apple/axlearn
Oct 2024 – Feb 2026
17 Months active
Languages Used
Python
Technical Skills
Audio ProcessingConvolutional Neural NetworksDeep LearningJAXMachine LearningPython
Dongseong Hwang
PROFILE
Dongseong Hwang
Over 17 months, this developer contributed to the apple/axlearn repository by building and optimizing deep learning infrastructure for audio, vision, and language models. They engineered scalable attention mechanisms, memory-efficient training with sliding window KV caches and gradient checkpointing, and robust distributed training workflows. Their work included refactoring convolution modules, enhancing metric computation with new summary types, and improving experiment reliability through CI/CD and logging fixes. Leveraging Python, JAX, and TensorFlow, they delivered features such as dynamic masking, segment-aware audio processing, and flexible embedding APIs. Their technical approach emphasized modularity, test coverage, and performance, enabling faster iteration and reliable model deployment.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
117Total
Bugs
19
Commits
117
Features
52
Lines of code
28,200
Activity Months17
Your Network
716 peopleSame Organization
@apple.com594
Work History
February 2026
4 Commits • 1 Features
Feb 1, 2026February 2026 (apple/axlearn): Delivered critical stability and reliability improvements for distributed training and decoding, with concrete bug fixes and a key embedding enhancement. The work reduces training interruptions on sharded deployments, mitigates rare crashes, and improves CI reliability and decoding performance, delivering measurable business value in model training throughput and development velocity.
4 Commits • 1 Features
Feb 1, 2026February 2026 (apple/axlearn): Delivered critical stability and reliability improvements for distributed training and decoding, with concrete bug fixes and a key embedding enhancement. The work reduces training interruptions on sharded deployments, mitigates rare crashes, and improves CI reliability and decoding performance, delivering measurable business value in model training throughput and development velocity.
February 2026
January 2026
10 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for apple/axlearn focused on delivering core feature enhancements, reliability improvements, and scalable patterns for variable-length inputs, with a clear emphasis on business value and technical excellence.
January 2026
10 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for apple/axlearn focused on delivering core feature enhancements, reliability improvements, and scalable patterns for variable-length inputs, with a clear emphasis on business value and technical excellence.
December 2025
4 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for apple/axlearn: Delivered key usability and reliability enhancements across the model and experiment pipeline. Implemented scalar metrics output and broadened convolution input type support, improved randomness through per-parameter PRNG keys in Variational Noise, and fixed logging reliability with W&B integration. These changes reduce setup time, improve monitoring, reproducibility, and stability across experiments, enabling faster iteration and better decision-making from experiment results.
4 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for apple/axlearn: Delivered key usability and reliability enhancements across the model and experiment pipeline. Implemented scalar metrics output and broadened convolution input type support, improved randomness through per-parameter PRNG keys in Variational Noise, and fixed logging reliability with W&B integration. These changes reduce setup time, improve monitoring, reproducibility, and stability across experiments, enabling faster iteration and better decision-making from experiment results.
December 2025
November 2025
4 Commits • 1 Features
Nov 1, 2025November 2025 (apple/axlearn) — Focused on performance, reliability, and maintainability of the decoder and attention stack. Key feature delivered: Decoder optimization and length-control feature, including the new StopOnMaxLength decoding condition to cap generated sequences, a refactored and faster decoder test suite, and type-safety improvements for summaries (get_summaries return type updated to dict[str, MetricSummary]). Also implemented SlidingWindowKVCache optimizations for scalable attention. These changes were implemented through the following commits: 0e28b7869e64221bc7b386a6d08d9b276af2fcc5; 5d97f2af4c9be6c2ad7d0d7ef043a935eae79778; 5717a6f20b46f84265cc8d220d6ae60680113309; b5b403ecab83317737ab8b1815ac0050a1492936. Major bugs fixed: No separate major bugs reported in this month. Improvements focus on generation length safety, test stability, and type-safety rather than urgent defect fixes. Overall impact and accomplishments: Enhanced generation reliability and throughput for longer outputs, reduced test runtime, and improved maintainability. The SlidingWindowKVCache optimization supports scalable attention, enabling larger models and workloads. These changes deliver measurable business value through more predictable generation behavior, faster CI, and a cleaner codebase. Technologies/skills demonstrated: Python typing and type-safety (dict[str, MetricSummary]), decoder performance optimizations, StopOnMaxLength, test refactoring for efficiency, SlidingWindowKVCache optimizations (ring buffer and one-hot matmul scatter), and attention scalability improvements.
November 2025
4 Commits • 1 Features
Nov 1, 2025November 2025 (apple/axlearn) — Focused on performance, reliability, and maintainability of the decoder and attention stack. Key feature delivered: Decoder optimization and length-control feature, including the new StopOnMaxLength decoding condition to cap generated sequences, a refactored and faster decoder test suite, and type-safety improvements for summaries (get_summaries return type updated to dict[str, MetricSummary]). Also implemented SlidingWindowKVCache optimizations for scalable attention. These changes were implemented through the following commits: 0e28b7869e64221bc7b386a6d08d9b276af2fcc5; 5d97f2af4c9be6c2ad7d0d7ef043a935eae79778; 5717a6f20b46f84265cc8d220d6ae60680113309; b5b403ecab83317737ab8b1815ac0050a1492936. Major bugs fixed: No separate major bugs reported in this month. Improvements focus on generation length safety, test stability, and type-safety rather than urgent defect fixes. Overall impact and accomplishments: Enhanced generation reliability and throughput for longer outputs, reduced test runtime, and improved maintainability. The SlidingWindowKVCache optimization supports scalable attention, enabling larger models and workloads. These changes deliver measurable business value through more predictable generation behavior, faster CI, and a cleaner codebase. Technologies/skills demonstrated: Python typing and type-safety (dict[str, MetricSummary]), decoder performance optimizations, StopOnMaxLength, test refactoring for efficiency, SlidingWindowKVCache optimizations (ring buffer and one-hot matmul scatter), and attention scalability improvements.
October 2025
5 Commits • 3 Features
Oct 1, 20252025-10 monthly summary for apple/axlearn: Delivered enhancements that improve training stability, flexibility, and correctness across TPU-based workflows. Key changes include expanded testing coverage for TPU attention forward propagation and gradient checks with zero-centered RMSNorm tests, added broadcasting support for live_targets in cross-entropy to support multi-dimensional and weighted losses, greater TPU Flash Attention configuration flexibility by removing head dimension divisibility restrictions, and a robust input dispatching fix to prevent misassignment of the feed index in distributed processing. These efforts improve numerical stability, scalability, and developer productivity for TPU-backed training pipelines.
5 Commits • 3 Features
Oct 1, 20252025-10 monthly summary for apple/axlearn: Delivered enhancements that improve training stability, flexibility, and correctness across TPU-based workflows. Key changes include expanded testing coverage for TPU attention forward propagation and gradient checks with zero-centered RMSNorm tests, added broadcasting support for live_targets in cross-entropy to support multi-dimensional and weighted losses, greater TPU Flash Attention configuration flexibility by removing head dimension divisibility restrictions, and a robust input dispatching fix to prevent misassignment of the feed index in distributed processing. These efforts improve numerical stability, scalability, and developer productivity for TPU-backed training pipelines.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for apple/axlearn. Focused on enabling broader EMA applicability by introducing non-floating weight support, enabling integer weights to be used directly in EMA calculations without interpolation. This strengthens optimization workflows and model evaluation across diverse data representations. No major bug fixes were reported this month. Key deliverable: EMA weight flexibility improvement integrated into the EMA module (commit 6e12a72251df63ccd884e24c7e08fe1df731272a).
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for apple/axlearn. Focused on enabling broader EMA applicability by introducing non-floating weight support, enabling integer weights to be used directly in EMA calculations without interpolation. This strengthens optimization workflows and model evaluation across diverse data representations. No major bug fixes were reported this month. Key deliverable: EMA weight flexibility improvement integrated into the EMA module (commit 6e12a72251df63ccd884e24c7e08fe1df731272a).
August 2025
1 Commits • 1 Features
Aug 1, 2025In August 2025, Apple/axlearn delivered a focused feature to enhance tensor metric accumulation by introducing MinSummary and MaxSummary classes, improving the accuracy and observability of metrics across tensor elements. This aligns with the team’s goals of more reliable model monitoring and faster iteration.
1 Commits • 1 Features
Aug 1, 2025In August 2025, Apple/axlearn delivered a focused feature to enhance tensor metric accumulation by introducing MinSummary and MaxSummary classes, improving the accuracy and observability of metrics across tensor elements. This aligns with the team’s goals of more reliable model monitoring and faster iteration.
August 2025
July 2025
3 Commits • 2 Features
Jul 1, 20252025-07 Apple AXLearn monthly summary focusing on performance and impact: Key features delivered include significant performance and memory optimizations for training and inference. Specifically, remat_in_scan was retired in the Repeat layer to simplify code and rely on remat_spec for memory efficiency, and a sliding window KV cache was added to the flash attention path with a standard attention fallback to reduce memory usage and speed up decoding. In addition, loss metric weighting was improved: CompositeLossMetrics now uses a weighted sum of losses, with weights derived from child metrics to improve loss accuracy and interpretability. No critical bugs reported this month; the focus was on delivering these capabilities with maintainable code and clear metrics. Overall impact: higher throughput, reduced memory footprint during training and inference, and clearer signals for model quality, enabling faster iteration and more scalable experiments. Technologies/skills demonstrated: Flash Attention optimizations, sliding window KV cache, memory optimization techniques, weighted loss metrics, Python/PyTorch, code refactoring for maintainability.
July 2025
3 Commits • 2 Features
Jul 1, 20252025-07 Apple AXLearn monthly summary focusing on performance and impact: Key features delivered include significant performance and memory optimizations for training and inference. Specifically, remat_in_scan was retired in the Repeat layer to simplify code and rely on remat_spec for memory efficiency, and a sliding window KV cache was added to the flash attention path with a standard attention fallback to reduce memory usage and speed up decoding. In addition, loss metric weighting was improved: CompositeLossMetrics now uses a weighted sum of losses, with weights derived from child metrics to improve loss accuracy and interpretability. No critical bugs reported this month; the focus was on delivering these capabilities with maintainable code and clear metrics. Overall impact: higher throughput, reduced memory footprint during training and inference, and clearer signals for model quality, enabling faster iteration and more scalable experiments. Technologies/skills demonstrated: Flash Attention optimizations, sliding window KV cache, memory optimization techniques, weighted loss metrics, Python/PyTorch, code refactoring for maintainability.
June 2025
5 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for the apple/axlearn repository. Focused on delivering features that improve training metrics reporting, reducing memory footprints to support longer contexts, and stabilizing the experimentation pipeline. Key work delivered across features and bugs includes enhanced metrics, memory optimizations, audio processing stabilization, and CI/logging reliability. Resulting business value includes clearer performance signals for model training, the ability to train longer sequences with the same or lower resource usage, and more reliable experimentation workflows.
5 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for the apple/axlearn repository. Focused on delivering features that improve training metrics reporting, reducing memory footprints to support longer contexts, and stabilizing the experimentation pipeline. Key work delivered across features and bugs includes enhanced metrics, memory optimizations, audio processing stabilization, and CI/logging reliability. Resulting business value includes clearer performance signals for model training, the ability to train longer sequences with the same or lower resource usage, and more reliable experimentation workflows.
June 2025
May 2025
11 Commits • 4 Features
May 1, 2025May 2025 highlights for apple/axlearn: Delivered memory-efficient training with gradient checkpointing and a sliding window KV cache to support longer sequences with reduced memory usage. Ensured inference semantics parity by applying logits_modifier during inference. Added per-type logging controls in SummaryWriter via write_every_n_steps_map for better performance and observability. Reworked audio processing pipeline for faster generation: LogMel front-end using jnp.fft.rfft, boolean-mask-based SpecAugmentation, and configurable max_len for fake_speech_source. Made core library improvements (safe_not, einops enhancements, rename einops.py) and stabilized CI by pinning transformers version to avoid conflicts.
May 2025
11 Commits • 4 Features
May 1, 2025May 2025 highlights for apple/axlearn: Delivered memory-efficient training with gradient checkpointing and a sliding window KV cache to support longer sequences with reduced memory usage. Ensured inference semantics parity by applying logits_modifier during inference. Added per-type logging controls in SummaryWriter via write_every_n_steps_map for better performance and observability. Reworked audio processing pipeline for faster generation: LogMel front-end using jnp.fft.rfft, boolean-mask-based SpecAugmentation, and configurable max_len for fake_speech_source. Made core library improvements (safe_not, einops enhancements, rename einops.py) and stabilized CI by pinning transformers version to avoid conflicts.
April 2025
9 Commits • 5 Features
Apr 1, 2025April 2025 - Apple AXLearn: Delivered scalable, reliable, and performance-oriented enhancements across attention, model sharding, and audio feature extraction. Key outcomes include enabling scalable training for Conformer models via double-weight sharding, removing external dependencies by reimplementing essential rearrange/repeat primitives in JAX, hardening attention configurations with relaxed shape checks and a Splash Attention NumPy mask for better JAX tracing and kv_cache dtype handling, and optimizing the Speech frontend with improved chunking and cross-platform benchmarking. Additionally, logmel feature extraction now adapts its upper bound to the sample rate (Nyquist), supporting accurate processing at higher rates such as 24 kHz. These changes collectively boost training throughput, reduce maintenance burden, and improve memory/performance efficiency, while aligning with broader reliability and cross-platform standards.
9 Commits • 5 Features
Apr 1, 2025April 2025 - Apple AXLearn: Delivered scalable, reliable, and performance-oriented enhancements across attention, model sharding, and audio feature extraction. Key outcomes include enabling scalable training for Conformer models via double-weight sharding, removing external dependencies by reimplementing essential rearrange/repeat primitives in JAX, hardening attention configurations with relaxed shape checks and a Splash Attention NumPy mask for better JAX tracing and kv_cache dtype handling, and optimizing the Speech frontend with improved chunking and cross-platform benchmarking. Additionally, logmel feature extraction now adapts its upper bound to the sample rate (Nyquist), supporting accurate processing at higher rates such as 24 kHz. These changes collectively boost training throughput, reduce maintenance burden, and improve memory/performance efficiency, while aligning with broader reliability and cross-platform standards.
April 2025
March 2025
14 Commits • 6 Features
Mar 1, 2025March 2025 performance summary for the apple/axlearn repository focused on delivering scalable transformer improvements, robust tooling, and reliability enhancements. The month drove measurable business value by boosting training/inference efficiency, reducing memory footprints, and equipping the team with visibility into cost drivers for deployment decisions.
March 2025
14 Commits • 6 Features
Mar 1, 2025March 2025 performance summary for the apple/axlearn repository focused on delivering scalable transformer improvements, robust tooling, and reliability enhancements. The month drove measurable business value by boosting training/inference efficiency, reducing memory footprints, and equipping the team with visibility into cost drivers for deployment decisions.
February 2025
8 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for apple/axlearn. Focused on delivering efficient attention mechanisms, improving robustness, and expanding test coverage across CPU/GPU and Flash Attention backends. Key outcomes include feature delivery for sliding window attention with KV cache, robustness improvements in KV cache handling and BiasAndResidual, a crash fix in the log-mel frontend after a JAX update, and expanded unit-test coverage for Flash Attention. Impact highlights: reduces memory footprint during decoding, enables near-infinite decoding with sliding window KV caches, and strengthens reliability across edge cases and backend configurations. Demonstrated proficiency in cross-backend testing, data-type handling for FFT, and comprehensive test design. Technologies/skills: JAX, Flash Attention, GPU/CPU testing, unit tests with edge-case validation, memory-conscious KV caching, and robust input handling for attention components.
8 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for apple/axlearn. Focused on delivering efficient attention mechanisms, improving robustness, and expanding test coverage across CPU/GPU and Flash Attention backends. Key outcomes include feature delivery for sliding window attention with KV cache, robustness improvements in KV cache handling and BiasAndResidual, a crash fix in the log-mel frontend after a JAX update, and expanded unit-test coverage for Flash Attention. Impact highlights: reduces memory footprint during decoding, enables near-infinite decoding with sliding window KV caches, and strengthens reliability across edge cases and backend configurations. Demonstrated proficiency in cross-backend testing, data-type handling for FFT, and comprehensive test design. Technologies/skills: JAX, Flash Attention, GPU/CPU testing, unit tests with edge-case validation, memory-conscious KV caching, and robust input handling for attention components.
February 2025
January 2025
8 Commits • 4 Features
Jan 1, 2025January 2025 monthly results for apple/axlearn (2025-01). The team focused on advancing sequence-to-sequence capability, performance optimizations, and reliability improvements across the core AxLearn models. Deliverables emphasize business value in model expressivity, faster iteration, and debugging usability.
January 2025
8 Commits • 4 Features
Jan 1, 2025January 2025 monthly results for apple/axlearn (2025-01). The team focused on advancing sequence-to-sequence capability, performance optimizations, and reliability improvements across the core AxLearn models. Deliverables emphasize business value in model expressivity, faster iteration, and debugging usability.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 — Apple AXLearn (apple/axlearn) monthly summary focusing on business value and technical achievements. The month delivered meaningful feature work, critical correctness fixes, and improved capabilities across convolution, DiT transformer decoding, and attention bias handling. Key features delivered: - Codebase maintenance: Refactored convolution-related classes into a dedicated module; frontend updated to support Short-Time Fourier Transform (STFT); added Learner unit tests to demonstrate API usage and verify forward/backward passes. Commits highlighted: 20568572183b5ab120b045b9f9c7e66765ec43e3, f91709f28b6c2bab11d4a1de27b21ca396a9b908, 6a7d2f0c9e17e13e17e05262e56c6f3ab0c4125a. - DiT transformer autoregressive decoding enhancements: Implemented init_states and extend_step for the DiT transformer to improve autoregressive decoding for both vision and speech applications. Commit: 3c21d93439f275d47356e7fa91f388717c6e0323. Major bugs fixed: - MaskFnAttentionBias correctness: ensured the mask_fn callback receives target_positions and source_positions tensors of the same rank, increasing reliability of attention bias. Commit: a7e2a952d321c650c869b43d3671acd5308f7ee9. Overall impact and accomplishments: - Improved code modularity and test coverage, enabling safer refactors and easier maintenance. - Expanded autoregressive decoding capabilities across modalities, broadening model applicability and usability. - Strengthened correctness in attention bias paths, reducing potential runtime errors in attention computations. Technologies/skills demonstrated: - Python, modular architecture, and unit testing (Learner tests) - Frontend integration for STFT and related preprocessing - DiT transformer internals (init_states, extend_step) and autoregressive decoding - Attention bias correctness and tensor shape handling
5 Commits • 2 Features
Dec 1, 2024December 2024 — Apple AXLearn (apple/axlearn) monthly summary focusing on business value and technical achievements. The month delivered meaningful feature work, critical correctness fixes, and improved capabilities across convolution, DiT transformer decoding, and attention bias handling. Key features delivered: - Codebase maintenance: Refactored convolution-related classes into a dedicated module; frontend updated to support Short-Time Fourier Transform (STFT); added Learner unit tests to demonstrate API usage and verify forward/backward passes. Commits highlighted: 20568572183b5ab120b045b9f9c7e66765ec43e3, f91709f28b6c2bab11d4a1de27b21ca396a9b908, 6a7d2f0c9e17e13e17e05262e56c6f3ab0c4125a. - DiT transformer autoregressive decoding enhancements: Implemented init_states and extend_step for the DiT transformer to improve autoregressive decoding for both vision and speech applications. Commit: 3c21d93439f275d47356e7fa91f388717c6e0323. Major bugs fixed: - MaskFnAttentionBias correctness: ensured the mask_fn callback receives target_positions and source_positions tensors of the same rank, increasing reliability of attention bias. Commit: a7e2a952d321c650c869b43d3671acd5308f7ee9. Overall impact and accomplishments: - Improved code modularity and test coverage, enabling safer refactors and easier maintenance. - Expanded autoregressive decoding capabilities across modalities, broadening model applicability and usability. - Strengthened correctness in attention bias paths, reducing potential runtime errors in attention computations. Technologies/skills demonstrated: - Python, modular architecture, and unit testing (Learner tests) - Frontend integration for STFT and related preprocessing - DiT transformer internals (init_states, extend_step) and autoregressive decoding - Attention bias correctness and tensor shape handling
December 2024
November 2024
18 Commits • 8 Features
Nov 1, 2024November 2024 (apple/axlearn): Delivered significant memory and performance improvements, robust padding correctness, and enhanced generation capabilities across the repository. Key outcomes include a quantization overhaul that eliminates one-hot vectors and returns IDs as int32 with a quantizer API, improved CAUSAL padding handling for stride > 1 and consistent partial-frame treatment across padding modes, multi-step transformer generation with optimized attention paths and KV cache, noticeable RLHF sampling speedups by replacing advanced indexing with dynamic_update_slice_in_dim, and the introduction of a unified ConvXDTranspose to support 1D/2D/3D transpose convolutions. Additional wins include model_analysis logging for detailed training state, Conv1DWithPadding support for sequence data, and targeted tests (e.g., bf16 in ConvSubSampler) and documentation updates to improve observability and reliability. These changes reduce memory usage, accelerate inference and generation, improve robustness, and strengthen tooling for performance analysis and debugging.
November 2024
18 Commits • 8 Features
Nov 1, 2024November 2024 (apple/axlearn): Delivered significant memory and performance improvements, robust padding correctness, and enhanced generation capabilities across the repository. Key outcomes include a quantization overhaul that eliminates one-hot vectors and returns IDs as int32 with a quantizer API, improved CAUSAL padding handling for stride > 1 and consistent partial-frame treatment across padding modes, multi-step transformer generation with optimized attention paths and KV cache, noticeable RLHF sampling speedups by replacing advanced indexing with dynamic_update_slice_in_dim, and the introduction of a unified ConvXDTranspose to support 1D/2D/3D transpose convolutions. Additional wins include model_analysis logging for detailed training state, Conv1DWithPadding support for sequence data, and targeted tests (e.g., bf16 in ConvSubSampler) and documentation updates to improve observability and reliability. These changes reduce memory usage, accelerate inference and generation, improve robustness, and strengthen tooling for performance analysis and debugging.
October 2024
7 Commits • 4 Features
Oct 1, 2024In October 2024, the apple/axlearn effort delivered tangible business value through robust audio/time-series processing, streamlined ASR data handling, and improved debugging ergonomics. Key features include: (1) Convolution and API enhancements: fixed padding inconsistencies, added causal convolution support, and exposed convolution utilities for downstream use; (2) Einops integration for ASR tensor reshaping, simplifying data handling; (3) Configurable RepeatedConformerLayer to enable streaming conformers via configuration; (4) Invocation wrapping controls and debugging annotations, including an environment variable to disable wrapping and @nowrap annotation to bypass wrapping when unnecessary. These changes reduce edge-case bugs, accelerate experimentation, and enable scalable, downstream-friendly ASR pipelines.
7 Commits • 4 Features
Oct 1, 2024In October 2024, the apple/axlearn effort delivered tangible business value through robust audio/time-series processing, streamlined ASR data handling, and improved debugging ergonomics. Key features include: (1) Convolution and API enhancements: fixed padding inconsistencies, added causal convolution support, and exposed convolution utilities for downstream use; (2) Einops integration for ASR tensor reshaping, simplifying data handling; (3) Configurable RepeatedConformerLayer to enable streaming conformers via configuration; (4) Invocation wrapping controls and debugging annotations, including an environment variable to disable wrapping and @nowrap annotation to bypass wrapping when unnecessary. These changes reduce edge-case bugs, accelerate experimentation, and enable scalable, downstream-friendly ASR pipelines.
October 2024
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Quality Metrics
Correctness96.6%
Maintainability87.6%
Architecture92.2%
Performance88.8%
AI Usage68.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
API developmentAttention MechanismsAudio ProcessingCI/CDCode RefactoringCommand Line ToolsComputer VisionConvolutional Neural NetworksData AnalysisData ManipulationData ParallelismData ProcessingData StructuresDeep LearningDistributed Systems
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