示例 import ray ray.init() # 从列表创建 Dataset dataset = ray.data.from_items([1, 2, 3, 4, 5]) # 显示数据 print(dataset.take(5)) # 输出 [1, 2, 3, 4, 5] 2. Blocks 定义 Block 是Dataset 的基本组成单元,表示一个分区的数据。
from tensorflow.keras.datasets import cifar10from tensorflow.keras.utils import to_categoricalray.init()def train_model(): (x_train, y_train), (x_test, y_test) = cifar10.load_data() y_train = to_categorical(y_train) y_test = to_categorical(y_test) x_train = x_train.astype('...
Runtime: 0.108 seconds, data: (0, 'Learning') (1, 'Ray') Runtime: 0.308 seconds, data: (2, 'Flexible') (3, 'Distributed') Runtime: 0.508 seconds, data: (4, 'Python') (5, 'for') Runtime: 0.709 seconds, data: (6, 'Data') (7, 'Science') 请注意,在while循环中,与其仅仅...
Ray Data用来处理机器学习中的数据。 $ pip install -U "ray[data]" 下面是使用Ray Data来并行处理数据。 from typing import Dict import numpy as np import ray # Create datasets from on-disk files, Python objects, and cloud storage like S3. ds = ray.data.read_csv("s3://anonymous@ray-exampl...
Ray带有一个实用函数from_iterators,可以创建并行迭代器,开发者可以用它包装data_generator生成器函数。 复制 importray def ray_generator(): num_parallel_processes=cpu_count()returnray.util.iter.from_iterators([data_generator]*num_parallel_processes).gather_async() ...
在Ray Data中,这样的推理流水线通过启动计划在GPU上运行的actors的转换来表达(num_gpus=1 和 compute=ActorPoolStrategy): import ray from ray.data import ActorPoolStrategy Model = ... preprocess_fn = ... # Define the model as a stateful class with cached setup. class MyModelCallableCls: def _...
from ray.util.sgd.torch import TrainingOperator # https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py from ray.util.sgd.torch.resnet import ResNet18 def cifar_creator(config): """Returns dataloaders to be used in `train` and `validate`.""" ...
import numpy as np import torch import torch.optim as optim from ray import tune from ray.tune.examples.mnist_pytorch import get_data_loaders, train, test import ray import sys if len(sys.argv) > 1: ray.init(redis_address=sys.argv[1]) ...
Requirement already satisfied:dataclassesin/usr/local/lib/python3.6/site-packages(from ray[default])(0.8)Collecting protobuf>=3.15.3Downloading https://mirrors.aliyun.com/pypi/packages/53/4e/e2db88d0bb0bda6a879eea62fddbaf813719ce3770d458bc5580512d9c95/protobuf-3.17.3-cp36-cp36m-manylinux...
When using ray data map_batches, the memory is not released once the reference to dataset goes out of scope, thus starting to build up memory and ultimately workers getting killed due to OOM. Versions / Dependencies Ray: 2.34 Python: 3.9 Reproduction script import ray import numpy as np fro...