Google Colab Guide

from google.colab import drive
drive.mount('/content/drive')

# Access files in Drive
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/research/dataset.csv')

# From URL
!wget -q https://example.com/dataset.zip -O /content/dataset.zip
!unzip -q /content/dataset.zip -d /content/data/

# From Kaggle
!pip install -q kaggle
!mkdir -p ~/.kaggle
# Upload kaggle.json API key first
!kaggle datasets download -d user/dataset-name -p /content/data/

# From Hugging Face
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("scientific_papers", "arxiv")

import shutil
from pathlib import Path

DRIVE_BASE = Path("/content/drive/MyDrive/research/experiment_001")
DRIVE_BASE.mkdir(parents=True, exist_ok=True)

def save_checkpoint(model, optimizer, epoch, loss):
    """Save training checkpoint to Google Drive."""
    checkpoint = {
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss
    }
    path = DRIVE_BASE / f"checkpoint_epoch_{epoch}.pt"
    torch.save(checkpoint, path)
    print(f"Checkpoint saved to {path}")

def save_results(df, name):
    """Save results DataFrame to Drive."""
    path = DRIVE_BASE / f"{name}.csv"
    df.to_csv(path, index=False)
    print(f"Results saved to {path}")

!pip install -q torch torchvision

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

# Automatic device selection
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")

model = MyModel().to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    model.train()
    total_loss = 0
    for batch in train_loader:
        inputs, labels = batch[0].to(device), batch[1].to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()

    avg_loss = total_loss / len(train_loader)
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")

    # Save checkpoint every 5 epochs
    if (epoch + 1) % 5 == 0:
        save_checkpoint(model, optimizer, epoch + 1, avg_loss)

!pip install -q transformers accelerate

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer

model_name = "allenai/scibert_scivocab_uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name, num_labels=5
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer
)
trainer.train()

# Save to Drive
model.save_pretrained(str(DRIVE_BASE / "fine_tuned_scibert"))

# Install specific versions for reproducibility
!pip install -q transformers==4.40.0 datasets==2.18.0 evaluate==0.4.1

# Install from GitHub
!pip install -q git+https://github.com/huggingface/peft.git

# Install system packages
!apt-get -qq install -y graphviz texlive-latex-base

import random
import numpy as np
import torch

def set_seed(seed=42):
    """Set all random seeds for reproducibility."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

set_seed(42)

# Generate requirements for reproducibility
!pip freeze > /content/drive/MyDrive/research/requirements.txt

# Restore environment in new session
!pip install -q -r /content/drive/MyDrive/research/requirements.txt

# Monitor GPU memory
!nvidia-smi

# Clear GPU cache
torch.cuda.empty_cache()

# Use mixed precision training for 2x speedup
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()

for batch in train_loader:
    optimizer.zero_grad()
    with autocast():
        outputs = model(inputs)
        loss = criterion(outputs, labels)
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

# Clone a research repository
!git clone https://github.com/user/research-repo.git /content/repo

# Push results back
%cd /content/repo
!git config user.email "researcher@university.edu"
!git config user.name "Researcher"
!git add results/
!git commit -m "Add experiment results from Colab"
!git push