68 lines
1.9 KiB
YAML
68 lines
1.9 KiB
YAML
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name: DEFAULT
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project: Spikey
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slurm:
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name: 'Spikey_{config[name]}'
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partitions:
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- single
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standard_output: ./reports/slurm/out_%A_%a.log
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standard_error: ./reports/slurm/err_%A_%a.log
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num_parallel_jobs: 50
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cpus_per_task: 4
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memory_per_cpu: 1000
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time_limit: 1440 # in minutes
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ntasks: 1
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venv: '.venv/bin/activate'
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sh_lines:
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- 'mkdir -p {tmp}/wandb'
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- 'mkdir -p {tmp}/local_pycache'
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- 'export PYTHONPYCACHEPREFIX={tmp}/local_pycache'
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runner: spikey
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scheduler:
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reps_per_version: 1
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agents_per_job: 1
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reps_per_agent: 1
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wandb:
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project: '{config[project]}'
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group: '{config[name]}'
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job_type: '{delta_desc}'
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name: '{job_id}_{task_id}:{run_id}:{rand}={config[name]}_{delta_desc}'
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tags:
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- '{config[env][name]}'
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- '{config[algo][name]}'
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sync_tensorboard: False
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monitor_gym: False
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save_code: False
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---
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name: Test
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preprocessing:
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use_delta_encoding: true # Whether to use delta encoding.
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predictor:
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type: lstm # Options: 'lstm', 'fixed_input_nn'
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input_size: 1 # Input size for the LSTM predictor.
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hidden_size: 128 # Hidden size for the LSTM or Fixed Input NN predictor.
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num_layers: 2 # Number of layers for the LSTM predictor.
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fixed_input_size: 10 # Input size for the Fixed Input NN predictor. Only used if type is 'fixed_input_nn'.
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training:
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epochs: 10 # Number of training epochs.
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batch_size: 32 # Batch size for training.
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learning_rate: 0.001 # Learning rate for the optimizer.
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eval_freq: 2 # Frequency of evaluation during training (in epochs).
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save_path: models # Directory to save the best model and encoder.
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num_points: 1000 # Number of data points to visualize
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bitstream_encoding:
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type: arithmetic # Use arithmetic encoding.
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data:
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url: https://content.neuralink.com/compression-challenge/data.zip # URL to download the dataset.
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directory: data # Directory to extract and store the dataset.
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split_ratio: 0.8 # Ratio to split the data into train and test sets.
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