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Orca pytorch estimator wrapper (intel-analytics#2624)
* add estimator wrapper * fix testcase * fix style * delete testcase & modify example
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# | ||
# Copyright 2018 Analytics Zoo Authors. | ||
# | ||
# 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. | ||
# | ||
from zoo.orca.learn.pytorch.training_operator import TrainingOperator | ||
from zoo.orca.learn.pytorch.pytorch_horovod_estimator import PyTorchHorovodEstimator | ||
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class Estimator(object): | ||
def fit(self, data, epochs, **kwargs): | ||
pass | ||
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def predict(self, data, **kwargs): | ||
pass | ||
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def evaluate(self, data, **kwargs): | ||
pass | ||
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@staticmethod | ||
def from_model_creator(*, | ||
model_creator, | ||
optimizer_creator, | ||
loss_creator=None, | ||
scheduler_creator=None, | ||
training_operator_cls=TrainingOperator, | ||
initialization_hook=None, | ||
config=None, | ||
scheduler_step_freq="batch", | ||
backend="ray"): | ||
assert backend == "ray", "only ray backend is supported for now" | ||
return PyTorchHorovodEstimatorWrapper(model_creator=model_creator, | ||
optimizer_creator=optimizer_creator, | ||
loss_creator=loss_creator, | ||
scheduler_creator=scheduler_creator, | ||
training_operator_cls=training_operator_cls, | ||
initialization_hook=initialization_hook, | ||
config=config, | ||
scheduler_step_freq=scheduler_step_freq) | ||
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class PyTorchHorovodEstimatorWrapper(Estimator): | ||
def __init__(self, | ||
*, | ||
model_creator, | ||
optimizer_creator, | ||
loss_creator=None, | ||
scheduler_creator=None, | ||
training_operator_cls=TrainingOperator, | ||
initialization_hook=None, | ||
config=None, | ||
scheduler_step_freq="batch"): | ||
self.estimator = PyTorchHorovodEstimator(model_creator=model_creator, | ||
optimizer_creator=optimizer_creator, | ||
loss_creator=loss_creator, | ||
scheduler_creator=scheduler_creator, | ||
training_operator_cls=training_operator_cls, | ||
initialization_hook=initialization_hook, | ||
config=config, | ||
scheduler_step_freq=scheduler_step_freq) | ||
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def fit(self, data, epochs=1, num_steps=None, profile=False, reduce_results=True, info=None): | ||
""" | ||
:param data: (callable) a funtion that takes a config dict as input and return a data | ||
loader containing the training data. | ||
:param epochs: (int) Number of epochs to train the model | ||
:param num_steps: (int) Number of batches to compute update steps on. | ||
This corresponds also to the number of times `TrainingOperator.train_batch`` is called. | ||
:param profile: (bool) Returns time stats for the training procedure. | ||
:param reduce_results: (bool) Whether to average all metrics across all workers into one | ||
dict. If a metric is a non-numerical value (or nested dictionaries), one value will be | ||
randomly selected among the workers. If False, returns a list of dicts. | ||
:param info: (dict) Optional dictionary passed to the training operator for ``train_epoch`` | ||
and ``train_batch``. | ||
:return: (list) A list of stats whose length will be equal to ``epochs``. | ||
stats is a dictionary of metrics for training. | ||
You can provide custom metrics by passing in a custom | ||
``training_operator_cls``. If ``reduce_results=False``, | ||
this will return a list of metric dictionaries whose | ||
length will be equal to ``num_workers``. | ||
""" | ||
stats_list = list() | ||
for i in range(epochs): | ||
stats = self.estimator.train(data_creator=data, num_steps=num_steps, profile=profile, | ||
reduce_results=reduce_results, info=info) | ||
stats_list.append(stats) | ||
return stats_list | ||
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def predict(self, data, **kwargs): | ||
pass | ||
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def evaluate(self, data, num_steps=None, profile=False, info=None): | ||
""" | ||
:param data: (callable) a funtion that takes a config dict as input and return | ||
a data loader containing the validation data. | ||
:param num_steps: (int) Number of batches to compute update steps on. | ||
This corresponds also to the number of times ``TrainingOperator.validate_batch`` | ||
is called. | ||
:param profile: (bool) Returns time stats for the evaluation procedure. | ||
:param info: (dict) Optional dictionary passed to the training operator for `validate` | ||
and `validate_batch`. | ||
:return: A dictionary of metrics for validation. | ||
You can provide custom metrics by passing in a custom ``training_operator_cls``. | ||
""" | ||
return self.estimator.validate(data_creator=data, num_steps=num_steps, profile=profile, | ||
info=info) |