removing paddlepaddle and tflite reference (#1805)

README.md

@@ -15,8 +15,6 @@ HTTP endpoint. It can serve the following model types out of the box:
 
 You can install extra extensions to enable the following models:
 
-- PaddlePaddle model
-- TFLite model
 - XGBoost model
 - LightGBM model
 - Sentencepiece model

benchmark/README.md

@@ -19,13 +19,10 @@ djl-bench currently support benchmark the following type of models:
 - TensorFlow SavedModel bundle
 - Apache MXNet model
 - ONNX model
-- PaddlePaddle model
-- TFLite model
 - TensorRT model
 - XGBoost model
 - LightGBM model
 - Python script model
-- Neo DLR (TVM) model
 
 You can build djl-bench from source if you need to benchmark fastText/BlazingText/Sentencepiece models.
 
@@ -187,9 +184,7 @@ By default, the above script will use MXNet as the default Engine, but you can a
 -e TensorFlow # TensorFlow
 -e PyTorch # PyTorch
 -e MXNet # Apache MXNet
--e PaddlePaddle # PaddlePaddle
 -e OnnxRuntime # pytorch
--e TFLite # TFLite
 -e TensorRT # TensorRT
 -e XGBoost # XGBoost
 -e LightGBM # LightGBM

benchmark/build.gradle

@@ -13,8 +13,6 @@ dependencies {
     runtimeOnly "ai.djl.pytorch:pytorch-model-zoo"
     runtimeOnly "ai.djl.tensorflow:tensorflow-model-zoo"
     runtimeOnly "ai.djl.mxnet:mxnet-model-zoo"
-    runtimeOnly "ai.djl.paddlepaddle:paddlepaddle-model-zoo"
-    runtimeOnly "ai.djl.tflite:tflite-engine"
     runtimeOnly "ai.djl.ml.xgboost:xgboost"
     runtimeOnly project(":engines:python")
     runtimeOnly "ai.djl.tensorrt:tensorrt"

benchmark/snapcraft/snapcraft.yaml

@@ -12,10 +12,8 @@ description: |
   - PyTorch
   - TensorFlow
   - Apache MXNet
-  - PaddlePaddle
   - ONNXRuntime
   - TensorRT
-  - TensorFlow Lite
   - XGBoost
   - LightGBM
   - Python

benchmark/src/main/java/ai/djl/benchmark/Benchmark.java

@@ -121,12 +121,6 @@ private static void configEngines(boolean multithreading) {
                 System.setProperty("ai.djl.onnxruntime.num_threads", "1");
             }
         }
-        if (System.getProperty("ai.djl.tflite.disable_alternative") == null) {
-            System.setProperty("ai.djl.tflite.disable_alternative", "true");
-        }
-        if (System.getProperty("ai.djl.paddlepaddle.disable_alternative") == null) {
-            System.setProperty("ai.djl.paddlepaddle.disable_alternative", "true");
-        }
         if (System.getProperty("ai.djl.onnx.disable_alternative") == null) {
             System.setProperty("ai.djl.onnx.disable_alternative", "true");
         }

serving/docs/configurations.md

@@ -57,12 +57,6 @@ DJLServing build on top of Deep Java Library (DJL). Here is a list of settings f
 | ai.djl.mxnet.static_shape         | system prop         | CachedOp options, default: true                                                |
 | ai.djl.use_local_parameter_server | system prop         | Use java parameter server instead of MXNet native implemention, default: false |
 
-### PaddlePaddle
-
-| Key                                     | Type                | Description                                      |
-|-----------------------------------------|---------------------|--------------------------------------------------|
-| PADDLE_LIBRARY_PATH                     | env var/system prop | User provided custom PaddlePaddle native library |
-| ai.djl.paddlepaddle.disable_alternative | system prop         | Disable alternative engine                       |
 
 ### Huggingface tokenizers
 

serving/docs/configurations_model.md

@@ -10,7 +10,7 @@ An example `serving.properties` can be found [here](https://github.com/deepjaval
 In `serving.properties`, you can set the following properties. Model properties are accessible to `Translator`
 and python handler functions.
 
-- `engine`: Which Engine to use, values include MXNet, PyTorch, TensorFlow, ONNX, PaddlePaddle, DeepSpeed, etc.
+- `engine`: Which Engine to use, values include MXNet, PyTorch, TensorFlow, ONNX, DeepSpeed, etc.
 - `load_on_devices`: A ; delimited devices list, which the model to be loaded on, default to load on all devices.
 - `translatorFactory`: Specify the TranslatorFactory.
 - `job_queue_size`: Specify the job queue size at model level, this will override global `job_queue_size`, default is `1000`.

serving/docs/modes.md

@@ -175,7 +175,6 @@ Next, you need to include a model file. DJL Serving supports model artifacts for
 - PyTorch (torchscript only)
 - TensorFlow
 - ONNX
-- PaddlePaddle
 
 You can also include any required artifacts in the model directory. For example, `ImageClassificationTranslator` may need a `synset.txt` file, you can put it in the same directory with your model file to define the labels.
 
@@ -443,7 +442,6 @@ DJL Serving supports model artifacts for the following engines:
 - PyTorch (torchscript only)
 - TensorFlow
 - ONNX
-- PaddlePaddle
 
 ### Packaging
 

serving/src/main/java/ai/djl/serving/plugins/DependencyManager.java

@@ -75,12 +75,6 @@ public void installEngine(String engineName) throws IOException {
                 installDependency("ai.djl.mxnet:mxnet-engine:" + djlVersion);
                 installDependency("ai.djl.mxnet:mxnet-model-zoo:" + djlVersion);
                 break;
-            case "PaddlePaddle":
-                installDependency("ai.djl.paddlepaddle:paddlepaddle-engine:" + djlVersion);
-                break;
-            case "TFLite":
-                installDependency("ai.djl.tflite:tflite-engine:" + djlVersion);
-                break;
             case "XGBoost":
                 installDependency("ai.djl.ml.xgboost:xgboost:" + djlVersion);
                 // TODO: Avoid hard code version

wlm/src/main/java/ai/djl/serving/wlm/ModelInfo.java

@@ -704,12 +704,6 @@ private String inferEngine() throws ModelException {
         } else if (Files.isRegularFile(modelDir.resolve(prefix + ".trt"))
                 || Files.isRegularFile(modelDir.resolve(prefix + ".uff"))) {
             return "TensorRT";
-        } else if (Files.isRegularFile(modelDir.resolve(prefix + ".tflite"))) {
-            return "TFLite";
-        } else if (Files.isRegularFile(modelDir.resolve("model"))
-                || Files.isRegularFile(modelDir.resolve("__model__"))
-                || Files.isRegularFile(modelDir.resolve("inference.pdmodel"))) {
-            return "PaddlePaddle";
         } else if (Files.isRegularFile(modelDir.resolve(prefix + ".json"))
                 || Files.isRegularFile(modelDir.resolve(prefix + ".xgb"))
                 || Files.isRegularFile(modelDir.resolve("model.xgb"))) {

wlm/src/test/java/ai/djl/serving/wlm/ModelInfoTest.java

@@ -176,18 +176,6 @@ public void testInitModel() throws IOException, ModelException {
         model.initialize();
         assertEquals(model.getEngineName(), "XGBoost");
 
-        Path paddle = modelDir.resolve("__model__");
-        Files.createFile(paddle);
-        model = new ModelInfo<>("build/models/test_model");
-        model.initialize();
-        assertEquals(model.getEngineName(), "PaddlePaddle");
-
-        Path tflite = modelDir.resolve("test_model.tflite");
-        Files.createFile(tflite);
-        model = new ModelInfo<>("build/models/test_model");
-        model.initialize();
-        assertEquals(model.getEngineName(), "TFLite");
-
         Path tensorRt = modelDir.resolve("test_model.uff");
         Files.createFile(tensorRt);
         model = new ModelInfo<>("build/models/test_model");