Open
Description
Import
Here we take LSTMForecaster as an example, same things will happen on LSTMForecaster, TCNForecaster, and Seq2SeqForecaster in the first round.
# which backend will be used by this will depend on a sys env CHRONOS_FORECASTER_BACKEND (default to 'torch')
# this is to support our legacy code
from bigdl.chronos.forecaster import LSTMForecaster
# These two will use the backend name stated in import
# tf2 is the same name as orca.learn
from bigdl.chronos.forecaster.pytorch import LSTMForecaster
from bigdl.chronos.forecaster.tf2 import LSTMForecaterInterface
I had a quick review of all APIs we have for pytorch and don't find any of them can not be supported in TF2. With the help of bigdl.nano.tf and bigdl.orca.learn.tf2. We should be able to support exactly the same API as pytorch.
class BaseTF2Forecaster(Forecaster):
def __init__(..., optimizer="Adam",
loss="mse",
lr=0.001,
metrics=["mse"],
seed=None,
distributed=False,
workers_per_node=1,
distributed_backend="tf2"):
```
no difference between pytorch
distributed_backend will support “horovod”, “tf2” or “spark”
```
def fit(self, data, epochs=1, batch_size=32):
```
no difference between pytorch
for data we will support
1. a numpy ndarray tuple (x, y)
2. a xshard item
3. a tf.data.Dataset
```
def predict(self, data, batch_size=32, quantize=False):
pass
def predict_with_onnx(self, data, batch_size=32):
pass
def evaluate(self, data, batch_size=32, multioutput="raw_values", quantize=False):
pass
def evaluate_with_onnx(self, data, batch_size=32, multioutput="raw_values"):
pass
def save(self, checkpoint_file, quantize_checkpoint_file=None):
pass
def load(self, checkpoint_file, quantize_checkpoint_file=None):
pass
def to_local(self):
pass
def get_model(self):
pass
def build_onnx(self, thread_num=None, sess_options=None):
pass
def export_onnx_file(self, dirname="model.onnx"):
pass
def quantize(self, calib_data,
val_data=None,
metric=None,
conf=None,
framework='tensorflow',
approach='static',
tuning_strategy='bayesian',
relative_drop=None,
absolute_drop=None,
timeout=0,
max_trials=1):
pass