axolotl/examples/swanlab/lora-swanlab-profiling.yml

# SwanLab LoRA Training Example with Performance Profiling
#
# This example demonstrates standard LoRA fine-tuning with SwanLab integration
# for performance profiling and optimization.
#
# Features enabled:
# - SwanLab experiment tracking
# - Performance profiling (training step, forward/backward pass timing)
# - Real-time metrics visualization
#
# To run:
#   export SWANLAB_API_KEY=your-api-key
#   accelerate launch -m axolotl.cli.train examples/swanlab/lora-swanlab-profiling.yml

# Model Configuration
base_model: NousResearch/Llama-3.2-1B

# Dataset Configuration
datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca

val_set_size: 0.1
output_dir: ./outputs/lora-swanlab-profiling-out

# LoRA Configuration
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

# Training Configuration
sequence_len: 2048
sample_packing: true
eval_sample_packing: true

micro_batch_size: 2
gradient_accumulation_steps: 2
num_epochs: 1

# Optimization
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
warmup_ratio: 0.1
weight_decay: 0.0

# Precision
bf16: auto
tf32: false

# Performance
gradient_checkpointing: true
flash_attention: true

# Checkpointing and Logging
logging_steps: 1
evals_per_epoch: 4
saves_per_epoch: 1

# Loss Monitoring
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

special_tokens:
  pad_token: "<|end_of_text|>"

# ============================================================================
# SwanLab Integration
# ============================================================================

plugins:
  - axolotl.integrations.swanlab.SwanLabPlugin

# Basic SwanLab Configuration
use_swanlab: true
swanlab_project: lora-profiling
swanlab_experiment_name: llama-3.2-1b-profiling-demo
swanlab_description: "LoRA fine-tuning with performance profiling"
swanlab_mode: cloud  # Options: cloud, local, offline, disabled

# SwanLab Authentication
# Recommended: Set via environment variable
#   export SWANLAB_API_KEY=your-api-key
# Or set in config (less secure):
# swanlab_api_key: your-api-key

# Optional: Team workspace
# swanlab_workspace: my-ml-team

# ============================================================================
# Performance Profiling
# ============================================================================
#
# SwanLab automatically profiles trainer methods when enabled.
# Profiling metrics appear in SwanLab dashboard under "profiling/" namespace.
#
# Built-in profiling:
# - Minimal overhead (< 0.1% per step)
# - High-precision timing (microsecond accuracy)
# - Exception-safe (logs duration even if method fails)
#
# View profiling metrics in SwanLab dashboard:
#   profiling/Time taken: AxolotlTrainer.training_step
#   profiling/Time taken: AxolotlTrainer.compute_loss
#   profiling/Time taken: AxolotlTrainer.prediction_step
#
# For custom profiling in your own trainer, see:
#   examples/swanlab/custom_trainer_profiling.py

# Completion logging is disabled for non-RLHF trainers
swanlab_log_completions: false  # Only works with DPO/KTO/ORPO/GRPO

# ============================================================================
# Optional: Compare with Multiple Runs
# ============================================================================
#
# To compare profiling metrics across different configurations:
#
# 1. Run baseline without flash attention:
#    swanlab_experiment_name: llama-3.2-1b-no-flash-attn
#    flash_attention: false
#
# 2. Run with gradient checkpointing:
#    swanlab_experiment_name: llama-3.2-1b-grad-checkpoint
#    gradient_checkpointing: true
#
# 3. Run with both:
#    swanlab_experiment_name: llama-3.2-1b-optimized
#    flash_attention: true
#    gradient_checkpointing: true
#
# Then compare profiling metrics in SwanLab dashboard to see performance impact

# ============================================================================
# Optional: Lark (Feishu) Team Notifications
# ============================================================================
#
# Get notified when profiling experiments complete:

# swanlab_lark_webhook_url: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxx
# swanlab_lark_secret: your-webhook-secret

# ============================================================================
# Profiling Best Practices
# ============================================================================
#
# 1. Run multiple epochs to see profiling trends over time
# 2. Ignore first ~10 steps (warmup period, slower)
# 3. Look for outliers (steps that take significantly longer)
# 4. Compare profiling metrics before/after optimization changes
# 5. Monitor per-rank profiling in distributed training
#
# Common bottlenecks to profile:
# - training_step: Overall step time (should be consistent)
# - compute_loss: Loss computation (scales with sequence length)
# - prediction_step: Evaluation time (can be slow for large val sets)
#
# If you see inconsistent timing:
# - Check for data loading bottlenecks
# - Monitor GPU utilization (may be CPU-bound)
# - Check for gradient accumulation effects
# - Verify CUDA kernel synchronization

# ============================================================================
# Disable WandB if you're migrating from it
# ============================================================================

# wandb_project:
# use_wandb: false