Difference between revisions of "Vectors/elmo/tutorial"
(→Training ELMo on Saga) |
|||
| Line 4: | Line 4: | ||
= Training ELMo on Saga = | = Training ELMo on Saga = | ||
| + | |||
| + | As of now, one should use ''Anaconda'' to get working GPU-enabled ''TensorFlow'' on Saga. | ||
| + | tensorflow-gpu Python package is then installed locally. | ||
| + | |||
| + | After that, the code from https://github.com/akutuzov/bilm-tf can be used to train a model. More instructions to appear later. | ||
Example SLURM file: | Example SLURM file: | ||
| Line 34: | Line 39: | ||
# <<< conda initialize <<< | # <<< conda initialize <<< | ||
conda activate python3.6 | conda activate python3.6 | ||
| − | python3 bin/train_elmo.py --train_prefix $DATA | + | python3 bin/train_elmo.py --train_prefix $DATA --size $SIZE --vocab_file $VOCAB --save_dir $OUT |
| + | |||
| + | $DATA is a path to the directory containing any number of (possibly gzipped) plain text files: your training corpus. $SIZE if the number of word tokens in $DATA (necessary to properly construct and log batches). $VOCAB is a (possibly gzipped) one-word-per-line vocabulary file; it should always contain at least <S>, </S> and <UNK>. $OUT is a directory where the TensorFlow checkpoints will be saved. | ||
Revision as of 19:20, 29 September 2019
Background
ELMo is a family of contextualized word embeddings first introduced in [Peter et al. 2018].
Training ELMo on Saga
As of now, one should use Anaconda to get working GPU-enabled TensorFlow on Saga. tensorflow-gpu Python package is then installed locally.
After that, the code from https://github.com/akutuzov/bilm-tf can be used to train a model. More instructions to appear later.
Example SLURM file:
#!/bin/bash
#SBATCH --job-name=elmo
#SBATCH --mail-type=FAIL
#SBATCH --account=nn9447k # Use your project number
#SBATCH --partition=accel # To use the accelerator nodes
#SBATCH --gres=gpu:2 # To specify how many GPUs to use
#SBATCH --time=10:00:00 # Max walltime is 14 days.
#SBATCH --mem-per-cpu=6G
#SBATCH --ntasks=8
set -o errexit # Recommended for easier debugging
module purge # Recommended for reproducibility
module load Anaconda3/2019.03
# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/cluster/software/Anaconda3/2019.03/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/cluster/software/Anaconda3/2019.03/etc/profile.d/conda.sh" ]; then
. "/cluster/software/Anaconda3/2019.03/etc/profile.d/conda.sh"
else
export PATH="/cluster/software/Anaconda3/2019.03/bin:$PATH"
fi
fi
unset __conda_setup
# <<< conda initialize <<<
conda activate python3.6
python3 bin/train_elmo.py --train_prefix $DATA --size $SIZE --vocab_file $VOCAB --save_dir $OUT
$DATA is a path to the directory containing any number of (possibly gzipped) plain text files: your training corpus. $SIZE if the number of word tokens in $DATA (necessary to properly construct and log batches). $VOCAB is a (possibly gzipped) one-word-per-line vocabulary file; it should always contain at least , and <UNK>. $OUT is a directory where the TensorFlow checkpoints will be saved.
