tchaton

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tchaton

lightning install quick-start
curl https://gist.githubusercontent.com/tchaton/b81c8d8ba0f4dd39a47bfa607d81d6d5/raw/8d9d70573a006d95bdcda8492e798d0771d7e61b/train_script.py > train_script.py

tchaton

import os
import torch
from torch import nn
import torchvision.transforms as T
from pytorch_lightning import LightningDataModule, LightningModule
from pytorch_lightning.utilities.cli import LightningCLI
from torch.nn import functional as F
from torchmetrics import Accuracy
from torchvision.datasets import MNIST

tchaton

import os
import torch
import torchvision.transforms as T
from pytorch_lightning import LightningDataModule, LightningModule
from pytorch_lightning.utilities.cli import LightningCLI
from torch.nn import functional as F
import torch.nn as nn
from torchmetrics import Accuracy
from torchvision.datasets import MNIST

tchaton

class Lite(LightningLite):

    def run(self):

        # Transfer and concatenate tensors across processes
        self.all_gather(...)

        # Transfer an object from one process to all the others
        self.broadcast(..., src=...)

tchaton

from torch import nn
from flash.image import ImageClassifier
from flash.core.classification import Logits
from functools import partial

head = nn.Sequential(
    nn.Linear(512, 512),
    nn.ReLU(True),
    nn.Dropout(),
    nn.Linear(512, 512),

tchaton

from flash.image import ImageClassifier, ImageClassificationData
class CIFAR10DataModule(ImageClassificationData):
    
    @property
    def num_classes(self):
        return 10
      
dm = CIFAR10DataModule.from_datasets(
    train_dataset=train_set,
    test_dataset=test_set,

tchaton

class Loop:

    def run(self):

        self.reset()
        self.on_run_start()

        while not self.done:
            self.advance()

tchaton

for epoch in range(num_epochs):
    TrainingEpochLoop(model, optimizer, dataloader).run()

tchaton

# FitLoop
for epoch in range(max_epochs):

    # TrainingEpochLoop
    for batch_idx, batch in enumerate(train_dataloader):

        # TrainingBatchLoop
        for split_batch in tbptt_split(batch):

          	if lightning_module.automatic_optimization: