code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
import copy
import glob
import logging
import os
from typing import Dict, Optional
from ray.util.debug import log_once
logger = logging.getLogger(__name__)
UNRESOLVED_SEARCH_SPACE = str(
"You passed a `{par}` parameter to {cls} that contained unresolved search "
"space definitions. {cls} should however be in... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/suggestion.py | 0.904605 | 0.324797 | suggestion.py | pypi |
from typing import Any, Dict, List, Optional
import numpy as np
import copy
import logging
from functools import partial
import pickle
from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
Normal, \
Quantized, \
Uniform
from ray.tune.sugg... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/hyperopt.py | 0.923221 | 0.324958 | hyperopt.py | pypi |
import copy
import os
import logging
import pickle
from typing import Dict, List, Optional, Union
try:
import sigopt as sgo
Connection = sgo.Connection
except ImportError:
sgo = None
Connection = None
from ray.tune.suggest import Searcher
logger = logging.getLogger(__name__)
class SigOptSearch(Sear... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/sigopt.py | 0.886181 | 0.263813 | sigopt.py | pypi |
from collections import defaultdict
import logging
import pickle
import json
from typing import Dict, List, Optional, Tuple
from ray.tune import ExperimentAnalysis
from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Domain, Float, Quantized
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_S... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/bayesopt.py | 0.943053 | 0.361531 | bayesopt.py | pypi |
import copy
from typing import Dict, List, Optional, Union
from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Float, Integer, LogUniform, \
Quantized, Uniform
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
from r... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/ax.py | 0.926752 | 0.394114 | ax.py | pypi |
import logging
import pickle
from typing import Any, Dict, List, Optional, Tuple, Union
from ray.tune.result import DEFAULT_METRIC, TRAINING_ITERATION
from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
Quantized, Uniform
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/optuna.py | 0.935028 | 0.38122 | optuna.py | pypi |
import copy
import logging
import pickle
from typing import Dict, List, Optional, Tuple, Union
from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Domain, Float, Integer, Quantized, \
LogUniform
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
UNDEFINED_METRIC_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/skopt.py | 0.918517 | 0.407333 | skopt.py | pypi |
from ray._private.utils import get_function_args
from ray.tune.suggest.search import SearchAlgorithm
from ray.tune.suggest.basic_variant import BasicVariantGenerator
from ray.tune.suggest.suggestion import Searcher, ConcurrencyLimiter
from ray.tune.suggest.search_generator import SearchGenerator
from ray.tune.suggest.v... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/__init__.py | 0.902776 | 0.333883 | __init__.py | pypi |
import copy
import glob
import itertools
import os
import uuid
from typing import Dict, List, Optional, Union
import warnings
from ray.tune.error import TuneError
from ray.tune.experiment import Experiment, convert_to_experiment_list
from ray.tune.config_parser import make_parser, create_trial_from_spec
from ray.tune.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/basic_variant.py | 0.894884 | 0.265833 | basic_variant.py | pypi |
import copy
import logging
from typing import Dict, List, Optional
import numpy as np
from ray.tune.suggest.suggestion import Searcher
logger = logging.getLogger(__name__)
TRIAL_INDEX = "__trial_index__"
"""str: A constant value representing the repeat index of the trial."""
def _warn_num_samples(searcher: Search... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/suggest/repeater.py | 0.935641 | 0.398582 | repeater.py | pypi |
from collections import Counter
from typing import Dict, List, Union
from tensorflow.keras.callbacks import Callback
from ray import tune
import os
class TuneCallback(Callback):
"""Base class for Tune's Keras callbacks."""
_allowed = [
"batch_begin",
"batch_end",
"epoch_begin",
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/keras.py | 0.954425 | 0.219683 | keras.py | pypi |
import os
from typing import Dict, Callable, Optional
import logging
from ray.tune.trainable import Trainable
from ray.tune.logger import Logger, LoggerCallback
from ray.tune.result import TRAINING_ITERATION
from ray.tune.trial import Trial
logger = logging.getLogger(__name__)
def _import_mlflow():
try:
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/mlflow.py | 0.835886 | 0.291242 | mlflow.py | pypi |
import os
import pickle
from collections.abc import Iterable
from multiprocessing import Process, Queue
from numbers import Number
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
from ray import logger
from ray.tune import Trainable
from ray.tune.function_runner import FunctionRunner
f... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/wandb.py | 0.831964 | 0.272478 | wandb.py | pypi |
from typing import Dict, List, Optional, Union
from pytorch_lightning import Callback, Trainer, LightningModule
from ray import tune
import os
class TuneCallback(Callback):
"""Base class for Tune's PyTorch Lightning callbacks."""
_allowed = [
"init_start", "init_end", "fit_start", "fit_end", "sanity... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/pytorch_lightning.py | 0.946088 | 0.350032 | pytorch_lightning.py | pypi |
import os
from typing import Any, Optional, Tuple
import subprocess
from ray import services, logger
from ray.autoscaler._private.command_runner import KubernetesCommandRunner
from ray.tune.syncer import NodeSyncer
from ray.tune.sync_client import SyncClient
def try_import_kubernetes():
try:
import kuber... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/kubernetes.py | 0.870886 | 0.174182 | kubernetes.py | pypi |
from typing import Dict, List, Union
from collections import OrderedDict
from ray import tune
import os
from ray.tune.utils import flatten_dict
from xgboost.core import Booster
try:
from xgboost.callback import TrainingCallback
except ImportError:
class TrainingCallback:
pass
class TuneCallback(Tr... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/xgboost.py | 0.966363 | 0.271436 | xgboost.py | pypi |
from contextlib import contextmanager
import os
import logging
import shutil
import tempfile
from typing import Callable, Dict, Generator, Optional, Type
import torch
from datetime import timedelta
import ray
from ray import tune
from ray.tune.result import RESULT_DUPLICATE
from ray.tune.logger import NoopLogger
from... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/torch.py | 0.813498 | 0.240351 | torch.py | pypi |
from typing import Callable, Dict, Type
from contextlib import contextmanager
import os
import logging
import shutil
import tempfile
from filelock import FileLock
import ray
from ray import tune
from ray.tune.resources import Resources
from ray.tune.utils.trainable import TrainableUtil
from ray.tune.result import RE... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/horovod.py | 0.846292 | 0.254295 | horovod.py | pypi |
from typing import Dict, List, Union
from ray import tune
import mxnet
from mxnet.model import save_checkpoint, BatchEndParam
import numpy as np
import os
class TuneCallback:
"""Base class for Tune's MXNet callbacks."""
pass
class TuneReportCallback(TuneCallback):
"""MXNet to Ray Tune reporting callb... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/mxnet.py | 0.954679 | 0.283295 | mxnet.py | pypi |
import json
import logging
import ray
import os
from ray import tune
from ray.tune.result import RESULT_DUPLICATE
from ray.tune.function_runner import wrap_function
from ray.tune.resources import Resources
from ray.util.sgd.utils import find_free_port
from ray.util.placement_group import remove_placement_group
from ra... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/integration/tensorflow.py | 0.728845 | 0.327857 | tensorflow.py | pypi |
from django.db import models
class JobRecord(models.Model):
"""Information of an AutoML Job."""
job_id = models.CharField(max_length=50)
name = models.CharField(max_length=20)
user = models.CharField(max_length=20)
type = models.CharField(max_length=20)
start_time = models.CharField(max_lengt... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automlboard/models/models.py | 0.818229 | 0.217587 | models.py | pypi |
from django.shortcuts import HttpResponse
from ray.tune.automlboard.models.models import JobRecord, TrialRecord
from ray.tune.trial import Trial
import json
def query_job(request):
"""Rest API to query the job info, with the given job_id.
The url pattern should be like this:
curl http://<server>:<port... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automlboard/frontend/query.py | 0.481698 | 0.318194 | query.py | pypi |
import logging
import os
import time
from threading import Thread
from ray.tune.automlboard.common.exception import CollectorError
from ray.tune.automlboard.common.utils import parse_json, \
parse_multiple_json, timestamp2date
from ray.tune.automlboard.models.models import JobRecord, \
TrialRecord, ResultReco... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automlboard/backend/collector.py | 0.756537 | 0.155527 | collector.py | pypi |
import logging
import json
import os
import time
def dump_json(json_info, json_file, overwrite=True):
"""Dump a whole json record into the given file.
Overwrite the file if the overwrite flag set.
Args:
json_info (dict): Information dict to be dumped.
json_file (str): File path to be dum... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automlboard/common/utils.py | 0.563138 | 0.185007 | utils.py | pypi |
# __import_lightning_begin__
import math
import torch
import pytorch_lightning as pl
from torch.utils.data import DataLoader, random_split
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
import os
# __import_lightning_end__
# __import_tune_begin__
import... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mnist_pytorch_lightning.py | 0.908252 | 0.527742 | mnist_pytorch_lightning.py | pypi |
import mxnet as mx
from ray import tune, logger
from ray.tune.integration.mxnet import TuneCheckpointCallback, \
TuneReportCallback
from ray.tune.schedulers import ASHAScheduler
def train_mnist_mxnet(config, mnist, num_epochs=10):
batch_size = config["batch_size"]
train_iter = mx.io.NDArrayIter(
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mxnet_example.py | 0.731922 | 0.401219 | mxnet_example.py | pypi |
import argparse
import tensorflow as tf
import numpy as np
import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.integration.keras import TuneReportCheckpointCallback
from ray.tune.integration.tensorflow import (DistributedTrainableCreator,
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/tf_distributed_keras_example.py | 0.851382 | 0.451145 | tf_distributed_keras_example.py | pypi |
import argparse
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
from tensorflow.keras.datasets.mnist import load_data
from ray import tune
MAX_TRAIN_BATCH = 10
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/tf_mnist_example.py | 0.927359 | 0.467696 | tf_mnist_example.py | pypi |
import json
import time
import os
import numpy as np
import ray
from ray import tune
from ray.tune import Trainable
from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
from ray.tune.suggest.bohb import TuneBOHB
class MyTrainableClass(Trainable):
"""Example agent whose learning curve is a random sigmoid.
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/bohb_example.py | 0.863579 | 0.309141 | bohb_example.py | pypi |
import math
import torch
from torch.nn import functional as F
import pytorch_lightning as pl
from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import os
from ray.tune.integration.pytorch_lightning import TuneReportCallback
import tempfile
from ray import tune
class LightningMNISTClassifier(pl.Lightn... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mnist_ptl_mini.py | 0.86977 | 0.533701 | mnist_ptl_mini.py | pypi |
from __future__ import print_function
import argparse
import os
import torch
import torch.optim as optim
import ray
from ray import tune
from ray.tune.schedulers import ASHAScheduler
from ray.tune.examples.mnist_pytorch import (train, test, get_data_loaders,
ConvNet)
# Ch... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mnist_pytorch_trainable.py | 0.845815 | 0.275308 | mnist_pytorch_trainable.py | pypi |
import argparse
import json
import os
import time
import numpy as np
import ray
from ray import tune
from ray.tune.schedulers import HyperBandScheduler
class MyTrainableClass(tune.Trainable):
"""Example agent whose learning curve is a random sigmoid.
The dummy hyperparameters "width" and "height" determin... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/hyperband_example.py | 0.826537 | 0.294462 | hyperband_example.py | pypi |
import time
from ray import tune
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.skopt import SkOptSearch
def evaluation_fn(step, width, height):
time.sleep(0.1)
return (0.1 + width * step / 100)**(-1) + height * 0.1
def easy_obj... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/skopt_example.py | 0.784443 | 0.288409 | skopt_example.py | pypi |
# __tutorial_imports_begin__
import argparse
import os
import numpy as np
import torch
import torch.optim as optim
from torchvision import datasets
from ray.tune.examples.mnist_pytorch import train, test, ConvNet,\
get_data_loaders
from ray import tune
from ray.tune.schedulers import PopulationBasedTraining
from ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_convnet_function_example.py | 0.834339 | 0.403273 | pbt_convnet_function_example.py | pypi |
from __future__ import print_function
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import (Input, Activation, Dense, Permute,
Dropout)
from tensorflow.keras.layers import add, dot, conca... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_memnn_example.py | 0.725454 | 0.359533 | pbt_memnn_example.py | pypi |
import sklearn.datasets
import sklearn.metrics
import os
from ray.tune.schedulers import ASHAScheduler
from sklearn.model_selection import train_test_split
import xgboost as xgb
from ray import tune
from ray.tune.integration.xgboost import TuneReportCheckpointCallback
def train_breast_cancer(config: dict):
# Thi... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/xgboost_example.py | 0.821116 | 0.47658 | xgboost_example.py | pypi |
import random
from ray import tune
from ray.tune.schedulers import PopulationBasedTraining
if __name__ == "__main__":
# Postprocess the perturbed config to ensure it's still valid
def explore(config):
# ensure we collect enough timesteps to do sgd
if config["train_batch_size"] < config["sgd_m... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_ppo_example.py | 0.672224 | 0.261885 | pbt_ppo_example.py | pypi |
import sys
import time
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.sigopt import SigOptSearch
def evaluate(step, width, height):
return (0.1 + width * step / 100)**(-1) + height * 0.01
def easy_objective(config):
# Hyperparameters
width, height = c... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/sigopt_example.py | 0.523664 | 0.268618 | sigopt_example.py | pypi |
"""Examples using MLfowLoggerCallback and mlflow_mixin.
"""
import os
import tempfile
import time
import mlflow
from ray import tune
from ray.tune.integration.mlflow import MLflowLoggerCallback, mlflow_mixin
def evaluation_fn(step, width, height):
return (0.1 + width * step / 100)**(-1) + height * 0.1
def eas... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mlflow_example.py | 0.689201 | 0.459925 | mlflow_example.py | pypi |
import argparse
import numpy as np
import time
import logging
import os
import ray
from ray import tune
from ray.tune import DurableTrainable
from ray.tune.sync_client import get_sync_client
from ray import cloudpickle
logger = logging.getLogger(__name__)
class MockDurableTrainable(DurableTrainable):
"""Mocks t... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/durable_trainable_example.py | 0.766818 | 0.220447 | durable_trainable_example.py | pypi |
# __import_begin__
from functools import partial
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import random_split
import torchvision
import torchvision.transforms as transforms
import ray
from ray import tune
from ray.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/cifar10_pytorch.py | 0.910649 | 0.420481 | cifar10_pytorch.py | pypi |
import numpy as np
import time
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.ax import AxSearch
def hartmann6(x):
alpha = np.array([1.0, 1.2, 3.0, 3.2])
A = np.array([
[10, 3, 17, 3.5, 1.7, 8],
[0.05, 10, 17, 0.1, 8, 14],
[3, 3.5, 1... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/ax_example.py | 0.606848 | 0.378919 | ax_example.py | pypi |
import argparse
from tensorflow.keras.datasets import mnist
from ray.tune.integration.keras import TuneReportCallback
parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
args, _ = parser.parse_known_args()
def train_mnist(config):
#... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/tune_mnist_keras.py | 0.823293 | 0.434581 | tune_mnist_keras.py | pypi |
# flake8: noqa
# yapf: disable
# __tutorial_imports_begin__
import argparse
import os
import numpy as np
import torch
import torch.optim as optim
from torchvision import datasets
from ray.tune.examples.mnist_pytorch import train, test, ConvNet,\
get_data_loaders
import ray
from ray import tune
from ray.tune.sche... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_convnet_example.py | 0.834474 | 0.47993 | pbt_convnet_example.py | pypi |
import argparse
import logging
import os
import torch
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel
import ray
from ray import tune
from ray.tune.examples.mnist_pytorch import (train, test, get_data_loaders,
ConvNet)
from ray.tune.integra... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/ddp_mnist_torch.py | 0.701509 | 0.262792 | ddp_mnist_torch.py | pypi |
HPO, and MLflow autologging all together."""
import os
import tempfile
import pytorch_lightning as pl
from pl_bolts.datamodules import MNISTDataModule
import mlflow
from ray import tune
from ray.tune.integration.mlflow import mlflow_mixin
from ray.tune.integration.pytorch_lightning import TuneReportCallback
from ray... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/mlflow_ptl.py | 0.69035 | 0.525186 | mlflow_ptl.py | pypi |
import argparse
import tempfile
from unittest.mock import MagicMock
import numpy as np
import wandb
from ray import tune
from ray.tune import Trainable
from ray.tune.integration.wandb import WandbLoggerCallback, \
WandbTrainableMixin, \
wandb_mixin
def train_function(config, checkpoint_dir=None):
for i ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/wandb_example.py | 0.771241 | 0.416085 | wandb_example.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import time
from ray import tune
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.suggest.dragonfly import DragonflySearc... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/dragonfly_example.py | 0.707506 | 0.182699 | dragonfly_example.py | pypi |
from __future__ import print_function
import argparse
import random
import mxnet as mx
import numpy as np
from mxnet import gluon, init
from mxnet import autograd as ag
from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
from gluoncv.model_zoo import get_model
from gluoncv.data import transform... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/tune_cifar10_gluon.py | 0.882592 | 0.24747 | tune_cifar10_gluon.py | pypi |
from __future__ import print_function
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.datasets import cifar10
from tensorflow.python.keras.layers import Input, Dense, Dropout, Flatten
from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D
from tensorflow.python.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_tune_cifar10_with_keras.py | 0.903008 | 0.490297 | pbt_tune_cifar10_with_keras.py | pypi |
import os
import ray
from ray import tune
from ray.tune import CLIReporter
from ray.tune.examples.pbt_transformers.utils import download_data, \
build_compute_metrics_fn
from ray.tune.schedulers import PopulationBasedTraining
from transformers import glue_tasks_num_labels, AutoConfig, \
AutoModelForSequenceCla... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/examples/pbt_transformers/pbt_transformers.py | 0.583559 | 0.281983 | pbt_transformers.py | pypi |
from typing import Dict, Optional
from copy import deepcopy
import logging
import numpy as np
import pandas as pd
from ray.tune import TuneError
from ray.tune.schedulers import PopulationBasedTraining
def import_pb2_dependencies():
try:
import GPy
except ImportError:
GPy = None
try:
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/schedulers/pb2.py | 0.859723 | 0.513668 | pb2.py | pypi |
import collections
import logging
from typing import Dict, List, Optional
import numpy as np
from ray.tune import trial_runner
from ray.tune.result import DEFAULT_METRIC
from ray.tune.trial import Trial
from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
logger = logging.getLogger(__name__)... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/schedulers/median_stopping_rule.py | 0.928944 | 0.401336 | median_stopping_rule.py | pypi |
import numpy as np
from scipy.optimize import minimize
import GPy
from GPy.kern import Kern
from GPy.core import Param
from sklearn.metrics import pairwise_distances
from sklearn.metrics.pairwise import euclidean_distances
class TV_SquaredExp(Kern):
""" Time varying squared exponential kernel.
For more i... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/schedulers/pb2_utils.py | 0.854004 | 0.548432 | pb2_utils.py | pypi |
from typing import Dict, Optional
from ray.tune import trial_runner
from ray.tune.result import DEFAULT_METRIC
from ray.tune.trial import Trial
class TrialScheduler:
"""Interface for implementing a Trial Scheduler class."""
CONTINUE = "CONTINUE" #: Status for continuing trial execution
PAUSE = "PAUSE" ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/schedulers/trial_scheduler.py | 0.951425 | 0.249421 | trial_scheduler.py | pypi |
from ray._private.utils import get_function_args
from ray.tune.schedulers.trial_scheduler import TrialScheduler, FIFOScheduler
from ray.tune.schedulers.hyperband import HyperBandScheduler
from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
from ray.tune.schedulers.async_hyperband import (AsyncHyperBandScheduler,
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/schedulers/__init__.py | 0.910947 | 0.419678 | __init__.py | pypi |
import time
import copy
import logging
from ray.tune.trial import Trial
from ray.tune.suggest import SearchAlgorithm
from ray.tune.experiment import convert_to_experiment_list
from ray.tune.suggest.variant_generator import generate_variants
from ray.tune.config_parser import make_parser, create_trial_from_spec
logger... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automl/search_policy.py | 0.718397 | 0.17692 | search_policy.py | pypi |
import random
import logging
import numpy as np
from ray.tune import grid_search
logger = logging.getLogger(__name__)
class ParameterSpace:
"""Base class of a single parameter's search space.
"""
def __init__(self, name):
"""Initialize ParameterSpace.
Arguments:
name (str):... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/automl/search_space.py | 0.751557 | 0.461138 | search_space.py | pypi |
from typing import Dict, List, Union
import copy
import json
import glob
import logging
import numbers
import os
import inspect
import threading
import time
import uuid
from collections import defaultdict, deque
from collections.abc import Mapping, Sequence
from datetime import datetime
from threading import Thread
fro... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/tune/utils/util.py | 0.764056 | 0.156073 | util.py | pypi |
from abc import ABC, abstractmethod
from ray.streaming.datastream import StreamSource
from ray.streaming.function import LocalFileSourceFunction
from ray.streaming.function import CollectionSourceFunction
from ray.streaming.function import SourceFunction
from ray.streaming.runtime.gateway_client import GatewayClient
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/context.py | 0.86806 | 0.442877 | context.py | pypi |
import importlib
import inspect
from abc import ABC, abstractmethod
from ray import cloudpickle
from ray.streaming.runtime import gateway_client
class Partition(ABC):
"""Interface of the partitioning strategy."""
@abstractmethod
def partition(self, record, num_partition: int):
"""Given a record ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/partition.py | 0.774924 | 0.453383 | partition.py | pypi |
from abc import ABC, abstractmethod
from ray.streaming import function
from ray.streaming import partition
class Stream(ABC):
"""
Abstract base class of all stream types. A Stream represents a stream of
elements of the same type. A Stream can be transformed into another Stream
by applying a transfo... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/datastream.py | 0.9666 | 0.3628 | datastream.py | pypi |
import enum
import importlib
import inspect
import sys
from abc import ABC, abstractmethod
from ray import cloudpickle
from ray.streaming.runtime import gateway_client
class Language(enum.Enum):
JAVA = 0
PYTHON = 1
class Function(ABC):
"""The base interface for all user-defined functions."""
def o... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/function.py | 0.803251 | 0.583915 | function.py | pypi |
import enum
import importlib
import logging
from abc import ABC, abstractmethod
from ray.streaming import function
from ray.streaming import message
from ray.streaming.collector import Collector
from ray.streaming.collector import CollectionCollector
from ray.streaming.function import SourceFunction
from ray.streaming... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/operator.py | 0.835047 | 0.313401 | operator.py | pypi |
import msgpack
import ray
class GatewayClient:
"""GatewayClient is used to interact with `PythonGateway` java actor"""
_PYTHON_GATEWAY_CLASSNAME = \
b"io.ray.streaming.runtime.python.PythonGateway"
def __init__(self):
self._python_gateway_actor = ray.java_actor_class(
Gateway... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/runtime/gateway_client.py | 0.83104 | 0.191158 | gateway_client.py | pypi |
import logging
from abc import ABC, abstractmethod
import ray.streaming.context as context
from ray.streaming import message
from ray.streaming.operator import OperatorType
logger = logging.getLogger(__name__)
class Processor(ABC):
"""The base interface for all processors."""
@abstractmethod
def open(s... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/streaming/runtime/processor.py | 0.905478 | 0.164483 | processor.py | pypi |
from typing import Any, List, Tuple, Dict, Optional
class CommandRunnerInterface:
"""Interface to run commands on a remote cluster node.
**Important**: This is an INTERNAL API that is only exposed for the purpose
of implementing custom node providers. It is not allowed to call into
CommandRunner meth... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/command_runner.py | 0.919353 | 0.310015 | command_runner.py | pypi |
import logging
from types import ModuleType
from typing import Any, Dict, List, Optional
from ray.autoscaler.command_runner import CommandRunnerInterface
from ray.autoscaler._private.command_runner import \
SSHCommandRunner, DockerCommandRunner
logger = logging.getLogger(__name__)
class NodeProvider:
"""Int... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/node_provider.py | 0.895555 | 0.321433 | node_provider.py | pypi |
from contextlib import contextmanager
from typing import Any, Callable, Dict, Iterator, List, Optional, Union
import json
import os
import tempfile
from ray.autoscaler._private import commands
from ray.autoscaler._private.event_system import ( # noqa: F401
CreateClusterEvent, # noqa: F401
global_event_syste... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/sdk.py | 0.921678 | 0.222912 | sdk.py | pypi |
from enum import Enum, auto
from typing import Any, Callable, Dict, List, Optional, Union
from ray.autoscaler._private.cli_logger import cli_logger
class CreateClusterEvent(Enum):
"""Events to track in ray.autoscaler.sdk.create_or_update_cluster.
Attributes:
up_started : Invoked at the beginning of ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/_private/event_system.py | 0.91067 | 0.213336 | event_system.py | pypi |
from ray.autoscaler._private import constants
from typing import List, Set, Tuple
class NodeTracker:
"""Map nodes to their corresponding logs.
We need to be a little careful here. At an given point in time, node_id <->
ip can be interchangeably used, but the node_id -> ip relation is not
bijective _a... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/_private/node_tracker.py | 0.909882 | 0.477798 | node_tracker.py | pypi |
import json
import logging
from http.client import RemoteDisconnected
from ray.autoscaler.node_provider import NodeProvider
from ray.autoscaler.tags import TAG_RAY_CLUSTER_NAME
logger = logging.getLogger(__name__)
class CoordinatorSenderNodeProvider(NodeProvider):
"""NodeProvider for automatically managed priva... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/autoscaler/_private/local/coordinator_node_provider.py | 0.674265 | 0.169681 | coordinator_node_provider.py | pypi |
import collections
from typing import List
from ray.util.timer import _Timer
class MetricsContext:
"""Metrics context object for a local iterator.
This object is accessible by all operators of a local iterator. It can be
used to store and retrieve global execution metrics for the iterator.
It can be... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/iter_metrics.py | 0.845129 | 0.442215 | iter_metrics.py | pypi |
import ray
class ActorPool:
"""Utility class to operate on a fixed pool of actors.
Arguments:
actors (list): List of Ray actor handles to use in this pool.
Examples:
>>> a1, a2 = Actor.remote(), Actor.remote()
>>> pool = ActorPool([a1, a2])
>>> print(list(pool.map(lambda ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/actor_pool.py | 0.874352 | 0.554169 | actor_pool.py | pypi |
import logging
from typing import Dict, Any, List, Optional, Tuple, Union
from ray._raylet import (
Count as CythonCount,
Histogram as CythonHistogram,
Gauge as CythonGauge,
) # noqa: E402
logger = logging.getLogger(__name__)
class Metric:
"""The parent class of custom metrics.
Ray's custom m... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/metrics.py | 0.941176 | 0.366476 | metrics.py | pypi |
import asyncio
from typing import Optional, Any, List, Dict
from collections.abc import Iterable
import ray
class Empty(Exception):
pass
class Full(Exception):
pass
class Queue:
"""A first-in, first-out queue implementation on Ray.
The behavior and use cases are similar to those of the asyncio.Q... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/queue.py | 0.931322 | 0.471649 | queue.py | pypi |
from abc import ABCMeta
from abc import abstractmethod
from ray.util.collective.types import AllReduceOptions, BarrierOptions, \
ReduceOptions, AllGatherOptions, BroadcastOptions, ReduceScatterOptions
class BaseGroup(metaclass=ABCMeta):
def __init__(self, world_size, rank, group_name):
"""Init the pr... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/base_collective_group.py | 0.922369 | 0.183228 | base_collective_group.py | pypi |
import numpy
import asyncio
try:
import pygloo
except ImportError:
raise ImportError("Can not import pygloo."
"Please run 'pip install pygloo' to install pygloo.")
import ray
from ray.util.collective.types import ReduceOp, torch_available
from ray.util.queue import _QueueActor
GLOO_REDUC... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/gloo_util.py | 0.772959 | 0.511534 | gloo_util.py | pypi |
import numpy
try:
import cupy
from cupy.cuda import nccl
from cupy.cuda import Device # noqa: F401
from cupy.cuda.nccl import get_version
from cupy.cuda.nccl import get_build_version
from cupy.cuda.nccl import NcclCommunicator
from cupy.cuda.nccl import groupStart # noqa: F401
from cup... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/nccl_util.py | 0.802207 | 0.184143 | nccl_util.py | pypi |
import logging
import threading
import cupy
from ray.util.collective.collective_group import nccl_util
from ray.util.collective.const import ENV
NCCL_STREAM_POOL_SIZE = 32
MAX_GPU_PER_ACTOR = 16
logger = logging.getLogger(__name__)
class StreamPool:
"""The class that represents a stream pool associated with a ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/cuda_stream.py | 0.787278 | 0.17749 | cuda_stream.py | pypi |
import logging
import datetime
import time
import ray
import cupy
from ray.util.collective.const import ENV
from ray.util.collective.collective_group import nccl_util
from ray.util.collective.collective_group.base_collective_group \
import BaseGroup
from ray.util.collective.const import get_store_name
from ray.ut... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/nccl_collective_group.py | 0.737064 | 0.212702 | nccl_collective_group.py | pypi |
import logging
import datetime
import time
import os
import shutil
import ray
from ray import ray_constants
import pygloo
import numpy
from ray.util.collective.collective_group import gloo_util
from ray.util.collective.collective_group.base_collective_group \
import BaseGroup
from ray.util.collective.types import... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/collective/collective_group/gloo_collective_group.py | 0.803444 | 0.178097 | gloo_collective_group.py | pypi |
import random
from typing import Iterable
from typing import List, Optional, Union
import pyarrow.parquet as pq
from pandas import DataFrame
import ray.util.iter as para_iter
from .dataset import MLDataset
from .interface import _SourceShard
class ParquetSourceShard(_SourceShard):
def __init__(self, data_pieces... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/data/parquet.py | 0.859354 | 0.463141 | parquet.py | pypi |
from collections import defaultdict
from typing import Iterable
import pandas as pd
from ray.util.data.dataset import MLDataset
from ray.util.data.parquet import read_parquet
from ray.util.iter import T, ParallelIterator
try:
import dataclasses
except: # noqa: E722
pass
else:
from dataclasses import is_... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/data/__init__.py | 0.919113 | 0.531392 | __init__.py | pypi |
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
import ray
import ray.util.data as ml_data
import ray.util.iter as parallel_it
from ray.util.sgd.torch.torch_dataset import TorchMLDataset
from ray.util.sgd.torch.torch_trainer import TorchTrainer
from ray.util.sg... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/data/examples/mlp_identity_torch.py | 0.924296 | 0.471041 | mlp_identity_torch.py | pypi |
from collections import OrderedDict
from collections.abc import Iterator
from operator import getitem
import uuid
import ray
from dask.base import quote
from dask.core import get as get_sync
from dask.compatibility import apply
try:
from dataclasses import is_dataclass, fields as dataclass_fields
except ImportEr... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/dask/common.py | 0.846133 | 0.331958 | common.py | pypi |
import collections
from contextlib import closing, contextmanager
import logging
import numpy as np
import socket
import time
import ray
from ray.exceptions import RayActorError
logger = logging.getLogger(__name__)
BATCH_COUNT = "batch_count"
NUM_SAMPLES = "num_samples"
BATCH_SIZE = "*batch_size"
class TimerStat:
... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/utils.py | 0.877135 | 0.37502 | utils.py | pypi |
import logging
from typing import Any, List, Optional
import tensorflow as tf
from ray.util.data import MLDataset
class TFMLDataset:
""" A TFMLDataset which converted from MLDataset
.. code-block:: python
ds = ml_dataset.to_tf(feature_columns=["x"], label_column="y")
shard = ds.get_shard(0... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/tf/tf_dataset.py | 0.81538 | 0.642489 | tf_dataset.py | pypi |
import io
import logging
import time
from collections import defaultdict
from datetime import timedelta
import ray
import torch
from ray.exceptions import RayActorError
from ray.util.sgd.torch.distributed_torch_runner import \
LocalDistributedRunner, DistributedTorchRunner
from ray.util.sgd.torch.torch_runner impo... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/worker_group.py | 0.880765 | 0.246103 | worker_group.py | pypi |
import functools
import logging
from collections import Iterator
from collections.abc import Iterable
from typing import Any, Callable, List, Optional
import numpy as np
import torch
import pandas as pd
from torch.utils.data import IterableDataset
from ray.util.data import MLDataset
def convert_to_tensor(df, featur... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/torch_dataset.py | 0.80213 | 0.398289 | torch_dataset.py | pypi |
import logging
import io
import itertools
import ray
import torch
from ray.util.sgd.torch.constants import USE_FP16, NUM_STEPS
from ray.util.sgd import utils
logger = logging.getLogger(__name__)
amp = None
try:
from apex import amp
except ImportError:
logger.debug("apex is not installed.")
pass
class ... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/torch_runner.py | 0.765155 | 0.224151 | torch_runner.py | pypi |
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=3,
stride... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/resnet.py | 0.945121 | 0.593521 | resnet.py | pypi |
import argparse
import numpy as np
import torch
import torch.nn as nn
from ray.util.sgd import TorchTrainer
from ray.util.sgd.torch import TrainingOperator
class LinearDataset(torch.utils.data.Dataset):
"""y = a * x + b"""
def __init__(self, a, b, size=1000):
x = np.arange(0, 10, 10 / size, dtype=np... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/train_example.py | 0.917326 | 0.461199 | train_example.py | pypi |
import torch
import torch.nn as nn
from ray.tune.utils import merge_dicts
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
import ray
from ray import tune
from ray.util.sgd.torch import TorchTrainer, TrainingOperator
from ray.util.sgd.utils import BATCH_SIZE
from ray.util.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/tune_example.py | 0.918118 | 0.449151 | tune_example.py | pypi |
import numpy as np
import os
import torch
import torch.nn as nn
import argparse
from filelock import FileLock
from ray import tune
from ray.tune.schedulers import PopulationBasedTraining
from torch.utils.data import DataLoader, Subset
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/cifar_pytorch_pbt.py | 0.833562 | 0.323968 | cifar_pytorch_pbt.py | pypi |
import os
import torch
import torch.nn as nn
import argparse
from filelock import FileLock
from torch.utils.data import DataLoader, Subset
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms
from tqdm import trange
import ray
from ray.util.sgd.torch import TorchTrainer, TrainingOpera... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/cifar_pytorch_example.py | 0.706292 | 0.291056 | cifar_pytorch_example.py | pypi |
import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Subset
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms
import ray
from ray import tune
from ray.tune import CLIReporter
from ray.tune.schedulers import Populatio... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/pytorch_pbt_failure.py | 0.758779 | 0.44083 | pytorch_pbt_failure.py | pypi |
import argparse
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
from filelock import FileLock
from tqdm import trange
from torch.autograd import Variable
from torch.... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/dcgan.py | 0.92895 | 0.427576 | dcgan.py | pypi |
import numpy as np
from PIL import Image
import random
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
ow, oh = img.size
padh = size - oh if oh < size el... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/segmentation/transforms.py | 0.821832 | 0.381076 | transforms.py | pypi |
import datetime
import os
import time
import torch
import torch.utils.data
from filelock import FileLock
from torch import nn
import torchvision
import ray
from ray.util.sgd.torch.examples.segmentation.coco_utils import get_coco
import ray.util.sgd.torch.examples.segmentation.transforms as T
import ray.util.sgd.torch... | /ray_delvewheel-1.3.0.0-cp38-cp38-win_amd64.whl/ray-1.3.0.data/purelib/ray/util/sgd/torch/examples/segmentation/train_segmentation.py | 0.788624 | 0.408336 | train_segmentation.py | pypi |
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