jwohlwend/flambe

An ML framework to accelerate research and its path to production.

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Welcome to Flambé, a PyTorch-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

Installation

From PIP:

pip install flambe  # CPU Version
# OR
pip install flambe[cuda]  # With GPU / CUDA support

From source:

git clone git@github.com:Open-ASAPP/flambe.git
cd flambe
pip install .

Getting started

Define an Experiment:

!Experiment

name: sst-text-classification

pipeline:

  # stage 0 - Load the Stanford Sentiment Treebank dataset and run preprocessing
  dataset: !SSTDataset
    transform:
      text: !TextField
      label: !LabelField

  # Stage 1 - Define a model
  model: !TextClassifier
      embedder: !Embedder
        embedding: !torch.Embedding  # automatically use pytorch classes
          num_embeddings: !@ dataset.text.vocab_size
          embedding_dim: 300
        embedding_dropout: 0.3
        encoder: !PooledRNNEncoder
          input_size: 300
          n_layers: !g [2, 3, 4]
          hidden_size: 128
          rnn_type: sru
          dropout: 0.3
      output_layer: !SoftmaxLayer
          input_size: !@ model.embedder.encoder.rnn.hidden_size
          output_size: !@ dataset.label.vocab_size

  # Stage 2 - Train the model on the dataset
  train: !Trainer
    dataset: !@ dataset
    model: !@ model
    train_sampler: !BaseSampler
    val_sampler: !BaseSampler
    loss_fn: !torch.NLLLoss
    metric_fn: !Accuracy
    optimizer: !torch.Adam
      params: !@ train.model.trainable_params
    max_steps: 10
    iter_per_step: 100

  # Stage 3 - Eval on the test set
  eval: !Evaluator
    dataset: !@ dataset
    model: !@ train.model
    metric_fn: !Accuracy
    eval_sampler: !BaseSampler

# Define how to schedule variants
schedulers:
  train: !tune.HyperBandScheduler

All objects in the pipeline are subclasses of Component, which are automatically registered to be used with YAML. Custom Component implementations must implement run to add custom behavior when being executed.

Now just execute:

flambe example.yaml

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):

Features

• Native support for hyperparameter search: using search tags (see !g in 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 Experiments to Clusters which will execute in a distributed way. Full AWS integration 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.

Next Steps

Full documentation, tutorials and much more in https://flambe.ai

Contact

You can reach us at flambe@asapp.com