Distributed swivel (#1441)

* Refactor to use distributed TF API; add driver script.

* Add explicit GPU flag so multiple cards can be used.

* Update docs to mention distributed.sh.

* Don't make use of HParams yet, since they're not available in TF1.0

swivel/README.md

@@ -42,6 +42,9 @@ This release includes the following programs.
 * `swivel.py` is a TensorFlow program that generates embeddings from the
   co-occurrence statistics.  It uses the files created by `prep.py` as input,
   and generates two text files as output: the row and column embeddings.
+* `distributed.sh` is a Bash script that is meant to act as a template for
+  launching "distributed" Swivel training; i.e., multiple processes that work in
+  parallel and communicate via a parameter server.
 * `text2bin.py` combines the row and column vectors generated by Swivel into a
   flat binary file that can be quickly loaded into memory to perform vector
   arithmetic.  This can also be used to convert embeddings from

swivel/distributed.sh

+#!/bin/bash
+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script launches a multi-process version of Swivel on a single machine.
+set -e
+
+# A comma-separated list of parameter server processes.
+PS_HOSTS="localhost:4000"
+
+# A comma-separated list of worker processes.
+WORKER_HOSTS="localhost:5000,localhost:5001,localhost:5002,localhost:5003"
+
+# Where the Swivel training data is located.  All processes must be able to read
+# from this directory, so it ought to be a network filesystem if you're running
+# on multiple servers.
+INPUT_BASE_PATH="${HOME}/tmp/swivel/in"
+
+# Where the output and working directory is located.
+OUTPUT_BASE_PATH="${HOME}/tmp/swivel/out"
+
+# Location of evaluation data, if you want to observe evaluation while training.
+EVAL_BASE_PATH="${HOME}/tmp/swivel/eval"
+
+ARGS="--ps_hosts ${PS_HOSTS}
+--worker_hosts ${WORKER_HOSTS}
+--input_base_path ${INPUT_BASE_PATH}
+--output_base_path ${OUTPUT_BASE_PATH}
+--eval_base_path ${EVAL_BASE_PATH}"
+
+# This configuration is for a two-GPU machine.  It starts four worker
+# processes, two for each GPU.
+python swivel.py --job_name ps --task_index 0 ${ARGS} >& /tmp/ps.0 &
+python swivel.py --job_name worker --task_index 0 --gpu_device 0 ${ARGS} >& /tmp/worker.0 &
+python swivel.py --job_name worker --task_index 1 --gpu_device 1 ${ARGS} >& /tmp/worker.1 &
+python swivel.py --job_name worker --task_index 2 --gpu_device 0 ${ARGS} >& /tmp/worker.2 &
+python swivel.py --job_name worker --task_index 3 --gpu_device 1 ${ARGS} >& /tmp/worker.3 &
+
+# Perhaps there is a more clever way to clean up the parameter server once all
+# the workers are done.
+wait %2 %3 %4 %5
+kill %1
+