def cleanup():
ts.stop_torchserve()
rm_dir('model_store')
rm_dir('logs')
# clean up residual from model-archiver IT suite.
rm_dir('model-archiver/model_archiver/htmlcov_ut model_archiver/model-archiver/htmlcov_it')
rm_file('ts_scripts/*_pb2*.py', True)
def cleanup():
ts.stop_torchserve()
rm_dir('model_store')
rm_dir('logs')
# clean up residual from model-archiver IT suite.
rm_dir(
'model-archiver/model_archiver/htmlcov_ut model_archiver/model-archiver/htmlcov_it'
)
"jmeter-plugins-manager-1.3.jar")
print("Downloading plugins cmdrunner...")
download_save(
"http://search.maven.org/remotecontent?filepath=kg/apc/cmdrunner/2.2/cmdrunner-2.2.jar",
f"{JMETER_HOME}\\lib\\", "cmdrunner-2.2.jar")
print("Downloading plugins jmeter-plugins-standard...")
download_save(
"https://jmeter-plugins.org/downloads/file/JMeterPlugins-Standard-1.4.0.zip"
)
unzip("JMeterPlugins-Standard-1.4.0.zip", os.getcwd(), "zip")
shutil.copy("lib\ext\JMeterPlugins-Standard.jar",
f"{JMETER_HOME}\\lib\\ext\\")
rm_file("JMeterPlugins-Standard-1.4.0.zip")
rm_dir("lib")
cmd = 'java -cp "{}\\apache-jmeter-5.3\\lib\\ext\\jmeter-plugins-manager-1.3.jar" org.jmeterplugins.repository.PluginManagerCMDInstaller'.format(
JMETER_INSTALL_PATH)
rc, out, _ = run(cmd)
if rc != 0:
print("Command execution failed : ", cmd)
else:
print(out)
print("Installing Plugins..")
cmd_plugins = '"{}\\apache-jmeter-5.3\\bin\\PluginsManagerCMD.bat" install jpgc-synthesis=2.1 jpgc-filterresults=2.1 jpgc-mergeresults=2.1 jpgc-cmd=2.1 jpgc-perfmon=2.1'.format(
JMETER_INSTALL_PATH)
rc, out, _ = run(cmd_plugins)
if rc != 0:
print("Command execution failed : ", cmd_plugins)
import tempfile
import sys
sys.path.append('../../ts_scripts')
from ts_scripts.shell_utils import rm_dir, rm_file, download_save
import os
import shutil
import subprocess
TMP_DIR = tempfile.gettempdir()
rm_file("torchhub.zip")
rm_dir(os.path.join(TMP_DIR, "DeepLearningExamples-torchhub"))
rm_dir("PyTorch")
download_save(
"https://github.com/nvidia/DeepLearningExamples/archive/torchhub.zip",
TMP_DIR)
shutil.unpack_archive(os.path.join(TMP_DIR, "torchhub.zip"),
extract_dir=TMP_DIR)
rm_file("tacotron.zip")
shutil.copytree(
os.path.join(TMP_DIR,
"DeepLearningExamples-torchhub/PyTorch/SpeechSynthesis/"),
"PyTorch/SpeechSynthesis/")
shutil.make_archive("tacotron", "zip", base_dir="PyTorch")
download_save(
"https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2pyt_fp32/versions/1/files/"
"nvidia_tacotron2pyt_fp32_20190306.pth")
download_save(
"https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/"
"nvidia_waveglowpyt_fp32_20190306.pth")
subprocess.run(
"torch-model-archiver --model-name waveglow_synthesizer --version 1.0 "