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# I setup my docker runner to cache those directories | |
# So the environments are only updated when different | |
variables: | |
XDG_CACHE_HOME: "/cache/pip" | |
PIP_CACHE_DIR: "/cache/pip" | |
OLYMPUS_DATA_PATH: "/cache/data" | |
ENV_CACHE_DIR: "/cache/env" | |
stages: | |
- tests |
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HIP_DB=3 HCC_SERIALIZE_KERNEL=0x6 python test/test_cuda.py TestCuda.test_multinomial_invalid_probs_cuda > log.out | |
HIP_DB=0x3 [api+sync] | |
hip-api pid:22173 tid:1:HIP initialized short_tid#1 (maps to full_tid: 0x7fd84c378740) | |
hip-sync pid:22173 tid:1:locking criticalData=0x7fd7ee7da798 for ctx:0x7fd7ee7da780.dev:0.. | |
hip-sync pid:22173 tid:1: addStream: stream:0.0 | |
hip-sync pid:22173 tid:1:created ctx with defaultStream=0x7fd7eee38670 (stream:0.0) | |
hip-sync pid:22173 tid:1:auto-unlocking criticalData=0x7fd7ee7da798 for ctx:0x7fd7ee7da780.dev:0... | |
hip-sync pid:22173 tid:1:pid=22173 <ihipInit> g_numLogicalThreads=4 | |
<<hip-api pid:22173 tid:1.1 hipGetDeviceCount (0x7ffcaee0a1c4) @370289587147796 | |
hip-api pid:22173 tid:1.1 hipGetDeviceCount ret= 0 (hipSuccess)>> +3657 ns |
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import torchvision | |
import torchvision.transforms as transforms | |
import time | |
import torch | |
import os | |
import h5py | |
import numpy as np | |
from MixedPrecision.tools.stats import StatStream |
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#include <thread> | |
#include <mutex> | |
#include <tuple> | |
#include <list> | |
#include <memory> | |
namespace pool{ | |
namespace internal { | |
template<typename T> |
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