Example Solutions¶

Determined includes several example machine learning models that have been ported to Determined’s APIs. These examples can be found in the examples/ subdirectory of the Determined GitHub repo; download links to each example can also be found below.

Each example consists of a model definition, along with one or more experiment configuration files. To run one of these examples, download the appropriate .tgz file, extract it, cd into the directory, and use det experiment create to create a new experiment, passing in the appropriate configuration file. For example, here is how to train the mnist_pytorch example with a fixed set of hyperparameters:

tar xzvf mnist_pytorch.tgz
cd mnist_pytorch
det experiment create const.yaml .

For an introduction to using the training API, please see the Training APIs section.

Computer Vision¶

Framework	Dataset	Filename
PyTorch	CIFAR-10	`cifar10_pytorch.tgz`
PyTorch	MNIST	`mnist_pytorch.tgz`
PyTorch	Imagenet	`imagenet_pytorch.tgz`
PyTorch	Penn-Fudan Dataset	`fasterrcnn_coco_pytorch.tgz`
PyTorch (Model Hub MMDetection)	COCO	`mmdetection.tgz`
PyTorch	COCO	`detr_coco_pytorch.tgz`
PyTorch	COCO	`deformabledetr_coco_pytorch.tgz`
PyTorch	COCO	`efficientdet_pytorch.tgz`
PyTorch	COCO	`detectron2_coco_pytorch.tgz`
PyTorch (Lightning Adapter)	MNIST	`mnist_pl.tgz`
TensorFlow (Estimator API)	MNIST	`mnist_estimator.tgz`
TensorFlow (tf.keras)	Fashion MNIST	`fashion_mnist_tf_keras.tgz`
TensorFlow (tf.keras)	CIFAR-10	`cifar10_tf_keras.tgz`
TensorFlow (tf.keras)	Iris Dataset	`iris_tf_keras.tgz`
TensorFlow (tf.keras)	Oxford-IIIT Pet Dataset	`unets_tf_keras.tgz`
PyTorch	CIFAR-10 / STL-10 / ImageNet	`byol_pytorch.tgz`

Natural Language Processing (NLP)¶

Framework	Dataset	Filename
PyTorch	SQuAD	`bert_squad_pytorch.tgz`
PyTorch	SQuAD 2.0	`albert_squad_pytorch.tgz`
PyTorch	GLUE	`bert_glue_pytorch.tgz`
PyTorch	WikiText-2	`word_language_model.tgz`
PyTorch (Model Hub Transformers)	WikiText-2	`language-modeling.tgz`
PyTorch (Model Hub Transformers)	SWAG	`multiple-choice.tgz`
PyTorch (Model Hub Transformers)	SQuAD v1 and v2	`question-answering.tgz`
PyTorch (Model Hub Transformers)	GLUE and XNLI	`text-classification.tgz`
PyTorch (Model Hub Transformers)	CoNLL-2003	`token-classification.tgz`

DeepSpeed¶

Framework	Dataset	Filename
DeepSpeed (PyTorch)	Enron Email Corpus	`gpt_neox.tgz`
DeepSpeed (PyTorch)	CIFAR-10	`cifar10_moe.tgz`
DeepSpeed (PyTorch)	CIFAR-10	`pipeline_parallelism.tgz`
DeepSpeed (PyTorch)	MNIST / CIFAR-10	`deepspeed_dcgan.tgz`
DeepSpeed (PyTorch)	CIFAR-10	`cifar10_cpu_offloading.tgz`

HP Search Benchmarking¶

Framework	Dataset	Filename
PyTorch	CIFAR-10	`darts_cifar10_pytorch.tgz`
PyTorch	Penn Treebank Dataset	`darts_penntreebank_pytorch.tgz`

Neural Architecture Search (NAS)¶

Framework	Dataset	Filename
PyTorch	DARTS	`gaea_pytorch.tgz`

Meta Learning¶

Framework	Dataset	Filename
PyTorch	Omniglot	`protonet_omniglot_pytorch.tgz`

Diffusion¶

Framework	Dataset	Filename
PyTorch	det_logos	`textual_inversion_stable_diffusion.tgz`

Generative Adversarial Networks (GANs)¶

Framework	Dataset	Filename
PyTorch	MNIST	`gan_mnist_pytorch.tgz`
TensorFlow (tf.keras)	MNIST	`dcgan_tf_keras.tgz`
TensorFlow (tf.keras)	pix2pix	`pix2pix_tf_keras.tgz`

Graphs¶

Framework	Dataset	Filename
PyTorch	PROTEINS	`proteins_pytorch_geometric.tgz`

Decision Trees¶

Framework	Dataset	Filename
TensorFlow (Estimator API)	Titanic	`gbt_titanic_estimator.tgz`

Features: Custom Reducers¶

Framework	Dataset	Filename
PyTorch	MNIST	`custom_reducers_mnist_pytorch.tgz`

Features: HP Search Constraints¶

Framework	Dataset	Filename
PyTorch	MNIST	`hp_constraints_mnist_pytorch.tgz`

Features: Custom Search Method¶

Framework	Dataset	Filename
PyTorch	MNIST	`asha_search_method.tgz`