repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
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ray | ray-master/doc/source/ray-core/_examples/datasets_train/datasets_train.py | # TODO(matt): Reformat script.
"""
Big Data Training
=================
"""
###############################################################################
# train
###############################################################################
import argparse
import collections
import os
import sys
import time
from ty... | 23,578 | 32.2567 | 88 | py |
ray | ray-master/doc/source/ray-overview/doc_test/ray_train.py | import torch
import ray.train as train
from ray.train.torch import TorchTrainer, TorchCheckpoint
from ray.air import ScalingConfig, session
def train_func():
# Setup model.
model = torch.nn.Linear(1, 1)
model = train.torch.prepare_model(model)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.... | 1,118 | 26.975 | 79 | py |
ray | ray-master/doc/source/ray-overview/doc_test/ray_rllib.py | from ray import air, tune
from ray.rllib.algorithms.ppo import PPO
tuner = tune.Tuner(
PPO,
run_config=air.RunConfig(
stop={"episode_len_mean": 20},
),
param_space={"env": "CartPole-v1", "framework": "torch", "log_level": "INFO"},
)
tuner.fit()
| 270 | 21.583333 | 82 | py |
ray | ray-master/doc/source/rllib/doc_code/rllib_in_60s.py | # flake8: noqa
# __rllib-in-60s-begin__
from ray.rllib.algorithms.ppo import PPOConfig
config = ( # 1. Configure the algorithm,
PPOConfig()
.environment("Taxi-v3")
.rollouts(num_rollout_workers=2)
.framework("torch")
.training(model={"fcnet_hiddens": [64, 64]})
.evaluation(evaluation_num_work... | 502 | 21.863636 | 48 | py |
ray | ray-master/doc/source/rllib/doc_code/rlmodule_guide.py | # flake8: noqa
from ray.rllib.utils.annotations import override
from ray.rllib.core.models.specs.typing import SpecType
from ray.rllib.core.models.specs.specs_base import TensorSpec
# __enabling-rlmodules-in-configs-begin__
import torch
from pprint import pprint
from ray.rllib.algorithms.ppo import PPOConfig
config... | 12,577 | 30.288557 | 85 | py |
ray | ray-master/doc/source/rllib/doc_code/catalog_guide.py | # flake8: noqa
"""
This file holds several examples for the Catalogs API that are used in the catalog
guide.
"""
# 1) Basic interaction with Catalogs in RLlib.
# __sphinx_doc_basic_interaction_begin__
import gymnasium as gym
from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
env = gym.make("CartPole-v1")
... | 4,940 | 34.546763 | 85 | py |
ray | ray-master/doc/source/train/doc_code/xgboost_train_predict.py | # flake8: noqa
# isort: skip_file
# __train_predict_start__
import numpy as np
import ray
from ray.train.xgboost import XGBoostTrainer, XGBoostPredictor
from ray.air.config import ScalingConfig
train_dataset = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
trainer = XGBoostTrainer(
label_column="... | 1,059 | 27.648649 | 85 | py |
ray | ray-master/doc/source/train/doc_code/gbdt_user_guide.py | # flake8: noqa
# isort: skip_file
# __xgboost_start__
import ray
from ray.train.xgboost import XGBoostTrainer
from ray.air.config import ScalingConfig
# Load data.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
# Split data into train and validation.
train_dataset, valid_dataset = d... | 4,984 | 24.564103 | 80 | py |
ray | ray-master/doc/source/train/doc_code/dl_guide.py | # flake8: noqa
MOCK = True
# __ft_initial_run_start__
from typing import Dict, Optional
import ray
from ray import air
from ray.air import session
from ray.train.torch import TorchCheckpoint, TorchTrainer
def get_datasets() -> Dict[str, ray.data.Dataset]:
return {"train": ray.data.from_items([{"x": i, "y": 2 *... | 2,602 | 25.292929 | 84 | py |
ray | ray-master/doc/source/train/doc_code/torchmetrics_example.py | # flake8: noqa
# isort: skip_file
# __start__
# First, pip install torchmetrics
# This code is tested with torchmetrics==0.7.3 and torch==1.12.1
import ray.train.torch
from ray.air import session, ScalingConfig
from ray.train.torch import TorchTrainer
import torch
import torch.nn as nn
import torchmetrics
from torc... | 2,364 | 26.823529 | 80 | py |
ray | ray-master/doc/source/train/doc_code/key_concepts.py | # flake8: noqa
# isort: skip_file
# __session_report_start__
from ray.air import session, ScalingConfig
from ray.train.data_parallel_trainer import DataParallelTrainer
def train_fn(config):
for i in range(10):
session.report({"step": i})
trainer = DataParallelTrainer(
train_loop_per_worker=train_fn... | 4,409 | 24.789474 | 83 | py |
ray | ray-master/doc/source/_ext/callouts.py | from docutils import nodes
from sphinx.util.docutils import SphinxDirective
from sphinx.transforms import SphinxTransform
from docutils.nodes import Node
# BASE_NUM = 2775 # black circles, white numbers
BASE_NUM = 2459 # white circle, black numbers
class CalloutIncludePostTransform(SphinxTransform):
"""Code b... | 5,307 | 25.54 | 86 | py |
ray | ray-master/doc/source/ray-air/examples/pytorch_tabular_starter.py | # flake8: noqa
# isort: skip_file
# __air_generic_preprocess_start__
import ray
# Load data.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
# Split data into train and validation.
train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)
# Create a test dataset by drop... | 3,788 | 28.146154 | 84 | py |
ray | ray-master/doc/source/ray-air/examples/xgboost_starter.py | # flake8: noqa
# isort: skip_file
# __air_generic_preprocess_start__
import ray
# Load data.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
# Split data into train and validation.
train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)
# Create a test dataset by drop... | 2,028 | 27.577465 | 80 | py |
ray | ray-master/doc/source/ray-air/examples/tf_tabular_starter.py | # flake8: noqa
# isort: skip_file
# __air_generic_preprocess_start__
import ray
# Load data.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
# Split data into train and validation.
train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)
# Create a test dataset by drop... | 3,869 | 27.880597 | 87 | py |
ray | ray-master/doc/source/ray-air/doc_code/computer_vision.py | def main():
for framework in "torch", "tensorflow":
for datasource in "tfrecords", "images", "numpy", "parquet":
test(framework=framework, datasource=datasource)
def test(*, framework: str, datasource: str):
assert framework in {"torch", "tensorflow"}
assert datasource in {"tfrecords",... | 13,310 | 30.100467 | 87 | py |
ray | ray-master/doc/source/ray-air/doc_code/air_ingest_migration.py | # flake8: noqa
# isort: skip_file
# __legacy_api__
import random
import ray
from ray.air.config import ScalingConfig, DatasetConfig
from ray.data.preprocessors.batch_mapper import BatchMapper
from ray.train.torch import TorchTrainer
train_ds = ray.data.range_tensor(1000)
test_ds = ray.data.range_tensor(10)
# A rand... | 1,549 | 23.603175 | 79 | py |
ray | ray-master/doc/source/ray-air/doc_code/report_metrics_and_save_checkpoints.py | # flake8: noqa
# isort: skip_file
# __air_session_start__
import tensorflow as tf
from ray.air import session
from ray.air.checkpoint import Checkpoint
from ray.air.config import ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
def build_model() -> tf.keras.Model:
model = tf.keras.Sequential(
... | 1,437 | 24.22807 | 77 | py |
ray | ray-master/doc/source/ray-air/doc_code/tf_starter.py | # flake8: noqa
# isort: skip_file
# __air_tf_train_start__
import ray
import tensorflow as tf
from ray.air import session
from ray.air.integrations.keras import ReportCheckpointCallback
from ray.train.tensorflow import TensorflowTrainer
from ray.air.config import ScalingConfig
# If using GPUs, set this to True.
use... | 2,135 | 26.384615 | 84 | py |
ray | ray-master/doc/source/ray-air/doc_code/torch_trainer.py | import torch
import torch.nn as nn
import ray
from ray import train
from ray.air import session, Checkpoint
from ray.train.torch import TorchTrainer
from ray.air.config import ScalingConfig
# If using GPUs, set this to True.
use_gpu = False
input_size = 1
layer_size = 15
output_size = 1
num_epochs = 3
class Neur... | 1,837 | 26.029412 | 83 | py |
ray | ray-master/doc/source/ray-air/doc_code/pytorch_starter.py | # flake8: noqa
# isort: skip_file
# __air_pytorch_preprocess_start__
from torchvision import datasets
from torchvision.transforms import ToTensor
# Download training data from open datasets.
training_data = datasets.FashionMNIST(
root="~/data",
train=True,
download=True,
transform=ToTensor(),
)
# Do... | 3,623 | 26.454545 | 78 | py |
ray | ray-master/doc/source/ray-air/doc_code/hvd_trainer.py | import ray
import ray.train as train
import ray.train.torch # Need this to use `train.torch.get_device()`
import horovod.torch as hvd
import torch
import torch.nn as nn
from ray.air import session, Checkpoint
from ray.train.horovod import HorovodTrainer
from ray.air.config import ScalingConfig
# If using GPUs, set th... | 2,168 | 28.712329 | 78 | py |
ray | ray-master/doc/source/ray-air/doc_code/air_key_concepts.py | # flake8: noqa
# isort: skip_file
# __air_preprocessors_start__
import ray
import pandas as pd
from sklearn.datasets import load_breast_cancer
from ray.data.preprocessors import *
# Split data into train and validation.
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
train_dataset, v... | 3,865 | 25.29932 | 88 | py |
ray | ray-master/doc/source/ray-air/doc_code/accelerate_trainer.py | import torch
import torch.nn as nn
from accelerate import Accelerator
import ray
from ray.air import session, Checkpoint
from ray.train.huggingface import AccelerateTrainer
from ray.air.config import ScalingConfig
# If using GPUs, set this to True.
use_gpu = False
input_size = 1
layer_size = 15
output_size = 1
nu... | 2,122 | 27.689189 | 85 | py |
ray | ray-master/doc/source/ray-air/doc_code/tuner.py | # flake8: noqa
# isort: skip_file
# __basic_start__
import ray
from ray import tune
from ray.tune import Tuner
from ray.train.xgboost import XGBoostTrainer
dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
trainer = XGBoostTrainer(
label_column="target",
params={
"objec... | 6,407 | 24.83871 | 86 | py |
ray | ray-master/doc/source/ray-air/doc_code/preprocessors.py | # flake8: noqa
# isort: skip_file
# __preprocessor_setup_start__
import pandas as pd
import ray
from ray.data.preprocessors import MinMaxScaler
from ray.data.preprocessors.scaler import StandardScaler
# Generate two simple datasets.
dataset = ray.data.range(8)
dataset1, dataset2 = dataset.split(2)
print(dataset1.tak... | 6,108 | 29.393035 | 99 | py |
ray | ray-master/doc/source/ray-air/doc_code/xgboost_trainer.py | import ray
from ray.train.xgboost import XGBoostTrainer
from ray.air.config import ScalingConfig
train_dataset = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
trainer = XGBoostTrainer(
label_column="y",
params={"objective": "reg:squarederror"},
scaling_config=ScalingConfig(num_workers=3),... | 385 | 26.571429 | 78 | py |
ray | ray-master/doc/source/ray-air/doc_code/predictors.py | # flake8: noqa
# isort: skip_file
import os
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
# __use_predictor_start__
import numpy as np
import tensorflow as tf
import ray
from ray.train.batch_predictor import BatchPredictor
from ray.train.tensorflow import (
TensorflowCheckpoint,
TensorflowPredictor,
)
... | 3,745 | 26.144928 | 85 | py |
ray | ray-master/doc/source/ray-air/doc_code/air_ingest_new.py | # flake8: noqa
# isort: skip_file
# __basic__
import ray
from ray.air import session
from ray.air.config import ScalingConfig
from ray.train.torch import TorchTrainer
import numpy as np
from typing import Dict
# Load the data.
train_ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
## Uncomm... | 4,304 | 28.087838 | 83 | py |
ray | ray-master/doc/source/tune/doc_code/pytorch_optuna.py | # flake8: noqa
import os
from filelock import FileLock
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
EPOCH_SIZE = 512
TEST_SIZE = 256
def train(model, optimizer, train_loader, device=None):
device = device or torch.device("cpu")
model.train()
for batc... | 3,411 | 27.915254 | 86 | py |
ray | ray-master/doc/source/tune/doc_code/faq.py | # flake8: noqa
# __reproducible_start__
import numpy as np
from ray import tune
from ray.air import session, ScalingConfig
def train(config):
# Set seed for trainable random result.
# If you remove this line, you will get different results
# each time you run the trial, even if the configuration
# is... | 12,698 | 24.551308 | 87 | py |
ray | ray-master/doc/source/tune/doc_code/trial_checkpoint.py | # flake8: noqa
# __class_api_checkpointing_start__
import os
import torch
from torch import nn
from ray import air, tune
class MyTrainableClass(tune.Trainable):
def setup(self, config):
self.model = nn.Sequential(
nn.Linear(config.get("input_size", 32), 32), nn.ReLU(), nn.Linear(32, 10)
... | 4,817 | 24.492063 | 85 | py |
ray | ray-master/doc/source/tune/doc_code/keras_hyperopt.py | # flake8: noqa
accuracy = 42
# __keras_hyperopt_start__
from ray import tune
from ray.tune.search.hyperopt import HyperOptSearch
import keras
def objective(config): # <1>
model = keras.models.Sequential()
model.add(keras.layers.Dense(784, activation=config["activation"]))
model.add(keras.layers.Dense(1... | 859 | 22.888889 | 85 | py |
ray | ray-master/doc/source/cluster/doc_code/xgboost_submit.py | from ray.job_submission import JobSubmissionClient
client = JobSubmissionClient("http://127.0.0.1:8265")
kick_off_xgboost_benchmark = (
# Clone ray. If ray is already present, don't clone again.
"git clone https://github.com/ray-project/ray || true;"
# Run the benchmark.
" python ray/release/air_tests... | 600 | 29.05 | 81 | py |
ray | ray-master/doc/source/cluster/doc_code/pytorch_training_e2e_submit.py | from ray.job_submission import JobSubmissionClient
client = JobSubmissionClient("http://127.0.0.1:8265")
kick_off_pytorch_benchmark = (
# Clone ray. If ray is already present, don't clone again.
"git clone -b ray-2.2.0 https://github.com/ray-project/ray || true;"
# Run the benchmark.
"python ray/relea... | 667 | 32.4 | 83 | py |
ray | ray-master/doc/source/data/doc_code/key_concepts.py | # flake8: noqa
# fmt: off
# __resource_allocation_1_begin__
import ray
from ray import tune
# This workload will use spare cluster resources for execution.
def objective(*args):
ray.data.range(10).show()
# Create a cluster with 4 CPU slots available.
ray.init(num_cpus=4)
# By setting `max_concurrent_trials=3`, ... | 2,655 | 26.102041 | 100 | py |
ray | ray-master/doc/source/serve/doc_code/tutorial_tensorflow.py | # fmt: off
# __doc_import_begin__
from ray import serve
import os
import tempfile
import numpy as np
from starlette.requests import Request
from typing import Dict
import tensorflow as tf
# __doc_import_end__
# fmt: on
# __doc_train_model_begin__
TRAINED_MODEL_PATH = os.path.join(tempfile.gettempdir(), "mnist_model.... | 2,226 | 28.302632 | 83 | py |
ray | ray-master/doc/source/serve/doc_code/object_detection.py | from contextlib import contextmanager
# __example_code_start__
import torch
from PIL import Image
import numpy as np
from io import BytesIO
from fastapi.responses import Response
from fastapi import FastAPI
from ray import serve
app = FastAPI()
@serve.deployment(num_replicas=1, route_prefix="/")
@serve.ingress(ap... | 1,969 | 23.625 | 82 | py |
ray | ray-master/doc/source/serve/doc_code/tutorial_pytorch.py | # fmt: off
# __doc_import_begin__
from ray import serve
from io import BytesIO
from PIL import Image
from starlette.requests import Request
from typing import Dict
import torch
from torchvision import transforms
from torchvision.models import resnet18
# __doc_import_end__
# fmt: on
# __doc_define_servable_begin__
@... | 1,678 | 29.527273 | 85 | py |
ray | ray-master/doc/source/serve/doc_code/multiplexed.py | # __serve_deployment_example_begin__
from ray import serve
import aioboto3
import torch
import starlette
@serve.deployment
class ModelInferencer:
def __init__(self):
self.bucket_name = "my_bucket"
@serve.multiplexed(max_num_models_per_replica=3)
async def get_model(self, model_id: str):
... | 1,109 | 26.073171 | 83 | py |
ray | ray-master/doc/source/serve/doc_code/distilbert.py | from contextlib import contextmanager
# __example_code_start__
from transformers import pipeline
from fastapi import FastAPI
from ray import serve
import torch
app = FastAPI()
@serve.deployment(num_replicas=1, route_prefix="/")
@serve.ingress(app)
class APIIngress:
def __init__(self, distilbert_model_handle) -... | 1,922 | 24.986486 | 84 | py |
ray | ray-master/doc/source/serve/doc_code/stable_diffusion.py | from contextlib import contextmanager
# __example_code_start__
from io import BytesIO
from fastapi import FastAPI
from fastapi.responses import Response
import torch
from ray import serve
app = FastAPI()
@serve.deployment(num_replicas=1, route_prefix="/")
@serve.ingress(app)
class APIIngress:
def __init__(se... | 2,765 | 25.596154 | 85 | py |
ray | ray-master/ci/env/check_minimal_install.py | """
This script ensures that some dependencies are _not_ installed in the
current python environment.
This is to ensure that tests with minimal dependencies are not tainted
by too many installed packages.
It also ensures the correct Python version.
"""
from typing import List
import argparse
import sys
# These are ... | 2,268 | 27.721519 | 84 | py |
ray | ray-master/ci/env/cleanup_test_state.py | """
This script is used to clean up state after running test scripts, including
on external services. For instance, this script can be used to remove the runs
from WandB that have been saved during unit testing or when running examples.
"""
import sys
def clear_wandb_project():
import wandb
# This is hardcod... | 1,705 | 27.433333 | 79 | py |
ray | ray-master/release/jobs_tests/workloads/jobs_specify_num_gpus.py | """Job submission test
This test checks that when using the Ray Jobs API with num_gpus
specified, the driver is run on a node that has a GPU.
Test owner: architkulkarni
Acceptance criteria: Should run through and print "PASSED"
"""
import argparse
import json
import os
import time
import torch
from typing import Op... | 2,955 | 29.474227 | 85 | py |
ray | ray-master/release/jobs_tests/workloads/jobs_check_cuda_available.py | """Job Submission CUDA available test
Checks that GPU resources are available in the job submission
driver script.
This file is a driver script to be submitted to a Ray cluster via
the Ray Jobs API. This is done by specifying `type: job` in
`release_tests.yaml` (as opposed to, say, `type: sdk_command`).
Release test... | 1,310 | 24.211538 | 78 | py |
ray | ray-master/release/long_running_distributed_tests/workloads/pytorch_pbt_failure.py | import argparse
import sys
import numpy as np
import ray
from ray import tune
from ray.air.config import CheckpointConfig, FailureConfig, RunConfig, ScalingConfig
from ray.train.examples.pytorch.tune_cifar_torch_pbt_example import train_func
from ray.train.torch import TorchConfig, TorchTrainer
from ray.tune.schedule... | 2,524 | 30.17284 | 88 | py |
ray | ray-master/release/rllib_tests/checkpointing_tests/test_learner_group_checkpointing.py | import gymnasium as gym
import itertools
import numpy as np
import tempfile
import unittest
import ray
from ray.rllib.core.learner.scaling_config import LearnerGroupScalingConfig
from ray.rllib.core.testing.utils import get_learner_group
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.test_u... | 5,031 | 38.622047 | 87 | py |
ray | ray-master/release/rllib_tests/checkpointing_tests/test_e2e_rl_module_restore.py | import gymnasium as gym
import numpy as np
import shutil
import tempfile
import tree
import unittest
import ray
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
from ray.rllib.algorithms.ppo.tf.ppo_tf_rl_module import PPOTfRLModule
from ray.rllib.algorithms.ppo... | 14,475 | 40.47851 | 88 | py |
ray | ray-master/release/rllib_tests/multi_gpu_with_lstm_learning_tests/run.py | """Multi-GPU + LSTM learning tests for RLlib (torch and tf).
"""
import json
import os
from pathlib import Path
from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
if __name__ == "__main__":
# Get path of this very script to look for yaml files.
abs_yaml_path = Path(__file__).parent
print... | 1,030 | 27.638889 | 80 | py |
ray | ray-master/release/rllib_tests/multi_gpu_with_attention_learning_tests/run.py | """Multi-GPU + GTrXL (attention net) learning tests for RLlib (torch and tf).
"""
import json
import os
from pathlib import Path
from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
if __name__ == "__main__":
# Get path of this very script to look for yaml files.
abs_yaml_path = Path(__file__)... | 1,052 | 28.25 | 85 | py |
ray | ray-master/release/rllib_tests/stress_tests/run_stress_tests.py | """Stress tests for RLlib (torch and tf).
Runs IMPALA on 4 GPUs and 100s of CPUs.
"""
import json
import os
from pathlib import Path
from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
... | 1,168 | 23.87234 | 72 | py |
ray | ray-master/release/rllib_tests/multi_gpu_learning_tests/run.py | """Multi-GPU learning tests for RLlib (torch and tf).
"""
import json
import os
from pathlib import Path
from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
if __name__ == "__main__":
# Get path of this very script to look for yaml files.
abs_yaml_path = Path(__file__).parent
print("abs_y... | 1,013 | 27.166667 | 72 | py |
ray | ray-master/release/rllib_tests/learning_tests/run.py | """Learning regression tests for RLlib (torch and tf).
Runs Atari/MuJoCo benchmarks for all major algorithms.
"""
import json
import os
from pathlib import Path
from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
... | 2,057 | 27.985915 | 84 | py |
ray | ray-master/release/serve_tests/workloads/serve_resnet_benchmark.py | """
Serve Resnet50 model benchmarking.
Including tasks:
1. Image downloading
2. Image convesion to tensors.
3. Batch tensors.
4. Inference with Restnet50 model
Beside last step, all steps are done inside the CPU, and model inference step is
finished on the GPU device.
In the benchmarking, the image download and tens... | 7,064 | 32.483412 | 88 | py |
ray | ray-master/release/benchmark-worker-startup/benchmark_worker_startup.py | #!/usr/bin/env python3
"""
$ ./benchmark_worker_startup.py --help
usage: benchmark_worker_startup.py [-h] --num_gpus_in_cluster
NUM_GPUS_IN_CLUSTER
--num_cpus_in_cluster
NUM_CPUS_IN_CLUSTER
... | 13,113 | 34.928767 | 87 | py |
ray | ray-master/release/benchmark-worker-startup/test_single_configuration.py | #!/usr/bin/env python3
"""
Helper file for benchmark_worker_startup.py. This file runs a particular test
configuration.
"""
import argparse
import ray
import sys
import time
@ray.remote
class Actor:
def run_code(self, should_import_torch: bool):
if should_import_torch:
import torch # noqa: F... | 4,363 | 30.395683 | 86 | py |
ray | ray-master/release/nightly_tests/dataset/inference.py | import json
import os
import time
from typing import Any, Dict
import numpy as np
import torch
from torchvision import transforms
from torchvision.models import resnet50
import ray
class ImageClassifier:
def __init__(self):
self.model = resnet50(pretrained=True).eval().half().cuda()
def __call__(se... | 1,639 | 22.098592 | 84 | py |
ray | ray-master/release/nightly_tests/dataset/data_ingest_benchmark.py | import numpy as np
import json
import os
import sys
import time
import argparse
import ray
from ray.data import Dataset
from ray.data import DatasetPipeline
import pandas as pd
import torch
GiB = 1024 * 1024 * 1024
@ray.remote
class ConsumingActor:
def __init__(self, rank):
self._rank = rank
def c... | 8,748 | 33.581028 | 109 | py |
ray | ray-master/release/nightly_tests/dataset/pipelined_training.py | from collections import OrderedDict
import argparse
import os
import json
import ray
import time
import timeit
import torch.optim as optim
import numpy as np
import torch
import horovod.torch as hvd
from horovod.ray import RayExecutor
from ray_shuffling_data_loader.data_generation import DATA_SPEC
from ray_shuffling_... | 11,123 | 31.717647 | 88 | py |
ray | ray-master/release/nightly_tests/dataset/dataset_shuffle_data_loader.py | import argparse
import os
import json
import time
import ray
from pyarrow import fs
import numpy as np
import torch
PATHS = {
"aws": [
f"s3://shuffling-data-loader-benchmarks/data/input_data_{i}.parquet.snappy"
for i in range(0, 25)
],
"gcp": [
f"gcs://shuffling-data-loader-benchm... | 4,327 | 30.136691 | 87 | py |
ray | ray-master/release/nightly_tests/dataset/iter_tensor_batches_benchmark.py | import argparse
import numpy as np
from typing import Optional, Union, List
import ray
from ray.data.dataset import Dataset
from benchmark import Benchmark
def iter_torch_batches(
ds: Dataset,
batch_size: Optional[int] = None,
local_shuffle_buffer_size: Optional[int] = None,
prefetch_batches: int = ... | 4,831 | 27.591716 | 122 | py |
ray | ray-master/release/lightgbm_tests/create_test_data.py | import argparse
import numpy as np
import os
from xgboost_ray.tests.utils import create_parquet
if __name__ == "__main__":
if "OMP_NUM_THREADS" in os.environ:
del os.environ["OMP_NUM_THREADS"]
parser = argparse.ArgumentParser(description="Create fake data.")
parser.add_argument(
"filename... | 1,350 | 25.490196 | 86 | py |
ray | ray-master/release/xgboost_tests/create_test_data.py | import argparse
import numpy as np
import os
from xgboost_ray.tests.utils import create_parquet
if __name__ == "__main__":
if "OMP_NUM_THREADS" in os.environ:
del os.environ["OMP_NUM_THREADS"]
parser = argparse.ArgumentParser(description="Create fake data.")
parser.add_argument(
"filename... | 1,350 | 25.490196 | 86 | py |
ray | ray-master/release/xgboost_tests/workloads/tune_small.py | """Small Ray Tune run (4 trials, 4 actors).
This training run will start 4 Ray Tune Trials, each starting 4 actors.
The cluster comprises 4 nodes.
Test owner: krfricke
Acceptance criteria: Should run through and report final results, as well
as the Ray Tune results table. No trials should error. All trials should
ru... | 1,834 | 24.136986 | 84 | py |
ray | ray-master/release/xgboost_tests/workloads/distributed_api_test.py | """Distributed XGBoost API test
This test runs unit tests on a distributed cluster. This will confirm that
XGBoost API features like custom metrics/objectives work with remote
trainables.
Test owner: krfricke
Acceptance criteria: Unit tests should pass (requires pytest).
"""
import ray
from xgboost_ray.tests.test_... | 877 | 24.085714 | 75 | py |
ray | ray-master/release/xgboost_tests/workloads/ft_small_elastic.py | """Fault tolerance test (small cluster, elastic training)
In this run, two training actors will die after some time. It is expected that
in both cases xgboost_ray stops training, but continues right away with the
remaining three actors. Shortly after, the actors will be restarted and
re-integrated into the training lo... | 3,052 | 31.136842 | 85 | py |
ray | ray-master/release/xgboost_tests/workloads/ft_small_non_elastic.py | """Fault tolerance test (small cluster, non-elastic training)
In this run, two training actors will die after some time. It is expected that
in both cases xgboost_ray stops training, restarts the dead actors, and
continues training with all four actors.
Test owner: krfricke
Acceptance criteria: Should run through an... | 2,121 | 28.472222 | 84 | py |
ray | ray-master/release/xgboost_tests/workloads/tune_32x4.py | """Moderate Ray Tune run (32 trials, 4 actors).
This training run will start 32 Ray Tune trials, each starting 4 actors.
The cluster comprises 32 nodes.
Test owner: krfricke
Acceptance criteria: Should run through and report final results, as well
as the Ray Tune results table. No trials should error. All trials sho... | 1,840 | 24.219178 | 84 | py |
ray | ray-master/release/xgboost_tests/workloads/tune_4x32.py | """Moderate Ray Tune run (4 trials, 32 actors).
This training run will start 4 Ray Tune trials, each starting 32 actors.
The cluster comprises 32 nodes.
Test owner: krfricke
Acceptance criteria: Should run through and report final results, as well
as the Ray Tune results table. No trials should error. All trials sho... | 1,841 | 24.232877 | 84 | py |
ray | ray-master/release/xgboost_tests/workloads/train_gpu.py | """Training on a GPU cluster.
This will train a small dataset on a distributed GPU cluster.
Test owner: krfricke
Acceptance criteria: Should run through and report final results.
Notes: The test will report output such as this:
```
[05:14:49] WARNING: ../src/gbm/gbtree.cc:350: Loading from a raw memory buffer
on CP... | 1,588 | 24.222222 | 80 | py |
ray | ray-master/release/xgboost_tests/workloads/release_test_util.py | import glob
import os
import time
import ray
from xgboost_ray import (
train,
RayDMatrix,
RayFileType,
RayDeviceQuantileDMatrix,
RayParams,
)
from xgboost_ray.session import get_actor_rank, put_queue
from xgboost.callback import TrainingCallback
from xgboost.rabit import get_world_size
if "OMP_NU... | 4,483 | 25.376471 | 84 | py |
ray | ray-master/release/xgboost_tests/workloads/train_small.py | """Small cluster training
This training run will start 4 workers on 4 nodes (including head node).
Test owner: krfricke
Acceptance criteria: Should run through and report final results.
"""
import json
import os
import time
import ray
from xgboost_ray import RayParams
from release_test_util import train_ray
if __... | 1,582 | 23.353846 | 74 | py |
ray | ray-master/release/xgboost_tests/workloads/train_moderate.py | """Moderate cluster training
This training run will start 32 workers on 32 nodes (including head node).
Test owner: krfricke
Acceptance criteria: Should run through and report final results.
"""
import json
import os
import time
import ray
from xgboost_ray import RayParams
from release_test_util import train_ray
... | 1,147 | 21.96 | 85 | py |
ray | ray-master/release/tune_tests/scalability_tests/create_test_data.py | import argparse
import numpy as np
import os
from xgboost_ray.tests.utils import create_parquet
if __name__ == "__main__":
if "OMP_NUM_THREADS" in os.environ:
del os.environ["OMP_NUM_THREADS"]
parser = argparse.ArgumentParser(description="Create fake data.")
parser.add_argument(
"filename... | 1,464 | 26.12963 | 86 | py |
ray | ray-master/release/tune_tests/scalability_tests/workloads/test_xgboost_sweep.py | """Large-scale XGBoost parameter sweep
In this run, we will start 32 trials of 32 actors each running distributed
XGBoost training. This test is more about making sure that the run succeeds
than about total runtime. However, it is expected that this is faster than
1 hour.
We fix the max_depth to 4 and the number of b... | 3,371 | 28.321739 | 88 | py |
ray | ray-master/release/long_running_tests/workloads/many_ppo.py | # This workload tests running many instances of PPO (many actors)
# This covers https://github.com/ray-project/ray/pull/12148
import ray
from ray.tune import run_experiments
from ray.tune.utils.release_test_util import ProgressCallback
from ray._private.test_utils import monitor_memory_usage
num_redis_shards = 5
redi... | 1,461 | 26.074074 | 73 | py |
ray | ray-master/release/golden_notebook_tests/workloads/torch_tune_serve_test.py | import argparse
import atexit
import json
import os
import time
import subprocess
import ray
from ray.air import session
from ray.air.config import ScalingConfig, RunConfig
from ray.air.util.node import _force_on_current_node
from ray.tune.tune_config import TuneConfig
import requests
import torch
import torch.nn as n... | 8,875 | 28.488372 | 86 | py |
ray | ray-master/release/air_tests/horovod/workloads/horovod_tune_test.py | import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
from torchvision.models import resnet18
import ray
from ray.air import RunConfig, session
from ray.air.config import ScalingConfig, FailureConfig, CheckpointConfi... | 6,383 | 31.406091 | 87 | py |
ray | ray-master/release/air_tests/air_benchmarks/mlperf-train/resnet50_ray_air.py | import tensorflow as tf
import numpy as np
import os
import pandas as pd
import time
import logging
import csv
import json
import ray
from ray.air import session
from ray.train.tensorflow import prepare_dataset_shard, TensorflowTrainer
from ray.air.config import ScalingConfig
from ray.data.preprocessors import BatchMa... | 21,807 | 33.781499 | 179 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/_torch_prepare.py | import torchvision
torchvision.datasets.FashionMNIST("/tmp/data_fashion_mnist", download=True)
| 96 | 23.25 | 75 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/_tensorflow_prepare.py | import tensorflow as tf
tf.keras.datasets.fashion_mnist.load_data()
| 69 | 16.5 | 43 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/pytorch_training_e2e.py | import click
import time
import json
import os
from typing import Dict
import numpy as np
from torchvision import transforms
from torchvision.models import resnet18
import torch.nn as nn
import torch.optim as optim
import ray
from ray.train.torch import TorchCheckpoint
from ray.data.preprocessors import BatchMapper, ... | 4,681 | 31.971831 | 88 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/xgboost_benchmark.py | from functools import wraps
import json
import multiprocessing
from multiprocessing import Process
import os
import time
import traceback
import xgboost as xgb
import ray
from ray import data
from ray.train.xgboost import (
XGBoostTrainer,
XGBoostCheckpoint,
XGBoostPredictor,
)
from ray.train.batch_predict... | 5,710 | 30.20765 | 88 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/tensorflow_benchmark.py | import json
import os
import time
from pathlib import Path
import click
import numpy as np
import tensorflow as tf
from typing import List, Tuple
CONFIG = {"lr": 1e-3, "batch_size": 64}
VANILLA_RESULT_JSON = "/tmp/vanilla_out.json"
def mnist_dataset(batch_size: int) -> tf.data.Dataset:
(x_train, y_train), _ = t... | 14,315 | 31.462585 | 87 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/torch_benchmark.py | import json
import os
import time
from pathlib import Path
from typing import Dict, Tuple
import click
import numpy as np
import torch
from torch import nn, distributed
from torch.utils.data import DataLoader, DistributedSampler
from torch.utils.data.dataloader import default_collate
from torchvision import datasets
f... | 18,324 | 29.643813 | 87 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/gpu_batch_prediction.py | import click
import time
import json
import os
import numpy as np
import torch
from torchvision import transforms
from torchvision.models import resnet18
import ray
from ray.train.torch import TorchCheckpoint, TorchPredictor
from ray.train.batch_predictor import BatchPredictor
from ray.data.preprocessors import Torch... | 2,970 | 29.316327 | 88 | py |
ray | ray-master/release/air_tests/air_benchmarks/workloads/tune_torch_benchmark.py | import json
import os
import time
import timeit
from typing import Optional, Dict
import click
import numpy as np
import ray
from ray.air import ScalingConfig
from ray.train.torch import TorchTrainer
CONFIG = {"lr": 1e-3, "batch_size": 64, "epochs": 20}
def prepare_mnist():
# Pre-download the data onto each n... | 5,255 | 26.375 | 88 | py |
ray | ray-master/release/ray_release/tests/test_alerts.py | import sys
import pytest
from ray_release.alerts import (
handle,
default,
# long_running_tests,
# rllib_tests,
# tune_tests,
# xgboost_tests,
)
from ray_release.test import Test
from ray_release.exception import ReleaseTestConfigError, ResultsAlert
from ray_release.result import (
Result,
... | 1,264 | 24.3 | 85 | py |
ray | ray-master/release/ray_release/alerts/xgboost_tests.py | from typing import Optional
from ray_release.test import Test
from ray_release.result import Result
def handle_result(
test: Test,
result: Result,
) -> Optional[str]:
test_name = test["name"]
time_taken = result.results.get("time_taken", float("inf"))
num_terminated = result.results.get("trial_s... | 1,822 | 28.403226 | 80 | py |
ray | ray-master/release/ray_release/alerts/tune_tests.py | from typing import Optional
from ray_release.test import Test
from ray_release.result import (
Result,
ResultStatus,
)
def handle_result(
test: Test,
result: Result,
) -> Optional[str]:
test_name = test["name"]
msg = ""
success = result.status == ResultStatus.SUCCESS.value
time_taken... | 2,275 | 30.611111 | 80 | py |
ray | ray-master/release/ray_release/alerts/handle.py | from ray_release.test import Test
from ray_release.exception import ReleaseTestConfigError, ResultsAlert
from ray_release.logger import logger
from ray_release.result import Result
from ray_release.alerts import (
default,
long_running_tests,
tune_tests,
xgboost_tests,
)
# The second bit in the tuple... | 1,577 | 28.773585 | 87 | py |
ray | ray-master/release/alpa_tests/train_opt_2_7b_minimum.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 17,044 | 31.842004 | 86 | py |
ray | ray-master/release/ml_user_tests/xgboost/train_gpu_connect.py | """Small cluster training
This training run will start 4 workers on 4 nodes (including head node).
Test owner: krfricke
Acceptance criteria: Should run through and report final results.
"""
import json
import os
import time
import ray
if __name__ == "__main__":
os.environ["RXGB_PLACEMENT_GROUP_TIMEOUT_S"] = "1... | 1,601 | 22.910448 | 88 | py |
ray | ray-master/release/ml_user_tests/xgboost/release_test_util.py | import glob
import os
import time
import ray
from xgboost_ray import (
train,
RayDMatrix,
RayFileType,
RayDeviceQuantileDMatrix,
RayParams,
)
from xgboost_ray.session import get_actor_rank, put_queue
from xgboost.callback import TrainingCallback
from xgboost.rabit import get_world_size
if "OMP_NU... | 4,483 | 25.376471 | 84 | py |
ray | ray-master/release/ml_user_tests/horovod/horovod_example.py | # This file is duplicated in ray/tests/horovod
import argparse
import os
from filelock import FileLock
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed
import horovod.torch as hvd
from horovod.ray import ... | 7,139 | 28.382716 | 86 | py |
ray | ray-master/release/ml_user_tests/train/train_torch_linear_test.py | import json
import os
import time
import ray
from ray.train.examples.pytorch.torch_linear_example import train_linear
if __name__ == "__main__":
start = time.time()
addr = os.environ.get("RAY_ADDRESS")
job_name = os.environ.get("RAY_JOB_NAME", "train_torch_linear_test")
if addr is not None and addr... | 842 | 23.794118 | 83 | py |
ray | ray-master/release/train_tests/horovod/train_horovod_multi_node_test.py | import json
import os
import time
import ray
from ray.air import ScalingConfig
from ray.air.constants import TRAINING_ITERATION
from ray.train.examples.horovod.horovod_example import (
train_func as horovod_torch_train_func,
)
from ray.train.horovod.horovod_trainer import HorovodTrainer
if __name__ == "__main__":... | 1,104 | 28.078947 | 67 | py |
ray | ray-master/release/lightning_tests/workloads/test_trainer.py | import os
import time
import json
from pytorch_lightning.loggers.csv_logs import CSVLogger
import ray
from ray.air.config import RunConfig, ScalingConfig
from ray.train.lightning import LightningTrainer, LightningConfigBuilder
from lightning_test_utils import MNISTClassifier, MNISTDataModule
if __name__ == "__main_... | 1,546 | 27.648148 | 84 | py |
ray | ray-master/release/lightning_tests/workloads/lightning_test_utils.py | import os
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from torchmetrics import Accuracy
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
from torchvision import transforms
class MNISTClassifier(pl.LightningModule):
def __init__(self, lr, ... | 2,981 | 34.5 | 86 | py |
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