add some updates

python/chronos/example/nano_qp/tcn/train_test.py → python/chronos/example/inference-acceleration/cpu_inference_acceleration.py

@@ -1,5 +1,20 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 import torch
-from bigdl.nano.pytorch import Trainer
+from bigdl.chronos.pytorch import TSTrainer as Trainer
 from bigdl.chronos.model.tcn import model_creator
 from bigdl.chronos.metric.forecast_metrics import Evaluator
 from bigdl.chronos.data.repo_dataset import get_public_dataset
@@ -8,11 +23,10 @@
 def gen_dataloader():
     tsdata_train, tsdata_val,\
         tsdata_test = get_public_dataset(name='nyc_taxi',
-                                        with_split=True,
-                                        val_ratio=0.1,
-                                        test_ratio=0.1
-                                        )
-    # carry out additional customized preprocessing on the dataset.
+                                         with_split=True,
+                                         val_ratio=0.1,
+                                         test_ratio=0.1)
+
     stand = StandardScaler()
     for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
         tsdata.deduplicate()\
@@ -25,18 +39,20 @@ def gen_dataloader():
     tsdata_valdataloader = tsdata_val.to_torch_data_loader(batch_size=32, shuffle=False)
     tsdata_testdataloader = tsdata_test.to_torch_data_loader(batch_size=32, shuffle=False)
 
-    return tsdata_traindataloader,\
-           tsdata_valdataloader,\
-           tsdata_testdataloader
+    return tsdata_traindataloader, tsdata_valdataloader, tsdata_testdataloader
 
 def predict_wraper(model, input_sample):
     model(input_sample)
 
 if __name__ == '__main__':
+
+    # create data loaders for train/valid/test
     tsdata_traindataloader,\
     tsdata_valdataloader,\
     tsdata_testdataloader = gen_dataloader()
 
+    # create a model
+    # This could be an arbitrary model, we choose to use a built-in model TCN here
     config = {'input_feature_num':8,
               'output_feature_num':1,
               'past_seq_len':48,
@@ -51,18 +67,24 @@ def predict_wraper(model, input_sample):
     loss = torch.nn.MSELoss()
     optimizer = torch.optim.Adam(lr=0.001, params=model.parameters())
     lit_model = Trainer.compile(model, loss, optimizer)
-    trainer = Trainer(max_epochs=3, val_check_interval=1.0,
-              accelerator='gpu', 
-              devices=1,
-              )
+
+    # train the model
+    # You may use any method to train the model either on gpu or cpu
+    trainer = Trainer(max_epochs=3,
+                      accelerator='gpu', 
+                      devices=1,
+                     )
     trainer.fit(lit_model, tsdata_traindataloader, tsdata_testdataloader)
-
+
+    # get an input sample
     x = None
     for x, _ in tsdata_traindataloader:
         break
     input_sample = x[0].unsqueeze(0)
 
-    print("original pytorch latency (ms):", Evaluator.get_latency(predict_wraper, lit_model, input_sample))
-
+    # speed up the model using Chronos TSTrainer
     speed_model = Trainer.trace(lit_model, accelerator="onnxruntime", input_sample=input_sample)
+
+    # evaluate the model's latency
+    print("original pytorch latency (ms):", Evaluator.get_latency(predict_wraper, lit_model, input_sample))
     print("onnxruntime latency (ms):", Evaluator.get_latency(predict_wraper, speed_model, input_sample))

python/chronos/example/inference-acceleration/readme.md

@@ -0,0 +1,20 @@
+# Accelerate the inference speed of model trained on other platform
+
+## Introduction
+Chronos has many built-in models wrapped in forecasters, detectors and simulators optimized on CPU (especially intel CPU) platform.
+
+While users may want to use their own model or built-in models trained on another platform (e.g. GPU) but prefer to carry out the inferencing process on CPU platform. Chronos can also help users to accelerate their model for inferencing.
+
+In this example, we show an example to train the model on GPU and accelerate the model by using onnxruntime on CPU.
+
+## How to run this example
+```bash
+python cpu_inference_acceleration.py
+```
+
+## Sample output
+```bash
+Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 288/288
+original pytorch latency (ms): {'p50': 1.236, 'p90': 1.472, 'p95': 1.612, 'p99': 32.989}
+onnxruntime latency (ms): {'p50': 0.124, 'p90': 0.129, 'p95': 0.148, 'p99': 0.363}
+```