repo stringlengths 7 54 | path stringlengths 4 223 | func_name stringlengths 1 134 | original_string stringlengths 75 104k | language stringclasses 1
value | code stringlengths 75 104k | code_tokens listlengths 20 28.4k | docstring stringlengths 1 46.3k | docstring_tokens listlengths 1 1.66k | sha stringlengths 40 40 | url stringlengths 87 315 | partition stringclasses 1
value | summary stringlengths 4 350 | obf_code stringlengths 7.85k 764k |
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alpacahq/pylivetrader | pylivetrader/misc/parallel_utils.py | parallelize | def parallelize(mapfunc, workers=None):
'''
Parallelize the mapfunc with multithreading. mapfunc calls will be
partitioned by the provided list of arguments. Each item in the list
will represent one call's arguments. They can be tuples if the function
takes multiple arguments, but one-tupling is not... | python | def parallelize(mapfunc, workers=None):
'''
Parallelize the mapfunc with multithreading. mapfunc calls will be
partitioned by the provided list of arguments. Each item in the list
will represent one call's arguments. They can be tuples if the function
takes multiple arguments, but one-tupling is not... | [
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alpacahq/pylivetrader | pylivetrader/data/data_portal.py | DataPortal.get_adjusted_value | def get_adjusted_value(
self,
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dt,
perspective_dt,
data_frequency):
'''
TODO:
for external data (fetch_csv) support, need to update logic here.
'''
return self.backend.get_spot_value(
... | python | def get_adjusted_value(
self,
assets,
field,
dt,
perspective_dt,
data_frequency):
'''
TODO:
for external data (fetch_csv) support, need to update logic here.
'''
return self.backend.get_spot_value(
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mlperf/training | data_generation/fractal_graph_expansions/util.py | load_df_from_file | def load_df_from_file(file_path, sep=",", header=0):
"""Wrapper around pandas' read_csv."""
with tf.gfile.Open(file_path) as infile:
df = pd.read_csv(infile, sep=sep, header=header)
return df | python | def load_df_from_file(file_path, sep=",", header=0):
"""Wrapper around pandas' read_csv."""
with tf.gfile.Open(file_path) as infile:
df = pd.read_csv(infile, sep=sep, header=header)
return df | [
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mlperf/training | data_generation/fractal_graph_expansions/util.py | serialize_to_file | def serialize_to_file(obj, file_name, append=False):
"""Pickle obj to file_name."""
logging.info("Serializing to file %s.", file_name)
with tf.gfile.Open(file_name, "a+" if append else "wb") as output_file:
pickle.dump(obj, output_file)
logging.info("Done serializing to file %s.", file_name) | python | def serialize_to_file(obj, file_name, append=False):
"""Pickle obj to file_name."""
logging.info("Serializing to file %s.", file_name)
with tf.gfile.Open(file_name, "a+" if append else "wb") as output_file:
pickle.dump(obj, output_file)
logging.info("Done serializing to file %s.", file_name) | [
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mlperf/training | data_generation/fractal_graph_expansions/util.py | savez_two_column | def savez_two_column(matrix, row_offset, file_name, append=False):
"""Savez_compressed obj to file_name."""
logging.info("Saving obj to file in two column .npz format %s.", file_name)
tc = []
for u, items in enumerate(matrix):
user = row_offset + u
for item in items:
tc.append([user, item])
n... | python | def savez_two_column(matrix, row_offset, file_name, append=False):
"""Savez_compressed obj to file_name."""
logging.info("Saving obj to file in two column .npz format %s.", file_name)
tc = []
for u, items in enumerate(matrix):
user = row_offset + u
for item in items:
tc.append([user, item])
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mlperf/training | data_generation/fractal_graph_expansions/util.py | sorted_product_set | def sorted_product_set(array_a, array_b):
"""Compute the product set of array_a and array_b and sort it."""
return np.sort(
np.concatenate(
[array_a[i] * array_b for i in xrange(len(array_a))], axis=0)
)[::-1] | python | def sorted_product_set(array_a, array_b):
"""Compute the product set of array_a and array_b and sort it."""
return np.sort(
np.concatenate(
[array_a[i] * array_b for i in xrange(len(array_a))], axis=0)
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/matcher.py | Matcher.set_low_quality_matches_ | def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
"""
Produce additional matches for predictions that have only low-quality matches.
Specifically, for each ground-truth find the set of predictions that have
maximum overlap with it (including ties); for each ... | python | def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
"""
Produce additional matches for predictions that have only low-quality matches.
Specifically, for each ground-truth find the set of predictions that have
maximum overlap with it (including ties); for each ... | [
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py | eval_detection_voc | def eval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_07_metric=False):
"""Evaluate on voc dataset.
Args:
pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields.
gt_boxlists(list[BoxList]): ground truth boxlist, has labels field.
iou_thresh: iou thresh
... | python | def eval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_07_metric=False):
"""Evaluate on voc dataset.
Args:
pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields.
gt_boxlists(list[BoxList]): ground truth boxlist, has labels field.
iou_thresh: iou thresh
... | [
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py | calc_detection_voc_prec_rec | def calc_detection_voc_prec_rec(gt_boxlists, pred_boxlists, iou_thresh=0.5):
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This function calculates precision and recall of
predicted bounding boxes obtained from a dataset which has :math:`N`
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"""Calculate precision and recall based on evaluation code of PASCAL VOC.
This function calculates precision and recall of
predicted bounding boxes obtained from a dataset which has :math:`N`
images.
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py | calc_detection_voc_ap | def calc_detection_voc_ap(prec, rec, use_07_metric=False):
"""Calculate average precisions based on evaluation code of PASCAL VOC.
This function calculates average precisions
from given precisions and recalls.
The code is based on the evaluation code used in PASCAL VOC Challenge.
Args:
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This function calculates average precisions
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/inference.py | RPNPostProcessor.add_gt_proposals | def add_gt_proposals(self, proposals, targets):
"""
Arguments:
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targets: list[BoxList]
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Arguments:
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targets: list[BoxList]
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/rpn/inference.py | RPNPostProcessor.forward_for_single_feature_map | def forward_for_single_feature_map(self, anchors, objectness, box_regression):
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box_regression: tensor of size N, A * 4, H, W
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mlperf/training | data_generation/fractal_graph_expansions/random_matrix_ops.py | shuffle_sparse_coo_matrix | def shuffle_sparse_coo_matrix(sparse_matrix, dropout_rate=0.0,
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mlperf/training | reinforcement/tensorflow/minigo/selfplay.py | play | def play(network):
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mlperf/training | reinforcement/tensorflow/minigo/selfplay.py | run_game | def run_game(load_file, selfplay_dir=None, holdout_dir=None,
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if sgf_dir is not None:
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uti... | python | def run_game(load_file, selfplay_dir=None, holdout_dir=None,
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mlperf/training | reinforcement/tensorflow/minigo/selfplay.py | main | def main(argv):
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holdout_pct=FLAGS.holdout_pct,
sgf_di... | python | def main(argv):
"""Entry point for running one selfplay game."""
del argv # Unused
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mlperf/training | data_generation/fractal_graph_expansions/graph_reduction.py | resize_matrix | def resize_matrix(usv, num_rows, num_cols):
"""Apply algorith 2 in https://arxiv.org/pdf/1901.08910.pdf.
Args:
usv: matrix to reduce given in SVD form with the spectrum s in
increasing order.
num_rows: number of rows in the output matrix.
num_cols: number of columns in the output matrix.
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"""Apply algorith 2 in https://arxiv.org/pdf/1901.08910.pdf.
Args:
usv: matrix to reduce given in SVD form with the spectrum s in
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num_rows: number of rows in the output matrix.
num_cols: number of columns in the output matrix.
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mlperf/training | data_generation/fractal_graph_expansions/graph_reduction.py | normalize_matrix | def normalize_matrix(matrix):
"""Fold all values of the matrix into [0, 1]."""
abs_matrix = np.abs(matrix.copy())
return abs_matrix / abs_matrix.max() | python | def normalize_matrix(matrix):
"""Fold all values of the matrix into [0, 1]."""
abs_matrix = np.abs(matrix.copy())
return abs_matrix / abs_matrix.max() | [
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mlperf/training | translation/tensorflow/transformer/translate.py | _get_sorted_inputs | def _get_sorted_inputs(filename):
"""Read and sort lines from the file sorted by decreasing length.
Args:
filename: String name of file to read inputs from.
Returns:
Sorted list of inputs, and dictionary mapping original index->sorted index
of each element.
"""
with tf.gfile.Open(filename) as f:
... | python | def _get_sorted_inputs(filename):
"""Read and sort lines from the file sorted by decreasing length.
Args:
filename: String name of file to read inputs from.
Returns:
Sorted list of inputs, and dictionary mapping original index->sorted index
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"""
with tf.gfile.Open(filename) as f:
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mlperf/training | translation/tensorflow/transformer/translate.py | _trim_and_decode | def _trim_and_decode(ids, subtokenizer):
"""Trim EOS and PAD tokens from ids, and decode to return a string."""
try:
index = list(ids).index(tokenizer.EOS_ID)
return subtokenizer.decode(ids[:index])
except ValueError: # No EOS found in sequence
return subtokenizer.decode(ids) | python | def _trim_and_decode(ids, subtokenizer):
"""Trim EOS and PAD tokens from ids, and decode to return a string."""
try:
index = list(ids).index(tokenizer.EOS_ID)
return subtokenizer.decode(ids[:index])
except ValueError: # No EOS found in sequence
return subtokenizer.decode(ids) | [
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mlperf/training | translation/tensorflow/transformer/translate.py | translate_file | def translate_file(
estimator, subtokenizer, input_file, output_file=None,
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"""Translate lines in file, and save to output file if specified.
Args:
estimator: tf.Estimator used to generate the translations.
subtokenizer: Subtokenizer object for encoding and decoding sou... | python | def translate_file(
estimator, subtokenizer, input_file, output_file=None,
print_all_translations=True):
"""Translate lines in file, and save to output file if specified.
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estimator: tf.Estimator used to generate the translations.
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mlperf/training | translation/tensorflow/transformer/translate.py | translate_text | def translate_text(estimator, subtokenizer, txt):
"""Translate a single string."""
encoded_txt = _encode_and_add_eos(txt, subtokenizer)
def input_fn():
ds = tf.data.Dataset.from_tensors(encoded_txt)
ds = ds.batch(_DECODE_BATCH_SIZE)
return ds
predictions = estimator.predict(input_fn)
translation... | python | def translate_text(estimator, subtokenizer, txt):
"""Translate a single string."""
encoded_txt = _encode_and_add_eos(txt, subtokenizer)
def input_fn():
ds = tf.data.Dataset.from_tensors(encoded_txt)
ds = ds.batch(_DECODE_BATCH_SIZE)
return ds
predictions = estimator.predict(input_fn)
translation... | [
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mlperf/training | rnn_translator/pytorch/seq2seq/data/tokenizer.py | Tokenizer.pad_vocabulary | def pad_vocabulary(self, vocab, pad):
"""
Pads vocabulary to a multiple of 'pad' tokens.
:param vocab: list with vocabulary
:param pad: integer
"""
vocab_size = len(vocab)
padded_vocab_size = (vocab_size + pad - 1) // pad * pad
for i in range(0, padded_vo... | python | def pad_vocabulary(self, vocab, pad):
"""
Pads vocabulary to a multiple of 'pad' tokens.
:param vocab: list with vocabulary
:param pad: integer
"""
vocab_size = len(vocab)
padded_vocab_size = (vocab_size + pad - 1) // pad * pad
for i in range(0, padded_vo... | [
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mlperf/training | rnn_translator/pytorch/seq2seq/data/tokenizer.py | Tokenizer.segment | def segment(self, line):
"""
Tokenizes single sentence and adds special BOS and EOS tokens.
:param line: sentence
returns: list representing tokenized sentence
"""
line = line.strip().split()
entry = [self.tok2idx[i] for i in line]
entry = [config.BOS] +... | python | def segment(self, line):
"""
Tokenizes single sentence and adds special BOS and EOS tokens.
:param line: sentence
returns: list representing tokenized sentence
"""
line = line.strip().split()
entry = [self.tok2idx[i] for i in line]
entry = [config.BOS] +... | [
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mlperf/training | rnn_translator/pytorch/seq2seq/data/tokenizer.py | Tokenizer.detokenize | def detokenize(self, inputs, delim=' '):
"""
Detokenizes single sentence and removes token separator characters.
:param inputs: sequence of tokens
:param delim: tokenization delimiter
returns: string representing detokenized sentence
"""
detok = delim.join([self... | python | def detokenize(self, inputs, delim=' '):
"""
Detokenizes single sentence and removes token separator characters.
:param inputs: sequence of tokens
:param delim: tokenization delimiter
returns: string representing detokenized sentence
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detok = delim.join([self... | [
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/backbone/fpn.py | FPN.forward | def forward(self, x):
"""
Arguments:
x (list[Tensor]): feature maps for each feature level.
Returns:
results (tuple[Tensor]): feature maps after FPN layers.
They are ordered from highest resolution first.
"""
last_inner = getattr(self, self... | python | def forward(self, x):
"""
Arguments:
x (list[Tensor]): feature maps for each feature level.
Returns:
results (tuple[Tensor]): feature maps after FPN layers.
They are ordered from highest resolution first.
"""
last_inner = getattr(self, self... | [
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mlperf/training | image_classification/tensorflow/official/utils/logs/benchmark_uploader.py | BigQueryUploader.upload_benchmark_run | def upload_benchmark_run(self, dataset_name, table_name, run_id):
"""Upload benchmark run information to Bigquery.
Args:
dataset_name: string, the name of bigquery dataset where the data will be
uploaded.
table_name: string, the name of bigquery table under the dataset where
the dat... | python | def upload_benchmark_run(self, dataset_name, table_name, run_id):
"""Upload benchmark run information to Bigquery.
Args:
dataset_name: string, the name of bigquery dataset where the data will be
uploaded.
table_name: string, the name of bigquery table under the dataset where
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mlperf/training | image_classification/tensorflow/official/utils/logs/benchmark_uploader.py | BigQueryUploader.upload_metric | def upload_metric(self, dataset_name, table_name, run_id):
"""Upload metric information to Bigquery.
Args:
dataset_name: string, the name of bigquery dataset where the data will be
uploaded.
table_name: string, the name of bigquery table under the dataset where
the metric data will ... | python | def upload_metric(self, dataset_name, table_name, run_id):
"""Upload metric information to Bigquery.
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dataset_name: string, the name of bigquery dataset where the data will be
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table_name: string, the name of bigquery table under the dataset where
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mlperf/training | translation/tensorflow/transformer/compute_bleu.py | bleu_wrapper | def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
"""Compute BLEU for two files (reference and hypothesis translation)."""
ref_lines = tf.gfile.Open(ref_filename).read().strip().splitlines()
hyp_lines = tf.gfile.Open(hyp_filename).read().strip().splitlines()
if len(ref_lines) != len(hyp_lines... | python | def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
"""Compute BLEU for two files (reference and hypothesis translation)."""
ref_lines = tf.gfile.Open(ref_filename).read().strip().splitlines()
hyp_lines = tf.gfile.Open(hyp_filename).read().strip().splitlines()
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mlperf/training | reinforcement/tensorflow/minigo/evaluate.py | play_match | def play_match(black_model, white_model, games, sgf_dir):
"""Plays matches between two neural nets.
Args:
black_model: Path to the model for black player
white_model: Path to the model for white player
"""
with utils.logged_timer("Loading weights"):
black_net = dual_net.DualNetw... | python | def play_match(black_model, white_model, games, sgf_dir):
"""Plays matches between two neural nets.
Args:
black_model: Path to the model for black player
white_model: Path to the model for white player
"""
with utils.logged_timer("Loading weights"):
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mlperf/training | reinforcement/tensorflow/minigo/evaluate.py | main | def main(argv):
"""Play matches between two neural nets."""
_, black_model, white_model = argv
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play_match(black_model, white_model, FLAGS.num_evaluation_games, FLAGS.eval_sgf_dir) | python | def main(argv):
"""Play matches between two neural nets."""
_, black_model, white_model = argv
utils.ensure_dir_exists(FLAGS.eval_sgf_dir)
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mlperf/training | rnn_translator/pytorch/scripts/filter_dataset.py | main | def main():
"""
Discards all pairs of sentences which can't be decoded by latin-1 encoder.
It aims to filter out sentences with rare unicode glyphs and pairs which
are most likely not valid English-German sentences.
Examples of discarded sentences:
✿★★★Hommage au king de la pop ★★★✿ ✿★★★Q... | python | def main():
"""
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Examples of discarded sentences:
✿★★★Hommage au king de la pop ★★★✿ ✿★★★Q... | [
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/box_coder.py | BoxCoder.encode | def encode(self, reference_boxes, proposals):
"""
Encode a set of proposals with respect to some
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Arguments:
reference_boxes (Tensor): reference boxes
proposals (Tensor): boxes to be encoded
"""
TO_REMOVE = 1 # TODO remove
... | python | def encode(self, reference_boxes, proposals):
"""
Encode a set of proposals with respect to some
reference boxes
Arguments:
reference_boxes (Tensor): reference boxes
proposals (Tensor): boxes to be encoded
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TO_REMOVE = 1 # TODO remove
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/box_coder.py | BoxCoder.decode | def decode(self, rel_codes, boxes):
"""
From a set of original boxes and encoded relative box offsets,
get the decoded boxes.
Arguments:
rel_codes (Tensor): encoded boxes
boxes (Tensor): reference boxes.
"""
boxes = boxes.to(rel_codes.dtype)
... | python | def decode(self, rel_codes, boxes):
"""
From a set of original boxes and encoded relative box offsets,
get the decoded boxes.
Arguments:
rel_codes (Tensor): encoded boxes
boxes (Tensor): reference boxes.
"""
boxes = boxes.to(rel_codes.dtype)
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mlperf/training | reinforcement/tensorflow/minigo/cluster/eval_server/launch_eval.py | launch_eval_job | def launch_eval_job(tag, m1_path, m2_path, job_name, completions):
"""Launches an evaluator job.
tag: name for this eval job (used as top level folder name)
m1_path, m2_path: full gs:// paths to the .pb files to match up
job_name: string, appended to the container, used to differentiate the job
name... | python | def launch_eval_job(tag, m1_path, m2_path, job_name, completions):
"""Launches an evaluator job.
tag: name for this eval job (used as top level folder name)
m1_path, m2_path: full gs:// paths to the .pb files to match up
job_name: string, appended to the container, used to differentiate the job
name... | [
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | launch_eval_job | def launch_eval_job(m1_path, m2_path, job_name,
bucket_name, completions=5, flags_path=None):
"""Launches an evaluator job.
m1_path, m2_path: full gs:// paths to the .pb files to match up
job_name: string, appended to the container, used to differentiate the job
names (e.g. 'minigo-cc-evaluator-... | python | def launch_eval_job(m1_path, m2_path, job_name,
bucket_name, completions=5, flags_path=None):
"""Launches an evaluator job.
m1_path, m2_path: full gs:// paths to the .pb files to match up
job_name: string, appended to the container, used to differentiate the job
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | same_run_eval | def same_run_eval(black_num=0, white_num=0, completions=4):
"""Shorthand to spawn a job matching up two models from the same run,
identified by their model number """
if black_num <= 0 or white_num <= 0:
print("Need real model numbers")
return
b = fsdb.get_model(black_num)
w = fsdb.... | python | def same_run_eval(black_num=0, white_num=0, completions=4):
"""Shorthand to spawn a job matching up two models from the same run,
identified by their model number """
if black_num <= 0 or white_num <= 0:
print("Need real model numbers")
return
b = fsdb.get_model(black_num)
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | _append_pairs | def _append_pairs(new_pairs):
""" Load the pairlist, add new stuff, save it out """
desired_pairs = restore_pairs() or []
desired_pairs += new_pairs
print("Adding {} new pairs, queue has {} pairs".format(len(new_pairs), len(desired_pairs)))
save_pairs(desired_pairs) | python | def _append_pairs(new_pairs):
""" Load the pairlist, add new stuff, save it out """
desired_pairs = restore_pairs() or []
desired_pairs += new_pairs
print("Adding {} new pairs, queue has {} pairs".format(len(new_pairs), len(desired_pairs)))
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | add_top_pairs | def add_top_pairs(dry_run=False, pair_now=False):
""" Pairs up the top twenty models against each other.
#1 plays 2,3,4,5, #2 plays 3,4,5,6 etc. for a total of 15*4 matches.
Default behavior is to add the pairs to the working pairlist.
`pair_now` will immediately create the pairings on the cluster.
... | python | def add_top_pairs(dry_run=False, pair_now=False):
""" Pairs up the top twenty models against each other.
#1 plays 2,3,4,5, #2 plays 3,4,5,6 etc. for a total of 15*4 matches.
Default behavior is to add the pairs to the working pairlist.
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | zoo_loop | def zoo_loop(sgf_dir=None, max_jobs=40):
"""Manages creating and cleaning up match jobs.
- Load whatever pairs didn't get queued last time, and whatever our most
recently seen model was.
- Loop and...
- If a new model is detected, create and append new pairs to the list
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"""Manages creating and cleaning up match jobs.
- Load whatever pairs didn't get queued last time, and whatever our most
recently seen model was.
- Loop and...
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mlperf/training | reinforcement/tensorflow/minigo/cluster/evaluator/launch_eval.py | cleanup | def cleanup(api_instance=None):
""" Remove completed jobs from the cluster """
api = api_instance or get_api()
r = api.list_job_for_all_namespaces()
delete_opts = kubernetes.client.V1DeleteOptions(
propagation_policy="Background")
for job in r.items:
if job.status.succeeded == jo... | python | def cleanup(api_instance=None):
""" Remove completed jobs from the cluster """
api = api_instance or get_api()
r = api.list_job_for_all_namespaces()
delete_opts = kubernetes.client.V1DeleteOptions(
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mlperf/training | object_detection/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py | convert_coco_stuff_mat | def convert_coco_stuff_mat(data_dir, out_dir):
"""Convert to png and save json with path. This currently only contains
the segmentation labels for objects+stuff in cocostuff - if we need to
combine with other labels from original COCO that will be a TODO."""
sets = ['train', 'val']
categories = []
... | python | def convert_coco_stuff_mat(data_dir, out_dir):
"""Convert to png and save json with path. This currently only contains
the segmentation labels for objects+stuff in cocostuff - if we need to
combine with other labels from original COCO that will be a TODO."""
sets = ['train', 'val']
categories = []
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mlperf/training | object_detection/pytorch/tools/cityscapes/convert_cityscapes_to_coco.py | convert_cityscapes_instance_only | def convert_cityscapes_instance_only(
data_dir, out_dir):
"""Convert from cityscapes format to COCO instance seg format - polygons"""
sets = [
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'gtFine_train',
'gtFine_test',
# 'gtCoarse_train',
# 'gtCoarse_val',
# 'gtCoarse_train_extra'
... | python | def convert_cityscapes_instance_only(
data_dir, out_dir):
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sets = [
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mlperf/training | reinforcement/tensorflow/minigo/cluster/ringmaster/setup_ringmaster.py | get_mg_path | def get_mg_path(model_run, model_num):
"""
model_run = integer, e.g. 15, 16, corresponding to the v-number
model_num = integer, e.g 939, for the model number in that run
"""
fsdb.switch_base("minigo-pub/v{:d}-19x19".format(model_run))
model = fsdb.get_model(model_num)
return os.path.join(fsd... | python | def get_mg_path(model_run, model_num):
"""
model_run = integer, e.g. 15, 16, corresponding to the v-number
model_num = integer, e.g 939, for the model number in that run
"""
fsdb.switch_base("minigo-pub/v{:d}-19x19".format(model_run))
model = fsdb.get_model(model_num)
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | make_single_array | def make_single_array(ds, batch_size=8*1024):
"""Create a single numpy array from a dataset.
The dataset must have only one dimension, that is,
the length of its `output_shapes` and `output_types`
is 1, and its output shape must be `[]`, that is,
every tensor in the dataset must be a scalar.
A... | python | def make_single_array(ds, batch_size=8*1024):
"""Create a single numpy array from a dataset.
The dataset must have only one dimension, that is,
the length of its `output_shapes` and `output_types`
is 1, and its output shape must be `[]`, that is,
every tensor in the dataset must be a scalar.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | _histogram_move_keys_by_game | def _histogram_move_keys_by_game(sess, ds, batch_size=8*1024):
"""Given dataset of key names, return histogram of moves/game.
Move counts are written by the game players, so
this is mostly useful for repair or backfill.
Args:
sess: TF session
ds: TF dataset containing game move keys.
... | python | def _histogram_move_keys_by_game(sess, ds, batch_size=8*1024):
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | _game_keys_as_array | def _game_keys_as_array(ds):
"""Turn keys of a Bigtable dataset into an array.
Take g_GGG_m_MMM and create GGG.MMM numbers.
Valuable when visualizing the distribution of a given dataset in
the game keyspace.
"""
ds = ds.map(lambda row_key, cell: row_key)
# want 'g_0000001234_m_133' is '000... | python | def _game_keys_as_array(ds):
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Take g_GGG_m_MMM and create GGG.MMM numbers.
Valuable when visualizing the distribution of a given dataset in
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | _delete_rows | def _delete_rows(args):
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The args are (BigtableSpec, row_keys), but are passed
as a single argument in order to work with
multiprocessing.Pool.map. This is also the reason why this is a
top-level function instead of a method.
"""
btspec... | python | def _delete_rows(args):
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The args are (BigtableSpec, row_keys), but are passed
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | set_fresh_watermark | def set_fresh_watermark(game_queue, count_from, window_size,
fresh_fraction=0.05, minimum_fresh=20000):
"""Sets the metadata cell used to block until some quantity of games have been played.
This sets the 'freshness mark' on the `game_queue`, used to block training
until enough new ... | python | def set_fresh_watermark(game_queue, count_from, window_size,
fresh_fraction=0.05, minimum_fresh=20000):
"""Sets the metadata cell used to block until some quantity of games have been played.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | get_unparsed_moves_from_last_n_games | def get_unparsed_moves_from_last_n_games(games, games_nr, n,
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column_family=TFEXAMPLE,
column='example',
... | python | def get_unparsed_moves_from_last_n_games(games, games_nr, n,
moves=2**21,
shuffle=True,
column_family=TFEXAMPLE,
column='example',
... | [
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | count_elements_in_dataset | def count_elements_in_dataset(ds, batch_size=1*1024, parallel_batch=8):
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Debugging function. The elements in a dataset cannot be counted
without enumerating all of them. By counting in batch and in
parallel, this method allows rapid traversal ... | python | def count_elements_in_dataset(ds, batch_size=1*1024, parallel_batch=8):
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.create | def create(self):
"""Create the table underlying the queue.
Create the 'metadata' and 'tfexample' column families
and their properties.
"""
if self.bt_table.exists():
utils.dbg('Table already exists')
return
max_versions_rule = bigtable_column_fa... | python | def create(self):
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Create the 'metadata' and 'tfexample' column families
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.games_by_time | def games_by_time(self, start_game, end_game):
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Returns [(time, game_number), ...]
The time will be a `datetime.datetime` and the game
number is the integer used as the basis of the row ID.
Note that when a cluster of ... | python | def games_by_time(self, start_game, end_game):
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.delete_row_range | def delete_row_range(self, format_str, start_game, end_game):
"""Delete rows related to the given game range.
Args:
format_str: a string to `.format()` by the game numbers
in order to create the row prefixes.
start_game: the starting game number of the deletion.
... | python | def delete_row_range(self, format_str, start_game, end_game):
"""Delete rows related to the given game range.
Args:
format_str: a string to `.format()` by the game numbers
in order to create the row prefixes.
start_game: the starting game number of the deletion.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.trim_games_since | def trim_games_since(self, t, max_games=500000):
"""Trim off the games since the given time.
Search back no more than max_games for this time point, locate
the game there, and remove all games since that game,
resetting the latest game counter.
If `t` is a `datetime.timedelta`,... | python | def trim_games_since(self, t, max_games=500000):
"""Trim off the games since the given time.
Search back no more than max_games for this time point, locate
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resetting the latest game counter.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.bleakest_moves | def bleakest_moves(self, start_game, end_game):
"""Given a range of games, return the bleakest moves.
Returns a list of (game, move, q) sorted by q.
"""
bleak = b'bleakest_q'
rows = self.bt_table.read_rows(
ROW_PREFIX.format(start_game),
ROW_PREFIX.format... | python | def bleakest_moves(self, start_game, end_game):
"""Given a range of games, return the bleakest moves.
Returns a list of (game, move, q) sorted by q.
"""
bleak = b'bleakest_q'
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.require_fresh_games | def require_fresh_games(self, number_fresh):
"""Require a given number of fresh games to be played.
Args:
number_fresh: integer, number of new fresh games needed
Increments the cell `table_state=metadata:wait_for_game_number`
by the given number of games. This will cause
... | python | def require_fresh_games(self, number_fresh):
"""Require a given number of fresh games to be played.
Args:
number_fresh: integer, number of new fresh games needed
Increments the cell `table_state=metadata:wait_for_game_number`
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... | [
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.wait_for_fresh_games | def wait_for_fresh_games(self, poll_interval=15.0):
"""Block caller until required new games have been played.
Args:
poll_interval: number of seconds to wait between checks
If the cell `table_state=metadata:wait_for_game_number` exists,
then block the caller, checking every ... | python | def wait_for_fresh_games(self, poll_interval=15.0):
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Args:
poll_interval: number of seconds to wait between checks
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.read_wait_cell | def read_wait_cell(self):
"""Read the value of the cell holding the 'wait' value,
Returns the int value of whatever it has, or None if the cell doesn't
exist.
"""
table_state = self.bt_table.read_row(
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filter_=bigtable_row_filters.ColumnRange... | python | def read_wait_cell(self):
"""Read the value of the cell holding the 'wait' value,
Returns the int value of whatever it has, or None if the cell doesn't
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"""
table_state = self.bt_table.read_row(
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.count_moves_in_game_range | def count_moves_in_game_range(self, game_begin, game_end):
"""Count the total moves in a game range.
Args:
game_begin: integer, starting game
game_end: integer, ending game
Uses the `ct_` keyspace for rapid move summary.
"""
rows = self.bt_table.read_rows(... | python | def count_moves_in_game_range(self, game_begin, game_end):
"""Count the total moves in a game range.
Args:
game_begin: integer, starting game
game_end: integer, ending game
Uses the `ct_` keyspace for rapid move summary.
"""
rows = self.bt_table.read_rows(... | [
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.moves_from_games | def moves_from_games(self, start_game, end_game, moves, shuffle,
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Args:
n: an integer indicating how many past games should be sourced.
moves: an integer indicating how man... | python | def moves_from_games(self, start_game, end_game, moves, shuffle,
column_family, column):
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n: an integer indicating how many past games should be sourced.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.moves_from_last_n_games | def moves_from_last_n_games(self, n, moves, shuffle,
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Args:
n: number of games at the end of this GameQueue to source.
moves: number of moves to be sampled from... | python | def moves_from_last_n_games(self, n, moves, shuffle,
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue._write_move_counts | def _write_move_counts(self, sess, h):
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not be needed except for backfill or repair.
Args:
sess: TF session to use for doing a Bigtable write.
tf_t... | python | def _write_move_counts(self, sess, h):
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sess: TF session to use for doing a Bigtable write.
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mlperf/training | reinforcement/tensorflow/minigo/bigtable_input.py | GameQueue.update_move_counts | def update_move_counts(self, start_game, end_game, interval=1000):
"""Used to update the move_count cell for older games.
Should not be needed except for backfill or repair.
move_count cells will be updated in both g_<game_id>_m_000 rows
and ct_<game_id>_<move_count> rows.
"""
... | python | def update_move_counts(self, start_game, end_game, interval=1000):
"""Used to update the move_count cell for older games.
Should not be needed except for backfill or repair.
move_count cells will be updated in both g_<game_id>_m_000 rows
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | get_filenames | def get_filenames(is_training, data_dir):
"""Return filenames for dataset."""
if is_training:
return [
os.path.join(data_dir, 'train-%05d-of-01024' % i)
for i in range(_NUM_TRAIN_FILES)]
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return [
os.path.join(data_dir, 'validation-%05d-of-00128' % i)
for i in range(12... | python | def get_filenames(is_training, data_dir):
"""Return filenames for dataset."""
if is_training:
return [
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | _parse_example_proto | def _parse_example_proto(example_serialized):
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The output of the build_image_data.py image preprocessing script is a dataset
containing serialized Example protocol buffers. Each Example proto contains
the following fields (values are included... | python | def _parse_example_proto(example_serialized):
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | parse_record | def parse_record(raw_record, is_training, dtype):
"""Parses a record containing a training example of an image.
The input record is parsed into a label and image, and the image is passed
through preprocessing steps (cropping, flipping, and so on).
Args:
raw_record: scalar Tensor tf.string containing a ser... | python | def parse_record(raw_record, is_training, dtype):
"""Parses a record containing a training example of an image.
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | input_fn | def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None,
dtype=tf.float32):
"""Input function which provides batches for train or eval.
Args:
is_training: A boolean denoting whether the input is for training.
data_dir: The directory containing the input data.
batch_size... | python | def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None,
dtype=tf.float32):
"""Input function which provides batches for train or eval.
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is_training: A boolean denoting whether the input is for training.
data_dir: The directory containing the input data.
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | _get_block_sizes | def _get_block_sizes(resnet_size):
"""Retrieve the size of each block_layer in the ResNet model.
The number of block layers used for the Resnet model varies according
to the size of the model. This helper grabs the layer set we want, throwing
an error if a non-standard size has been selected.
Args:
resn... | python | def _get_block_sizes(resnet_size):
"""Retrieve the size of each block_layer in the ResNet model.
The number of block layers used for the Resnet model varies according
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resn... | [
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mlperf/training | image_classification/tensorflow/official/resnet/imagenet_main.py | imagenet_model_fn | def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
# Warmup and higher lr may not be valid for fine tuning with small batches
# and smaller numbers of training images.
if params['fine_tune']:
base_lr = .1
else:
base_lr = .128
learnin... | python | def imagenet_model_fn(features, labels, mode, params):
"""Our model_fn for ResNet to be used with our Estimator."""
# Warmup and higher lr may not be valid for fine tuning with small batches
# and smaller numbers of training images.
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base_lr = .1
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | gnmt_print | def gnmt_print(*args, **kwargs):
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Wrapper for MLPerf compliance logging calls.
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workers. 'sync' should be set to True for all compliance tags that requi... | python | def gnmt_print(*args, **kwargs):
"""
Wrapper for MLPerf compliance logging calls.
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | init_lstm_ | def init_lstm_(lstm, init_weight=0.1):
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Initializes weights of LSTM layer.
Weights and biases are initialized with uniform(-init_weight, init_weight)
distribution.
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:param init_weight: range for the uniform initializer
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# Initialize hidden-hid... | python | def init_lstm_(lstm, init_weight=0.1):
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Initializes weights of LSTM layer.
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | setup_seeds | def setup_seeds(master_seed, epochs, device):
"""
Generates seeds from one master_seed.
Function returns (worker_seeds, shuffling_seeds), worker_seeds are later
used to initialize per-worker random number generators (mostly for
dropouts), shuffling_seeds are for RNGs resposible for reshuffling the
... | python | def setup_seeds(master_seed, epochs, device):
"""
Generates seeds from one master_seed.
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | barrier | def barrier():
"""
Works as a temporary distributed barrier, currently pytorch
doesn't implement barrier for NCCL backend.
Calls all_reduce on dummy tensor and synchronizes with GPU.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
torch.distributed.all_red... | python | def barrier():
"""
Works as a temporary distributed barrier, currently pytorch
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Calls all_reduce on dummy tensor and synchronizes with GPU.
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | get_rank | def get_rank():
"""
Gets distributed rank or returns zero if distributed is not initialized.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
rank = torch.distributed.get_rank()
else:
rank = 0
return rank | python | def get_rank():
"""
Gets distributed rank or returns zero if distributed is not initialized.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
rank = torch.distributed.get_rank()
else:
rank = 0
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | get_world_size | def get_world_size():
"""
Gets total number of distributed workers or returns one if distributed is
not initialized.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
else:
world_size = 1
return wor... | python | def get_world_size():
"""
Gets total number of distributed workers or returns one if distributed is
not initialized.
"""
if torch.distributed.is_available() and torch.distributed.is_initialized():
world_size = torch.distributed.get_world_size()
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | setup_logging | def setup_logging(log_file=os.devnull):
"""
Configures logging.
By default logs from all workers are printed to the console, entries are
prefixed with "N: " where N is the rank of the worker. Logs printed to the
console don't include timestaps.
Full logs with timestamps are saved to the log_file... | python | def setup_logging(log_file=os.devnull):
"""
Configures logging.
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | set_device | def set_device(cuda, local_rank):
"""
Sets device based on local_rank and returns instance of torch.device.
:param cuda: if True: use cuda
:param local_rank: local rank of the worker
"""
if cuda:
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device = torch.device('cuda')
else:
d... | python | def set_device(cuda, local_rank):
"""
Sets device based on local_rank and returns instance of torch.device.
:param cuda: if True: use cuda
:param local_rank: local rank of the worker
"""
if cuda:
torch.cuda.set_device(local_rank)
device = torch.device('cuda')
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | init_distributed | def init_distributed(cuda):
"""
Initializes distributed backend.
:param cuda: (bool) if True initializes nccl backend, if False initializes
gloo backend
"""
world_size = int(os.environ.get('WORLD_SIZE', 1))
distributed = (world_size > 1)
if distributed:
backend = 'nccl' if c... | python | def init_distributed(cuda):
"""
Initializes distributed backend.
:param cuda: (bool) if True initializes nccl backend, if False initializes
gloo backend
"""
world_size = int(os.environ.get('WORLD_SIZE', 1))
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | log_env_info | def log_env_info():
"""
Prints information about execution environment.
"""
logging.info('Collecting environment information...')
env_info = torch.utils.collect_env.get_pretty_env_info()
logging.info(f'{env_info}') | python | def log_env_info():
"""
Prints information about execution environment.
"""
logging.info('Collecting environment information...')
env_info = torch.utils.collect_env.get_pretty_env_info()
logging.info(f'{env_info}') | [
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | debug_tensor | def debug_tensor(tensor, name):
"""
Simple utility which helps with debugging.
Takes a tensor and outputs: min, max, avg, std, number of NaNs, number of
INFs.
:param tensor: torch tensor
:param name: name of the tensor (only for logging)
"""
logging.info(name)
tensor = tensor.detach... | python | def debug_tensor(tensor, name):
"""
Simple utility which helps with debugging.
Takes a tensor and outputs: min, max, avg, std, number of NaNs, number of
INFs.
:param tensor: torch tensor
:param name: name of the tensor (only for logging)
"""
logging.info(name)
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mlperf/training | rnn_translator/pytorch/seq2seq/utils.py | AverageMeter.reduce | def reduce(self, op):
"""
Reduces average value over all workers.
:param op: 'sum' or 'mean', reduction operator
"""
if op not in ('sum', 'mean'):
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/data/build.py | build_dataset | def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
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Arguments:
dataset_list (list[str]): Contains the names of the datasets, i.e.,
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transforms (callable): transforms to apply to each (image, target) sample
datase... | python | def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
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Arguments:
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/structures/image_list.py | to_image_list | def to_image_list(tensors, size_divisible=0):
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mlperf/training | object_detection/pytorch/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py | ROIKeypointHead.forward | def forward(self, features, proposals, targets=None):
"""
Arguments:
features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
Returns:
... | python | def forward(self, features, proposals, targets=None):
"""
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features (list[Tensor]): feature-maps from possibly several levels
proposals (list[BoxList]): proposal boxes
targets (list[BoxList], optional): the ground-truth targets.
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | get_inference_input | def get_inference_input():
"""Set up placeholders for input features/labels.
Returns the feature, output tensors that get passed into model_fn."""
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"""Set up placeholders for input features/labels.
Returns the feature, output tensors that get passed into model_fn."""
return (tf.placeholder(tf.float32,
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | model_fn | def model_fn(features, labels, mode, params):
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features: tensor with shape
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... | python | def model_fn(features, labels, mode, params):
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Create the model for estimator api
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features: tensor with shape
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labels: dict from string to tensor with shape
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | model_inference_fn | def model_inference_fn(features, training, params):
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Args:
features: input features tensor.
training: True if the model is training.
params: A dictionary
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... | python | def model_inference_fn(features, training, params):
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params: A dictionary
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | tpu_model_inference_fn | def tpu_model_inference_fn(features):
"""Builds the model graph suitable for running on TPU.
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1) Mark all weights as constant, which improves TPU inference performance
because it prevents the weights being transferred to the TPU every call
to Session.run().
2) Adds ... | python | def tpu_model_inference_fn(features):
"""Builds the model graph suitable for running on TPU.
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | bootstrap | def bootstrap():
"""Initialize a tf.Estimator run with random initial weights."""
# a bit hacky - forge an initial checkpoint with the name that subsequent
# Estimator runs will expect to find.
#
# Estimator will do this automatically when you call train(), but calling
# train() requires data, a... | python | def bootstrap():
"""Initialize a tf.Estimator run with random initial weights."""
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | export_model | def export_model(model_path):
"""Take the latest checkpoint and copy it to model_path.
Assumes that all relevant model files are prefixed by the same name.
(For example, foo.index, foo.meta and foo.data-00000-of-00001).
Args:
model_path: The path (can be a gs:// path) to export model
"""
... | python | def export_model(model_path):
"""Take the latest checkpoint and copy it to model_path.
Assumes that all relevant model files are prefixed by the same name.
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model_path: The path (can be a gs:// path) to export model
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] | 1c6ae725a81d15437a2b2df05cac0673fde5c3a4 | https://github.com/mlperf/training/blob/1c6ae725a81d15437a2b2df05cac0673fde5c3a4/reinforcement/tensorflow/minigo/dual_net.py#L602-L619 | train | Take the latest checkpoint and copy it to model_path. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | freeze_graph_tpu | def freeze_graph_tpu(model_path):
"""Custom freeze_graph implementation for Cloud TPU."""
assert model_path
assert FLAGS.tpu_name
if FLAGS.tpu_name.startswith('grpc://'):
tpu_grpc_url = FLAGS.tpu_name
else:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
... | python | def freeze_graph_tpu(model_path):
"""Custom freeze_graph implementation for Cloud TPU."""
assert model_path
assert FLAGS.tpu_name
if FLAGS.tpu_name.startswith('grpc://'):
tpu_grpc_url = FLAGS.tpu_name
else:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
... | [
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mlperf/training | reinforcement/tensorflow/minigo/dual_net.py | DualNetwork.initialize_weights | def initialize_weights(self, save_file):
"""Initialize the weights from the given save_file.
Assumes that the graph has been constructed, and the
save_file contains weights that match the graph. Used
to set the weights to a different version of the player
without redifining the e... | python | def initialize_weights(self, save_file):
"""Initialize the weights from the given save_file.
Assumes that the graph has been constructed, and the
save_file contains weights that match the graph. Used
to set the weights to a different version of the player
without redifining the e... | [
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mlperf/training | reinforcement/tensorflow/minigo/mask_flags.py | parse_helpfull_output | def parse_helpfull_output(help_output, regex=FLAG_HELP_RE_PY):
"""Parses the output of --helpfull.
Args:
help_output: str, the full output of --helpfull.
Returns:
A set of flags that are valid flags.
"""
valid_flags = set()
for _, no_prefix, flag_name in regex.findall(help_outp... | python | def parse_helpfull_output(help_output, regex=FLAG_HELP_RE_PY):
"""Parses the output of --helpfull.
Args:
help_output: str, the full output of --helpfull.
Returns:
A set of flags that are valid flags.
"""
valid_flags = set()
for _, no_prefix, flag_name in regex.findall(help_outp... | [
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mlperf/training | reinforcement/tensorflow/minigo/mask_flags.py | filter_flags | def filter_flags(parsed_flags, valid_flags):
"""Return the subset of `parsed_flags` that are found in the list `valid_flags`"""
def valid_argv(argv):
"""Figures out if a flag parsed from the flagfile matches a flag in
the command about to be run."""
flagname_match = FLAG_RE.match(argv)
... | python | def filter_flags(parsed_flags, valid_flags):
"""Return the subset of `parsed_flags` that are found in the list `valid_flags`"""
def valid_argv(argv):
"""Figures out if a flag parsed from the flagfile matches a flag in
the command about to be run."""
flagname_match = FLAG_RE.match(argv)
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mlperf/training | reinforcement/tensorflow/minigo/mask_flags.py | prepare_subprocess_cmd | def prepare_subprocess_cmd(subprocess_cmd):
"""Prepares a subprocess command by running --helpfull and masking flags.
Args:
subprocess_cmd: List[str], what would be passed into subprocess.call()
i.e. ['python', 'train.py', '--flagfile=flags']
Returns:
['python', 'train.py', '--... | python | def prepare_subprocess_cmd(subprocess_cmd):
"""Prepares a subprocess command by running --helpfull and masking flags.
Args:
subprocess_cmd: List[str], what would be passed into subprocess.call()
i.e. ['python', 'train.py', '--flagfile=flags']
Returns:
['python', 'train.py', '--... | [
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mlperf/training | reinforcement/tensorflow/minigo/mask_flags.py | run | def run(cmd):
"""Prepare and run a subprocess cmd, returning a CompletedProcess."""
print("Preparing the following cmd:")
cmd = prepare_subprocess_cmd(cmd)
print("Running the following cmd:")
print('\n'.join(cmd))
return subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stderr) | python | def run(cmd):
"""Prepare and run a subprocess cmd, returning a CompletedProcess."""
print("Preparing the following cmd:")
cmd = prepare_subprocess_cmd(cmd)
print("Running the following cmd:")
print('\n'.join(cmd))
return subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stderr) | [
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mlperf/training | reinforcement/tensorflow/minigo/mask_flags.py | checked_run | def checked_run(cmd):
"""Prepare and run a subprocess cmd, checking for successful completion."""
completed_process = run(cmd)
if completed_process.returncode > 0:
print("Command failed! Hanging around in case someone needs a "
"docker connection. (Ctrl-C to quit now)")
time.s... | python | def checked_run(cmd):
"""Prepare and run a subprocess cmd, checking for successful completion."""
completed_process = run(cmd)
if completed_process.returncode > 0:
print("Command failed! Hanging around in case someone needs a "
"docker connection. (Ctrl-C to quit now)")
time.s... | [
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