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#!/usr/bin/env python3 | |
""" | |
Stream-ordering race using CuPy with RMM's async memory resource. | |
This should trigger compute-sanitizer's stream-ordered race detection. | |
""" | |
import cupy as cp | |
import rmm.mr | |
from rmm.allocators.cupy import rmm_cupy_allocator |
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#!/usr/bin/env python3 | |
""" | |
Convert cudf-polars traces from JSONL format to Chrome Tracing format. | |
Chrome Tracing format can be viewed in chrome://tracing or similar tools. | |
""" | |
import json | |
import sys | |
from pathlib import Path |
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""" | |
Outputs the following (parital) IR for query 3. Note the multiple `Select` nodes. | |
Sort( | |
schema={'l_orderkey': <DataType(polars=Int64, plc=<type_id.INT64: 4>)>, 'revenue': <DataType(polars=Float64, plc=<type_id.FLOAT64: 10>)>, 'o_orderdate': <DataType(polars=Datetime(time_unit='ms', time_zone=None), plc=<type_id.TIMESTAMP_MILLISECONDS: 14>)>, 'o_shippriority': <DataType(polars=Int32, plc=<type_id.INT32: 3>)>}, | |
by=(NamedExpr(revenue, Col(<DataType(polars=Float64, plc=<type_id.FLOAT64: 10>)>, 'revenue')), NamedExpr(o_orderdate, Col(<DataType(polars=Datetime(time_unit='ms', time_zone=None), plc=<type_id.TIMESTAMP_MILLISECONDS: 14>)>, 'o_orderdate'))), | |
order=(<order.DESCENDING: 1>, <order.ASCENDING: 0>), | |
null_order=(<null_order.AFTER: 0>, <null_order.BEFORE: 1>), | |
stable=False, | |
zlice=(0, 10), |
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# /// script | |
# requires-python = ">=3.13" | |
# dependencies = [ | |
# "dask-cuda", | |
# "kvikio-cu12", | |
# "nvidia-ml-py", | |
# "psutil", | |
# "rich", | |
# ] | |
# /// |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8"> | |
</head> | |
<body> | |
<div id="app"></div> | |
<script>var pyinstrumentHTMLRenderer=function(){"use strict";var is=Object.defineProperty;var ns=(F,ve,Pe)=>ve in F?is(F,ve,{enumerable:!0,configurable:!0,writable:!0,value:Pe}):F[ve]=Pe;var A=(F,ve,Pe)=>ns(F,typeof ve!="symbol"?ve+"":ve,Pe);function F(){}function ve(i){return i()}function Pe(){return Object.create(null)}function oe(i){i.forEach(ve)}function pt(i){return typeof i=="function"}function re(i,e){return i!=i?e==e:i!==e||i&&typeof i=="object"||typeof i=="function"}function ki(i){return Object.keys(i).length===0}function St(i,...e){if(i==null){for(const n of e)n(void 0);return F}const t=i.subscribe(...e);return t.unsubscribe?()=>t.unsubscribe():t}function ge(i,e,t){i.$$.on_destroy.push(St(e,t))}function Ci(i,e,t){return i.set(t),e}function u(i,e){i.appendChild(e)}function S(i,e,t){i.insertBefore(e,t||null)}function L |
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Ran query=1 in 12.8907s | |
Ran query=1 in 1.4123s | |
Ran query=1 in 2.0518s | |
Ran query=2 in 1.9878s | |
Ran query=2 in 1.1087s | |
Ran query=2 in 0.5492s | |
Ran query=3 in 6.8526s | |
Ran query=3 in 3.0100s | |
Ran query=3 in 3.1382s | |
Ran query=4 in 1.5490s |
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""" | |
Sample demonstrating slowness of cudf.read_parquet with kvikio from S3. | |
# Instructions | |
Download the dev wheels (skip if you have it built locally; rquires vpn access) | |
curl -LO https://downloads.rapids.ai/ci/cudf/pull-request/17957/793d0b2/cudf_wheel_python_cudf_cu12_py312_x86_64.tar.gz | |
curl -LO https://downloads.rapids.ai/ci/cudf/pull-request/17957/793d0b2/cudf_wheel_cpp_libcudf_cu12_x86_64.tar.gz |
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#!/usr/bin/env bash | |
set -e | |
PATH=/opt/homebrew/bin:$PATH | |
# The exact UUID doesn't matter, but this should match what you select in the UI. | |
UUID=edef356f-55b1-4132-b311-e5e8c10c6a33 | |
# This path is MacOS specific. | |
OUTPUT=$HOME/Library/Containers/com.microsoft.teams2/Data/Library/Application\ Support/Microsoft/MSTeams/Backgrounds/Uploads |
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""" | |
dali + Zarr (GPU) example. | |
This script adapts the GPU example from | |
https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/general/data_loading/external_input.html | |
to use Zarr for storage. | |
To run it, you'll currently need to use my fork of zarr-python: | |
pip install git+https://github.com/TomAugspurger/zarr-python/@tom/fix/gpu |
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