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
From PIP
:
pip install flambe # CPU Version
# OR
pip install flambe[cuda] # With GPU / CUDA support
From source:
git clone git@github.com:asappresearch/flambe.git
cd flambe
pip install .
Define an Experiment
:
!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: !@ 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: !@ model[embedder][encoder].rnn.hidden_size # also use inner-links
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: 100
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: !ray.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):
- 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
toClusters
which will execute in a distributed way. FullAWS
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.
Full documentation, tutorials and much more in https://flambe.ai
You can reach us at flambe@asapp.com