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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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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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about-time==4.2.1
absl-py==1.4.0
accelerate==0.19.0
adal==1.2.7
aim==3.17.5
aim-ui==3.17.5
aimrecords==0.0.7
aimrocks==0.4.0
aioboto3==11.2.0
aiobotocore==2.5.0

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# Minimal Example adapted from https://huggingface.co/docs/transformers/training
import deepspeed
import evaluate
import torch
from datasets import load_dataset
from deepspeed.accelerator import get_accelerator
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
    AutoModelForSequenceClassification,

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import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,

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import os
import evaluate
import numpy as np
from datasets import load_dataset
from ray.train import RunConfig, ScalingConfig, CheckpointConfig, Checkpoint
from ray.train.torch import TorchTrainer
from transformers import AutoTokenizer
from transformers import (
    AutoModelForSequenceClassification,
    DataCollatorWithPadding,

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PyTorch Lightning User Guides

Converting an existing training loop

You should replace the arguments in pl.Trainer with Ray Train's implementations.

import pytorch_lightning as pl
+ from ray.train.lightning import (
+     get_devices,
+ prepare_trainer,

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import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from filelock import FileLock
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision.datasets import MNIST
from torchvision import transforms

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class MNISTClassifier(pl.LightningModule):
    def __init__(self, config):
        super(MNISTClassifier, self).__init__()
        self.accuracy = Accuracy()
        
        # [!] Determine your data augmentation strategy here
        self.batch_size = config["batch_size"]
        self.aug_strategy = config["augmentation_strategy"]
        

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import ray
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset, load_metric
import numpy as np
from ray.train.torch import TorchTrainer