DeepR: Build and Train Deep Learning Pipelines for Production

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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 or gin config in a lot of ways.

To start with deepr read the blogpost then go to quickstart on colab

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 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 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. Once your custom components are packaged in a library, it is easy to run configs with

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 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 or use the command line.

pip install deepr[cpu] faiss_cpu
cd deepr/examples/movielens/configs
deepr run config.json macros.json



Make sure you use python>=3.6 and an up-to-date version of pip and setuptools

python --version
pip install -U pip setuptools

It is recommended to install deepr in a new virtual environment. For example

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, 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

pip install deepr[cpu]
pip install deepr[gpu]
pip install -e ".[cpu]"
pip install -e ".[gpu]"

Or alternatively, pre-install Tensorflow separately like so

pip install tensorflow==1.15.2
pip install deepr

From Source

First, clone the deepr repo on your local machine with

git clone
cd deepr

To install from source in editable mode, run

make install-cpu

Or to install on a GPU enabled machine

make install-gpu

To install development tools and test requirements, run

make install-dev


To run unit tests in your current environment, run

make test

To run integration tests in your current environment, run

make integration

To run lint + unit and integration tests in a fresh virtual environment, run

make venv-lint-test-integration


To run mypy, pylint and black --check:

make lint

To auto-format the code using black

make black

Command Line Tools

To get a list of available commands, run

deepr --help



Change log


Main contributors

Main contributors and maintainers for deepr are Guillaume Genthial, Romain Beaumont, Denis Kuzin, Amine Benhalloum