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deepr-2.9.1
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DeepR: Build and Train Deep Learning Pipelines for Production
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepr-2.9.1 |
| نام | deepr |
| نسخه کتابخانه | 2.9.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Criteo |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deepr/ |
| مجوز | - |
DeepR: Build and Train Deep Learning Pipelines for Production
=============================================================
|pypi|_ |ci|_
.. |pypi| image:: https://img.shields.io/pypi/v/deepr.svg
.. _pypi: https://pypi.python.org/pypi/deepr
.. |ci| image:: https://github.com/criteo/deepr/workflows/Continuous%20integration/badge.svg
.. _ci: https://github.com/criteo/deepr/actions?query=workflow%3A%22Continuous+integration%22
DeepR is a library for Deep Learning on top of Tensorflow 1.x that focuses on production capabilities. It makes it easy to define pipelines (via the ``Job`` abstraction), preprocess data (via the ``Prepro`` abstraction), design models (via the ``Layer`` abstraction) and train them either locally or on a Yarn cluster. It also integrates nicely with MLFlow and Graphite, allowing for production ready logging capabilities.
It can be seen as a collection of generic tools and abstractions to be extended for more specific use cases. See the ``Use DeepR`` section for more information.
Submitting jobs and defining flexible pipelines is made possible thanks to a config system based off simple dictionaries and import strings. It is similar to `Thinc config system <https://thinc.ai/docs>`_ or `gin config <https://github.com/google/gin-config>`_ in a lot of ways.
To start with deepr read the `blogpost <https://medium.com/criteo-labs/deepr-training-tensorflow-models-for-production-dda34a914c3b?source=friends_link&sk=91949017f33714dba3323956035f76e0>`_ then go to `quickstart on colab <https://colab.research.google.com/github/criteo/deepr/blob/master/docs/getting_started/quickstart.ipynb>`_
Why a Deep Learning Library based on TF1.x
------------------------------------------
Tensorflow 1.x provides great production oriented capabilities, centered around the ``tf.Estimator`` API. It makes it possible to deploy models using a ``protobuf`` with no ``python`` code, and optimize computational graphs with XLA compilation.
Although ``DeepR`` comes with a ``Layer`` interface (most similar to `google TRAX <https://github.com/google/trax>`_ and very close to most modern frameworks) that makes it easy to define models using a functional programming approach, most of its capabilities are orthogonal to it. Most of the building blocks expect generic ``python`` types (for example, a ``Layer`` is merely a function ``fn(tensors, mode)``).
Use DeepR
---------
You can use ``DeepR`` as a simple python library, reusing only a subset of the concepts (the config system is generic for example) or build your own extension as a standalone python package that depends on ``deepr``.
Have a look at the submodule `examples <../deepr/examples>`_ of ``deepr`` that illustrates what packages built on top of deepr would look like. It defines custom jobs, layers, preprocessors, macros as well as `configs <../deepr/examples/multiply/configs>`_. Once your custom components are packaged in a library, it is easy to run configs with
.. code-block::
deepr run config.json macros.json
MovieLens Example
-----------------
You can try using DeepR on the MovieLens dataset, consisting of movie ratings aggregated by users. The submodule `movielens <../deepr/examples/movielens>`_ implements an AverageModel, a Transformer Model and a BPR loss as well as jobs to build and evaluate on this dataset.
You can jump to the notebook on `Colab <https://colab.research.google.com/github/criteo/deepr/blob/master/docs/movielens/movielens.ipynb>`_ or use the command line.
.. code-block::
pip install deepr[cpu] faiss_cpu
cd deepr/examples/movielens/configs
wget http://files.grouplens.org/datasets/movielens/ml-20m.zip
unzip ml-20m.zip
deepr run config.json macros.json
Installation
------------
Prerequisites
~~~~~~~~~~~~~
Make sure you use ``python>=3.6`` and an up-to-date version of ``pip`` and ``setuptools``
.. code-block::
python --version
pip install -U pip setuptools
It is recommended to install ``deepr`` in a new virtual environment. For example
.. code-block::
python -m venv deepr
source deepr/bin/activate
pip install -U pip setuptools
pip install deepr[cpu]
Using Pip
~~~~~~~~~
If installing using pip and your own ``requirements.txt`` file, be aware that ``Tensorflow`` is listed in ``extras_require`` in the ``setup.py``, which means that ``pip install deepr`` WON'T INSTALL Tensorflow. This is because the Tensorflow requirement is different depending on the platform (GPU or CPU-only).
You can specify which extras to use using the ``[cpu]`` or ``[gpu]`` argument like in the following examples
.. code-block::
pip install deepr[cpu]
pip install deepr[gpu]
pip install -e ".[cpu]"
pip install -e ".[gpu]"
Or alternatively, pre-install Tensorflow separately like so
.. code-block::
pip install tensorflow==1.15.2
pip install deepr
From Source
~~~~~~~~~~~
First, clone the ``deepr`` repo on your local machine with
.. code-block::
git clone https://github.com/criteo/deepr.git
cd deepr
To install from source in editable mode, run
.. code-block::
make install-cpu
Or to install on a GPU enabled machine
.. code-block::
make install-gpu
To install development tools and test requirements, run
.. code-block::
make install-dev
Test
----
To run unit tests in your current environment, run
.. code-block::
make test
To run integration tests in your current environment, run
.. code-block::
make integration
To run lint + unit and integration tests in a fresh virtual environment, run
.. code-block::
make venv-lint-test-integration
Lint
----
To run ``mypy``, ``pylint`` and ``black --check``:
.. code-block::
make lint
To auto-format the code using ``black``
.. code-block::
make black
Command Line Tools
------------------
To get a list of available commands, run
.. code-block::
deepr --help
Contributing
------------
See `CONTRIBUTING <CONTRIBUTING.rst>`_
Change log
----------
See `CHANGELOG <CHANGELOG.rst>`_
Main contributors
-----------------
Main contributors and maintainers for deepr are
`Guillaume Genthial <https://github.com/guillaumegenthial>`_,
`Romain Beaumont <https://github.com/rom1504>`_,
`Denis Kuzin <https://github.com/denkuzin>`_,
`Amine Benhalloum <https://github.com/bamine>`_
نحوه نصب
نصب پکیج whl deepr-2.9.1:
pip install deepr-2.9.1.whl
نصب پکیج tar.gz deepr-2.9.1:
pip install deepr-2.9.1.tar.gz