- Support HBM-DRAM-SSD storage in EmbeddingVariable multi-tier storage.
- Support multi-tier EmbeddingVariable initialized based on frequency when restore model.
- Support to lookup location of ids of EmbeddingVariable.
- Support kv_initialized_op for GPU Embedding Variable.
- Support restore compatibility of EmbeddingVariable using init_from_proto.
- Improve performance of apply/gather ops for EmbeddingVariable.
- Add Eviction Manager in EmbeddingVariable Multi-tier storage.
- Add unified thread pool for cache of Multi-tier storage in EmbeddingVariable.
- Save frequencies and versions of features in SSDHash and LevelDB storage of EmbeddingVariable.
- Avoid invalid eviction use HBM-DRAM storage of EmbeddingVariable.
- Preventing from accessing uninitialized data use EmbeddingVariable.
- Optimize Async EmbeddingLookup by placement optimization.
- Place VarHandlerOp to Compute main graph for SmartStage.
- Support independent thread pool for stage subgraph to avoid thread contention.
- Implement device placement optimization.
- Support CUDA Graph execution by adding CUDA Graph mode session.
- Support CUDA Graph execution in JIT mode.
- Support intra task cost estimate in CostModel in Executor.
- Support tf.stream and tf.colocate python API for CUDA multi-stream.
- Support embedding subgraphs partition policy when use CUDA multi-stream.
- Optimize CUDA multi-stream by merging copy stream into compute stream.
- Add a list of Quantized* and _MklQuantized* ops.
- Implement GPU version of SparseFillEmptyRows.
- Implement c version of spin_lock to support multi-architectures.
- Upgrade the OneDNN version to v2.7.
- Support distributed training use SOK based on EmbeddingVariable.
- Add NETWORK_MAX_CONNECTION_TIMEOUT to support connection timeout configurable in StarServer.
- Upgrade the SOK version to v4.2.
- Add TF_NEED_PARQUET_DATASET to enable ParquetDataset.
- Optimize embedding lookup performance by disable feature filter when serving.
- Optimize error code for user when parse request or response failed.
- Support independent update model threadpool to avoid performance jitter.
- Add MaskNet Model.
- Add PLE Model.
- Support variable type BF16 in DCN model.
- Fix tf.nn.embedding_lookup interface bug and session hang bug when enabling async embedding.
- Fix warmup failed bug when user set warmup file path.
- Fix build failure in ev_allocator.cc and hash.cc on ARM.
- Fix build failure in arrow when build on ARM
- Fix redefined error in NEON header file for ARM.
- Fix _mm_malloc build failure in sparsehash on ARM.
- Fix warmup failed bug when use session_group.
- Fix build save graph bug when creating partitioned EmbeddingVariable in feature_column API.
- Fix the colocation error when using EmbeddingVariable in distribution.
- Fix HostNameToIp fails by replacing gethostbyname by getaddrinfo in StarServer.
More details of features: https://deeprec.readthedocs.io/zh/latest/
alideeprec/deeprec-release:deeprec2210-cpu-py36-ubuntu18.04
alideeprec/deeprec-release:deeprec2210-gpu-py36-cu116-ubuntu18.04
Duyi-Wang, Locke, shijieliu, Honglin Zhu, chenxujun, GosTraight2020, LALBJ, Nanno
- Fix a list of Quantized* and _MklQuantized* ops not found issue.
- Fix build save graph bug when creating partitioned EmbeddingVariable in feature_column API.
- Fix warmup failed bug when user set warmup file path.
- Fix warmup failed bug when use session_group.
alideeprec/deeprec-release:deeprec2208u1-cpu-py36-ubuntu18.04
alideeprec/deeprec-release:deeprec2208u1-gpu-py36-cu116-ubuntu18.04
- Multi-tier of EmbeddingVariable support HBM, add async compactor in SSDHashKV.
- Support tf.feature_column.shard_embedding_columns, SequenceCategoricalColumn and WeightedCategoricalColumn API for EmbeddingVariable.
- Support save and restore checkpoint of GPU EmbeddingVariable.
- Support EmbeddingVariable OpKernel with REAL_NUMBER_TYPES.
- Support user defined default_value for feature filter.
- Support feature column API for MultiHash.
- Add FP32 fused l2 normalize op and grad op and tf.nn.fused_layer_normalize API.
- Add Concat+Cast fusion ops.
- Optimize SmartStage performance on GPU.
- Add macro to control to optimize mkl_layout_pass.
- Support asynchronous embedding lookup.
- CPUAllocator, avoid multiple threads cleanup at the same time.
- Support independent intra threadpool for each session and intra threadpool be pinned to cpuset.
- Support multi-stream with virtual device.
- Implement ApplyFtrl, ResourceApplyFtrl, ApplyFtrlV2 and ResourceApplyFtrlV2 GPU kernels.
- Optimize BatchMatmul GPU kernel.
- Integrate cuBLASlt into backend and use BlasLtMatmul in batch_matmul_op.
- Support GPU fusion of matmal+bias+(activation).
- Merge NV-TF r1.15.5+22.06.
- Support AdamW optimizer for EmbeddingVariable.
- Support asynchronously restore EmbeddingVariable from checkpoint.
- Support EmbeddingVariable in init_from_checkpoint.
- Add go/java/python client SDK and demo.
- Support GPU multi-streams in SessionGroup.
- Support independent inter thread pool for each session in SessionGroup.
- Support multi-tiered Embedding.
- Support immutable EmbeddingVariable.
- Add low precision optimization tool, support BF16, FP16, INT8 for savedmodel and checkpoint.
- Add embedding variable quantization.
- Optimize DIN's BF16 performance.
- Add DCN & DCNv2 models and MLPerf recommendation benchmark.
- Add detail information for RecvTensor in timeline.
- Add ubuntu 22.04 dockerfile and images with gcc11.2 and python3.8.6.
- Add cuda11.2, cuda11.4, cuda11.6, cuda11.7 docker images and use cuda 11.6 as default GPU image.
- Update default TF_CUDA_COMPUTE_CAPABILITIES to 6.0,6.1,7.0,7.5,8.0.
- Upgrade bazel version to 0.26.1.
- Support for building DeepRec on ROCm2.10.0.
- Fix build failures with gcc11 & gcc12.
- StarServer, remove user packet split to avoid multiple user packet out-of-order issue.
- Fix the 'NodeIsInGpu is not declare' issue.
- Fix the placement bug of worker devices when distributed training in Modelzoo.
- Fix out of range issue for BiasAddGrad op when enable AVX512.
- Avoid loading invalid model when model update in serving.
More details of features: https://deeprec.readthedocs.io/zh/latest/
alideeprec/deeprec-release:deeprec2208-cpu-py36-ubuntu18.04
alideeprec/deeprec-release:deeprec2208-gpu-py36-cu116-ubuntu18.04
- Multi-tier of EmbeddingVariable, add SSD_HashKV which is better performance than LevelDB.
- Support GPU EmbeddingVariable which gather/apply ops place on GPU.
- Add user API to record frequence and version for EmbeddingVariable.
- Add Embedding Fusion ops for CPU/GPU.
- Optimize SmartStage performance on GPU.
- Executor, support cost-based and critical path ops first.
- GPUAllocator, support CUDA malloc async allocator. (need to use >= CUDA 11.2)
- CPUAllocator, automatically memory allocation policy generation.
- PMEMAllocator, optimize allocator and add statistic.
- Implement SparseReshape, SparseApplyAdam, SparseApplyAdagrad, SparseApplyFtrl, ApplyAdamAsync, SparseApplyAdamAsync, KvSparseApplyAdamAsync GPU kernels.
- Optimize UnSortedSegment on CPU.
- Upgrade OneDNN to v2.6.
- ParquetDataset, add parquet dataset which could reduce storage and improve performance.
- Asynchronous restore EmbeddingVariable from checkpoint.
- SessionGroup, highly improve QPS and RT in inference.
- Add models SimpleMultiTask, ESSM, DBMTL, MMoE, BST.
- Support for mapping of operators and real thread ids in timeline.
- Fix EmbeddingVariable core when EmbeddingVariable only has primary embedding value.
- Fix abnormal behavior in L2-norm calculation.
- Fix save checkpoint issue when use LevelDB in EmbeddingVariable.
- Fix delete old checkpoint failure when use incremental checkpoint.
- Fix build failure with CUDA 11.6.
More details of features: https://deeprec.readthedocs.io/zh/latest/
alideeprec/deeprec-release:deeprec2206-cpu-py36-ubuntu18.04
alideeprec/deeprec-release:deeprec2206-gpu-py36-cu110-ubuntu18.04
- Fix saving checkpoint issue when use EmbeddingVariable. (DeepRec-AI#167)
- Fix inputs from different frames issue when use auto graph fusion. (DeepRec-AI#144)
- Fix embedding_lookup_sparse graph issue.
alideeprec/deeprec-release:deeprec2204u1-cpu-py36-ubuntu18.04
alideeprec/deeprec-release:deeprec2204u1-gpu-py36-cu110-ubuntu18.04
- Support hybrid storage of EmbeddingVariable (DRAM, PMEM, LevelDB)
- Support memory-continuous storage of multi-slot EmbeddingVariable.
- Optimize beta1_power and beta2_power slots of EmbeddingVariable.
- Support restore frequency of features in EmbeddingVariable.
- Integrate SOK in DeepRec.
- Auto Graph Fusion, support float32/int32/int64 type for select fusion.
- SmartStage, fix graph contains circle bug when enable SmartStage optimization.
- GPUTensorPoolAllocator, which reduce GPU memory usage and improve performance.
- PMEMAllocator, support allocation in persistent memory.
- Optimize AdamOptimizer performance.
- Change fused MatMul layout type and number thread for small size inputs.
- KafkaGroupIODataset, support consumer rebalance.
- Support dump incremental graph info.
- Add serving module (ODL processor), which support Online Deep Learning (ODL).
More details of features: https://deeprec.readthedocs.io/zh/latest/
registry.cn-shanghai.aliyuncs.com/pai-dlc-share/deeprec-training:deeprec2204-cpu-py36-ubuntu18.04
registry.cn-shanghai.aliyuncs.com/pai-dlc-share/deeprec-training:deeprec2204-gpu-py36-cu110-ubuntu18.04
Some user report issue when use Embedding Variable, such as DeepRec-AI#167. The bug is fixed in r1.15.5-deeprec2204u1.
This is the first release of DeepRec. DeepRec has super large-scale distributed training capability, supporting model training of trillion samples and 100 billion Embedding Processing. For sparse model scenarios, in-depth performance optimization has been conducted across CPU and GPU platform.
- Embedding Variable (including feature eviction and feature filter)
- Dynamic Dimension Embedding Variable
- Adaptive Embedding
- Multi-Hash Variable
- GRPC++
- StarServer
- Synchronous Training - SOK
- Auto Micro Batch
- Auto Graph Fusion
- Embedding Fusion
- Smart Stage
- CPU Memory Optimization
- GPU Memory Optimization
- GPU Virtual Memory
- Incremental Checkpoint
- AdamAsync Optimizer
- AdagradDecay Optimizer
- Operators Optimization: Unique, Gather, DynamicStitch, BiasAdd, Select, Transpose, SparseSegmentReduction, where, DynamicPartition, SparseConcat tens of ops' CPU/GPU optimization.
- support oneDNN & BFloat16(BF16) & Advanced Matrix Extension(AMX)
- Support TensorFloat-32(TF32)
- WorkQueue
- KafkaDataset
- KafkaGroupIODataset
More details of features: DeepRec Document
registry.cn-shanghai.aliyuncs.com/pai-dlc-share/deeprec-training:deeprec2201-cpu-py36-ubuntu18.04
registry.cn-shanghai.aliyuncs.com/pai-dlc-share/deeprec-training:deeprec2201-gpu-py36-cu110-ubuntu18.04