mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
add loggers registry & logger docs sections
This commit is contained in:
svlandeg
parent
72a87095d9
commit
5230529de2
|
|
@ -272,7 +272,7 @@ def train_while_improving(
|
|||
step (int): How many steps have been completed.
|
||||
score (float): The main score form the last evaluation.
|
||||
other_scores: : The other scores from the last evaluation.
|
||||
loss: The accumulated losses throughout training.
|
||||
losses: The accumulated losses throughout training.
|
||||
checkpoints: A list of previous results, where each result is a
|
||||
(score, step, epoch) tuple.
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -296,24 +296,25 @@ factories.
|
|||
> i += 1
|
||||
> ```
|
||||
|
||||
| Registry name | Description |
|
||||
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
|
||||
| `assets` | Registry for data assets, knowledge bases etc. |
|
||||
| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
|
||||
| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
|
||||
| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
|
||||
| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
|
||||
| `loggers` | Registry for functions that log [training results](/usage/training). |
|
||||
| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
|
||||
| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
|
||||
| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
|
||||
| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
|
||||
| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
|
||||
| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
|
||||
| Registry name | Description |
|
||||
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `annotation_setters` | Registry for functions that store Tok2Vec annotations on `Doc` objects. |
|
||||
| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
|
||||
| `assets` | Registry for data assets, knowledge bases etc. |
|
||||
| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
|
||||
| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
|
||||
| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
|
||||
| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
|
||||
| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
|
||||
| `loggers` | Registry for functions that log [training results](/usage/training). |
|
||||
| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
|
||||
| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
|
||||
| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
|
||||
| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
|
||||
| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
|
||||
| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
|
||||
|
||||
### spacy-transformers registry {#registry-transformers}
|
||||
|
||||
|
|
@ -327,18 +328,69 @@ See the [`Transformer`](/api/transformer) API reference and
|
|||
> ```python
|
||||
> import spacy_transformers
|
||||
>
|
||||
> @spacy_transformers.registry.annotation_setters("my_annotation_setter.v1")
|
||||
> def configure_custom_annotation_setter():
|
||||
> def annotation_setter(docs, trf_data) -> None:
|
||||
> # Set annotations on the docs
|
||||
> @spacy_transformers.registry.span_getters("my_span_getter.v1")
|
||||
> def configure_custom_span_getter() -> Callable:
|
||||
> def span_getter(docs: List[Doc]) -> List[List[Span]]:
|
||||
> # Transform each Doc into a List of Span objects
|
||||
>
|
||||
> return annotation_sette
|
||||
> return span_getter
|
||||
> ```
|
||||
|
||||
| Registry name | Description |
|
||||
| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| [`span_getters`](/api/transformer#span_getters) | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences. |
|
||||
| [`annotation_setters`](/api/transformer#annotation_setters) | Registry for functions that create annotation setters. Annotation setters are functions that take a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. |
|
||||
|
||||
| Registry name | Description |
|
||||
| ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| [`span_getters`](/api/transformer#span_getters) | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences. |
|
||||
|
||||
## Loggers {#loggers source="spacy/gold/loggers.py" new="3"}
|
||||
|
||||
A logger records the training results for each step. When a logger is created,
|
||||
it returns a `log_step` function and a `finalize` function. The `log_step`
|
||||
function is called by the [training script](/api/cli#train) and receives a
|
||||
dictionary of information, including
|
||||
|
||||
# TODO
|
||||
|
||||
> #### Example config
|
||||
>
|
||||
> ```ini
|
||||
> [training.logger]
|
||||
> @loggers = "spacy.ConsoleLogger.v1"
|
||||
> ```
|
||||
|
||||
Instead of using one of the built-in batchers listed here, you can also
|
||||
[implement your own](/usage/training#custom-code-readers-batchers), which may or
|
||||
may not use a custom schedule.
|
||||
|
||||
#### spacy.ConsoleLogger.v1 {#ConsoleLogger tag="registered function"}
|
||||
|
||||
Writes the results of a training step to the console in a tabular format.
|
||||
|
||||
#### spacy.WandbLogger.v1 {#WandbLogger tag="registered function"}
|
||||
|
||||
> #### Installation
|
||||
>
|
||||
> ```bash
|
||||
> $ pip install wandb
|
||||
> $ wandb login
|
||||
> ```
|
||||
|
||||
Built-in logger that sends the results of each training step to the dashboard of
|
||||
the [Weights & Biases`](https://www.wandb.com/) dashboard. To use this logger,
|
||||
Weights & Biases should be installed, and you should be logged in. The logger
|
||||
will send the full config file to W&B, as well as various system information
|
||||
such as GPU
|
||||
|
||||
| Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `project_name` | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet. ~~str~~ |
|
||||
|
||||
> #### Example config
|
||||
>
|
||||
> ```ini
|
||||
> [training.logger]
|
||||
> @loggers = "spacy.WandbLogger.v1"
|
||||
> project_name = "monitor_spacy_training"
|
||||
> ```
|
||||
|
||||
## Batchers {#batchers source="spacy/gold/batchers.py" new="3"}
|
||||
|
||||
|
|
|
|||
|
|
@ -605,6 +605,24 @@ to your Python file. Before loading the config, spaCy will import the
|
|||
$ python -m spacy train config.cfg --output ./output --code ./functions.py
|
||||
```
|
||||
|
||||
#### Example: Custom logging function {#custom-logging}
|
||||
|
||||
During training, the results of each step are passed to a logger function in a
|
||||
dictionary providing the following information:
|
||||
|
||||
| Key | Value |
|
||||
| -------------- | ---------------------------------------------------------------------------------------------- |
|
||||
| `epoch` | How many passes over the data have been completed. ~~int~~ |
|
||||
| `step` | How many steps have been completed. ~~int~~ |
|
||||
| `score` | The main score form the last evaluation, measured on the dev set. ~~float~~ |
|
||||
| `other_scores` | The other scores from the last evaluation, measured on the dev set. ~~Dict[str, Any]~~ |
|
||||
| `losses` | The accumulated training losses. ~~Dict[str, float]~~ |
|
||||
| `checkpoints` | A list of previous results, where each result is a (score, step, epoch) tuple. ~~List[Tuple]~~ |
|
||||
|
||||
By default, these results are written to the console with the [`ConsoleLogger`](/api/top-level#ConsoleLogger)
|
||||
|
||||
# TODO
|
||||
|
||||
#### Example: Custom batch size schedule {#custom-code-schedule}
|
||||
|
||||
For example, let's say you've implemented your own batch size schedule to use
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user