Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
For stable background and recommendation level, see:
Minimal Bridge override:
from megatron.bridge.training.comm_overlap import CommOverlapConfig
cfg.model.tensor_model_parallel_size = 4
cfg.model.sequence_parallel = True
cfg.model.pipeline_model_parallel_size = 4
cfg.model.virtual_pipeline_model_parallel_size = 2
cfg.comm_overlap = CommOverlapConfig(
tp_comm_overlap=True,
)
cfg.ddp.use_distributed_optimizer = True
cfg.ddp.overlap_grad_reduce = True
cfg.ddp.overlap_param_gather = True
Optional TP preset:
from megatron.bridge.training.comm_overlap import userbuffers_bf16_h100_h12288_tp4_mbs1_seqlen2048
cfg.comm_overlap.tp_comm_overlap_cfg = userbuffers_bf16_h100_h12288_tp4_mbs1_seqlen2048
Precision knobs belong to mixed precision:
cfg.mixed_precision.grad_reduce_in_fp32 = False
cfg.mixed_precision.fp8_param_gather = False
Bridge overlap gating:
if self.user_comm_overlap_cfg.tp_comm_overlap is True:
if model_cfg.tensor_model_parallel_size < 2:
...
elif not model_cfg.sequence_parallel:
...
elif not HAVE_TE:
...
PP overlap selection:
if model_cfg.pipeline_model_parallel_size > 1:
if vp_size > 1:
comm_overlap_cfg.overlap_p2p_comm = True
comm_overlap_cfg.batch_p2p_comm = False
else:
comm_overlap_cfg.overlap_p2p_comm = False
comm_overlap_cfg.batch_p2p_comm = True
DP overlap defaults:
if self.data_parallel_size > 1:
comm_overlap_cfg.bucket_size = 128 * 1024 * 1024
comm_overlap_cfg.overlap_grad_reduce = True
comm_overlap_cfg.overlap_param_gather = True
Launch-time env tuning:
executor.env_vars["CUDA_DEVICE_MAX_CONNECTIONS"] = str(cuda_device_max_connections)
...
executor.env_vars["NVTE_FWD_LAYERNORM_SM_MARGIN"] = str(self.layernorm_sm_margin)
executor.env_vars["NVTE_BWD_LAYERNORM_SM_MARGIN"] = str(self.layernorm_sm_margin)
sequence_parallel=False or Transformer Engine is unavailable.overlap_p2p_comm=True when PP > 1 and VPP > 1.bucket_size is a parameter-count knob, not a byte-size knob.grad_reduce_in_fp32 and fp8_param_gather should be set through mixed precision, not as standalone DDP tuning first.CUDA_DEVICE_MAX_CONNECTIONS and LayerNorm SM margin are launch-time plugin settings, not CommOverlapConfig fields.Use the checked-in overlap unit coverage first:
uv run python -m pytest tests/unit_tests/training/test_comm_overlap.py -q
Optional second check if nemo_run is available:
uv run python -m pytest tests/unit_tests/recipes/test_run_plugins.py -q
Success criteria:
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
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Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
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Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
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