Deep Learning Workflow

Deep Learning Workflow (2025)

Your up-to-date guide for running deep learning assignments on the **Faraday** cluster.

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Where to Start (JupyterHub)

All machine learning assignments should now be run on **Faraday** using its dedicated JupyterHub instance: https://jupyter.cluster.earlham.edu/hub/login

Log in with your Earlham credentials. This environment: - Has preloaded TensorFlow, PyTorch - Gives direct access to Faraday's GPU nodes - Supports running long experiments and interactive debugging

To confirm GPU access in a notebook:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

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What Python Version to Use

Faraday’s default deep learning environment uses **Python 3.12**, which is fully compatible with the latest machine learning frameworks.

Supported with: - **TensorFlow** - **PyTorch**

To list all available modules:

$ module avail

Compatibility references: - TensorFlow: https://www.tensorflow.org/install/source#gpu - PyTorch: https://pytorch.org/get-started/previous-versions/

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Using GPUs for Training

Training on GPUs is essential for modern deep learning tasks. Faraday is equipped with high-performance NVIDIA GPUs hat massively accelerate training.

To verify GPU access:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

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Using Pretrained Models with Keras

If you’re using **MobileNetV2**, **InceptionV3**, **VGG19**, or **VGG16**, you can easily load them through Keras:

from keras.applications import MobileNetV2, InceptionV3, VGG19, VGG16

Each model can be initialized with pretrained weights (e.g., from ImageNet):

base_model = MobileNetV2(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

These models run efficiently on Faraday's GPUs when used with `tensorflow` and `keras`. Ideal for transfer learning, feature extraction, or fine-tuning workflows.

To freeze layers during transfer learning:

for layer in base_model.layers:
    layer.trainable = False

You can then add your own classifier on top using `tf.keras.Sequential` or the functional API.

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Last updated: May 2025

Deep Learning Workflow

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