Migration Guide¶
This page contains information about changes between major versions and how you can migrate from one version to another.
0.11.x to 0.12.0¶
Warning
This is a release breaking backwards compatibility. Unfortunately, it is not possible to load previously trained models (as the stored file formats have changed as well as the configuration and metadata). Please make sure to retrain a model before trying to use it with this improved version.
model configuration¶
We have split the configuration in a model configuration and parameters used
to configure the server, train, and evaluate scripts. The configuration
file now only contains the pipeline
as well as the language
parameters. Example:
langauge: "en" pipeline: - name: "nlp_spacy" model: "en" # parameter of the spacy component - name: "ner_synonyms"
All other parameters have either been moved to the scripts for training (Train a Model), serving models (Server Parameters), or put into the pipeline configuration (Processing Pipeline).
persistors:¶
- renamed
AWS_REGION
toAWS_DEFAULT_REGION
- always make sure to specify the bucket using env
BUCKET_NAME
- are now configured solely over environment variables
0.9.x to 0.10.0¶
- We introduced a new concept called a
project
. You can have multiple versions of a model trained for a project. E.g. you can train an initial model and add more training data and retrain that project. This will result in a new model version for the same project. This allows you to, allways request the latest model version from the http server and makes the model handling more structured. - If you want to reuse trained models you need to move them in a directory named
after the project. E.g. if you already got a trained model in directory
my_root/model_20170628-002704
you need to move that tomy_root/my_project/model_20170628-002704
. Your new projects name will bemy_project
and you can query the model using the http server usingcurl http://localhost:5000/parse?q=hello%20there&project=my_project
- Docs moved to https://rasahq.github.io/rasa_nlu/
- Renamed
name
parameter toproject
. This means for training requests you now need to pass theproject parameter instead of ``name
, e.g.POST /train?project=my_project_name
with the body of the request containing the training data - Adapted remote cloud storages to support projects. This is a backwards incompatible change, and unfortunately you need to retrain uploaded models and reupload them.
0.8.x to 0.9.x¶
- add
tokenizer_spacy
to trained spacy_sklearn models metadata (right after thenlp_spacy
). alternative is to retrain the model
0.7.x to 0.8.x¶
The training and loading capability for the spacy entity extraction was dropped in favor of the new CRF extractor. That means models need to be retrained using the crf extractor.
The parameter and configuration value name of
backend
changed topipeline
.There have been changes to the model metadata format. You can either retrain your models or change the stored metadata.json:
- rename
language_name
tolanguage
- rename
backend
topipeline
- for mitie models you need to replace
feature_extractor
withmitie_feature_extractor_fingerprint
. That fingerprint depends on the language you are using, foren
it is"mitie_feature_extractor_fingerprint": 10023965992282753551
.
- rename
0.6.x to 0.7.x¶
The parameter and configuration value name of
server_model_dir
changed toserver_model_dirs
.The parameter and configuration value name of
write
changed toresponse_log
. It now configures the directory where the logs should be written to (not a file!)The model metadata format has changed. All paths are now relative with respect to the
path
specified in the configuration during training and loading. If you want to run models that are trained with a version prev to 0.7 you need to adapt the paths manually inmetadata.json
from{ "trained_at": "20170304-191111", "intent_classifier": "model_XXXX_YYYY_ZZZZ/intent_classifier.pkl", "training_data": "model_XXXX_YYYY_ZZZZ/training_data.json", "language_name": "en", "entity_extractor": "model_XXXX_YYYY_ZZZZ/ner", "feature_extractor": null, "backend": "spacy_sklearn" }
to something along the lines of this (making all paths relative to the models base dir, which is
model_XXXX_YYYY_ZZZZ/
):{ "trained_at": "20170304-191111", "intent_classifier": "intent_classifier.pkl", "training_data": "training_data.json", "language_name": "en", "entity_synonyms": null, "entity_extractor": "ner", "feature_extractor": null, "backend": "spacy_sklearn" }