From dc255099507a8e72f35227f10ba94a1257905077 Mon Sep 17 00:00:00 2001 From: Alexandre Lissy Date: Thu, 30 Jan 2020 08:44:00 +0100 Subject: [PATCH] Remove unused benchmark_nc --- bin/benchmark_nc.py | 504 ------------------ bin/benchmark_plotter.py | 145 ----- requirements.txt | 5 - taskcluster/tc-benchmark-tests.sh | 106 ---- .../test-benchmark-linux-amd64-opt.yml | 14 - 5 files changed, 774 deletions(-) delete mode 100755 bin/benchmark_nc.py delete mode 100755 bin/benchmark_plotter.py delete mode 100755 taskcluster/tc-benchmark-tests.sh delete mode 100644 taskcluster/test-benchmark-linux-amd64-opt.yml diff --git a/bin/benchmark_nc.py b/bin/benchmark_nc.py deleted file mode 100755 index 6a98dcaa..00000000 --- a/bin/benchmark_nc.py +++ /dev/null @@ -1,504 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, division, print_function - -import os -import sys - -# To use util.tc -sys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(sys.argv[0])))) -import util.taskcluster as tcu -from util.helpers import keep_only_digits - -import paramiko -import argparse -import tempfile -import shutil -import subprocess -import stat -import numpy -import matplotlib.pyplot as plt -import scipy.stats as scipy_stats -import csv -import getpass -import zipfile - -from six import iteritems -from six.moves import range, map -from functools import cmp_to_key - -r''' - Tool to: - - remote local or remote (ssh) native_client - - handles copying models (as protocolbuffer files) - - run native_client in benchmark mode - - collect timing results - - compute mean values (with wariances) - - output as CSV -''' - -ssh_conn = None -def exec_command(command, cwd=None): - r''' - Helper to exec locally (subprocess) or remotely (paramiko) - ''' - - rc = None - stdout = stderr = None - if ssh_conn is None: - ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')} - p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=ld_library_path, cwd=cwd) - stdout, stderr = p.communicate() - rc = p.returncode - else: - # environment= requires paramiko >= 2.1 (fails with 2.0.2) - final_command = command if cwd is None else 'cd %s && %s %s' % (cwd, 'LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH', command) - ssh_stdin, ssh_stdout, ssh_stderr = ssh_conn.exec_command(final_command) - stdout = ''.join(ssh_stdout.readlines()) - stderr = ''.join(ssh_stderr.readlines()) - rc = ssh_stdout.channel.recv_exit_status() - - return rc, stdout, stderr - -def assert_valid_dir(dir): - if dir is None: - raise AssertionError('Invalid temp directory') - return True - -def get_arch_string(): - r''' - Check local or remote system arch, to produce TaskCluster proper link. - ''' - rc, stdout, stderr = exec_command('uname -sm') - if rc > 0: - raise AssertionError('Error checking OS') - - stdout = stdout.lower().strip() - if not b'linux' in stdout: - raise AssertionError('Unsupported OS') - - if b'armv7l' in stdout: - return 'arm' - - if b'x86_64' in stdout: - nv_rc, nv_stdout, nv_stderr = exec_command('nvidia-smi') - nv_stdout = nv_stdout.lower().strip() - if b'NVIDIA-SMI' in nv_stdout: - return 'gpu' - else: - return 'cpu' - - raise AssertionError('Unsupported arch:', stdout) - -def maybe_download_binaries(dir): - assert_valid_dir(dir) - tcu.maybe_download_tc(target_dir=dir, tc_url=tcu.get_tc_url(get_arch_string()), progress=True) - -def extract_native_client_tarball(dir): - r''' - Download a native_client.tar.xz file from TaskCluster and extract it to dir. - ''' - assert_valid_dir(dir) - - target_tarball = os.path.join(dir, 'native_client.tar.xz') - if os.path.isfile(target_tarball) and os.stat(target_tarball).st_size == 0: - return - - subprocess.check_call(['pixz', '-d', 'native_client.tar.xz'], cwd=dir) - subprocess.check_call(['tar', 'xf', 'native_client.tar'], cwd=dir) - os.unlink(os.path.join(dir, 'native_client.tar')) - open(target_tarball, 'w').close() - -def is_zip_file(models): - r''' - Ensure that a path is a zip file by: - - checking length is 1 - - checking extension is '.zip' - ''' - ext = os.path.splitext(models[0])[1] - return (len(models) == 1) and (ext == '.zip') - -def maybe_inspect_zip(models): - r''' - Detect if models is a list of protocolbuffer files or a ZIP file. - If the latter, then unzip it and return the list of protocolbuffer files - that were inside. - ''' - - if not(is_zip_file(models)): - return models - - if len(models) > 1: - return models - - if len(models) < 1: - raise AssertionError('No models at all') - - return zipfile.ZipFile(models[0]).namelist() - -def all_files(models=[]): - r''' - Return a list of full path of files matching 'models', sorted in human - numerical order (i.e., 0 1 2 ..., 10 11 12, ..., 100, ..., 1000). - - Files are supposed to be named identically except one variable component - e.g. the list, - test.weights.e5.lstm1200.ldc93s1.pb - test.weights.e5.lstm1000.ldc93s1.pb - test.weights.e5.lstm800.ldc93s1.pb - gets sorted: - test.weights.e5.lstm800.ldc93s1.pb - test.weights.e5.lstm1000.ldc93s1.pb - test.weights.e5.lstm1200.ldc93s1.pb - ''' - - def nsort(a, b): - fa = os.path.basename(a).split('.') - fb = os.path.basename(b).split('.') - elements_to_remove = [] - - assert len(fa) == len(fb) - - for i in range(0, len(fa)): - if fa[i] == fb[i]: - elements_to_remove.append(fa[i]) - - for e in elements_to_remove: - fa.remove(e) - fb.remove(e) - - assert len(fa) == len(fb) - assert len(fa) == 1 - - fa = int(keep_only_digits(fa[0])) - fb = int(keep_only_digits(fb[0])) - - if fa < fb: - return -1 - if fa == fb: - return 0 - if fa > fb: - return 1 - - base = list(map(lambda x: os.path.abspath(x), maybe_inspect_zip(models))) - base.sort(key=cmp_to_key(nsort)) - - return base - -def copy_tree(dir): - assert_valid_dir(dir) - - sftp = ssh_conn.open_sftp() - # IOError will get triggered if the path does not exists remotely - try: - if stat.S_ISDIR(sftp.stat(dir).st_mode): - print('Directory already existent: %s' % dir) - except IOError: - print('Creating remote directory: %s' % dir) - sftp.mkdir(dir) - - print('Copy files to remote') - for fname in os.listdir(dir): - fullpath = os.path.join(dir, fname) - local_stat = os.stat(fullpath) - try: - remote_mode = sftp.stat(fullpath).st_mode - except IOError: - remote_mode = 0 - - if not stat.S_ISREG(remote_mode): - print('Copying %s ...' % fullpath) - remote_mode = sftp.put(fullpath, fullpath, confirm=True).st_mode - - if local_stat.st_mode != remote_mode: - print('Setting proper remote mode: %s' % local_stat.st_mode) - sftp.chmod(fullpath, local_stat.st_mode) - - sftp.close() - -def delete_tree(dir): - assert_valid_dir(dir) - - sftp = ssh_conn.open_sftp() - # IOError will get triggered if the path does not exists remotely - try: - if stat.S_ISDIR(sftp.stat(dir).st_mode): - print('Removing remote files') - for fname in sftp.listdir(dir): - fullpath = os.path.join(dir, fname) - remote_stat = sftp.stat(fullpath) - if stat.S_ISREG(remote_stat.st_mode): - print('Removing %s ...' % fullpath) - sftp.remove(fullpath) - - print('Removing directory %s ...' % dir) - sftp.rmdir(dir) - - sftp.close() - except IOError: - print('No remote directory: %s' % dir) - -def setup_tempdir(dir, models, wav, lm_binary, trie, binaries): - r''' - Copy models, libs and binary to a directory (new one if dir is None) - ''' - if dir is None: - dir = tempfile.mkdtemp(suffix='dsbench') - - sorted_models = all_files(models=models) - if binaries is None: - maybe_download_binaries(dir) - else: - print('Using local binaries: %s' % (binaries)) - shutil.copy2(binaries, dir) - extract_native_client_tarball(dir) - - filenames = map(lambda x: os.path.join(dir, os.path.basename(x)), sorted_models) - missing_models = list(filter(lambda x: not os.path.isfile(x), filenames)) - if len(missing_models) > 0: - # If we have a ZIP file, directly extract it to the proper path - if is_zip_file(models): - print('Extracting %s to %s' % (models[0], dir)) - zipfile.ZipFile(models[0]).extractall(path=dir) - print('Extracted %s.' % models[0]) - else: - # If one model is missing, let's copy everything again. Be safe. - for f in sorted_models: - print('Copying %s to %s' % (f, dir)) - shutil.copy2(f, dir) - - for extra_file in [ wav, lm_binary, trie ]: - if extra_file and not os.path.isfile(os.path.join(dir, os.path.basename(extra_file))): - print('Copying %s to %s' % (extra_file, dir)) - shutil.copy2(extra_file, dir) - - if ssh_conn: - copy_tree(dir) - - return dir, sorted_models - -def teardown_tempdir(dir): - r''' - Cleanup temporary directory. - ''' - - if ssh_conn: - delete_tree(dir) - - assert_valid_dir(dir) - shutil.rmtree(dir) - -def get_sshconfig(): - r''' - Read user's SSH configuration file - ''' - - with open(os.path.expanduser('~/.ssh/config')) as f: - cfg = paramiko.SSHConfig() - cfg.parse(f) - ret_dict = {} - for d in cfg._config: - _copy = dict(d) - # Avoid buggy behavior with strange host definitions, we need - # Hostname and not Host. - del _copy['host'] - for host in d['host']: - ret_dict[host] = _copy['config'] - - return ret_dict - -def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True): - r''' - Establish a SSH connection to a remote host. It should be able to use - SSH's config file Host name declarations. By default, will not automatically - add trust for hosts, will use SSH agent and will try to load keys. - ''' - - def password_prompt(username, hostname): - r''' - If the Host is relying on password authentication, lets ask it. - Relying on SSH itself to take care of that would not work when the - remote authentication is password behind a SSH-key+2FA jumphost. - ''' - return getpass.getpass('No SSH key for %s@%s, please provide password: ' % (username, hostname)) - - ssh_conn = None - if target is not None: - ssh_conf = get_sshconfig() - cfg = { - 'hostname': None, - 'port': 22, - 'allow_agent': allow_agent, - 'look_for_keys': look_keys - } - if ssh_conf.has_key(target): - user_config = ssh_conf.get(target) - - # If ssh_config file's Host defined 'User' instead of 'Username' - if user_config.has_key('user') and not user_config.has_key('username'): - user_config['username'] = user_config['user'] - del user_config['user'] - - for k in ('username', 'hostname', 'port'): - if k in user_config: - cfg[k] = user_config[k] - - # Assume Password auth. If we don't do that, then when connecting - # through a jumphost we will run into issues and the user will - # not be able to input his password to the SSH prompt. - if 'identityfile' in user_config: - cfg['key_filename'] = user_config['identityfile'] - else: - cfg['password'] = password_prompt(cfg['username'], cfg['hostname'] or target) - - # Should be the last one, since ProxyCommand will issue connection to remote host - if 'proxycommand' in user_config: - cfg['sock'] = paramiko.ProxyCommand(user_config['proxycommand']) - - else: - cfg['username'] = target.split('@')[0] - cfg['hostname'] = target.split('@')[1].split(':')[0] - cfg['password'] = password_prompt(cfg['username'], cfg['hostname']) - try: - cfg['port'] = int(target.split('@')[1].split(':')[1]) - except IndexError: - # IndexError will happen if no :PORT is there. - # Default value 22 is defined above in 'cfg'. - pass - - ssh_conn = paramiko.SSHClient() - if auto_trust: - ssh_conn.set_missing_host_key_policy(paramiko.AutoAddPolicy()) - - ssh_conn.connect(**cfg) - - return ssh_conn - -def run_benchmarks(dir, models, wav, lm_binary=None, trie=None, iters=-1): - r''' - Core of the running of the benchmarks. We will run on all of models, against - the WAV file provided as wav. - ''' - - assert_valid_dir(dir) - - inference_times = [ ] - - for model in models: - model_filename = model - - current_model = { - 'name': model, - 'iters': [ ], - 'mean': numpy.infty, - 'stddev': numpy.infty - } - - if lm_binary and trie: - cmdline = './deepspeech --model "%s" --lm "%s" --trie "%s" --audio "%s" -t' % (model_filename, lm_binary, trie, wav) - else: - cmdline = './deepspeech --model "%s" --audio "%s" -t' % (model_filename, wav) - - for it in range(iters): - sys.stdout.write('\rRunning %s: %d/%d' % (os.path.basename(model), (it+1), iters)) - sys.stdout.flush() - rc, stdout, stderr = exec_command(cmdline, cwd=dir) - if rc == 0: - inference_time = float(stdout.split(b'\n')[1].split(b'=')[-1]) - # print("[%d] model=%s inference=%f" % (it, model, inference_time)) - current_model['iters'].append(inference_time) - else: - print('exec_command("%s") failed with rc=%d' % (cmdline, rc)) - print('stdout: %s' % stdout) - print('stderr: %s' % stderr) - raise AssertionError('Execution failure: rc=%d' % (rc)) - - sys.stdout.write('\n') - sys.stdout.flush() - current_model['mean'] = numpy.mean(current_model['iters']) - current_model['stddev'] = numpy.std(current_model['iters']) - inference_times.append(current_model) - - return inference_times - -def produce_csv(input, output): - r''' - Take an input dictionnary and write it to the object-file output. - ''' - output.write('"model","mean","std"\n') - for model_data in input: - output.write('"%s",%f,%f\n' % (model_data['name'], model_data['mean'], model_data['stddev'])) - output.flush() - output.close() - print("Wrote as %s" % output.name) - -def handle_args(): - parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.') - parser.add_argument('--target', required=False, - help='SSH user:pass@host string for remote benchmarking. This can also be a name of a matching \'Host\' in your SSH config.') - parser.add_argument('--autotrust', action='store_true', default=False, - help='SSH Paramiko policy to automatically trust unknown keys.') - parser.add_argument('--allowagent', action='store_true', dest='allowagent', - help='Allow the use of a SSH agent.') - parser.add_argument('--no-allowagent', action='store_false', dest='allowagent', - help='Disallow the use of a SSH agent.') - parser.add_argument('--lookforkeys', action='store_true', dest='lookforkeys', - help='Allow to look for SSH keys in ~/.ssh/.') - parser.add_argument('--no-lookforkeys', action='store_false', dest='lookforkeys', - help='Disallow to look for SSH keys in ~/.ssh/.') - parser.add_argument('--dir', required=False, default=None, - help='Local directory where to copy stuff. This will be mirrored to the remote system if needed (make sure to use path that will work on both).') - parser.add_argument('--models', nargs='+', required=False, - help='List of files (protocolbuffer) to work on. Might be a zip file.') - parser.add_argument('--wav', required=False, - help='WAV file to pass to native_client. Supply again in plotting mode to draw realine line.') - parser.add_argument('--lm_binary', required=False, - help='Path to the LM binary file used by the decoder.') - parser.add_argument('--trie', required=False, - help='Path to the trie file used by the decoder.') - parser.add_argument('--iters', type=int, required=False, default=5, - help='How many iterations to perfom on each model.') - parser.add_argument('--keep', required=False, action='store_true', - help='Keeping run files (binaries & models).') - parser.add_argument('--csv', type=argparse.FileType('w'), required=False, - help='Target CSV file where to dump data.') - parser.add_argument('--binaries', required=False, default=None, - help='Specify non TaskCluster native_client.tar.xz to use') - return parser.parse_args() - -def do_main(): - cli_args = handle_args() - - if not cli_args.models or not cli_args.wav: - raise AssertionError('Missing arguments (models or wav)') - - if cli_args.dir is not None and not os.path.isdir(cli_args.dir): - raise AssertionError('Inexistent temp directory') - - if cli_args.binaries is not None and cli_args.binaries.find('native_client.tar.xz') == -1: - raise AssertionError('Local binaries must be bundled in a native_client.tar.xz file') - - global ssh_conn - ssh_conn = establish_ssh(target=cli_args.target, auto_trust=cli_args.autotrust, allow_agent=cli_args.allowagent, look_keys=cli_args.lookforkeys) - - tempdir, sorted_models = setup_tempdir(dir=cli_args.dir, models=cli_args.models, wav=cli_args.wav, lm_binary=cli_args.lm_binary, trie=cli_args.trie, binaries=cli_args.binaries) - - dest_sorted_models = list(map(lambda x: os.path.join(tempdir, os.path.basename(x)), sorted_models)) - dest_wav = os.path.join(tempdir, os.path.basename(cli_args.wav)) - - if cli_args.lm_binary and cli_args.trie: - dest_lm_binary = os.path.join(tempdir, os.path.basename(cli_args.lm_binary)) - dest_trie = os.path.join(tempdir, os.path.basename(cli_args.trie)) - inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, wav=dest_wav, lm_binary=dest_lm_binary, trie=dest_trie, iters=cli_args.iters) - else: - inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, wav=dest_wav, iters=cli_args.iters) - - if cli_args.csv: - produce_csv(input=inference_times, output=cli_args.csv) - - if not cli_args.keep: - teardown_tempdir(dir=tempdir) - -if __name__ == '__main__' : - do_main() diff --git a/bin/benchmark_plotter.py b/bin/benchmark_plotter.py deleted file mode 100755 index 3952f11c..00000000 --- a/bin/benchmark_plotter.py +++ /dev/null @@ -1,145 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from __future__ import absolute_import, division, print_function - -import os -import sys - -# To use util.tc -sys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(sys.argv[0])))) -from util.helpers import keep_only_digits - -import argparse -import numpy -import matplotlib.pyplot as plt -import scipy.stats as scipy_stats -import scipy.io.wavfile as wav -import csv -import getpass - -from six import iteritems -from six.moves import range, map - -r''' - Tool to: - - ingest CSV file produced by benchmark_nc and produce nice plots -''' - -def reduce_filename(f): - r''' - Expects something like /tmp/tmpAjry4Gdsbench/test.weights.e5.XXX.YYY.pb - Where XXX is a variation on the model size for example - And where YYY is a const related to the training dataset - ''' - - f = os.path.basename(f).split('.') - return int(keep_only_digits(f[-3])) - -def ingest_csv(datasets=None, range=None): - existing_files = filter(lambda x: os.path.isfile(x[1]), datasets) - assert len(list(datasets)) == len(list(existing_files)) - - if range: - range = map(int, range.split(',')) - - data = {} - for (dsname, dsfile) in datasets: - print('Reading %s from %s' % (dsname, dsfile)) - with open(dsfile) as f: - d = csv.DictReader(f) - data[dsname] = [] - for e in d: - if range: - re = reduce_filename(e['model']) - in_range = (re >= range[0] and re <= range[1]) - if in_range: - data[dsname].append(e) - else: - data[dsname].append(e) - - return data - -def produce_plot(input=None, output=None): - x = range(len(input)) - xlabels = list(map(lambda a: a['name'], input)) - y = list(map(lambda a: a['mean'], input)) - yerr = list(map(lambda a: a['stddev'], input)) - - print('y=', y) - print('yerr=', yerr) - plt.errorbar(x, y, yerr=yerr) - plt.show() - print("Wrote as %s" % output.name) - -def produce_plot_multiseries(input=None, output=None, title=None, size=None, fig_dpi=None, source_wav=None): - fig, ax = plt.subplots() - # float() required because size.split()[] is a string - fig.set_figwidth(float(size.split('x')[0]) / fig_dpi) - fig.set_figheight(float(size.split('x')[1]) / fig_dpi) - - nb_items = len(input[list(input.keys())[0]]) - x_all = list(range(nb_items)) - for serie, serie_values in iteritems(input): - xtics = list(map(lambda a: reduce_filename(a['model']), serie_values)) - y = list(map(lambda a: float(a['mean']), serie_values)) - yerr = list(map(lambda a: float(a['std']), serie_values)) - linreg = scipy_stats.linregress(x_all, y) - ylin = linreg.intercept + linreg.slope * numpy.asarray(x_all) - - ax.errorbar(x_all, y, yerr=yerr, label=('%s' % serie), fmt='-', capsize=4, elinewidth=1) - ax.plot(x_all, ylin, label=('%s ~= %0.4f*x+%0.4f (R=%0.4f)' % (serie, linreg.slope, linreg.intercept, linreg.rvalue))) - - plt.xticks(x_all, xtics, rotation=60) - - if source_wav: - audio = wav.read(source_wav) - print('Adding realtime') - for rt_factor in [ 0.5, 1.0, 1.5, 2.0 ]: - rt_secs = len(audio[1]) / audio[0] * rt_factor - y_rt = numpy.repeat(rt_secs, nb_items) - ax.plot(x_all, y_rt, label=('Realtime: %0.4f secs [%0.1f]' % (rt_secs, rt_factor))) - - ax.set_title(title) - ax.set_xlabel('Model size') - ax.set_ylabel('Execution time (s)') - legend = ax.legend(loc='best') - - plot_format = os.path.splitext(output.name)[-1].split('.')[-1] - - plt.grid() - plt.tight_layout() - plt.savefig(output, transparent=False, frameon=True, dpi=fig_dpi, format=plot_format) - -def handle_args(): - parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.') - parser.add_argument('--wav', required=False, - help='WAV file to pass to native_client. Supply again in plotting mode to draw realine line.') - parser.add_argument('--dataset', action='append', nargs=2, metavar=('name','source'), - help='Include dataset NAME from file SOURCE. Repeat the option to add more datasets.') - parser.add_argument('--title', default=None, help='Title of the plot.') - parser.add_argument('--plot', type=argparse.FileType('wb'), required=False, - help='Target file where to plot data. Format will be deduced from extension.') - parser.add_argument('--size', default='800x600', - help='Size (px) of the resulting plot.') - parser.add_argument('--dpi', type=int, default=96, - help='Set plot DPI.') - parser.add_argument('--range', default=None, - help='Range of model size to use. Comma-separated string of boundaries: min,max') - return parser.parse_args() - -def do_main(): - cli_args = handle_args() - - if not cli_args.dataset or not cli_args.plot: - raise AssertionError('Missing arguments (dataset or target file)') - - # This is required to avoid errors about missing DISPLAY env var - plt.switch_backend('agg') - all_inference_times = ingest_csv(datasets=cli_args.dataset, range=cli_args.range) - - if cli_args.plot: - produce_plot_multiseries(input=all_inference_times, output=cli_args.plot, title=cli_args.title, size=cli_args.size, fig_dpi=cli_args.dpi, source_wav=cli_args.wav) - -if __name__ == '__main__' : - do_main() diff --git a/requirements.txt b/requirements.txt index ed1b8c76..befe6f54 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,8 +20,3 @@ bs4 requests librosa soundfile - -# Miscellaneous scripts -paramiko >= 2.1 -scipy -matplotlib diff --git a/taskcluster/tc-benchmark-tests.sh b/taskcluster/tc-benchmark-tests.sh deleted file mode 100755 index 2a585f35..00000000 --- a/taskcluster/tc-benchmark-tests.sh +++ /dev/null @@ -1,106 +0,0 @@ -#!/bin/bash - -set -xe - -source $(dirname "$0")/tc-tests-utils.sh - -exec_benchmark() -{ - model_file="$1" - run_postfix=$2 - - mkdir -p /tmp/bench-ds/ || true - mkdir -p /tmp/bench-ds-nolm/ || true - - csv=${TASKCLUSTER_ARTIFACTS}/benchmark-${run_postfix}.csv - csv_nolm=${TASKCLUSTER_ARTIFACTS}/benchmark-nolm-${run_postfix}.csv - png=${TASKCLUSTER_ARTIFACTS}/benchmark-${run_postfix}.png - svg=${TASKCLUSTER_ARTIFACTS}/benchmark-${run_postfix}.svg - - python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_nc.py \ - --dir /tmp/bench-ds/ \ - --models ${model_file} \ - --wav /tmp/LDC93S1.wav \ - --lm_binary /tmp/lm.binary \ - --trie /tmp/trie \ - --csv ${csv} - - python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_nc.py \ - --dir /tmp/bench-ds-nolm/ \ - --models ${model_file} \ - --wav /tmp/LDC93S1.wav \ - --csv ${csv_nolm} - - python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_plotter.py \ - --dataset "TaskCluster model" ${csv} \ - --dataset "TaskCluster model (no LM)" ${csv_nolm} \ - --title "TaskCluster model benchmark" \ - --wav /tmp/LDC93S1.wav \ - --plot ${png} \ - --size 1280x720 - - python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_plotter.py \ - --dataset "TaskCluster model" ${csv} \ - --dataset "TaskCluster model (no LM)" ${csv_nolm} \ - --title "TaskCluster model benchmark" \ - --wav /tmp/LDC93S1.wav \ - --plot ${svg} \ - --size 1280x720 -} - -pyver=3.5.6 - -unset PYTHON_BIN_PATH -unset PYTHONPATH -export PYENV_ROOT="${HOME}/ds-test/.pyenv" -export PATH="${PYENV_ROOT}/bin:$PATH" - -mkdir -p ${TASKCLUSTER_ARTIFACTS} || true -mkdir -p ${PYENV_ROOT} || true - -# We still need to get model, wav and alphabet -download_data - -# Follow benchmark naming from parameters in bin/run-tc-ldc93s1.sh -# Okay, it's not really the real LSTM sizes, just a way to verify how things -# actually behave. -for size in 100 200 300 400 500 600 700 800 900; -do - cp /tmp/${model_name} /tmp/test.frozen.e75.lstm${size}.ldc93s1.pb -done; - -# Let's make it a ZIP file. We don't want the directory structure. -zip --junk-paths -r9 /tmp/test.frozen.e75.lstm100-900.ldc93s1.zip /tmp/test.frozen.e75.lstm*.ldc93s1.pb && rm /tmp/test.frozen.e75.lstm*.ldc93s1.pb - -# And prepare for multiple files on the CLI -model_list="" -for size in 10 20 30 40 50 60 70 80 90; -do - cp /tmp/${model_name} /tmp/test.frozen.e75.lstm${size}.ldc93s1.pb - model_list="${model_list} /tmp/test.frozen.e75.lstm${size}.ldc93s1.pb" -done; - -# Let's prepare another model for single-model codepath -mv /tmp/${model_name} /tmp/test.frozen.e75.lstm494.ldc93s1.pb - -export TASKCLUSTER_SCHEME=${DEEPSPEECH_ARTIFACTS_ROOT}/native_client.tar.xz - -install_pyenv "${PYENV_ROOT}" -install_pyenv_virtualenv "$(pyenv root)/plugins/pyenv-virtualenv" - -PYENV_NAME=deepspeech-test -pyenv install ${pyver} -pyenv virtualenv ${pyver} ${PYENV_NAME} -source ${PYENV_ROOT}/versions/${pyver}/envs/${PYENV_NAME}/bin/activate - -set -o pipefail -pip install --upgrade pip==19.3.1 setuptools==45.0.0 wheel==0.33.6 | cat -pip install -r ${DS_ROOT_TASK}/DeepSpeech/ds/requirements.txt | cat -set +o pipefail - -exec_benchmark "/tmp/test.frozen.e75.lstm494.ldc93s1.pb" "single-model" -exec_benchmark "/tmp/test.frozen.e75.lstm100-900.ldc93s1.zip" "zipfile-model" -exec_benchmark "${model_list}" "multi-model" - -deactivate -pyenv uninstall --force ${PYENV_NAME} diff --git a/taskcluster/test-benchmark-linux-amd64-opt.yml b/taskcluster/test-benchmark-linux-amd64-opt.yml deleted file mode 100644 index 5b3f53f4..00000000 --- a/taskcluster/test-benchmark-linux-amd64-opt.yml +++ /dev/null @@ -1,14 +0,0 @@ -build: - template_file: test-linux-opt-base.tyml - dependencies: - - "linux-amd64-cpu-opt" - - "test-training_16k-linux-amd64-py36m-opt" - test_model_task: "test-training_16k-linux-amd64-py36m-opt" - system_setup: - > - apt-get -qq -y install ${python.packages_trusty.apt} zip - args: - tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-benchmark-tests.sh" - metadata: - name: "DeepSpeech Linux AMD64 CPU benchmarking" - description: "Benchmarking DeepSpeech for Linux/AMD64, CPU only, optimized version"