def get_resource_file(resource_file):
bucket = config.s3_detector_bucket()
tarball_name = resource_file + '.tar.gz'
s3client.download_from_s3(bucket, tarball_name, tarball_name)
with tarfile.open(tarball_name, 'r:*') as tar:
tar.extractall()
assert os.path.isfile(resource_file)
def create_new_tm_version(cls, classifier_model_files_dir, op_dir=None):
"""Creates new versioned tarball and uploads it to the s3 affine detector bucket"""
assert os.path.isdir(
classifier_model_files_dir
), "Directory missing : %s" % classifier_model_files_dir
if op_dir is None:
op_dir = mkdtemp()
if not os.path.isdir(op_dir):
os.makedirs(op_dir)
print 'Storing model files at location: %s' % op_dir
# get new version and folder with appropriate name
timestamp_int = cls.get_current_timestamp()
# create tarball with version from input folder
versioned_tarball_path, versioned_tarball_name = cls.create_versioned_tarball(
classifier_model_files_dir, op_dir, timestamp_int)
# upload to s3
bucket = config.s3_detector_bucket()
s3client.upload_to_s3(bucket,
versioned_tarball_name,
versioned_tarball_path,
public=False)
# create new row in DB
tmv = cls(detector_type='topic_model',
timestamp=datetime.utcfromtimestamp(timestamp_int))
session.flush()
return tmv
def grab_files(self):
bucket = config.s3_detector_bucket()
retry_operation(s3client.download_tarball,
bucket,
self.tarball_basename,
self.local_dir(),
sleep_time=0.1,
error_class=IOError)
def grab_projection_files(self):
download_dir = os.path.join(config.scratch_detector_path(), self.PCA_TAR_BALL_NAME)
bucket = config.s3_detector_bucket()
logger.info('Downloading files from s3')
s3client.download_tarball(bucket, self.PCA_TAR_BALL_NAME, download_dir)
pca_file = os.path.join(download_dir, 'pca.xml')
assert os.path.exists(pca_file), pca_file
return pca_file
def fetch_domain_stopwords(self):
self.stop_dict = defaultdict(set)
download_tarball(config.s3_detector_bucket(), DOMAIN_WF, DOMAIN_WF)
for pp in os.listdir(DOMAIN_WF):
pickle_file = os.path.join(DOMAIN_WF, pp)
with open(pickle_file, 'rb') as fi:
wf = pickle.load(fi)
for i in wf:
if wf[i] > STOP_THRESH:
self.stop_dict[pp.replace('.pickle', '')].add(i)
def get_negative_set_s3(config, inter_dir, train_pos, test_pos):
''' Predetermined set of negative images from s3 '''
bucket = affine_config.s3_detector_bucket() # CHANGE
min_neg_lines_to_use = int(
config['train_detector_params']['neg_train_min_num_frames'])
neg_train = get_set_lines(bucket, min_neg_lines_to_use, train_pos,
inter_dir, 's3_neg_test_info_readymade.txt')
min_neg_lines_to_use = int(
config['train_detector_params']['neg_test_min_num_frames'])
neg_test = get_set_lines(bucket, min_neg_lines_to_use, test_pos, inter_dir,
's3_neg_train_info_readymade.txt')
return neg_train, neg_test
def tar_and_upload(self, clf):
""" Creates tarball with model files/directories and upload to s3
Args:
clf: classifier or model that has a tarball_basename property
The uploaded tarball will have this name with a .tar.gz
extension.
"""
tarball_path = self.create_tarball(self.model_paths,
clf.tarball_basename,
op_dir=self.model_dir)
bucket = config.s3_detector_bucket()
s3client.upload_to_s3(bucket,
os.path.basename(tarball_path),
tarball_path,
public=False)
def write_neg_json(self, config_dict):
"""Creates train/test files containing negative data"""
skip_labels = [
int(i) for i in config_dict['youtube_data_ignore_labels']
]
bucket = config.s3_detector_bucket()
tarball_name = config_dict['neg_tarball_s3']
fname = tarball_name.rstrip('.tar.gz')
s3client.download_from_s3(bucket, tarball_name, tarball_name)
with tarfile.open(tarball_name, 'r:*') as tar:
tar.extractall()
assert os.path.isfile(fname)
test_split = config_dict['test_split']
neg_train_json = config_dict['neg_train_json']
neg_test_json = config_dict['neg_test_json']
fo_train = open(neg_train_json, 'w')
fo_test = open(neg_test_json, 'w')
count = 0
skip_labels_set = set(skip_labels)
skip_lines = set()
for lnum, jsn in enumerate(open(fname)):
yvt = YoutubeVideoText.to_object(jsn)
if yvt.label_id in skip_labels_set:
skip_lines.add(lnum)
count += 1
rr = range(count)
random.shuffle(rr)
line_nums = list(set(rr) - skip_lines)
test_size = len(line_nums) * test_split
test_line_nums = set(line_nums[:int(test_size)])
for lnum, jsn in enumerate(open(fname)):
if lnum in skip_lines:
continue
if lnum in test_line_nums:
fo_test.write(jsn)
else:
fo_train.write(jsn)
fo_train.close()
fo_test.close()
def grab_s3_files(self):
bucket = config.s3_detector_bucket()
tar_ball_name = 'classifier_model_files_%d' % self.current_version
logger.info("downloading files from S3")
s3client.download_tarball(bucket, tar_ball_name, self.model_files_dir)
logger.info("done downloading files from S3")
def grab_s3_files(self):
"""Download model files from s3 and untar them to destination dir."""
bucket = config.s3_detector_bucket()
logger.info("downloading files from S3")
s3client.download_tarball(bucket, 'asr_model', self.model_dir)
logger.info("done downloading files from S3")
