config.yaml

defaults:
  - _self_
  - cluster: bcm  # Set to bcm for BCM and BCP clusters. Set to k8s for a k8s cluster.
  - data_curation: common_crawl/curate_common_crawl
  - data_preparation: gpt3/download_gpt3_pile #steerlm/steerlm_data_prep1 or steerlm/steerlm_data_prep2_reg
  - training: gpt3/5b
  - conversion: gpt3/convert_gpt3
  - conversion_hf2nemo: hf_llama2/convert_llama2_nemo
  - fw_inference: null
  - external_conversion: null
  - fine_tuning: null
  - generic: null
  - peft: null
  - prompt_learning: null
  - adapter_learning: null
  - ia3_learning: null
  - evaluation: gpt3/evaluate_all
  - export: gpt3/export_gpt3
  - rlhf_rm: gpt3/2b_rm
  - rlhf_ppo: gpt3/2b_ppo
  - steerlm_reg : ac_sft/gpt_sft # either rw_sft/training_rm or ac_sft/gpt_sft
  - ptq: model/quantization
  - rag_indexing: bert/340m
  - rag_generating: gpt3/125m
  - override hydra/job_logging: stdout

hydra:
  run:
    dir: .
  output_subdir: null

debug: False

stages:
  #- data_preparation
  #- training
  - conversion
  #- conversion_hf2nemo
  #- prompt_learning
  #- adapter_learning
  #- peft
  #- ia3_learning
  #- evaluation
  #- export
  #- steerlm_reg

cluster_type: bcm  # bcm, bcp, or k8s. If bcm or k8s, it must match - cluster above.
launcher_scripts_path: ???  # Path to NeMo Megatron Launch scripts, should ends with /launcher_scripts
data_dir: ${launcher_scripts_path}/data  # Location to store and read the data.
base_results_dir: ${launcher_scripts_path}/results  # Location to store the results, checkpoints and logs.
container_mounts: # List of additional paths to mount to container. They will be mounted to same path.
  - null
container: nvcr.io/nvidia/nemo:24.09

wandb_api_key_file: null  # File where the w&B api key is stored. Key must be on the first line.
wandb_api_bcp_secret_key: null  # For BCP clusters, read the W&B api key directly from the environment variable set as a secret from BCP. The value must match the name of the environment variable in BCP, such as WANDB_TOKEN.

bcp_no_redirect: True  # If True, all stdout and stderr will not be redirected and appear in the standard logs. If False, all stdout and stderr output will be redirected to individual files on a per-rank basis. Ignored for non-BCP clusters.

enable_vboost: False # if True, sets video boost slider that rebalances the power budget between the compute engine and the memory such that the compute engines have greater power allocation. This can help improve the performance of compute-bound worklaods.

env_vars:
  NCCL_TOPO_FILE: null # Should be a path to an XML file describing the topology
  UCX_IB_PCI_RELAXED_ORDERING: null # Needed to improve Azure performance
  NCCL_IB_PCI_RELAXED_ORDERING: null # Needed to improve Azure performance
  NCCL_IB_TIMEOUT: null # InfiniBand Verbs Timeout. Set to 22 for Azure
  NCCL_DEBUG: null # Logging level for NCCL. Set to "INFO" for debug information
  NCCL_PROTO: null # Protocol NCCL will use. Set to "simple" for AWS
  TRANSFORMERS_OFFLINE: 0
  TORCH_NCCL_AVOID_RECORD_STREAMS: 1
  NCCL_NVLS_ENABLE: 0

# GPU Mapping
numa_mapping:
  enable: True  # Set to False to disable all mapping (performance will suffer).
  mode: unique_contiguous  # One of: all, single, single_unique, unique_interleaved or unique_contiguous.
  scope: node  # Either node or socket.
  cores: all_logical  # Either all_logical or single_logical.
  balanced: True  # Whether to assing an equal number of physical cores to each process.
  min_cores: 1  # Minimum number of physical cores per process.
  max_cores: 8  # Maximum number of physical cores per process. Can be null to use all available cores.

# Do not modify below, use the values above instead.
data_preparation_config: ${hydra:runtime.choices.data_preparation}
data_curation_config: ${hydra:runtime.choices.data_curation}
training_config: ${hydra:runtime.choices.training}
fine_tuning_config: ${hydra:runtime.choices.fine_tuning}
peft_config: ${hydra:runtime.choices.peft}
prompt_learning_config: ${hydra:runtime.choices.prompt_learning}
adapter_learning_config: ${hydra:runtime.choices.adapter_learning}
ia3_learning_config: ${hydra:runtime.choices.ia3_learning}
evaluation_config: ${hydra:runtime.choices.evaluation}
conversion_config: ${hydra:runtime.choices.conversion}
export_config: ${hydra:runtime.choices.export}
rlhf_rm_config: ${hydra:runtime.choices.rlhf_rm}
rlhf_ppo_config: ${hydra:runtime.choices.rlhf_ppo}
steerlm_reg_config : ${hydra:runtime.choices.steerlm_reg}
conversion_hf2nemo_config: ${hydra:runtime.choices.conversion_hf2nemo}
fw_inference_config: ${hydra:runtime.choices.fw_inference}
external_conversion_config: ${hydra:runtime.choices.external_conversion}
ptq_config: ${hydra:runtime.choices.ptq}
rag_indexing_config: ${hydra:runtime.choices.rag_indexing}
rag_generating_config: ${hydra:runtime.choices.rag_generating}