Merge remote-tracking branch 'origin/master' into lidar-annotation-geometry-fix

Author: flubstep

.circleci/config.yml

@@ -1,16 +1,20 @@
 # CircleCI jobs are only enabled to on Pull Requests and commits to master branch.
 # "Only build pull requests" enabled in Project's Advanced Settings.
 version: 2.1
+orbs:
+  slack: circleci/slack@4.4.2
+
 jobs:
   build_test:
     docker:
-      - image: python:3.6-slim-buster
+      - image: python:3.6-buster
     resource_class: small
     steps:
       - checkout # checkout source code to working directory
       - run:
           name: Install Environment Dependencies
           command: | # install dependencies
+            apt-get -y install curl
             pip install --upgrade pip 
             pip install poetry
             poetry install
@@ -38,15 +42,17 @@ jobs:
             poetry run coverage run --include=nucleus/* -m pytest -s -v --junitxml=test_results/junit.xml
             poetry run coverage report
             poetry run coverage html
-
       - store_test_results:
           path: htmlcov
-
       - store_test_results:
           path: test_results
-      
       - store_artifacts:
           path: test_results
+      - slack/notify:
+          branch_pattern: master
+          event: fail
+          template: basic_fail_1
+
   pypi_publish:
     docker:
       - image: cimg/python:3.6
@@ -83,9 +89,21 @@ jobs:
             fi
             poetry publish --username=$PYPI_USERNAME --password=$PYPI_PASSWORD
 workflows:
+  nightly_build_test:
+    triggers:
+      - schedule:
+          cron: "0 0 * * *"
+          filters:
+            branches:
+              only:
+                - master
+    jobs:
+      - build_test:
+          context: Nucleus
   build_test_publish:
     jobs:
       - build_test:
+          context: Nucleus
           filters:
             tags:
               only: /^v\d+\.\d+\.\d+$/ # Runs only for tags with the format [v1.2.3]

README.md

@@ -28,168 +28,7 @@ pip install --upgrade scale-nucleus
 
 ## Usage
 
-The first step to using the Nucleus library is instantiating a client object.
-The client abstractions serves to authenticate the user and act as the gateway
-for users to interact with their datasets, models, and model runs.
-
-### Create a client object
-
-```python
-import nucleus
-client = nucleus.NucleusClient("YOUR_API_KEY_HERE")
-```
-
-### Create Dataset
-
-```python
-dataset = client.create_dataset("My Dataset")
-```
-
-### List Datasets
-
-```python
-datasets = client.list_datasets()
-```
-
-### Delete a Dataset
-
-By specifying target dataset id.
-A response code of 200 indicates successful deletion.
-
-```python
-client.delete_dataset("YOUR_DATASET_ID")
-```
-
-### Append Items to a Dataset
-
-You can append both local images and images from the web. Simply specify the location and Nucleus will automatically infer if it's remote or a local file.
-
-```python
-dataset_item_1 = DatasetItem(image_location="./1.jpeg", reference_id="1", metadata={"key": "value"})
-dataset_item_2 = DatasetItem(image_location="s3://srikanth-nucleus/9-1.jpg", reference_id="2", metadata={"key": "value"})
-```
-
-The append function expects a list of `DatasetItem` objects to upload, like this:
-
-```python
-response = dataset.append([dataset_item_1, dataset_item_2])
-```
-
-### Get Dataset Info
-
-Tells us the dataset name, number of dataset items, model_runs, and slice_ids.
-
-```python
-dataset.info
-```
-
-### Access Dataset Items
-
-There are three methods to access individual Dataset Items:
-
-(1) Dataset Items are accessible by reference id
-
-```python
-item = dataset.refloc("my_img_001.png")
-```
-
-(2) Dataset Items are accessible by index
-
-```python
-item = dataset.iloc(0)
-```
-
-(3) Dataset Items are accessible by the dataset_item_id assigned internally
-
-```python
-item = dataset.loc("dataset_item_id")
-```
-
-### Add Annotations
-
-Upload groundtruth annotations for the items in your dataset.
-Box2DAnnotation has same format as https://dashboard.scale.com/nucleus/docs/api#add-ground-truth
-
-```python
-annotation_1 = BoxAnnotation(reference_id="1", label="label", x=0, y=0, width=10, height=10, annotation_id="ann_1", metadata={})
-annotation_2 = BoxAnnotation(reference_id="2", label="label", x=0, y=0, width=10, height=10, annotation_id="ann_2", metadata={})
-response = dataset.annotate([annotation_1, annotation_2])
-```
-
-For particularly large payloads, please reference the accompanying scripts in **references**
-
-### Add Model
-
-The model abstraction is intended to represent a unique architecture.
-Models are independent of any dataset.
-
-```python
-model = client.add_model(name="My Model", reference_id="newest-cnn-its-new", metadata={"timestamp": "121012401"})
-```
-
-### Upload Predictions to ModelRun
-
-This method populates the model_run object with predictions. `ModelRun` objects need to reference a `Dataset` that has been created.
-Returns the associated model_id, human-readable name of the run, status, and user specified metadata.
-Takes a list of Box2DPredictions within the payload, where Box2DPrediction
-is formulated as in https://dashboard.scale.com/nucleus/docs/api#upload-model-outputs
-
-```python
-prediction_1 = BoxPrediction(reference_id="1", label="label", x=0, y=0, width=10, height=10, annotation_id="pred_1", confidence=0.9)
-prediction_2 = BoxPrediction(reference_id="2", label="label", x=0, y=0, width=10, height=10, annotation_id="pred_2", confidence=0.2)
-
-model_run = model.create_run(name="My Model Run", metadata={"timestamp": "121012401"}, dataset=dataset, predictions=[prediction_1, prediction_2])
-```
-
-### Commit ModelRun
-
-The commit action indicates that the user is finished uploading predictions associated
-with this model run. Committing a model run kicks off Nucleus internal processes
-to calculate performance metrics like IoU. After being committed, a ModelRun object becomes immutable.
-
-```python
-model_run.commit()
-```
-
-### Get ModelRun Info
-
-Returns the associated model_id, human-readable name of the run, status, and user specified metadata.
-
-```python
-model_run.info
-```
-
-### Accessing ModelRun Predictions
-
-You can access the modelRun predictions for an individual dataset_item through three methods:
-
-(1) user specified reference_id
-
-```python
-model_run.refloc("my_img_001.png")
-```
-
-(2) Index
-
-```python
-model_run.iloc(0)
-```
-
-(3) Internally maintained dataset_item_id
-
-```python
-model_run.loc("dataset_item_id")
-```
-
-### Delete ModelRun
-
-Delete a model run using the target model_run_id.
-
-A response code of 200 indicates successful deletion.
-
-```python
-client.delete_model_run("model_run_id")
-```
+For the most up to date documentation, reference: https://dashboard.scale.com/nucleus/docs/api?language=python.
 
 ## For Developers