This repository is largely adapted from material for the SC18 tutorial: Deep Learning at Scale. Please see sc18-dl-tutorial on GitHub, and also see their readme below.
Here you will links to slides and resources as well as all the code for the hands-on sessions. It contains specifications for a few datasets, a couple of CNN models, and all the training code to enable training the models in a distributed fashion using Horovod.
As part of the tutorial, you will
- Train a simple CNN to classify images from the CIFAR10 dataset on a single node
- Train a ResNet model to classify the same images on multiple nodes
Contents
Presentation slides: https://drive.google.com/drive/folders/1m_BEBlkbgBHiNg1bzzo74uRh6lJFBjLL?usp=sharing
Join the Slack workspace: https://join.slack.com/t/sc18-tut177/shared_invite/enQtNDY3OTUyNzcwNjI4LWYyNTlkMTI0ZGM5YzI4MDFkNWJiZjBlMjAxMzdiMzI1NTZhYmE5NmNhMzcyYzFhYzQ0ZjczNmJkMzRkZWIyNGY
NERSC JupyterHub: https://jupyter-dev.nersc.gov
- Start a terminal on Cori, either via ssh or from the Jupyter interface.
- Clone the repository using git:
git clone https://github.com/NERSC/sc18-dl-tutorial.git
That's it! The rest of the software (Keras, TensorFlow) is pre-installed on Cori and loaded via the scripts used below.
train.py - the main training script which can be steered with YAML
configuration files.
data/ - folder containing the specifications of the datasets. Each dataset
has a corresponding name which is mapped to the specification in data/__init__.py
models/ - folder containing the Keras model definitions. Again, each model
has a name which is interpreted in models/__init__.py.
configs/ - folder containing the configuration files. Each
configuration specifies a dataset, a model, and all relevant configuration
options (with some exceptions like the number of nodes, which is specified
instead to SLURM via the command line).
scripts/ - contains an environment setup script and some SLURM scripts
for easily submitting the example jobs to the Cori batch system.
utils/ - contains additional useful code for the training script, e.g.
custom callbacks, device configuration, and optimizers logic.
Go through the following steps as directed by the tutorial presenters. Discuss the questions with your neighbors.
We will start with single node training of a simple CNN to classify images from the CIFAR10 dataset.
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Take a look at the simple CNN model defined here: models/cnn.py. Consider the following things:
- Note how the model is constructed as a sequence of layers
- Note the structure of alternating convolutions, pooling, and dropout
- Identify the classifier head of the model; the part which computes the class probabilities.
- Can you figure out what the
Flatten()layer does here, and why it is needed?
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Now take a look at the dataset code for CIFAR10: data/cifar10.py
- Keras has a convenient API for CIFAR10 which will automatically download the dataset for you.
- Ask yourself: why do we scale the dataset by 1/255?
- Note where we convert the labels (integers) to categorical class vectors. Ask yourself: why do we have to do this?
- What kinds of data augmentation are we applying?
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Next, take a look at the training script: train.py.
- Identify the part where we retrieve the dataset.
- Identify the section where we retrieve the CNN model, the optimizer, and compile the model.
- Now identify the part where we do the actual training.
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Finally, look at the configuration file: configs/cifar10_cnn.yaml.
- YAML allows to express configurations in rich, human-readable, hierarchical structure.
- Identify where you would edit to modify the optimizer, learning-rate, batch-size, etc.
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Now we are ready to submit our training job to the Cori batch system. We have provided SLURM scripts to make this as simple as possible. To run the simple CNN training on CIFAR10 on a single KNL node, simply do:
sbatch scripts/cifar_cnn.sh- Important: the first time you run a CIFAR10 example, it will autmoatically download the dataset. If you have more than one job attempting this download simultaneously it will likely fail.
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Check on the status of your job by running
sqs. Once the job starts running, you should see the output start to appear in the slurm log filelogs/cifar-cnn-*.out. -
When the job is finished, check the log to identify how well your model learned to solve the CIFAR10 classification task. For every epoch you should see the loss and accuracy reported for both the training set and the validation set. Take note of the best validation accuracy achieved.
To demonstrate scaling to multiple nodes, we will switch to a larger, more complex ResNet model. This model can achieve higher accuracy than our simple CNN, but it is quite a bit slower to train. By parallelizing the training across nodes we should be able to achieve a better result than our simple CNN in a practical amount of time.
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Check out the ResNet model code in models/resnet.py. Note this is quite a bit more complex than the simple CNN! In fact the model code is broken into multiple functions for easy reuse. We provide here two versions of ResNet models: a standard ResNet50 (with 50 layers) and a smaller ResNet consisting of 26 layers.
- Identify the identy block and conv block functions. How many convolutional layers do each of these have?
- Identify the functions that build the ResNet50 and the ResNetSmall. Given how many layers are in each block, see if you can confirm how many layers (conv and dense) are in the models. Hint: we don't normally count the convolution applied to the shortcuts.
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Inspect the optimizer setup in utils/optimizers.py.
- Note how we scale the learning rate (
lr) according to the number of processes (ranks). - Note how we construct our optimizer and then wrap it in the Horovod DistributedOptimizer.
- Note how we scale the learning rate (
-
Inspect train.py once again.
- Identify the
init_workersfunction where we initialize Horovod. Note where this is invoked in the main() function (right away). - Identify where we setup our training callbacks.
- Which callback ensures we have consistent model weights at the start of training?
- Identify the callbacks responsible for the learning rate schedule (warmup and decay).
- Identify the
That's mostly it for the code. Note that in general when training distributed you might want to use more complicated data handling, e.g. to ensure different workers are always processing different samples of your data within a training epoch. In this case we aren't worrying about that and are, for simplicity, relying on the independent random shuffling of the data by each worker as well as the random data augmentation.
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(optional) To gain an appreciation for the speedup of training on multiple nodes, you can first try to train the ResNet model on a single node. Adjust the configuration in configs/cifar10_resnet.yaml to train for just 1 epoch and then submit the job with
sbatch -N 1 scripts/cifar_resnet.sh -
Now we are ready to train our ResNet model on multiple nodes using Horovod and MPI! If you changed the config to 1 epoch above, be sure to change it back to 32 epochs for this step. To launch the ResNet training on 8 nodes, do:
sbatch -N 8 scripts/cifar_resnet.sh -
As before, watch the log file (
logs/cifar-resnet-*.out) when the job starts. You'll see some printouts from every worker. Others are only printed from rank 0. -
When the job is finished, look at the log and compare to the simple CNN case above. If you ran step 4, compare the time to train one epoch between single-node and multi-node. Did your model manage to converge to a better validation accuracy than the simple CNN?
Now that you've finished the main tutorial material, try to play with the code and/or configuration to see the effect on the training results. You can try changing things like
- Change the optimizer (search for Keras optimizers on google).
- Change the nominal learning rate, number of warmup epochs, decay schedule
- Change the learning rate scaling (e.g. try "sqrt" scaling instead of linear)
Most of these things can be changed entirely within the configuration. See configs/imagenet_resnet.yaml for examples.
We may not have the time and compute resources to do this (certainly not for all attendees), but this repository also includes a more advanced ResNet50 example and a 100-class subset of the ImageNet dataset. ResNet and ImageNet are a fairly standard benchmark for scalable deep learning methods. The configuration is available in configs/imagenet_resnet.yaml
Please check with the presenters before submitting large scale training jobs with this example, as we have a limited reservation of nodes on Cori and we want to make sure all tutorial attendees are able to complete the core content and have a chance to play with settings in the ResNet-CIFAR example.
Keras ResNet50 official model: https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py
Horovod ResNet + ImageNet example: https://github.com/uber/horovod/blob/master/examples/keras_imagenet_resnet50.py
CIFAR10 CNN and ResNet examples: https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py https://github.com/keras-team/keras/blob/master/examples/cifar10_resnet.py