horovod VS ray_snowflake

Compare horovod vs ray_snowflake and see what are their differences.

horovod

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod] (by uber)
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horovod ray_snowflake
1 1
11,889 4
- -
9.4 6.6
over 2 years ago 7 months ago
Python Python
GNU General Public License v3.0 or later -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

horovod

Posts with mentions or reviews of horovod. We have used some of these posts to build our list of alternatives and similar projects.

ray_snowflake

Posts with mentions or reviews of ray_snowflake. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing horovod and ray_snowflake you can also consider the following projects:

tf-encrypted - A Framework for Encrypted Machine Learning in TensorFlow

aws-data-wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). [Moved to: https://github.com/aws/aws-sdk-pandas]

rxray - Ray distributed computing integration for RxPY

SnowDDL - Declarative-style object management tool for Snowflake.

seq2seq - A general-purpose encoder-decoder framework for Tensorflow

horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

client - DagsHub client libraries

snowflake-provisioning - Snowflake Database, Schema, and Warehouse provisioning with Access Roles & Generating and Provisioning of Functional Roles & Snowflake Source Export & Snowflake cloning tool

deep-significance - Enabling easy statistical significance testing for deep neural networks.

mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.