https://github.com/asappresearch/flambe

An ML framework to accelerate research and its path to production.
https://github.com/asappresearch/flambe

deep-learning distributed machine-learning ml python pytorch research

Last synced: 5 days ago
JSON representation

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

• Host: GitHub
• URL: https://github.com/asappresearch/flambe
• Owner: asappresearch
• License: mit
• Created: 2019-07-29T20:57:45.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-09-03T20:58:51.000Z (4 months ago)
• Last Synced: 2024-12-21T17:03:24.639Z (12 days ago)
• Topics: deep-learning, distributed, machine-learning, ml, python, pytorch, research
• Language: Python
• Homepage: https://flambe.ai
• Size: 13.2 MB
• Stars: 264
• Watchers: 9
• Forks: 29
• Open Issues: 22
• Metadata Files:
  • Readme: README-pypi.rst
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - flambe
• Awesome-pytorch-list - flambe

README

        
Flambé

------

|

.. image:: https://github.com/asappresearch/flambe/workflows/Run%20fast%20tests/badge.svg

    :target: https://github.com/asappresearch/flambe/actions

    :alt: Fast tests

.. image:: https://github.com/asappresearch/flambe/workflows/Run%20slow%20tests/badge.svg

    :target: https://github.com/asappresearch/flambe/actions

    :alt: Slow tests

.. image:: https://readthedocs.org/projects/flambe/badge/?version=latest

    :target: https://flambe.ai/en/latest/?badge=latest

    :alt: Documentation Status

.. image:: https://badge.fury.io/py/flambe.svg

    :target: https://badge.fury.io/py/flambe

    :alt: PyPI version

|

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``:

.. code-block:: bash

    pip install flambe

**From source**:

.. code-block:: bash

    git clone [email protected]:asappresearch/flambe.git

    cd flambe

    pip install .

Getting started

---------------

Define an ``Experiment``:

.. code-block:: yaml

    !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: !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:

.. code-block:: bash

    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 [email protected]