def _serve_metadata(self, request):
run = request.args.get('run')
if run is None:
return Respond(request, 'query parameter "run" is required',
'text/plain', 400)
name = request.args.get('name')
if name is None:
return Respond(request, 'query parameter "name" is required',
'text/plain', 400)
num_rows = _parse_positive_int_param(request, 'num_rows')
if num_rows == -1:
return Respond(request,
'query parameter num_rows must be integer > 0',
'text/plain', 400)
if run not in self.configs:
return Respond(request, 'Unknown run: %s' % run, 'text/plain', 400)
config = self.configs[run]
fpath = self._get_metadata_file_for_tensor(name, config)
if not fpath:
return Respond(
request,
'No metadata file found for tensor %s in the config file %s' %
(name, self.config_fpaths[run]), 'text/plain', 400)
if not file_io.file_exists(fpath) or file_io.is_directory(fpath):
return Respond(request, '%s is not a file' % fpath, 'text/plain',
400)
num_header_rows = 0
with file_io.FileIO(fpath, 'r') as f:
lines = []
# Stream reading the file with early break in case the file doesn't fit in
# memory.
for line in f:
lines.append(line)
if len(lines) == 1 and '\t' in lines[0]:
num_header_rows = 1
if num_rows and len(lines) >= num_rows + num_header_rows:
break
return Respond(request, ''.join(lines), 'text/plain')
def _serve_runs(self, request):
"""Returns a list of runs that have embeddings."""
return Respond(request, list(self.configs.keys()), 'application/json')