hybridbackend - HybridBackend 1.0.0

Objectives:

• Memory-efficient loading of categorical data
• Communication-efficient training and evaluation at scale
• Easy to use with existing AI workflows

Features:

1. Performance:

   • Support ORC format in data loading.
   • Support data deduplication.
   • Improve performance of data transfer.
   • Improve performance of loading and shuffling string data.
   • Support workers with unbalanced training data via SyncReplicasDataset.
   • Support pipeline-based semi-synchronous training.
   • Support a hierarchical embedding lookup.

2. Usability

   • Support standalone evaluation and prediction APIs of estimator and keras.

3. Bugfixes:

   • Fix shape calculation of tf.feature_column.shared_embeddings

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Published by francktcheng over 2 years ago

hybridbackend - HybridBackend 0.8.0

Objectives:

1. Memory-efficient loading of categorical data
2. Communication-efficient training and evaluation at scale

Features:

1. Performance

   • Support of automatic embedding fusion on PAI DLC / PAI DSW
   • Support of row-wise shuffling
   • Improves data transfer prefetching

2. Usability

   • Support of embedding_lookup_* API
   • Support of new composable Dataset API

- C++
Published by 2sin18 almost 3 years ago

hybridbackend - HybridBackend v0.7.0

Objectives:

1. Memory-efficient loading of categorical data
2. GPU-efficient orchestration of embedding layers
3. Communication-efficient training and evaluation at scale
4. Easy to use with existing AI workflows

Features:

1. Performance

   • Support of data transfer prefetching

2. Usability

   • Support of Keras Model API
   • Support direct pip install via Pypi

- C++
Published by 2sin18 over 3 years ago

hybridbackend - HybridBackend 0.5.4

Objectives:

• Easy to use with existing AI workflows

Features：

• Support fixed length list in ParquetDataset
• Support schema parsing in ParquetDataset
• Provide validation tools for parquet files

Bug Fixes:

• Fixes indices calculation in rebatching

- C++
Published by 2sin18 over 3 years ago

hybridbackend - HybridBackend v0.5.3

Objectives:

• Easy to use with existing AI workflows

Features:

• Support working with GPU
• Support building on macOS

- C++
Published by 2sin18 over 3 years ago

hybridbackend - HybridBackend v0.6.0

Objectives:

1. Communication-efficient training and evaluation at scale
2. Easy to use with existing AI workflows

Features:

1. Data-Parallel Training and Evaluation

   • Bucketized Gradients Aggregation using AllReduce
   • Global Metric Operations
   • Out-Of-Range Coordination

2. Hybrid-Parallel Embedding Learning

   • Bucketized Embedding Exchanging using AllToAllv
   • Fusion and Quantization of AllToAllv
   • Fusion of Partitioning and Stitching

3. Usability

   • Support of MonitoredSession and Estimator
   • Declarative API for Model Definition

4. Compatibility

   • Support of NVIDIA TensorFlow and DeepRec

5. Interoperability

   • Inference Pipeline Needs No Change
   • Support of SavedModel
   • Support of Variable, XDL HashTable and PAI Embedding Variable

Bug Fixes:

[#46] Fixes rebatching in ParquetDataset.

- C++
Published by 2sin18 almost 4 years ago

hybridbackend - HybridBackend v0.5.2

Objectives: - Memory-efficient loading of categorical data - Easy to use with existing AI workflows

Features: 1. Parquet Dataset - Reading batch of tensors from numeric fields in zero-copy way - Reading batch of sparse tensors from numeric list fields in zero-copy way - Support of string fields - Support of local filesystem, HDFS, S3 and OSS

1. Data Pipeline Functions

   • Resizing batch of tensors and ragged tensors
   • Converting ragged tensors to sparse tensors
   • Objective: "Easy to use with existing AI workflows"

2. Compatibility

   • Support of TensorFlow 1.15 and Tensorflow 1.14
   • GitHub actions for uploading wheels to PyPI

Bug Fixes:

• [#11][#12][#13] Supports manylinux224 platform.

- C++
Published by 2sin18 about 4 years ago