82 lines
2.6 KiB
YAML
82 lines
2.6 KiB
YAML
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: 8
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memory_per_cpu: 4000
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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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import: $
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latent_projector:
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type: fc # Options: 'fc', 'rnn'
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input_size: 50 # Input size for the Latent Projector (length of snippets).
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latent_size: 8 # Size of the latent representation before message passing.
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layer_shapes: [16, 32] # List of layer sizes for the latent projector (if type is 'fc').
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activations: ['relu', 'relu'] # Activation functions for the latent projector layers (if type is 'fc').
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rnn_hidden_size: 32 # Hidden size for the RNN projector (if type is 'rnn').
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rnn_num_layers: 2 # Number of layers for the RNN projector (if type is 'rnn').
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middle_out:
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output_size: 16 # Size of the latent representation after message passing.
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num_peers: 3 # Number of most correlated peers to consider.
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predictor:
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layer_shapes: [32, 16] # List of layer sizes for the predictor.
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activations: ['relu', 'relu'] # Activation functions for the predictor layers.
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training:
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epochs: 128 # Number of training epochs.
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batch_size: 8 # Batch size for training.
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learning_rate: 0.001 # Learning rate for the optimizer.
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eval_freq: 8 # 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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evaluation:
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full_compression: false # Perform full compression during evaluation
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bitstream_encoding:
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type: identity # Options: 'arithmetic', 'no_compression', 'bzip2'
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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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cut_length: None # Optional length to cut sequences to.
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profiler:
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enable: false |