Source code for mars.learn.contrib.tensorflow.run_script

# Copyright 1999-2020 Alibaba Group Holding Ltd.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import itertools
import os
import json
from collections import defaultdict

import numpy as np

from ....context import get_context, RunningMode
from .... import opcodes as OperandDef
from ....remote.run_script import RunScript
from ....serialize import BytesField, Int32Field, DictField, StringField
from ....utils import to_binary
from ..utils import pick_workers

class RunTensorFlow(RunScript):
    _op_type_ = OperandDef.RUN_TENSORFLOW

    _code = BytesField('code')
    _n_workers = Int32Field('n_workers')
    _n_ps = Int32Field('n_ps')
    _tf_config = DictField('tf_config')
    _port = Int32Field('port')
    # used for chunk op
    _tf_task_type = StringField('tf_task_type')
    _tf_task_index = Int32Field('tf_task_index')

    def __init__(self, n_workers=None, n_ps=None, tf_config=None, port=None,
                 tf_task_type=None, tf_task_index=None, gpu=None, **kw):
        super().__init__(mode='spawn', _n_workers=n_workers, _n_ps=n_ps,
                         _tf_config=tf_config, _port=port, _tf_task_type=tf_task_type,
                         _tf_task_index=tf_task_index, _gpu=gpu, **kw)

    def n_workers(self):
        return self._n_workers

    def n_ps(self):
        return self._n_ps or 0

    def n_roles(self):
        return self._n_workers + self._n_ps

    def tf_config(self):
        return self._tf_config

    def port(self):
        return self._port

    def tf_task_type(self):
        return self._tf_task_type

    def tf_task_index(self):
        return self._tf_task_index

    def __call__(self):
        return self.new_tileable(None)

    def tile(cls, op):
        ctx = get_context()

        port = op.port or 2221
        cluster_conf = {'worker': []}
        if op.n_ps > 0:
            cluster_conf['ps'] = []
        n_workers = op.n_workers

        out_chunks = []
        if ctx.running_mode != RunningMode.distributed:
            worker_addr = ''
            port_iter = itertools.count(port)

            for i in range(op.n_roles):
                chunk_op = op.copy().reset_key()
                addr = f'{worker_addr}:{next(port_iter)}'
                chunk_op._tf_task_type = tp = 'worker' if i < n_workers else 'ps'
                chunk_op._tf_task_index = idx = i if i < n_workers else i - n_workers
                chunk_op._tf_config = {'cluster': cluster_conf,
                                       'task': {'type': tp, 'index': idx}}
                out_chunks.append(chunk_op.new_chunk(None, index=(i,)))
            worker_addresses = ctx.get_worker_addresses()
            picked_workers = pick_workers(worker_addresses, op.n_roles)
            worker_to_port_iter = defaultdict(lambda: itertools.count(port))

            for i, worker in enumerate(picked_workers):
                worker_addr = worker.rsplit(':', 1)[0]
                chunk_op = op.copy().reset_key()
                addr = f'{worker_addr}:{next(worker_to_port_iter[worker_addr])}'
                # tell graph actor that the chunk should be executed on the exact worker
                chunk_op._expect_worker = worker
                tp = 'worker' if i < n_workers else 'ps'
                chunk_op._tf_task_type = tp
                idx = i if i < n_workers else i - n_workers
                chunk_op._tf_task_index = idx
                chunk_op._tf_config = {'cluster': cluster_conf,
                                       'task': {'type': tp, 'index': idx}}
                out_chunks.append(chunk_op.new_chunk(None, index=(i,)))

        new_op = op.copy()
        return new_op.new_tileables(op.inputs, chunks=out_chunks,
                                    nsplits=(tuple(np.nan for _ in range(len(out_chunks))),))

    def _build_envs(cls, ctx, op):
        envs = super()._build_envs(ctx, op)
        envs.update({'TF_CONFIG': json.dumps(op.tf_config)})
        return envs

    def execute(cls, ctx, op):
        if op.merge:
            return super().execute(ctx, op)

        assert ctx.get_local_address() == op.expect_worker

        super().execute(ctx, op)

        if op.tf_task_type == 'worker' and op.tf_task_index == 0:
            ctx[op.outputs[0].key] = {'status': 'ok'}
            ctx[op.outputs[0].key] = {}

[docs]def run_tensorflow_script(script, n_workers, n_ps=0, gpu=None, command_argv=None, retry_when_fail=False, session=None, run_kwargs=None, port=None): """ Run TensorFlow script in Mars cluster. :param script: script to run :type script: str or file-like object :param n_workers: number of TensorFlow workers :param n_ps: number of TensorFlow ps, optional :param gpu: run TensorFlow script on GPU :param command_argv: extra command args for script :param retry_when_fail: bool, default False. If True, retry when function failed. :param session: Mars session, if not provided, will use default one :param run_kwargs: extra kwargs for :param port: port of TensorFlow worker or ps, will automatically increase for the same worker :return: return {'status': 'ok'} if succeeded, or error raised """ if int(n_workers) <= 0: raise ValueError('n_workers should be at least 1') if int(n_ps) < 0: raise ValueError('n_ps should be at least 0') if hasattr(script, 'read'): code = else: with open(os.path.abspath(script), 'rb') as f: code = op = RunTensorFlow(code=to_binary(code), n_workers=int(n_workers), n_ps=int(n_ps), retry_when_fail=retry_when_fail, gpu=gpu, port=port, command_args=command_argv) return op().execute(session=session, **(run_kwargs or {})).fetch(session=session)