ayulockin

# Demonstrative changes to this file: https://github.com/Lightning-AI/lit-gpt/blob/main/finetune/lora.py

from lightning.pytorch.loggers import WandbLogger


def setup(
    data_dir: Path = Path("data/alpaca"),
    checkpoint_dir: Path = Path("checkpoints/stabilityai/stablelm-base-alpha-3b"),
    out_dir: Path = Path("out/lora/alpaca"),
    precision: Optional[str] = None,

ayulockin

import tensorflow as tf

import wandb
from wandb.keras import (
    # WandBMetricsLogger,
    WandbModelCheckpoint,
    # WandbGradientLogger,
    # ModelLogger,
    # FLOPsLogger,
)

ayulockin

import tensorflow as tf

import wandb
from wandb.keras import WandBMetricsLogger

with wandb.init(project="mnist", job_type="dev-wandb-metrics-logger"):

    fashion_mnist = tf.keras.datasets.fashion_mnist

    (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

ayulockin

import wandb

import tensorflow as tf
from keras_cv import bounding_box

class WandbTablesBuilder:
    """
    Utility class that contains useful methods to create W&B Tables,
    and log it to W&B.
    """

ayulockin

# Imports
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *

import numpy as np

import wandb
from wandb.keras import WandbCallback

ayulockin

class Net(nn.Module):
	def __init__(self):
		super(Net, self).__init__()
		
		self.conv1 = nn.Conv2d(3, 32, 3, 1)
	  torch.nn.init.kaiming_uniform_(self.conv1.weight, mode='fan_in', nonlinearity='relu')

	  self.conv2 = nn.Conv2d(32, 32, 3, 1)
		torch.nn.init.kaiming_uniform_(self.conv2.weight, mode='fan_in', nonlinearity='relu')

ayulockin

class NetforExplode(nn.Module):
  def __init__(self):
    super(NetforExplode, self).__init__()
    
    self.conv1 = nn.Conv2d(1, 32, 3, 1)
    self.conv1.weight.data.fill_(100)
    self.conv1.bias.data.fill_(-100)
    
    self.conv2 = nn.Conv2d(32, 64, 3, 1)
    self.conv2.weight.data.fill_(100)

ayulockin

lr_finder = LRFinder(net, optimizer, device)

lr_finder.range_test(trainloader, end_lr=10, num_iter=100, logwandb=True)

ayulockin

if logwandb:
  wandb.log({'lr': lr_schedule.get_lr()[0], 'loss': loss})

ayulockin

net = Net().to(device)

optimizer = optim.Adam(net.parameters())
wandb.init(project='pytorchw_b')
wandb.watch(net, log='all')

for epoch in range(10):
  train(net, device, trainloader, optimizer, epoch)
  test(net, device, testloader, classes)