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# Step 1 - Import necessary Packages | |
import cudf as cudf | |
import pyblazing | |
from pyblazing import DriverType, FileSystemType, EncryptionType | |
from pyblazing import SchemaFrom | |
# Step 2 - Register a File System | |
def register_hdfs(): | |
print('*** Register a HDFS File System ***') | |
fs_status = pyblazing.register_file_system( |
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# Install BlazingSQL for CUDA 10.0 | |
conda install -y -c conda-forge -c defaults -c nvidia -c rapidsai -c blazingsql/label/cuda10.0 -c blazingsql blazingsql-calcite blazingsql-orchestrator blazingsql-ral blazingsql-python python=3.7 cudatoolkit=10.0 | |
pip install flatbuffers && \ | |
pip install jupyterlab |
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#!/usr/bin/env python | |
# coding: utf-8 | |
# # BlazingSQL TPC-H Benchmark Notebook | |
# | |
# In this notebook we will setup a distributed BlazingSQL cluster with Dask running cuDF on every node as well. We will query data residing on Google Cloud Storage (GCS) in Apache Parquet files. | |
# | |
# First we will import necessary packages and launch the distributed servicers. | |
# In[ ]: |
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