An ML framework to accelerate research and its path to production.
Welcome to Flambé, a
PyTorch <https://pytorch.org/>_-based library that allows users to:
- Run complex experiments with multiple training and processing stages
- Search over hyperparameters, and select the best trials
- Run experiments remotely over many workers, including full AWS integration
- Easily share experiment configurations, results, and model weights with others
pip install flambe
git clone [email protected]:asappresearch/flambe.git cd flambe pip install .
!Experiment name: sst-text-classification pipeline: # stage 0 - Load the Stanford Sentiment Treebank dataset and run preprocessing dataset: !SSTDataset # this is a simple Python object, and the arguments to build it transform: # these arguments are passed to the init method text: !TextField label: !LabelField # Stage 1 - Define a model model: !TextClassifier embedder: !Embedder embedding: !torch.Embedding # automatically use pytorch classes num_embeddings: [email protected] dataset.text.vocab_size # link to other components, and attributes embedding_dim: 300 embedding_dropout: 0.3 encoder: !PooledRNNEncoder input_size: 300 n_layers: !g [2, 3, 4] # grid search over any parameters hidden_size: 128 rnn_type: sru dropout: 0.3 output_layer: !SoftmaxLayer input_size: [email protected] model[embedder][encoder].rnn.hidden_size # also use inner-links output_size: [email protected] dataset.label.vocab_size # Stage 2 - Train the model on the dataset train: !Trainer dataset: [email protected] dataset model: [email protected] model train_sampler: !BaseSampler val_sampler: !BaseSampler loss_fn: !torch.NLLLoss metric_fn: !Accuracy optimizer: !torch.Adam params: [email protected] train[model].trainable_params max_steps: 100 iter_per_step: 100 # Stage 3 - Eval on the test set eval: !Evaluator dataset: [email protected] dataset model: [email protected] train.model metric_fn: !Accuracy eval_sampler: !BaseSampler # Define how to schedule variants schedulers: train: !ray.HyperBandScheduler
All objects in the
pipeline are subclasses of
are automatically registered to be used with YAML. Custom
implementations must implement
run to add custom behavior when being executed.
Now just execute:
Note that defining objects like model and dataset ahead of time is optional; it's useful if you want to reference the same model architecture multiple times later in the pipeline.
Progress can be monitored via the Report Site (with full integration with Tensorboard):
- Native support for hyperparameter search: using search tags (see
!gin the example) users can define multi variant pipelines. More advanced search algorithms will be available in a coming release!
- Remote and distributed experiments: users can submit
Clusterswhich will execute in a distributed way. Full
AWSintegration is supported.
- Visualize all your metrics and meaningful data using Tensorboard: log scalars, histograms, images, hparams and much more.
- Add custom code and objects to your pipelines: extend flambé functionality using our easy-to-use extensions mechanism.
- Modularity with hierarchical serialization: save different components from pipelines and load them safely anywhere.
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