<jupyter_start><jupyter_text>Test without batch optimizations<jupyter_code>params.resume = False
params.use_intra_attention = True
params.gru_encode = False
# Change to 32 for testing withouth optimization
params.batch_size = 32
params.projection_dim = 200
params.use_optimization = False
params.patience = 30

train(params)<jupyter_output><empty_output><jupyter_text>Test with batch optimization<jupyter_code>params.resume = False
params.use_intra_attention = True
params.gru_encode = False
params.batch_size = 10
params.use_optimization = True
params.projection_dim = 200
params.patience = 30

train(params)<jupyter_output><empty_output><jupyter_text>Test without batch optimizations for bilstm max pooling<jupyter_code>params.projection_dim = 300
params.hidden_dim = 300
params.batch_size = 10
params.dropout = 0.2
params.resume = None
params.extra_debug = True
params.use_optimizations = True
train(params, model_name="bilstm_max")
%%bash
cp /scratch/as10656/nli_models/early_stopping_checkpoint /scratch/as10656/nli_models/bilstm_max_drop_0.5.pth.tar<jupyter_output><empty_output><jupyter_text>Test with optimizations for bilstm max pooling<jupyter_code>params.projection_dim = 300
params.batch_size = 10
params.dropout = 0.5
params.resume = False
# params.resume = '/scratch/as10656/nli_models/early_stopping_checkpoint'
params.use_optimizations = True
train(params, model_name="bilstm_max")
%%bash
cp /scratch/as10656/nli_models/early_stopping_checkpoint /scratch/as10656/nli_models/bilstm_max_drop_opti_0.5.pth.tar
params.projection_dim = 300
params.batch_size = 10
params.dropout = 0.2
params.use_optimizations = True
train(params, model_name="bilstm_max")
%%bash
nvidia-smi<jupyter_output><empty_output>
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{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '552396', 'license': 'MIT', 'n_tokens_mistral': 669, 'n_tokens_neox': 608, 'n_words': 103, 'path': 'parikh-et-al-and-bilstm-max-pool/parikh.ipynb', 'repo_name': 'apsdehal/nli-batch-optimizations', 'size': '1713'}