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Training Keras with the SLURM Scheduler

Training Keras with the SLURM Scheduler#

This simple example demonstrates how to plug TensorFlow Datasets (TFDS) into a Keras model and submit a training job in HPC environments. Although NJIT is quoted the approach must be the same for NYU.

Copyright 2020 The TensorFlow Datasets Authors, Licensed under the Apache License, Version 2.0

View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook 
import tensorflow as tf
import tensorflow_datasets as tfds

2023-10-03 09:29:30.258272: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-03 09:29:30.258321: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-03 09:29:30.258358: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

Set 0: VPN and Login#

You need to install a VPN client and establish a VPN connection to NJIT data center. Consult https://ist.njit.edu/vpn for help in doing so.

# select from the two options below and ssh into the HPC server
ssh ucid@HPC_HOST.njit.edu 
ssh ucid@wulver.njit.edu

Step 1: Create your input pipeline#

Start by building an efficient input pipeline using advices from:

Load a dataset#

Load the MNIST dataset with the following arguments:

  • shuffle_files=True: The MNIST data is only stored in a single file, but for larger datasets with multiple files on disk, it’s good practice to shuffle them when training.

  • as_supervised=True: Returns a tuple (img, label) instead of a dictionary {'image': img, 'label': label}.

(ds_train, ds_test), ds_info = tfds.load(
    'mnist',
    split=['train', 'test'],
    shuffle_files=True,
    as_supervised=True,
    with_info=True,
)

2023-10-03 09:29:33.682941: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

Upload dataset to server#

command: scp -r /path_of_file_locally ucid@HPC_HOST.njit.edu:/path_in_server or scp -r /path_of_file_locally ucid@wulver.njit.edu:/path_in_server

Build a training pipeline#

Apply the following transformations:

  • tf.data.Dataset.map: TFDS provide images of type tf.uint8, while the model expects tf.float32. Therefore, you need to normalize images.

  • tf.data.Dataset.cache As you fit the dataset in memory, cache it before shuffling for a better performance.
    Note: Random transformations should be applied after caching.

  • tf.data.Dataset.shuffle: For true randomness, set the shuffle buffer to the full dataset size.
    Note: For large datasets that can’t fit in memory, use buffer_size=1000 if your system allows it.

  • tf.data.Dataset.batch: Batch elements of the dataset after shuffling to get unique batches at each epoch.

  • tf.data.Dataset.prefetch: It is good practice to end the pipeline by prefetching for performance.

def normalize_img(image, label):
  """Normalizes images: `uint8` -> `float32`."""
  return tf.cast(image, tf.float32) / 255., label

ds_train = ds_train.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.AUTOTUNE)

Build an evaluation pipeline#

Your testing pipeline is similar to the training pipeline with small differences:

  • You don’t need to call tf.data.Dataset.shuffle.

  • Caching is done after batching because batches can be the same between epochs.

ds_test = ds_test.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.AUTOTUNE)

Run code on server#

create a .sh file first and here is a sample .sh file which runs your_job_name in general partition, you might need to change nodelist,nodes,gres,mem in your .sh file. If you want to comment something in .sh file, use several # to make sure it is comment, such as #####your comment.

#!/bin/bash -1

#SBATCH --job-name=your_job_name
#SBATCH --output=xxxx.out
#SBATCH --partition=general
#SBATCH --nodelist=node816
#SBATCH --nodes=1
#SBATCH --gres=gpu:1
#SBATCH --mem=16G

Use: sinfo on terminal to check if the job is run successful on the server

Use: squeue -p gpu to check usage of gpu

It is hard to visualize things in server so make sure you save the output of everything into a file in server and download into your local enviroment to visualize if needed.

command: scp ucid@HPC_HOST.njit.edu:/path_in_server /path_of_file_locally or scp ucid@wulver.njit.edu:/path_in_server /path_of_file_locally

Step 2: Create and train the model#

Plug the TFDS input pipeline into a simple Keras model, compile the model, and train it.

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10)
])
model.compile(
    optimizer=tf.keras.optimizers.Adam(0.001),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)

model.fit(
    ds_train,
    epochs=6,
    validation_data=ds_test,
)

Epoch 1/6

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469/469 [==============================] - 4s 4ms/step - loss: 0.3621 - sparse_categorical_accuracy: 0.9011 - val_loss: 0.1925 - val_sparse_categorical_accuracy: 0.9463
Epoch 2/6

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469/469 [==============================] - 1s 3ms/step - loss: 0.1602 - sparse_categorical_accuracy: 0.9543 - val_loss: 0.1392 - val_sparse_categorical_accuracy: 0.9588
Epoch 3/6

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469/469 [==============================] - 1s 2ms/step - loss: 0.1174 - sparse_categorical_accuracy: 0.9664 - val_loss: 0.1084 - val_sparse_categorical_accuracy: 0.9693
Epoch 4/6

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469/469 [==============================] - 1s 3ms/step - loss: 0.0911 - sparse_categorical_accuracy: 0.9743 - val_loss: 0.0968 - val_sparse_categorical_accuracy: 0.9714
Epoch 5/6

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469/469 [==============================] - 1s 2ms/step - loss: 0.0738 - sparse_categorical_accuracy: 0.9790 - val_loss: 0.0881 - val_sparse_categorical_accuracy: 0.9735
Epoch 6/6

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469/469 [==============================] - 1s 2ms/step - loss: 0.0617 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.0793 - val_sparse_categorical_accuracy: 0.9749

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