mirror of https://github.com/kubeflow/examples.git
Add new TF-Serving component with sample task (#152)
* Add new TF-Serving component with sample task * Unify nmslib and t2t packages, need to be cohesive * [WIP] update references to the package * Replace old T2T problem * Add representative code for encoding/decoding from tf serving service * Add rest API port to TF serving (replaces custom http proxy) * Fix linting * Add NMSLib creator and server components * Add docs to CLI module
This commit is contained in:
Sanyam Kapoor
k8s-ci-robot
parent
11b75edfd9
commit
c1b2802313
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@ -86,11 +86,11 @@ $ gcloud auth configure-docker
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* Build and push the image
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```
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$ PROJECT=my-project ./language_task/build_image.sh
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$ PROJECT=my-project ./build_image.sh
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```
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and a GPU image
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```
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$ GPU=1 PROJECT=my-project ./language_task/build_image.sh
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$ GPU=1 PROJECT=my-project ./build_image.sh
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```
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See [GCR Pushing and Pulling Images](https://cloud.google.com/container-registry/docs/pushing-and-pulling) for more.
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@ -1,28 +1,27 @@
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# NOTE: The context for this build must be the `language_task` directory
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# NOTE: The context for this build must be the `app` directory
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ARG BASE_IMAGE_TAG=1.8.0-py3
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FROM tensorflow/tensorflow:$BASE_IMAGE_TAG
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ADD requirements.txt /
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ADD . /app
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RUN pip3 --no-cache-dir install -r /requirements.txt &&\
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WORKDIR /app
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ENV T2T_USR_DIR=/app/code_search/t2t
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RUN pip3 --no-cache-dir install . &&\
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apt-get update && apt-get install -y jq &&\
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rm -rf /var/lib/apt/lists/*
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VOLUME ["/data", "/output"]
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ADD t2t_problems/* /t2t_problems/
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ADD t2t-entrypoint.sh /usr/local/sbin/t2t-entrypoint
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ENV T2T_USR_DIR=/t2t_problems
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WORKDIR /t2t_problems
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#ENTRYPOINT ["/usr/local/sbin/t2t-entrypoint"]
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rm -rf /var/lib/apt/lists/* &&\
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ln -s /app/t2t-entrypoint.sh /usr/local/sbin/t2t-entrypoint
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# TODO(sanyamkapoor): A workaround for tensorflow/tensor2tensor#879
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RUN apt-get update && apt-get install -y curl python &&\
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curl https://sdk.cloud.google.com | bash &&\
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rm -rf /var/lib/apt/lists/*
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VOLUME ["/data", "/output"]
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EXPOSE 8008
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ENTRYPOINT ["bash"]
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@ -0,0 +1,90 @@
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"""
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This module serves as the entrypoint to either create an nmslib index or
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start a Flask server to serve the index via a simple REST interface. It
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internally talks to TF Serving for inference related tasks. The
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two entrypoints `server` and `creator` are exposed as `nmslib-create`
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and `nmslib-serve` binaries (see `setup.py`). Use `-h` to get a list
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of input CLI arguments to both.
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"""
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import sys
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import os
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import argparse
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import numpy as np
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from code_search.nmslib.gcs import maybe_download_gcs_file, maybe_upload_gcs_file
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from code_search.nmslib.search_engine import CodeSearchEngine
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from code_search.nmslib.search_server import CodeSearchServer
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def parse_server_args(args):
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parser = argparse.ArgumentParser(prog='nmslib Flask Server')
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parser.add_argument('--tmp-dir', type=str, metavar='', default='/tmp/nmslib',
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help='Path to temporary data directory')
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parser.add_argument('--index-file', type=str, required=True,
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help='Path to index file created by nmslib')
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parser.add_argument('--problem', type=str, required=True,
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help='Name of the T2T problem')
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parser.add_argument('--data-dir', type=str, metavar='', default='/tmp',
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help='Path to working data directory')
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parser.add_argument('--serving-url', type=str, required=True,
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help='Complete URL to TF Serving Inference server')
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parser.add_argument('--host', type=str, metavar='', default='0.0.0.0',
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help='Host to start server on')
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parser.add_argument('--port', type=int, metavar='', default=8008,
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help='Port to bind server to')
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args = parser.parse_args(args)
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args.tmp_dir = os.path.expanduser(args.tmp_dir)
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args.index_file = os.path.expanduser(args.index_file)
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args.data_dir = os.path.expanduser(args.data_dir)
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return args
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def parse_creator_args(args):
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parser = argparse.ArgumentParser(prog='nmslib Index Creator')
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parser.add_argument('--data-file', type=str, required=True,
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help='Path to csv data file for human-readable data')
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parser.add_argument('--index-file', type=str, metavar='', default='/tmp/index.nmslib',
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help='Path to output index file')
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parser.add_argument('--tmp-dir', type=str, metavar='', default='/tmp/nmslib',
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help='Path to temporary data directory')
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return parser.parse_args(args)
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def server():
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args = parse_server_args(sys.argv[1:])
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if not os.path.isdir(args.tmp_dir):
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os.makedirs(args.tmp_dir, exist_ok=True)
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# Download relevant files if needed
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index_file = maybe_download_gcs_file(args.index_file, args.tmp_dir)
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search_engine = CodeSearchEngine(args.problem, args.data_dir, args.serving_url,
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index_file)
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search_server = CodeSearchServer(engine=search_engine,
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host=args.host, port=args.port)
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search_server.run()
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def creator():
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args = parse_creator_args(sys.argv[1:])
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if not os.path.isdir(args.tmp_dir):
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os.makedirs(args.tmp_dir, exist_ok=True)
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data_file = maybe_download_gcs_file(args.data_file, args.tmp_dir)
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# TODO(sanyamkapoor): parse data file into a numpy array
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data = np.load(data_file)
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tmp_index_file = os.path.join(args.tmp_dir, os.path.basename(args.index_file))
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CodeSearchEngine.create_index(data, tmp_index_file)
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maybe_upload_gcs_file(tmp_index_file, args.index_file)
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@ -0,0 +1,69 @@
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import json
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import requests
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import nmslib
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import numpy as np
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from tensor2tensor import problems # pylint: disable=unused-import
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from code_search.t2t.query import get_encoder_decoder, encode_query
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class CodeSearchEngine:
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"""This is a utility class which takes an nmslib
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index file and a data file to return data from"""
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def __init__(self, problem: str, data_dir: str, serving_url: str,
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index_file: str):
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self._serving_url = serving_url
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self._problem = problem
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self._data_dir = data_dir
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self._index_file = index_file
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self.index = CodeSearchEngine.nmslib_init()
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self.index.loadIndex(index_file)
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def embed(self, query_str):
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"""This function gets the vector embedding from
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the target inference server. The steps involved are
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encoding the input query and decoding the responses
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from the TF Serving service
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TODO(sanyamkapoor): This code is still under construction
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and only representative of the steps needed to build the
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embedding
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"""
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encoder, decoder = get_encoder_decoder(self._problem, self._data_dir)
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encoded_query = encode_query(encoder, query_str)
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data = {"instances": [{"input": {"b64": encoded_query}}]}
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response = requests.post(url=self._serving_url,
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headers={'content-type': 'application/json'},
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data=json.dumps(data))
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result = response.json()
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for prediction in result['predictions']:
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prediction['outputs'] = decoder.decode(prediction['outputs'])
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return result['predicts'][0]['outputs']
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def query(self, query_str: str, k=2):
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embedding = self.embed(query_str)
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idxs, dists = self.index.knnQuery(embedding, k=k)
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# TODO(sanyamkapoor): initialize data map and return
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# list of dicts
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# [
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# {'src': self.data_map[idx], 'dist': dist}
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# for idx, dist in zip(idxs, dists)
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# ]
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return idxs, dists
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@staticmethod
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def nmslib_init():
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"""Initializes an nmslib index object"""
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index = nmslib.init(method='hnsw', space='cosinesimil')
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return index
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@staticmethod
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def create_index(data: np.array, save_path: str):
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"""Add numpy data to the index and save to path"""
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index = CodeSearchEngine.nmslib_init()
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index.addDataPointBatch(data)
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index.createIndex({'post': 2}, print_progress=True)
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index.saveIndex(save_path)
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from flask import Flask, request, abort, jsonify, make_response
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from code_search.nmslib.search_engine import CodeSearchEngine
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class CodeSearchServer:
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"""This utility class wraps the search engine into
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an HTTP server based on Flask"""
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def __init__(self, engine, host='0.0.0.0', port=8008):
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def __init__(self, engine: CodeSearchEngine, host='0.0.0.0', port=8008):
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self.app = Flask(__name__)
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self.host = host
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self.port = port
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@ -24,7 +25,7 @@ class CodeSearchServer:
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abort(make_response(
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jsonify(status=400, error="empty query"), 400))
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result = self.engine.search(query_str)
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result = self.engine.query(query_str)
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return make_response(jsonify(result=result))
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def run(self):
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@ -0,0 +1 @@
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import code_search.t2t.similarity_transformer
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@ -0,0 +1,32 @@
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import base64
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import tensorflow as tf
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from tensor2tensor.data_generators import text_encoder
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from tensor2tensor.utils import registry
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def get_encoder_decoder(problem_name, data_dir):
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"""Get encoder from the T2T problem.This might
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vary by problem, keeping generic as a reference
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"""
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problem = registry.problem(problem_name)
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hparams = tf.contrib.training.HParams(data_dir=data_dir)
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problem.get_hparams(hparams)
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return problem.feature_info["inputs"].encoder, \
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problem.feature_info["targets"].encoder
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def encode_query(encoder, query_str):
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"""Encode the input query string using encoder. This
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might vary by problem but keeping generic as a reference.
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Note that in T2T problems, the 'targets' key is needed
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even though it is ignored during inference.
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See tensorflow/tensor2tensor#868"""
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encoded_str = encoder.encode(query_str) + [text_encoder.EOS_ID]
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features = {"inputs": tf.train.Feature(int64_list=tf.train.Int64List(value=encoded_str)),
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"targets": tf.train.Feature(int64_list=tf.train.Int64List(value=[0]))}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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return base64.b64encode(example.SerializeToString()).decode('utf-8')
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def decode_result(decoder, list_ids):
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return decoder.decode(list_ids)
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@ -0,0 +1,8 @@
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tensor2tensor~=1.6.0
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tensorflow~=1.8.0
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oauth2client~=4.1.0
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Flask~=1.0.0
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nmslib~=1.7.0
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numpy~=1.14.0
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google-cloud-storage~=1.10.0
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requests~=2.18.0
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@ -3,10 +3,10 @@ from setuptools import setup, find_packages
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with open('requirements.txt', 'r') as f:
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install_requires = f.readlines()
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VERSION = '0.1.0'
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VERSION = '0.0.1'
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setup(name='code-search-index-server',
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description='Kubeflow Code Search Demo - Index Server',
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setup(name='code-search',
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description='Kubeflow Code Search Demo',
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url='https://www.github.com/kubeflow/examples',
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author='Sanyam Kapoor',
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author_email='sanyamkapoor@google.com',
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@ -17,7 +17,7 @@ setup(name='code-search-index-server',
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extras_require={},
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entry_points={
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'console_scripts': [
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'nmslib-serve=nmslib_flask.cli:server',
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'nmslib-create=nmslib_flask.cli:creator',
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'nmslib-serve=code_search.nmslib.cli:server',
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'nmslib-create=code_search.nmslib.cli:creator',
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]
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})
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@ -1,5 +1,11 @@
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#!/usr/bin/env bash
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##
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# This script builds and pushes a Docker image containing
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# "app" to Google Container Registry. It automatically tags
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# a unique image for every run.
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#
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set -ex
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PROJECT=${PROJECT:-}
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@ -17,7 +23,7 @@ BUILD_IMAGE_TAG="code-search:v$(date +%Y%m%d)$([[ ${GPU} = "1" ]] && echo '-gpu'
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# Directory of this script used as docker context
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_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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pushd "$_SCRIPT_DIR"
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pushd "${_SCRIPT_DIR}/app"
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docker build -t ${BUILD_IMAGE_TAG} --build-arg BASE_IMAGE_TAG=${BASE_IMAGE_TAG} .
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@ -1,9 +0,0 @@
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FROM python:3.6
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ADD . /app
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WORKDIR /app
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RUN pip install .
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ENTRYPOINT ["sh"]
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@ -1,21 +0,0 @@
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#!/usr/bin/env bash
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set -e
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PROJECT=${PROJECT:-}
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BUILD_IMAGE_TAG=${BUILD_IMAGE_TAG:-nmslib:devel}
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# Directory of this script used as docker context
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_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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pushd "$_SCRIPT_DIR"
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docker build -t ${BUILD_IMAGE_TAG} .
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# Push image to GCR if PROJECT available
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if [[ ! -z "${PROJECT}" ]]; then
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docker tag ${BUILD_IMAGE_TAG} gcr.io/${PROJECT}/${BUILD_IMAGE_TAG}
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docker push gcr.io/${PROJECT}/${BUILD_IMAGE_TAG}
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fi
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popd
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@ -1,71 +0,0 @@
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import sys
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import os
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import argparse
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import numpy as np
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from nmslib_flask.gcs import maybe_download_gcs_file, maybe_upload_gcs_file
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from nmslib_flask.search_engine import CodeSearchEngine
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from nmslib_flask.search_server import CodeSearchServer
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def parse_server_args(args):
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parser = argparse.ArgumentParser(prog='nmslib Flask Server')
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parser.add_argument('--index-file', type=str, required=True,
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help='Path to index file created by nmslib')
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parser.add_argument('--data-file', type=str, required=True,
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help='Path to csv file for human-readable data')
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parser.add_argument('--data-dir', type=str, metavar='', default='/tmp',
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help='Path to working data directory')
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parser.add_argument('--host', type=str, metavar='', default='0.0.0.0',
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help='Host to start server on')
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parser.add_argument('--port', type=int, metavar='', default=8008,
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help='Port to bind server to')
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return parser.parse_args(args)
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def parse_creator_args(args):
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parser = argparse.ArgumentParser(prog='nmslib Index Creator')
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parser.add_argument('--data-file', type=str, required=True,
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help='Path to csv data file for human-readable data')
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parser.add_argument('--output-file', type=str, metavar='', default='/tmp/index.nmslib',
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help='Path to output index file')
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parser.add_argument('--data-dir', type=str, metavar='', default='/tmp',
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help='Path to working data directory')
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return parser.parse_args(args)
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def server():
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args = parse_server_args(sys.argv[1:])
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if not os.path.isdir(args.data_dir):
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os.makedirs(args.data_dir, exist_ok=True)
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# Download relevant files if needed
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index_file = maybe_download_gcs_file(args.index_file, args.data_dir)
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data_file = maybe_download_gcs_file(args.data_file, args.data_dir)
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search_engine = CodeSearchEngine(index_file, data_file)
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search_server = CodeSearchServer(engine=search_engine,
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host=args.host, port=args.port)
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search_server.run()
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def creator():
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args = parse_creator_args(sys.argv[1:])
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if not os.path.isdir(args.data_dir):
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os.makedirs(args.data_dir, exist_ok=True)
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data_file = maybe_download_gcs_file(args.data_file, args.data_dir)
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# TODO(sanyamkapoor): parse data file into a numpy array
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data = np.load(data_file)
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tmp_output_file = os.path.join(args.data_dir, os.path.basename(args.output_file))
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CodeSearchEngine.create_index(data, tmp_output_file)
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maybe_upload_gcs_file(tmp_output_file, args.output_file)
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@ -1,46 +0,0 @@
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import nmslib
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import numpy as np
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class CodeSearchEngine:
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"""This is a utility class which takes an nmslib
|
||||
index file and a data file to return data from"""
|
||||
def __init__(self, index_file: str, data_file: str):
|
||||
self._index_file = index_file
|
||||
self._data_file = data_file
|
||||
|
||||
self.index = CodeSearchEngine.nmslib_init()
|
||||
self.index.loadIndex(index_file)
|
||||
|
||||
# TODO: load the reverse-index map for actual code data
|
||||
# self.data_map =
|
||||
|
||||
def embed(self, query_str):
|
||||
# TODO load trained model and embed input strings
|
||||
raise NotImplementedError
|
||||
|
||||
def query(self, query_str: str, k=2):
|
||||
embedding = self.embed(query_str)
|
||||
idxs, dists = self.index.knnQuery(embedding, k=k)
|
||||
|
||||
# TODO(sanyamkapoor): initialize data map and return
|
||||
# list of dicts
|
||||
# [
|
||||
# {'src': self.data_map[idx], 'dist': dist}
|
||||
# for idx, dist in zip(idxs, dists)
|
||||
# ]
|
||||
return idxs, dists
|
||||
|
||||
@staticmethod
|
||||
def nmslib_init():
|
||||
"""Initializes an nmslib index object"""
|
||||
index = nmslib.init(method='hnsw', space='cosinesimil')
|
||||
return index
|
||||
|
||||
@staticmethod
|
||||
def create_index(data: np.array, save_path: str):
|
||||
"""Add numpy data to the index and save to path"""
|
||||
index = CodeSearchEngine.nmslib_init()
|
||||
index.addDataPointBatch(data)
|
||||
index.createIndex({'post': 2}, print_progress=True)
|
||||
index.saveIndex(save_path)
|
||||
|
|
@ -1,4 +0,0 @@
|
|||
Flask~=1.0.0
|
||||
nmslib~=1.7.0
|
||||
numpy~=1.14.0
|
||||
google-cloud-storage~=1.10.0
|
||||
|
|
@ -14,6 +14,12 @@ libraries:
|
|||
refSpec: master
|
||||
name: tf-job
|
||||
registry: kubeflow
|
||||
tf-serving:
|
||||
gitVersion:
|
||||
commitSha: e1b2aee865866b2e7e4f8c41b34ae03b4c4bb0db
|
||||
refSpec: master
|
||||
name: tf-serving
|
||||
registry: kubeflow
|
||||
name: kubeflow
|
||||
registries:
|
||||
incubator:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,7 @@
|
|||
local k = import "k.libsonnet";
|
||||
local nms = import "nms.libsonnet";
|
||||
|
||||
local env = std.extVar("__ksonnet/environments");
|
||||
local params = std.extVar("__ksonnet/params").components["nms-creator"];
|
||||
|
||||
std.prune(k.core.v1.list.new(nms.parts(params, env).creator))
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
local k = import "k.libsonnet";
|
||||
local nms = import "nms.libsonnet";
|
||||
|
||||
local env = std.extVar("__ksonnet/environments");
|
||||
local params = std.extVar("__ksonnet/params").components["nms-server"];
|
||||
|
||||
std.prune(k.core.v1.list.new(nms.parts(params, env).server))
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
local baseParams = std.extVar("__ksonnet/params").components["nmslib"];
|
||||
|
||||
{
|
||||
|
||||
nmsContainer(params, env):: {
|
||||
apiVersion: "extensions/v1beta1",
|
||||
kind: "Deployment",
|
||||
metadata: {
|
||||
name: params.name + "-deployment",
|
||||
namespace: env.namespace,
|
||||
labels: {
|
||||
app: params.name,
|
||||
}
|
||||
},
|
||||
spec: {
|
||||
replicas: params.replicas,
|
||||
selector: {
|
||||
matchLabels: {
|
||||
app: params.name,
|
||||
},
|
||||
},
|
||||
template: {
|
||||
metadata: {
|
||||
labels: {
|
||||
app: params.name,
|
||||
}
|
||||
},
|
||||
spec: {
|
||||
containers: [
|
||||
{
|
||||
name: params.name,
|
||||
image: params.image,
|
||||
args: params.args,
|
||||
ports: [
|
||||
{
|
||||
containerPort: 8008,
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
|
||||
service(params, env):: {
|
||||
apiVersion: "v1",
|
||||
kind: "Service",
|
||||
metadata: {
|
||||
labels: {
|
||||
app: params.name,
|
||||
},
|
||||
name: params.name,
|
||||
namespace: env.namespace,
|
||||
annotations: {
|
||||
"getambassador.io/config":
|
||||
std.join("\n", [
|
||||
"---",
|
||||
"apiVersion: ambassador/v0",
|
||||
"kind: Mapping",
|
||||
"name: http-mapping-" + params.name,
|
||||
"prefix: /code-search/",
|
||||
"rewrite: /",
|
||||
"method: GET",
|
||||
"service: " + params.name + "." + env.namespace + ":8008",
|
||||
]),
|
||||
},
|
||||
},
|
||||
spec: {
|
||||
type: "ClusterIP",
|
||||
selector: {
|
||||
app: params.name,
|
||||
},
|
||||
ports: [
|
||||
{
|
||||
name: "nmslib-serve-http",
|
||||
port: 8008,
|
||||
targetPort: 8008,
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
|
||||
parts(newParams, env):: {
|
||||
local params = baseParams + newParams,
|
||||
|
||||
creator:: {
|
||||
local creatorParams = params + {
|
||||
args: [
|
||||
"nmslib-create",
|
||||
"--data-file=" + params.dataFile,
|
||||
"--index-file=" + params.indexFile,
|
||||
],
|
||||
},
|
||||
|
||||
all: [
|
||||
$.nmsContainer(creatorParams, env),
|
||||
],
|
||||
}.all,
|
||||
|
||||
server:: {
|
||||
local serverParams = params + {
|
||||
args: [
|
||||
"nmslib-serve",
|
||||
"--data-file=" + params.dataFile,
|
||||
"--index-file=" + params.indexFile,
|
||||
"--problem=" + params.problem,
|
||||
"--data-dir=" + params.dataDir,
|
||||
"--serving-url=" + params.servingUrl,
|
||||
],
|
||||
},
|
||||
|
||||
all: [
|
||||
$.service(serverParams, env),
|
||||
$.nmsContainer(serverParams, env),
|
||||
],
|
||||
}.all,
|
||||
}
|
||||
}
|
||||
|
|
@ -32,6 +32,18 @@
|
|||
gsOutputDir: "null",
|
||||
},
|
||||
|
||||
"nmslib": {
|
||||
name: null,
|
||||
replicas: 1,
|
||||
image: "gcr.io/kubeflow-dev/code-search:v20180621-266e689",
|
||||
|
||||
dataFile: null,
|
||||
indexFile: null,
|
||||
problem: null,
|
||||
dataDir: null,
|
||||
servingUrl: null,
|
||||
},
|
||||
|
||||
"t2t-translate-datagen": {
|
||||
jobType: "datagen",
|
||||
|
||||
|
|
@ -70,5 +82,22 @@
|
|||
model: "transformer",
|
||||
hparams_set: "transformer_base_single_gpu",
|
||||
},
|
||||
|
||||
"t2t-translate-serving": {
|
||||
name: "t2t-translate",
|
||||
modelName: "t2t-translate",
|
||||
modelPath: "gs://kubeflow-examples/t2t-translate/translate_ende_wmt32k/output/export/Servo",
|
||||
modelServerImage: "gcr.io/kubeflow-images-public/tensorflow-serving-1.8:latest",
|
||||
cloud: "gcp",
|
||||
gcpCredentialSecretName: "gcp-credentials",
|
||||
},
|
||||
|
||||
"nms-creator": {
|
||||
name: "nms-creator",
|
||||
},
|
||||
|
||||
"nms-server": {
|
||||
name: "nms-server",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,21 @@
|
|||
local env = std.extVar("__ksonnet/environments");
|
||||
local params = std.extVar("__ksonnet/params").components["t2t-translate-serving"];
|
||||
|
||||
local k = import "k.libsonnet";
|
||||
|
||||
// ksonnet appears to require name be a parameter of the prototype which is why we handle it differently.
|
||||
local name = params.name;
|
||||
|
||||
// updatedParams includes the namespace from env by default.
|
||||
// We can override namespace in params if needed
|
||||
local updatedParams = env + params;
|
||||
|
||||
local tfServingBase = import "kubeflow/tf-serving/tf-serving.libsonnet";
|
||||
local tfServing = tfServingBase {
|
||||
// Override parameters with user supplied parameters.
|
||||
params+: updatedParams {
|
||||
name: name,
|
||||
},
|
||||
};
|
||||
|
||||
std.prune(k.core.v1.list.new(tfServing.components))
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
<!-- START doctoc generated TOC please keep comment here to allow auto update -->
|
||||
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
|
||||
**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
|
||||
|
||||
- [tf-serving](#tf-serving)
|
||||
- [Quickstart](#quickstart)
|
||||
- [Using the library](#using-the-library)
|
||||
- [io.ksonnet.pkg.tf-serving](#ioksonnetpkgtf-serving)
|
||||
- [Example](#example)
|
||||
- [Parameters](#parameters)
|
||||
|
||||
<!-- END doctoc generated TOC please keep comment here to allow auto update -->
|
||||
|
||||
# tf-serving
|
||||
|
||||
> TensorFlow serving is a server for TensorFlow models.
|
||||
|
||||
|
||||
* [Quickstart](#quickstart)
|
||||
* [Using Prototypes](#using-prototypes)
|
||||
* [io.ksonnet.pkg.tf-serving](#io.ksonnet.pkg.tf-serving)
|
||||
|
||||
## Quickstart
|
||||
|
||||
*The following commands use the `io.ksonnet.pkg.tf-serving` prototype to generate Kubernetes YAML for tf-serving, and then deploys it to your Kubernetes cluster.*
|
||||
|
||||
First, create a cluster and install the ksonnet CLI (see root-level [README.md](rootReadme)).
|
||||
|
||||
If you haven't yet created a [ksonnet application](linkToSomewhere), do so using `ks init <app-name>`.
|
||||
|
||||
Finally, in the ksonnet application directory, run the following:
|
||||
|
||||
```shell
|
||||
# Expand prototype as a Jsonnet file, place in a file in the
|
||||
# `components/` directory. (YAML and JSON are also available.)
|
||||
$ ks prototype use io.ksonnet.pkg.tf-serving tf-serving \
|
||||
--name tf-serving \
|
||||
--namespace default
|
||||
|
||||
# Apply to server.
|
||||
$ ks apply -f tf-serving.jsonnet
|
||||
```
|
||||
|
||||
## Using the library
|
||||
|
||||
The library files for tf-serving define a set of relevant *parts* (_e.g._, deployments, services, secrets, and so on) that can be combined to configure tf-serving for a wide variety of scenarios. For example, a database like Redis may need a secret to hold the user password, or it may have no password if it's acting as a cache.
|
||||
|
||||
This library provides a set of pre-fabricated "flavors" (or "distributions") of tf-serving, each of which is configured for a different use case. These are captured as ksonnet *prototypes*, which allow users to interactively customize these distributions for their specific needs.
|
||||
|
||||
These prototypes, as well as how to use them, are enumerated below.
|
||||
|
||||
### io.ksonnet.pkg.tf-serving
|
||||
|
||||
TensorFlow serving
|
||||
#### Example
|
||||
|
||||
```shell
|
||||
# Expand prototype as a Jsonnet file, place in a file in the
|
||||
# `components/` directory. (YAML and JSON are also available.)
|
||||
$ ks prototype use io.ksonnet.pkg.tf-serving tf-serving \
|
||||
--name YOUR_NAME_HERE \
|
||||
--model_path YOUR_MODEL_PATH_HERE
|
||||
```
|
||||
|
||||
#### Parameters
|
||||
|
||||
The available options to pass prototype are:
|
||||
|
||||
* `--name=<name>`: Name to give to each of the components [string]
|
||||
* `--model_path=<model_path>`: Path to the model. This can be a GCS path. [string]
|
||||
|
||||
|
||||
[rootReadme]: https://github.com/ksonnet/mixins
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
{
|
||||
"name": "tf-serving",
|
||||
"apiVersion": "0.0.1",
|
||||
"kind": "ksonnet.io/parts",
|
||||
"description": "TensorFlow serving is a server for TensorFlow models.\n",
|
||||
"author": "kubeflow team <kubeflow-team@google.com>",
|
||||
"contributors": [
|
||||
{
|
||||
"name": "Jeremy Lewi",
|
||||
"email": "jlewi@google.com"
|
||||
}
|
||||
],
|
||||
"repository": {
|
||||
"type": "git",
|
||||
"url": "https://github.com/kubeflow/kubeflow"
|
||||
},
|
||||
"bugs": {
|
||||
"url": "https://github.com/kubeflow/kubeflow/issues"
|
||||
},
|
||||
"keywords": [
|
||||
"kubeflow",
|
||||
"tensorflow",
|
||||
"database"
|
||||
],
|
||||
"quickStart": {
|
||||
"prototype": "io.ksonnet.pkg.tf-serving",
|
||||
"componentName": "tf-serving",
|
||||
"flags": {
|
||||
"name": "tf-serving",
|
||||
"namespace": "default"
|
||||
},
|
||||
"comment": "Run TensorFlow Serving"
|
||||
},
|
||||
"license": "Apache 2.0"
|
||||
}
|
||||
24
code_search/kubeflow/vendor/kubeflow/tf-serving/prototypes/tf-serving-all-features.jsonnet
vendored
Normal file
24
code_search/kubeflow/vendor/kubeflow/tf-serving/prototypes/tf-serving-all-features.jsonnet
vendored
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
// @apiVersion 0.1
|
||||
// @name io.ksonnet.pkg.tf-serving
|
||||
// @description TensorFlow serving
|
||||
// @shortDescription A TensorFlow serving deployment
|
||||
// @param name string Name to give to each of the components
|
||||
|
||||
local k = import "k.libsonnet";
|
||||
|
||||
// ksonnet appears to require name be a parameter of the prototype which is why we handle it differently.
|
||||
local name = import "param://name";
|
||||
|
||||
// updatedParams includes the namespace from env by default.
|
||||
// We can override namespace in params if needed
|
||||
local updatedParams = env + params;
|
||||
|
||||
local tfServingBase = import "kubeflow/tf-serving/tf-serving.libsonnet";
|
||||
local tfServing = tfServingBase {
|
||||
// Override parameters with user supplied parameters.
|
||||
params+: updatedParams {
|
||||
name: name,
|
||||
},
|
||||
};
|
||||
|
||||
std.prune(k.core.v1.list.new(tfServing.components))
|
||||
|
|
@ -0,0 +1,387 @@
|
|||
{
|
||||
util:: import "kubeflow/tf-serving/util.libsonnet",
|
||||
|
||||
// Parameters are intended to be late bound.
|
||||
params:: {
|
||||
name: null,
|
||||
numGpus: 0,
|
||||
labels: {
|
||||
app: $.params.name,
|
||||
},
|
||||
modelName: $.params.name,
|
||||
modelPath: null,
|
||||
modelStorageType: "cloud",
|
||||
|
||||
version: "v1",
|
||||
firstVersion: true,
|
||||
|
||||
deployIstio: false,
|
||||
|
||||
deployHttpProxy: false,
|
||||
defaultHttpProxyImage: "gcr.io/kubeflow-images-public/tf-model-server-http-proxy:v20180606-9dfda4f2",
|
||||
httpProxyImage: "",
|
||||
httpProxyImageToUse: if $.params.httpProxyImage == "" then
|
||||
$.params.defaultHttpProxyImage
|
||||
else
|
||||
$.params.httpProxyImage,
|
||||
|
||||
serviceType: "ClusterIP",
|
||||
|
||||
// If users want to override the image then can override defaultCpuImage and/or defaultGpuImage
|
||||
// in which case the image used will still depend on whether GPUs are used or not.
|
||||
// Users can also override modelServerImage in which case the user supplied value will always be used
|
||||
// regardless of numGpus.
|
||||
defaultCpuImage: "gcr.io/kubeflow-images-public/tensorflow-serving-1.7:v20180604-0da89b8a",
|
||||
defaultGpuImage: "gcr.io/kubeflow-images-public/tensorflow-serving-1.6gpu:v20180604-0da89b8a",
|
||||
modelServerImage: if $.params.numGpus == 0 then
|
||||
$.params.defaultCpuImage
|
||||
else
|
||||
$.params.defaultGpuImage,
|
||||
|
||||
|
||||
// Whether or not to enable s3 parameters
|
||||
s3Enable:: false,
|
||||
|
||||
// Which cloud to use
|
||||
cloud:: null,
|
||||
},
|
||||
|
||||
// Parametes specific to GCP.
|
||||
gcpParams:: {
|
||||
gcpCredentialSecretName: "",
|
||||
} + $.params,
|
||||
|
||||
// Parameters that control S3 access
|
||||
// params overrides s3params because params can be overwritten by the user to override the defaults.
|
||||
s3params:: {
|
||||
// Name of the k8s secrets containing S3 credentials
|
||||
s3SecretName: "",
|
||||
// Name of the key in the k8s secret containing AWS_ACCESS_KEY_ID.
|
||||
s3SecretAccesskeyidKeyName: "",
|
||||
|
||||
// Name of the key in the k8s secret containing AWS_SECRET_ACCESS_KEY.
|
||||
s3SecretSecretaccesskeyKeyName: "",
|
||||
|
||||
// S3 region
|
||||
s3AwsRegion: "us-west-1",
|
||||
|
||||
// TODO(jlewi): We should use util.toBool to automatically conver to actual boolean values.
|
||||
// The use of strings is left over from when they were prototype parameters which only supports string type.
|
||||
|
||||
// true Whether or not to use https for S3 connections
|
||||
s3UseHttps: "true",
|
||||
|
||||
// Whether or not to verify https certificates for S3 connections
|
||||
s3VerifySsl: "true",
|
||||
|
||||
// URL for your s3-compatible endpoint.
|
||||
s3Endpoint: "http://s3.us-west-1.amazonaws.com,",
|
||||
} + $.params,
|
||||
|
||||
|
||||
components:: {
|
||||
|
||||
all:: [
|
||||
// Default routing rule for the first version of model.
|
||||
if $.util.toBool($.params.deployIstio) && $.util.toBool($.params.firstVersion) then
|
||||
$.parts.defaultRouteRule,
|
||||
] +
|
||||
// TODO(jlewi): It would be better to structure s3 as a mixin.
|
||||
// As an example it would be great to allow S3 and GCS parameters
|
||||
// to be enabled simultaneously. This should be doable because
|
||||
// each entails adding a set of environment variables and volumes
|
||||
// to the containers. These volumes/environment variables shouldn't
|
||||
// overlap so there's no reason we shouldn't be able to just add
|
||||
// both modifications to the base container.
|
||||
// I think we want to restructure things as mixins so they can just
|
||||
// be added.
|
||||
if $.params.s3Enable then
|
||||
[
|
||||
$.s3parts.tfService,
|
||||
$.s3parts.tfDeployment,
|
||||
]
|
||||
else if $.params.cloud == "gcp" then
|
||||
[
|
||||
$.gcpParts.tfService,
|
||||
$.gcpParts.tfDeployment,
|
||||
]
|
||||
else
|
||||
[
|
||||
$.parts.tfService,
|
||||
$.parts.tfDeployment,
|
||||
],
|
||||
}.all,
|
||||
|
||||
parts:: {
|
||||
// We define the containers one level beneath parts because combined with jsonnet late binding
|
||||
// this makes it easy for users to override specific bits of the container.
|
||||
tfServingContainerBase:: {
|
||||
name: $.params.name,
|
||||
image: $.params.modelServerImage,
|
||||
imagePullPolicy: "IfNotPresent",
|
||||
args: [
|
||||
"/usr/bin/tensorflow_model_server",
|
||||
"--port=9000",
|
||||
"--rest_api_port=8000",
|
||||
"--model_name=" + $.params.modelName,
|
||||
"--model_base_path=" + $.params.modelPath,
|
||||
],
|
||||
ports: [
|
||||
{
|
||||
containerPort: 9000,
|
||||
},
|
||||
{
|
||||
containerPort: 8000,
|
||||
},
|
||||
],
|
||||
// TODO(jlewi): We should add readiness and liveness probes. I think the blocker is that
|
||||
// model-server doesn't have something we can use out of the box.
|
||||
resources: {
|
||||
requests: {
|
||||
memory: "1Gi",
|
||||
cpu: "1",
|
||||
},
|
||||
limits: {
|
||||
memory: "4Gi",
|
||||
cpu: "4",
|
||||
},
|
||||
},
|
||||
// The is user and group should be defined in the Docker image.
|
||||
// Per best practices we don't run as the root user.
|
||||
securityContext: {
|
||||
runAsUser: 1000,
|
||||
fsGroup: 1000,
|
||||
},
|
||||
volumeMounts+: if $.params.modelStorageType == "nfs" then [{
|
||||
name: "nfs",
|
||||
mountPath: "/mnt",
|
||||
}]
|
||||
else [],
|
||||
}, // tfServingContainer
|
||||
|
||||
tfServingContainer+: $.parts.tfServingContainerBase +
|
||||
if $.params.numGpus > 0 then
|
||||
{
|
||||
resources+: {
|
||||
limits+: {
|
||||
"nvidia.com/gpu": $.params.numGpus,
|
||||
},
|
||||
},
|
||||
}
|
||||
else {},
|
||||
|
||||
tfServingMetadata+: {
|
||||
labels: $.params.labels { version: $.params.version },
|
||||
annotations: {
|
||||
"sidecar.istio.io/inject": if $.util.toBool($.params.deployIstio) then "true",
|
||||
},
|
||||
},
|
||||
|
||||
httpProxyContainer:: {
|
||||
name: $.params.name + "-http-proxy",
|
||||
image: $.params.httpProxyImageToUse,
|
||||
imagePullPolicy: "IfNotPresent",
|
||||
command: [
|
||||
"python",
|
||||
"/usr/src/app/server.py",
|
||||
"--port=8000",
|
||||
"--rpc_port=9000",
|
||||
"--rpc_timeout=10.0",
|
||||
],
|
||||
env: [],
|
||||
ports: [
|
||||
{
|
||||
containerPort: 8000,
|
||||
},
|
||||
],
|
||||
resources: {
|
||||
requests: {
|
||||
memory: "1Gi",
|
||||
cpu: "1",
|
||||
},
|
||||
limits: {
|
||||
memory: "4Gi",
|
||||
cpu: "4",
|
||||
},
|
||||
},
|
||||
securityContext: {
|
||||
runAsUser: 1000,
|
||||
fsGroup: 1000,
|
||||
},
|
||||
}, // httpProxyContainer
|
||||
|
||||
|
||||
tfDeployment: {
|
||||
apiVersion: "extensions/v1beta1",
|
||||
kind: "Deployment",
|
||||
metadata: {
|
||||
name: $.params.name + "-" + $.params.version,
|
||||
namespace: $.params.namespace,
|
||||
labels: $.params.labels,
|
||||
},
|
||||
spec: {
|
||||
template: {
|
||||
metadata: $.parts.tfServingMetadata,
|
||||
spec: {
|
||||
containers: [
|
||||
$.parts.tfServingContainer,
|
||||
if $.util.toBool($.params.deployHttpProxy) then
|
||||
$.parts.httpProxyContainer,
|
||||
],
|
||||
volumes+: if $.params.modelStorageType == "nfs" then
|
||||
[{
|
||||
name: "nfs",
|
||||
persistentVolumeClaim: {
|
||||
claimName: $.params.nfsPVC,
|
||||
},
|
||||
}]
|
||||
else [],
|
||||
},
|
||||
},
|
||||
},
|
||||
}, // tfDeployment
|
||||
|
||||
tfService: {
|
||||
apiVersion: "v1",
|
||||
kind: "Service",
|
||||
metadata: {
|
||||
labels: $.params.labels,
|
||||
name: $.params.name,
|
||||
namespace: $.params.namespace,
|
||||
annotations: {
|
||||
"getambassador.io/config":
|
||||
std.join("\n", [
|
||||
"---",
|
||||
"apiVersion: ambassador/v0",
|
||||
"kind: Mapping",
|
||||
"name: tfserving-mapping-" + $.params.name + "-get",
|
||||
"prefix: /models/" + $.params.name + "/",
|
||||
"rewrite: /",
|
||||
"method: GET",
|
||||
"service: " + $.params.name + "." + $.params.namespace + ":8000",
|
||||
"---",
|
||||
"apiVersion: ambassador/v0",
|
||||
"kind: Mapping",
|
||||
"name: tfserving-mapping-" + $.params.name + "-post",
|
||||
"prefix: /models/" + $.params.name + "/",
|
||||
"rewrite: /model/" + $.params.name + ":predict",
|
||||
"method: POST",
|
||||
"service: " + $.params.name + "." + $.params.namespace + ":8000",
|
||||
]),
|
||||
}, //annotations
|
||||
},
|
||||
spec: {
|
||||
ports: [
|
||||
{
|
||||
name: "grpc-tf-serving",
|
||||
port: 9000,
|
||||
targetPort: 9000,
|
||||
},
|
||||
{
|
||||
name: "http-tf-serving-proxy",
|
||||
port: 8000,
|
||||
targetPort: 8000,
|
||||
},
|
||||
],
|
||||
selector: $.params.labels,
|
||||
type: $.params.serviceType,
|
||||
},
|
||||
}, // tfService
|
||||
|
||||
defaultRouteRule: {
|
||||
apiVersion: "config.istio.io/v1alpha2",
|
||||
kind: "RouteRule",
|
||||
metadata: {
|
||||
name: $.params.name + "-default",
|
||||
namespace: $.params.namespace,
|
||||
},
|
||||
spec: {
|
||||
destination: {
|
||||
name: $.params.name,
|
||||
},
|
||||
precedence: 0,
|
||||
route: [
|
||||
{
|
||||
labels: { version: $.params.version },
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
|
||||
}, // parts
|
||||
|
||||
// Parts specific to S3
|
||||
s3parts:: $.parts {
|
||||
s3Env:: [
|
||||
{ name: "AWS_ACCESS_KEY_ID", valueFrom: { secretKeyRef: { name: $.s3params.s3SecretName, key: $.s3params.s3SecretAccesskeyidKeyName } } },
|
||||
{ name: "AWS_SECRET_ACCESS_KEY", valueFrom: { secretKeyRef: { name: $.s3params.s3SecretName, key: $.s3params.s3SecretSecretaccesskeyKeyName } } },
|
||||
{ name: "AWS_REGION", value: $.s3params.s3AwsRegion },
|
||||
{ name: "S3_REGION", value: $.s3params.s3AwsRegion },
|
||||
{ name: "S3_USE_HTTPS", value: $.s3params.s3UseHttps },
|
||||
{ name: "S3_VERIFY_SSL", value: $.s3params.s3VerifySsl },
|
||||
{ name: "S3_ENDPOINT", value: $.s3params.s3Endpoint },
|
||||
],
|
||||
|
||||
tfServingContainer: $.parts.tfServingContainer {
|
||||
env+: $.s3parts.s3Env,
|
||||
},
|
||||
|
||||
tfDeployment: $.parts.tfDeployment {
|
||||
spec: +{
|
||||
template: +{
|
||||
metadata: $.parts.tfServingMetadata,
|
||||
spec: +{
|
||||
containers: [
|
||||
$.s3parts.tfServingContainer,
|
||||
if $.util.toBool($.params.deployHttpProxy) then
|
||||
$.parts.httpProxyContainer,
|
||||
],
|
||||
},
|
||||
},
|
||||
},
|
||||
}, // tfDeployment
|
||||
}, // s3parts
|
||||
|
||||
// Parts specific to GCP
|
||||
gcpParts:: $.parts {
|
||||
gcpEnv:: [
|
||||
if $.gcpParams.gcpCredentialSecretName != "" then
|
||||
{ name: "GOOGLE_APPLICATION_CREDENTIALS", value: "/secret/gcp-credentials/key.json" },
|
||||
],
|
||||
|
||||
tfServingContainer: $.parts.tfServingContainer {
|
||||
env+: $.gcpParts.gcpEnv,
|
||||
volumeMounts+: [
|
||||
if $.gcpParams.gcpCredentialSecretName != "" then
|
||||
{
|
||||
name: "gcp-credentials",
|
||||
mountPath: "/secret/gcp-credentials",
|
||||
},
|
||||
],
|
||||
},
|
||||
|
||||
tfDeployment: $.parts.tfDeployment {
|
||||
spec+: {
|
||||
template+: {
|
||||
metadata: $.parts.tfServingMetadata,
|
||||
spec+: {
|
||||
containers: [
|
||||
$.gcpParts.tfServingContainer,
|
||||
if $.util.toBool($.params.deployHttpProxy) then
|
||||
$.parts.httpProxyContainer,
|
||||
],
|
||||
volumes: [
|
||||
if $.gcpParams.gcpCredentialSecretName != "" then
|
||||
{
|
||||
name: "gcp-credentials",
|
||||
secret: {
|
||||
secretName: $.gcpParams.gcpCredentialSecretName,
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
},
|
||||
}, // tfDeployment
|
||||
}, // gcpParts
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
// Some useful routines.
|
||||
{
|
||||
// Convert a string to upper case.
|
||||
upper:: function(x) {
|
||||
local cp(c) = std.codepoint(c),
|
||||
local upLetter(c) = if cp(c) >= 97 && cp(c) < 123 then
|
||||
std.char(cp(c) - 32)
|
||||
else c,
|
||||
result:: std.join("", std.map(upLetter, std.stringChars(x))),
|
||||
}.result,
|
||||
|
||||
// Convert non-boolean types like string,number to a boolean.
|
||||
// This is primarily intended for dealing with parameters that should be booleans.
|
||||
toBool:: function(x) {
|
||||
result::
|
||||
if std.type(x) == "boolean" then
|
||||
x
|
||||
else if std.type(x) == "string" then
|
||||
$.upper(x) == "TRUE"
|
||||
else if std.type(x) == "number" then
|
||||
x != 0
|
||||
else
|
||||
false,
|
||||
}.result,
|
||||
}
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
tensor2tensor~=1.6.0
|
||||
oauth2client~=4.1.0
|
||||
|
|
@ -1,2 +0,0 @@
|
|||
from . import function_summarizer
|
||||
from . import docstring_lm
|
||||
Loading…
Reference in New Issue