In this example, we show an example to train the model on GPU and accelerate the model by using onnxruntime on CPU.
+
+
+
diff --git a/python/chronos/example/inference-acceleration/cpu_inference_acceleration.py b/python/chronos/example/inference-acceleration/cpu_inference_acceleration.py
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+#
+# 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.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
+from sklearn.preprocessing import StandardScaler
+
+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)
+
+ stand = StandardScaler()
+ for tsdata in [tsdata_train, tsdata_val, tsdata_test]:
+ tsdata.deduplicate()\
+ .impute()\
+ .gen_dt_feature()\
+ .scale(stand, fit=tsdata is tsdata_train)\
+ .roll(lookback=48,horizon=1)
+
+ tsdata_traindataloader = tsdata_train.to_torch_data_loader(batch_size=32)
+ 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
+
+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,
+ 'future_seq_len':1,
+ 'kernel_size':3,
+ 'repo_initialization':True,
+ 'dropout':0.1,
+ 'seed': 0,
+ 'num_channels':[30]*7
+ }
+ model = model_creator(config)
+ loss = torch.nn.MSELoss()
+ optimizer = torch.optim.Adam(lr=0.001, params=model.parameters())
+ lit_model = Trainer.compile(model, loss, optimizer)
+
+ # 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)
+
+ # 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))
diff --git a/python/chronos/example/inference-acceleration/readme.md b/python/chronos/example/inference-acceleration/readme.md
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+# 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}
+```
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View source on GitHub
+