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flambe-0.4.9
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مشاهده بیشترتوضیحات
Pytorch based library for robust prototyping, standardized benchmarking, and effortless experiment management
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | flambe-0.4.9 |
| نام | flambe |
| نسخه کتابخانه | 0.4.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | ASAPP Inc. |
| ایمیل نویسنده | flambe@asapp.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/flambe/ |
| مجوز | - |
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 <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
Installation
------------
**From** ``PIP``:
.. code-block:: bash
pip install flambe
**From source**:
.. code-block:: bash
git clone git@github.com: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 flambe@asapp.com
نیازمندی
| مقدار | نام |
|---|---|
| ~=0.3.9 | colorama |
| ~=1.3.0 | torch |
| ~=1.16.0 | numpy |
| <0.16.5,>=0.15.87 | ruamel.yaml |
| >=0.23.4 | pandas |
| ~=4.28 | tqdm |
| ~=0.20.3 | scikit-learn |
| ~=2.7.1 | paramiko |
| >=1.9.100 | boto3 |
| ==0.7.6 | ray |
| ~=2.21.0 | requests |
| ~=1.0.0 | Flask |
| ==1.7.2 | tensorboardx-hparams |
| ~=2.1.11 | GitPython |
| ~=2.2.1 | sru |
| ~=2.3.1 | pygments |
| ~=3.4.1 | nltk |
| >=0.3.1 | dill |
| ==3.7.3 | gensim |
| ~=19.1 | pip |
| ~=1.17 | awscli |
| ~=2.2.1 | transformers |
| ~=2.10.1 | jinja2 |
| ~=1.9.0 | ninja |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.1 | Python |
نحوه نصب
نصب پکیج whl flambe-0.4.9:
pip install flambe-0.4.9.whl
نصب پکیج tar.gz flambe-0.4.9:
pip install flambe-0.4.9.tar.gz