Updated gunicorn config for demos running in Docker.

PiperOrigin-RevId: 646542614

README.md

@@ -1,6 +1,6 @@
 # 🔥 Learning Interpretability Tool (LIT)
 
-<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-22' } *-->
+<!--* freshness: { owner: 'lit-dev' reviewed: '2024-06-25' } *-->
 
 The Learning Interpretability Tool (🔥LIT, formerly known as the Language
 Interpretability Tool) is a visual, interactive ML model-understanding tool that
@@ -21,8 +21,8 @@ LIT is built to answer questions such as:
 LIT supports a variety of debugging workflows through a browser-based UI.
 Features include:
 
-*   **Local explanations** via salience maps, attention, and rich visualization
-    of model predictions.
+*   **Local explanations** via salience maps and rich visualization of model
+    predictions.
 *   **Aggregate analysis** including custom metrics, slicing and binning, and
     visualization of embedding spaces.
 *   **Counterfactual generation** via manual edits or generator plug-ins to
@@ -90,8 +90,8 @@ git clone https://github.com/PAIR-code/lit.git && cd lit
 ```
 
 
-Note: be sure you are running Python 3.10. If you have a different version on  
-your system, use the `conda` instructions below to set up a Python 3.10 
+Note: be sure you are running Python 3.9+. If you have a different version on
+your system, use the `conda` instructions below to set up a Python 3.9
 environment.
 
 Set up a Python environment with `venv`:
@@ -106,7 +106,7 @@ Or set up a Python environment using `conda`:
 ```sh
 conda create --name lit-nlp
 conda activate lit-nlp
-conda install python=3.10
+conda install python=3.9
 conda install pip
 ```
 
@@ -142,13 +142,7 @@ To explore classification and regression models tasks from the popular
 [GLUE benchmark](https://gluebenchmark.com/):
 
 ```sh
-python -m lit_nlp.examples.glue_demo --port=5432 --quickstart
-```
-
-Or, using `docker`:
-
-```sh
-docker run --rm -e DEMO_NAME=glue_demo -p 5432:5432 -t lit-nlp --quickstart
+python -m lit_nlp.examples.glue.demo --port=5432 --quickstart
 ```
 
 Navigate to http://localhost:5432 to access the LIT UI.
@@ -160,19 +154,6 @@ but you can switch to
 [STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or
 [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the
 gear icon in the upper right.
-
-### Quick-start: language modeling
-
-To explore predictions from a pre-trained language model (BERT or GPT-2), run:
-
-```sh
-python -m lit_nlp.examples.lm_demo --models=bert-base-uncased --port=5432
-```
-
-Or, using `docker`:
-
-```sh
-docker run --rm -e DEMO_NAME=lm_demo -p 5432:5432 -t lit-nlp --models=bert-base-uncased
 ```
 
 And navigate to http://localhost:5432 for the UI.
@@ -192,7 +173,7 @@ See [lit_nlp/examples](./lit_nlp/examples). Most are run similarly to the
 quickstart example above:
 
 ```sh
-python -m lit_nlp.examples.<example_name> --port=5432 [optional --args]
+python -m lit_nlp.examples.<example_name>.demo --port=5432 [optional --args]
 ```
 
 ## User Guide

lit_nlp/examples/gunicorn_config.py

@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Config for gunicorn for cloud-hosted demos."""
+"""gunicorn configuration for cloud-hosted demos."""
 
 import os
 
-_DEMO_NAME = os.getenv('DEMO_NAME', 'glue_demo')
+_DEMO_NAME = os.getenv('DEMO_NAME', 'glue')
 _DEMO_PORT = os.getenv('DEMO_PORT', '5432')
 
 bind = f'0.0.0.0:{_DEMO_PORT}'
 timeout = 3600
 threads = 8
 worker_class = 'gthread'
-wsgi_app = f'lit_nlp.examples.{_DEMO_NAME}:get_wsgi_app()'
+wsgi_app = f'lit_nlp.examples.{_DEMO_NAME}.demo:get_wsgi_app()'

lit_nlp/examples/tydi/demo.py

@@ -12,6 +12,7 @@
 
 from absl import app
 from absl import flags
+from absl import logging
 from lit_nlp import dev_server
 from lit_nlp import server_flags
 from lit_nlp.components import word_replacer
@@ -40,7 +41,9 @@ def get_wsgi_app() -> Optional[dev_server.LitServerType]:
   # Parse flags without calling app.run(main), to avoid conflict with
   # gunicorn command line flags.
   unused = flags.FLAGS(sys.argv, known_only=True)
-  return main(unused)
+  if unused:
+    logging.info("tydi_demo:get_wsgi_app() called with unused args: %s", unused)
+  return main([])
 
 
 def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:

website/sphinx_src/docker.md

@@ -49,13 +49,11 @@ below.
 ```shell
 # DEMO_NAME is used to complete the Python module path
 #
-#     "lit_nlp.examples.$DEMO_NAME"
+#     "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()"
 #
 # Therefore, valid values for DEMO_NAME are Python module paths in the
-# lit_nlp/examples directory, such as
-#
-#   * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc.
-docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp
+# lit_nlp/examples directory, such as glue, penguin, tydi, etc.
+docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
 
 # Use the DEMO_PORT environment variable as to change the port that LIT uses in
 # the container. Be sure to also change the -p option to map the container's
@@ -66,8 +64,8 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp
 # containers on your machine using the combination of the DEMO_NAME and
 # DEMO_PORT arguments, and docker run with the -d flag to run the container in
 # the background.
-docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp
-docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp
+docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp
+docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp
 ```
 
 ## Integrating Custom LIT Instances with the Default Docker Image