Distributed Training with TensorFlow Java
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- java, tensorflow
- Domain
- distributed-systems, machine-learning
Research direction
Start by reading the TensorFlow distributed training guide and comparing its tf.distribute.Strategy API with the Java bindings in this repository. Determine which distributed-training capabilities and Java API surface are in scope, then define tests or examples that demonstrate multi-GPU or multi-machine training before implementation.
Written by the indexing model from the issue text.
Description
Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template
System information
- TensorFlow version (you are using): 2.X
- Are you willing to contribute it (Yes/No): Yes, when able and available
Describe the feature and the current behavior/state.
Tensorflow on Python has tf.distribute.Strategy API to distribute training across multiple GPUs or multiple machines.
Will this change the current api? How?
Yes, it will add a new awesome feature
Who will benefit with this feature?
- Anyone that requires to speed up training a DL model
- Anyone that requires to train a DL model with big data
- Anyone who wants to create or add Java support for APIs that leverages tf.distribute.Strategy such as TensorflowOnSpark, Spark Tensorflow Distributor or Horovod
Any Other info.
https://www.tensorflow.org/guide/distributed_training
- Dominant language
- Java
- Stars
- 928
- Forks
- 227
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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