Kaggle GPU Tutorial for Deep Learning with Optimal Configs

Kaggle is one of the fastest methods for data scientists and ML engineers to turn ideas into working models with its free GPU and TPU resources, rich dataset ecosystem, and ready‑to‑use notebook environment. In this article, you will learn a complete guide on Kaggle GPU Training with the Best Configs.

This guide is a practical setup for Kaggle users who want to train models efficiently with GPUs, from turning on accelerators and verifying PyTorch and TensorFlow setup, to knowing when to move workloads to dedicated infrastructure on PerLod Hosting, if you need more scalable or persistent GPU resources.

Why train with a GPU on Kaggle?

Training deep learning models on a CPU can take a very long time, especially with large image or text datasets. Kaggle’s free GPUs make training much faster because they use powerful hardware and come with all the tools and libraries you need already set up.

With Kaggle GPU Training:

• Experiments and testing different settings happen faster.
• You get access to strong GPUs like Tesla P100 and T4, with up to 16 GB of memory.
• Everything is ready to use, including datasets, notebooks, and tools for competitions are built right in.

And when you need more power, longer runtimes, or dedicated resources for teams, you can move your best Kaggle notebooks and models onto GPU Dedicated Servers, which keep the same workflow while scaling beyond free Kaggle limits.

Explore Kaggle GPU and TPU Options

Kaggle allows you to choose an accelerator for each notebook session, which defines whether you use CPU only, GPU, or TPU. Selecting the right option is the most important key note for performance and resource limits.

GPU options include:

• NVIDIA Tesla P100: 16 GB HBM2 VRAM, strong raw throughput for CNNs and many standard models.
• NVIDIA Tesla T4 or T4 x2: 16 GB GDDR6 VRAM per card, efficient and often the default option, with T4 x2 offering two GPUs in some setups.

TPU options include:

• Kaggle used to provide TPU v3‑8 and now provides TPU v5e‑8. These have 8 cores and a lot of fast memory, which helps train large TensorFlow models.
• Best for TensorFlow workloads that follow TPU‑friendly input pipelines and training loops.

Kaggle Resource and Time Limits

Before launching long runs, you must understand Kaggle’s runtime and usage limits. Planning with these limits in mind helps you avoid your session suddenly stopping and losing your work:

Session duration:

• Sessions usually can run for about 9 hours before they stop, so you should save checkpoints often so you do not lose work.

Weekly GPU quota:

• Free users only get a certain number of GPU hours each week, so very heavy use can hit the limit, and you may need to wait for it to reset.
• A good approach is to test quickly on CPU, then run the main training on GPU to save your GPU hours.

Storage and RAM:

• Your working folder only has limited disk space, and memory is also limited, so you should use sensible batch sizes and avoid loading very large datasets into memory all at once.

How to Enable GPU in Kaggle Notebooks?

Before any setup, you must enable GPU in the Kaggle UI. If you do not enable it, your notebook will silently train on the CPU and be much slower.

To enable GPU in Kaggle notebooks, follow the steps below:

1. Open or create a notebook:

• Go to the “Code” or “Notebooks” section on Kaggle and create a new notebook or open an existing one.
• Ensure your account is fully verified. Phone verification is often required to unlock GPU access.

2. Turn on the accelerator:

• In the notebook, open the Settings or “Session options” panel on the right.
• Under “Accelerator”, choose “GPU” and, if available, select P100 or T4/T4 x2, then apply; Kaggle will restart the environment with the GPU attached.

3. Confirm GPU activation:

• Check the resource bar; it should show an active GPU label or icon when GPU is enabled.​
• If the GPU option is greyed out, re‑check verification, switch to the latest environment, and retry after refreshing the page.

Which Kaggle Accelerators to Use: P100, T4, or TPU?

Not every model runs the same on each type of accelerator, so pick the right option based on what you are training and which framework you use.​ Here are the recommendations to choose from accelerators:

P100:

• Suitable for heavy image models with large batch sizes due to its strong raw compute and memory bandwidth.
• It is a safe option when training vision models, standard CNNs, or medium-sized transformers.

T4 and T4 x2:

• Very efficient and good for many mixed‑precision workloads; T4 x2 gives two GPUs for certain advanced setups.
• It is useful when P100 is busy or if your model fits comfortably into 16 GB VRAM.

TPU:

• Designed for TensorFlow models with TPU‑compatible input pipelines and distribution strategies.
• Best for very large models and image workloads that can use a lot of parallel processing and bandwidth, but it needs a bit more setup work.

Check PyTorch GPU in the Kaggle Notebook

Before training your model, you must quickly check that PyTorch can use the GPU. Run the following command in your Kaggle notebook to check if CUDA is available and how many GPUs are detected:

import torch

print("CUDA available:", torch.cuda.is_available())
print("GPU count:", torch.cuda.device_count())

if torch.cuda.is_available():
    print("Current device index:", torch.cuda.current_device())
    print("GPU name:", torch.cuda.get_device_name(0))

Note: If “CUDA available” is False or the GPU count is 0, your notebook is running on CPU only.

Check TensorFlow and Keras GPU in Kaggle Notebook

At this point, you must use the following command to determine which devices are available and whether a GPU is visible:

import tensorflow as tf

print("TensorFlow version:", tf.__version__)
print("All physical devices:", tf.config.list_physical_devices())
print("GPU devices:", tf.config.list_physical_devices('GPU'))

If the “GPU devices” list comes back empty, your notebook is running on CPU only, and you should double‑check that the accelerator is set to GPU in the notebook settings.

PyTorch Device Setup for Kaggle GPU Training

In Kaggle PyTorch notebooks, it is important to send both your model and your data to the same device so training runs on the GPU when it is available.

Define the device and move the model with the command below:

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = MyModel()          # replace with your model class
model = model.to(device)

Move the data to the same device inside the training loop with the command below:

for inputs, targets in train_loader:
    inputs = inputs.to(device)
    targets = targets.to(device)

    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()

This structure ensures both model and tensors are on the GPU when available and avoids device mismatch errors.

Mixed Precision Training for Faster Kaggle GPU Runs (PyTorch)

On Kaggle GPUs like P100 and T4, mixed precision allows PyTorch to train models faster while using less GPU memory, which is especially helpful for larger batches and deeper networks.

You can use the following automatic mixed precision:

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = torch.nn.CrossEntropyLoss()

USE_AMP = True
scaler = torch.cuda.amp.GradScaler(enabled=USE_AMP)

for epoch in range(num_epochs):
    model.train()
    for inputs, targets in train_loader:
        inputs = inputs.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

        optimizer.zero_grad()

        with torch.cuda.amp.autocast(enabled=USE_AMP):
            outputs = model(inputs)
            loss = criterion(outputs, targets)

        if USE_AMP:
            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()
        else:
            loss.backward()
            optimizer.step()

This training loop pattern turns mixed precision on in one place, keeps the rest of your code clean, and still keeps training stable by using higher precision where it matters.

Safer GPU Memory Use with TensorFlow and Keras on Kaggle

On Kaggle, TensorFlow and Keras will use the GPU automatically when it is available, but they can grab all VRAM by default and causing out‑of‑memory errors. Turning on GPU memory growth at the start of your notebook lets TensorFlow slowly allocate only the memory it needs, so your Keras model can train more safely on shared Kaggle GPUs.

Add this at the top of your notebook:

import tensorflow as tf

gpus = tf.config.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print("Enabled memory growth for GPUs.")
    except RuntimeError as e:
        print(e)

Now define and train your Keras model with:

model = build_model()  # your model definition

model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy"]
)

history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=EPOCHS
)

When the GPU is enabled and detected, TensorFlow uses it automatically for supported operations.

PyTorch DataLoader Setup for Fast Kaggle GPU Training

On Kaggle, a well‑configured DataLoader keeps the GPU busy by feeding batches quickly from CPU to GPU. Adjusting batch size, number of workers, and transforms for your dataset and VRAM helps you avoid slowdowns and stay within Kaggle’s runtime limits.

You can use a DataLoader like this:

from torch.utils.data import DataLoader

BATCH_SIZE = 32  # tune based on VRAM

train_loader = DataLoader(
    train_dataset,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2,
    pin_memory=True,
)

val_loader = DataLoader(
    val_dataset,
    batch_size=BATCH_SIZE,
    shuffle=False,
    num_workers=2,
    pin_memory=True,
)

TensorFlow tf.data Pipeline for Kaggle

On Kaggle, a well‑built tf.data pipeline keeps your GPU busy by preparing the next batch while the current one is training.

Using map, shuffle, batch, and prefetch with AUTOTUNE lets TensorFlow overlap preprocessing with GPU work, which improves throughput and reduces training slowdowns.

Here is a typical TensorFlow tf.data Pipeline for faster Kaggle GPU training:

import tensorflow as tf

BATCH_SIZE = 32

def preprocess(example):
    # apply parsing, decoding, augmentations, etc.
    return example

train_ds = (
    raw_train_ds
    .map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
    .shuffle(10_000)
    .batch(BATCH_SIZE)
    .prefetch(tf.data.AUTOTUNE)
)

val_ds = (
    raw_val_ds
    .map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(BATCH_SIZE)
    .prefetch(tf.data.AUTOTUNE)
)

Recommended Batch Sizes and Settings for Kaggle GPUs

On Kaggle GPUs like P100 and T4, both with 16 GB VRAM, using reasonable batch sizes, tuned learning rates, and frequent checkpoints helps you avoid out‑of‑memory errors and session timeouts.

Batch size recommendations:

• For standard image models like ResNet‑50 at 224×224, a batch size between 16 and 64 usually works in 16 GB VRAM on P100 or T4.
• Large models such as transformers and big CNNs may require smaller batches or gradient accumulation to avoid out‑of‑memory errors.

Learning rate and schedulers:

• You can start with defaults, such as 1e‑3 for Adam or AdamW and scaled values for SGD.
• Use cosine decay or step decay, and consider warmup when using large batches or mixed precision.

Checkpointing strategy:

• Save checkpoints regularly to /kaggle/working so progress is not lost when sessions hit the 9‑hour limit.​
• Reload the latest checkpoint when restarting training to stay within weekly GPU hour limits.

Monitor Kaggle GPU Usage and Avoid Bottlenecks

Kaggle notebooks show live GPU, CPU, RAM, and disk usage in the resource panel, which helps you see whether the GPU is busy or waiting on data.

If GPU utilization stays low while CPU use is high, your data loading or preprocessing is likely the bottleneck, so you may need to optimize your input pipeline instead of the model.

Also, you can track overall GPU hours and remaining quota from your Kaggle profile and Notebooks pages, where usage bars show how much weekly accelerator time you have left.

Plan short, focused GPU runs and stop idle sessions so you do not waste limited GPU hours and can spread experiments across the whole week.

End‑to‑End PyTorch Training Template for Kaggle GPUs

This ready‑made PyTorch template shows a full training loop designed for Kaggle GPUs, including efficient DataLoaders, AMP mixed‑precision, validation, and checkpoint saving to /kaggle/working. You can paste it into a new notebook, plug in your own dataset and model, and have a solid, GPU‑friendly starting point for most Kaggle deep learning projects.

import torch
import random
import numpy as np
from torch.utils.data import DataLoader

# -----------------------------
# 1. Reproducibility and device
# -----------------------------
SEED = 42

torch.manual_seed(SEED)
np.random.seed(SEED)
random.seed(SEED)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device.type == "cuda":
    torch.cuda.manual_seed_all(SEED)

print("Using device:", device)

# -----------------------------
# 2. Datasets and DataLoaders
# -----------------------------
# Define your dataset classes or use existing ones
# train_dataset = YourTrainDataset(...)
# val_dataset   = YourValDataset(...)

BATCH_SIZE = 32

train_loader = DataLoader(
    train_dataset,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2,
    pin_memory=True,
)

val_loader = DataLoader(
    val_dataset,
    batch_size=BATCH_SIZE,
    shuffle=False,
    num_workers=2,
    pin_memory=True,
)

# -----------------------------
# 3. Model, optimizer, loss
# -----------------------------
model = MyModel()  # replace with your model
model = model.to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = torch.nn.CrossEntropyLoss()

USE_AMP = True
scaler = torch.cuda.amp.GradScaler(enabled=USE_AMP)

NUM_EPOCHS = 10

# -----------------------------
# 4. Training and validation
# -----------------------------
for epoch in range(NUM_EPOCHS):
    # --- TRAIN ---
    model.train()
    running_loss = 0.0

    for inputs, targets in train_loader:
        inputs = inputs.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

        optimizer.zero_grad()

        with torch.cuda.amp.autocast(enabled=USE_AMP):
            outputs = model(inputs)
            loss = criterion(outputs, targets)

        if USE_AMP:
            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()
        else:
            loss.backward()
            optimizer.step()

        running_loss += loss.item() * inputs.size(0)

    train_loss = running_loss / len(train_loader.dataset)

    # --- VALIDATE ---
    model.eval()
    val_loss = 0.0
    correct = 0

    with torch.no_grad():
        for inputs, targets in val_loader:
            inputs = inputs.to(device, non_blocking=True)
            targets = targets.to(device, non_blocking=True)

            outputs = model(inputs)
            loss = criterion(outputs, targets)

            val_loss += loss.item() * inputs.size(0)
            preds = outputs.argmax(dim=1)
            correct += (preds == targets).sum().item()

    val_loss /= len(val_loader.dataset)
    val_acc = correct / len(val_loader.dataset)

    print(
        f"Epoch {epoch+1}/{NUM_EPOCHS} "
        f"- train_loss: {train_loss:.4f} "
        f"- val_loss: {val_loss:.4f} "
        f"- val_acc: {val_acc:.4f}"
    )

    # --- SAVE CHECKPOINT ---
    checkpoint_path = f"/kaggle/working/model_epoch_{epoch+1}.pth"
    torch.save(
        {
            "epoch": epoch + 1,
            "model_state": model.state_dict(),
            "optimizer_state": optimizer.state_dict(),
        },
        checkpoint_path,
    )
    print("Saved checkpoint to:", checkpoint_path)

FAQs

Conclusion

Using GPUs effectively on Kaggle turns the platform into a powerful and free lab for deep learning, which allows you to train models much faster than on CPU‑only setups.

By understanding how to enable accelerators, choose between P100, T4, and TPU, configure PyTorch or TensorFlow correctly, and understand Kaggle’s resource limits, you can make every GPU minute count and focus on improving your models instead of fighting the infrastructure.

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