use recipe generated by grayskull
-# Note: there are many handy hints in comments in this example -- remove them when you've finalized your recipe
-
-# Jinja variables help maintain the recipe as you'll update the version only here.
-# Using the name variable with the URL in line 14 is convenient
-# when copying and pasting from another recipe, but not really needed.
{% set name = "sparkflow" %}
{% set version = "0.7.0" %}
+
package:
name: {{ name|lower }}
version: {{ version }}
source:
- url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
+ url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/sparkflow-{{ version }}.tar.gz
sha256: 532e5055f6d21333460b5162dfddbe8d53408eaca95d89d0654d6a95e6b2cb57
build:
number: 0
- script: "
- {{ PYTHON }} -m pip install . -vv
- skip: True # [py<35]
- "
+ noarch: python
+ script: {{ PYTHON }} -m pip install . -vv
requirements:
host:
- - python =3.6
- pip
+ - python
run:
- - six =1.11.0
- - pandas =0.23.4
- - h5py =2.8.0
- - pillow =4.1.1
- dill
- - tensorflow =1.10.0
- flask
+ - protobuf
+ - python
- requests
- - python =3.6
+ - tensorflow
test:
- # Some packages might need a `test/commands` key to check CLI.
- # List all the packages/modules that `run_test.py` imports.
imports:
+ - examples
- sparkflow
- - sparkflow.tests
+ commands:
+ - pip check
+ requires:
+ - pip
about:
home: https://github.com/lifeomic/sparkflow
+ summary: Deep learning on Spark with Tensorflow
license: MIT
- license_family: MIT
license_file: LICENSE
- summary: 'Deep learning on Spark with Tensorflow'
-
- description: |
- sparkflow is an implementation of TensorFlow on Spark. The goal of this library is to provide a simple,
- understandable interface in using TensorFlow on Spark. With SparkFlow, you can easily integrate your
- deep learning model with a ML Spark Pipeline. Underneath, SparkFlow uses a parameter server to train the
- TensorFlow network in a distributed manner. Through the api, the user can specify the style of training,
- whether that is Hogwild or async with locking.
- doc_url: https://github.com/lifeomic/sparkflow#documentation
- dev_url: https://github.com/lifeomic/sparkflow
extra:
recipe-maintainers:
