regression model with cuML on 20 million rows of public NYC Taxi Data loaded with BlazingSQL. |[![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BlazingDB/bsql-demos/blob/master/colab_notebooks/taxi_fare_prediction.ipynb)| | BlazingSQL vs. Apache Spark | Analyze over 73 million rows of net flow data to compare BlazingSQL and Apache Spark timings for the same workload. |[![Google Colab Badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BlazingDB/bsql-demos/blob/master/colab_notebooks/vs_pyspark_netflow.ipynb)| +### BlazingSQL Notebooks +Run demos free in BlazingSQL Notebooks, a web-based Jupyter Notebook that lets you quickly run BlazingSQL + RAPIDS AI. We will walk you through each demo, but feel free to modify each demo for your own needs. + +| Notebook Title | Description | Try Now | +| -------------- | ----------- | ------- | +| Welcome Notebook | An introduction to BlazingSQL Notebooks and the GPU Data Science Ecosystem. | Launch on BlazingSQL Notebooks | +| The DataFrame | Learn how to use BlazingSQL and cuDF to create GPU DataFrames with SQL and Pandas-like APIs. | Launch on BlazingSQL Notebooks | +| Data Visualization | Plug in your favorite Python visualization packages, or use GPU accelerated visualization tools to render millions of rows in a flash. | Launch on BlazingSQL Notebooks | +| Machine Learning | Learn about cuML, mirrored after the Scikit-Learn API, it offers GPU accelerated machine learning on GPU DataFrames. | Launch on BlazingSQL Notebooks | + ## Getting Started with BlazingSQL You can install BlazingSQL simply by running the [python script](https://github.com/rapidsai/notebooks-contrib/tree/branch-0.12/utils/sql_check.py) `sql_check.py` found in the `notebooks-contrib/utils/` directory. -#### Stable (v0.11) +#### Stable (v0.13) -You can find the latest install scripts [in our docs here](https://docs.blazingdb.com/docs/install-via-conda) or just below. +You can find the latest install scripts [in our docs here](https://docs.blazingdb.com/docs/install-via-conda), [in our main GitHub repo](https://github.com/blazingdb/blazingsql#install-using-conda) or just below. ```bash -# for CUDA 9.2 & Python 3.7 -conda install -c blazingsql/label/cuda9.2 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 cudatoolkit=9.2 +# for CUDA 10.0 & Python 3.6 +conda install -c blazingsql/label/cuda10.0 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.6 -# for CUDA 10.0 & Python 3.7 -conda install -c blazingsql/label/cuda10.0 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 cudatoolkit=10.0 +# for CUDA 10.2 & Python 3.7 +conda install -c blazingsql/label/cuda10.2 -c blazingsql -c rapidsai -c nvidia -c conda-forge -c defaults blazingsql python=3.7 ``` #### Nightly ```bash -conda install -c blazingsql-nightly/label/cuda10.0 -c blazingsql-nightly -c rapidsai-nightly -c conda-forge -c defaults blazingsql +conda install -c blazingsql-nightly/label/cuda10.0 -c blazingsql-nightly -c rapidsai-nightly -c nvidia -c conda-forge -c defaults blazingsql python=3.7 ``` -Note: BlazingSQL-Nightly is supported only on Linux, with CUDA 9.2 or 10 and Python 3.6 or 3.7. - ## Troubleshooting ### On RAPIDS Docker