Utilities and boilerplate code which allows using Weights & Biases to tune the hypereparameters for any AllenNLP model without a single line of extra code!
-
Log a single run or a hyperparameter search sweep without any extra code, just using configuration files.
-
Use Weights & Biases' bayesian hyperparameter search engine + hyperband in any AllenNLP project.
$ pip install wandb-allennlp
$ echo wandb_allennlp >> .allennlp_plugins
-
Create your model using AllenNLP along with a training configuration file as you would normally do.
-
Add a trainer callback in your config file. Use one of the following based on your AllenNLP version:
...,
trainer: {
type: 'callback',
callbacks: [
...,
{
type: 'wandb_allennlp',
files_to_save: ['config.json'],
files_to_save_at_end: ['*.tar.gz'],
},
...,
],
...,
}
...
...
- Execute the
allennlp train-with-wandbcommand instead ofallennlp train. It supports all the arguments present inallennlp train. However, the--overrideshave to be specified in the--kw valueor--kw=valueform, wherekwis the parameter to override andvalueis its value. Use the dot notation for nested parameters. For instance,{'model': {'embedder': {'type': xyz}}}can be provided as--model.embedder.type xyz.
allennlp train-with-wandb model_configs/my_config.jsonnet --include-package=package_with_my_registered_classes --include-package=another_package --wandb-run-name=my_first_run --wandb-tags=any,set,of,non-unique,tags,that,identify,the,run,without,spaces
- Create your model using AllenNLP along with a training configuration file as you would normally do. For example:
local data_path = std.extVar('DATA_PATH');
local a = std.parseJson(std.extVar('a'));
local bool_value = std.parseJson(std.extVar('bool_value'));
local int_value = std.parseJson(std.extVar('int_value'));
{
type: 'train_test_log_to_wandb',
evaluate_on_test: true,
dataset_reader: {
type: 'snli',
token_indexers: {
tokens: {
type: 'single_id',
lowercase_tokens: true,
},
},
},
train_data_path: data_path + '/snli_1.0_test/snli_1.0_train.jsonl',
validation_data_path: data_path + '/snli_1.0_test/snli_1.0_dev.jsonl',
test_data_path: data_path + '/snli_1.0_test/snli_1.0_test.jsonl',
model: {
type: 'parameter-tying',
a: a,
b: a,
d: 0,
bool_value: bool_value,
bool_value_not: !bool_value,
int_value: int_value,
int_value_10: int_value + 10,
},
data_loader: {
batch_sampler: {
type: 'bucket',
batch_size: 64,
},
},
trainer: {
optimizer: {
type: 'adam',
lr: 0.001,
weight_decay: 0.0,
},
cuda_device: -1,
num_epochs: 2,
callbacks: [
{
type: 'wandb_allennlp',
files_to_save: ['config.json'],
files_to_save_at_end: ['*.tar.gz'],
},
],
},
}
- Create a sweep configuration file and generate a sweep on the wandb server. Note that the tied parameters that are accepted through environment variables are specified using the prefix
env.in the sweep config. For example:
name: parameter_tying_test_console_script_v0.2.4
program: allennlp
command:
- ${program} #omit the interpreter as we use allennlp train command directly
- "train-with-wandb" # subcommand
- "configs/parameter_tying_v0.2.4.jsonnet"
- "--include-package=models" # add all packages containing your registered classes here
- "--include-package=allennlp_models"
- ${args}
method: bayes
metric:
name: training_loss
goal: minimize
parameters:
# hyperparameters start with overrides
# Ranges
# Add env. to tell that it is a top level parameter
env.a:
min: 1
max: 10
distribution: uniform
env.bool_value:
values: [true, false]
env.int_value:
values: [-1, 0, 1, 10]
model.d:
value: 1
- Create the sweep on wandb.
$ wandb sweep path_to_sweep.yaml
- Set the other environment variables required by your jsonnet.
export DATA_DIR=./data
- Start the search agents.
wandb agent <sweep_id>
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