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accelerate==0.19.0
adal==1.2.7
aiofiles==22.1.0
aiohttp==3.8.5
aiohttp-cors==0.7.0
aiorwlock==1.3.0
aiosignal==1.3.1
aiosqlite==0.19.0
alabaster==0.7.13
anyio==3.7.1

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import os
import tempfile

import torch
from torch import nn
from torch.nn.parallel import DistributedDataParallel

import ray
from ray.train import Checkpoint, CheckpointConfig, RunConfig, ScalingConfig
from ray.train.torch import TorchTrainer

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import torch
import torch.nn as nn
import torch.optim as optim
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_backend  # noqa: F401

from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer, prepare_model
from ray.train.torch.xla import TorchXLAConfig

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# This script is tested with the PR(https://github.com/ray-project/ray/pull/39130) from AWS team.
# It configures the required environment variables for Neuron XLA.

import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_backend  # noqa: F401

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"""
Cluster: 16 x A10G GPUs
Command: python precompute_latents.py --subset_size 50 --mode debug
"""

import argparse
import io
import pandas as pd
import pyarrow.dataset as pds
import os

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import os
import time
import torch
from torch import nn

from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer
from ray.train.torch.xla import TorchXLAConfig

from torchvision.datasets import mnist

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import ray
import ray.train
import numpy as np
from ray.train.torch import TorchTrainer
from ray.train import ScalingConfig
from dataclasses import dataclass


@dataclass
class DummyDataclass:

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#!/usr/bin/env python3
# pylint: skip-file
import os

import torch
from torch import distributed as dist
from torchvision.models import resnet18
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor, Normalize, Compose
from torch.utils.data import DataLoader, DistributedSampler

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import ray

ray.init()

node_resources = {}
for node in ray.nodes():
    print(node, "\n")
    node_resources[node["NodeID"]] = node["Resources"]

import ray

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from collections import defaultdict
from ray.train._internal.utils import get_address_and_port
import ray
import os

import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
import time