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arihanv 2025-05-18 16:36:28 -07:00
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environments/optimizer/FOB/pytorch_fob/engine/default.yaml Normal file
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engine:
accelerator: gpu # Whether to train on cpu or gpu
check_finite: true # Check if 'early_stopping_metric' is finite during training. Aborts training if not. Only active when 'early_stopping' is not null.
data_dir: ./data # Where you want to store the training data
deterministic: warn # 'warn' tries to use deterministic algorithms if possible, also accepts true or false.
detect_anomaly: false # Lightning trainer argument with same name.
devices: null # This is set by each task by default, but can be overridden
early_stopping: null # The number of epochs to wait before stopping if no improvement is found. Set to null to disable.
early_stopping_metric: null # Metric to use for early stopping. If null, uses 'task.target_metric'.
gradient_clip_alg: norm # {value, norm} to disable gradient clipping: set 'gradient_clip_val' to null
gradient_clip_val: null # DEFAULT: don't clip gradients, expects value in [0, 1]
log_extra: false # Activate logging of gradients and more. Can be bool or a dict with the options supported by callback `LogTrainingStats` in `pytorch_fob/engine/callbacks.py`.
logging_interval: 50 # Number of steps between each logging step.
optimize_memory: false # Use nondeterministic, but memory-efficient algorithms for self-attention
output_dir: ./experiments # Where you want to store the results
plot: true # Whether to plot the results.
precision: bf16-mixed # Floating precision of training, see https://lightning.ai/docs/pytorch/stable/common/precision_basic.html
restrict_train_epochs: null # Only train for a specific number of epochs. Set to null to disable. The epochs set here are counted from start of training, so this works with 'resume'.
resume: true # You can either pass the path to your checkpoint here or set to true, which loads the last checkpoint.
run_scheduler: sequential # How to schedule the runs of the experiment. Supported values:
# 'sequential': runs are performed sequentially
# 'single:N' where N is the number of the run starting from 1.
# 'slurm_array': runs are scheduled using a SLURM array job.
# 'slurm_jobs': runs are scheduled using independent SLURM jobs
save_sbatch_scripts: null # Path to directory where sbatch scripts will be saved. If null, sbatch scripts will not be saved.
sbatch_time_factor: 1 # Time factor for SLURM. Multiplies all default times by this factor.
sbatch_args: # Additional arguments to pass to sbatch. Only used if run_scheduler is 'slurm_array'.
# ntasks-per-node and gres are set to 'devices' by default
# cpus-per-task is set to 'workers' by default
nodes: 1
mem-per-cpu: 2gb
time: 00:30:00 # Each task has their own default time (assumes A100 or similar gpu). Format: HH:MM:SS or seconds.
sbatch_script_template: null # Path to template for the sbatch script. Script can contain placeholder '__FOB_COMMAND__'. Otherwise it will be executed before the experiment. 'sbatch_args' will be added to the beginning of the script.
slurm_log_dir: null # Default: 'output_dir/slurm_logs' for run_scheduler 'slurm_array' and 'run_dir/slurm_logs' for run_scheduler 'slurm_jobs'
seed: 42 # The seed to use for the experiment
seed_mode: fixed # Currently only supports 'fixed'
silent: false # whether to hide progress bars. Recommended when writing outputs to a log file.
test: true # Whether to test the model.
train: true # Whether to train the model.
validate: false # Whether to validate the model after training (only useful if you are interested in the results, for example for HPO).
workers: 16 # The number of processes to use for dataloading