tchaton

import lightning as L
import torch
import torch.nn.functional as F
from lightning.pytorch.demos import Transformer, WikiText2
from torch.utils.data import DataLoader, random_split
import os
from time import sleep


class LanguageModel(L.LightningModule):

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# Copyright The Lightning AI team.
#
# 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-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

tchaton

# main.py
# ! pip install torchvision
import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F
import lightning as L

# --------------------------------
# Step 1: Define a LightningModule
# --------------------------------
# A LightningModule (nn.Module subclass) defines a full *system*
# (ie: an LLM, diffusion model, autoencoder, or simple image classifier).

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# Copyright The Lightning AI team.
#
# 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-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

tchaton

import os
import tarfile
import torch
import torchvision.transforms.functional as fn
from PIL import Image
from tqdm import tqdm as tq

FOLDER_IN = "/data/imagenet"
FOLDER_OUT = "/data/imagenet-final"
FILENAME = "ILSVRC2012_img_{}.tar"

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This gist describes the steps process to add a private bucket on Lightning AI

# 1. Go on your AWS account and search for IAM 
# 2. From the left tab, create a policy on AWS with the following JSON. Replace the bucket with yours.
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PermissionForObjectOperations",
            "Effect": "Allow",

tchaton

import React, { useState } from 'react';
import ReactDOM from 'react-dom/client';
import './index.css';

function calculateWinner(squares) {
    const lines = [
      [0, 1, 2],
      [3, 4, 5],
      [6, 7, 8],
      [0, 3, 6],

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import os

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer


class RandomDataset(Dataset):
    def __init__(self, size, length):

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import os.path as ops

from lightning import LightningApp

from lightning_hpo import HPOCloudCompute, Sweep
from lightning_hpo.algorithm.optuna import OptunaAlgorithm
from lightning_hpo.distributions.distributions import Categorical, IntUniform, LogUniform, Uniform

app = LightningApp(
    Sweep(

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import torch
from pytorch_lightning import LightningModule, Trainer
from torch.utils.data import DataLoader, Dataset


class RandomDataset(Dataset):
    def __init__(self, size: int, length: int):
        self.len = length
        self.data = torch.randn(length, size)