Fix Tensorflow -> TensorFlow

doc/design/alps_submitter.md

@@ -158,7 +158,7 @@ LABEL class
 ```
 
 ### Semantic Analyze
-Feature Expressions except for Tensorflow Feature Column API should raise an error.
+Feature Expressions except for TensorFlow Feature Column API should raise an error.
 ```sql
 /* Not supported */
 select * from kaggle_credit_fraud_training_data

doc/design/codegen_couler_use_ir.md

@@ -10,12 +10,12 @@ In this design, we'll only have `codegen_couler.go` to generate programs to run
 
 For example, to run the training steps, we intend to use couler API call: `couler.{xgboost,tensorflow,elasticdl}.train(model_def, data)` to generate a training step. The `train` function must take lots of parameters to run the training job as the SQLFlow statement describes, see [here](https://github.com/sql-machine-learning/sqlflow/blob/develop/python/runtime/tensorflow/train.py#L52) as an example.
 
-To implement the single `codegen_couler.go` to support generate code that can run either Tensorflow/XGBoost/ElasticDL/ALPS programs, we have below two choices:
+To implement the single `codegen_couler.go` to support generate code that can run either TensorFlow/XGBoost/ElasticDL/ALPS programs, we have below two choices:
 
 1. `couler.{tensorflow/xgboost/elasticdl}.train` have different arguments defined, so we can do:
 
     ```go
-        if ir.ModelType == "Tensorflow":
+        if ir.ModelType == "TensorFlow":
             tfFiller := TFFiller{
                 Estimator: generateTFEstimatorCode(ir),
                 FeatureColumns: generateFeatureColumnsCode(ir),
@@ -55,6 +55,6 @@ To implement the single `codegen_couler.go` to support generate code that can ru
 
 We intend to use the solution **No.2** described above for these reasons.
 
-1. If a data scientist needs to add a new type of engine (SVM, SKLearn, PyTorch, etc.) other than Tensorflow/XGBoost/ElasticDL, he/she can use python only to define a `couler.{new_engine}.train` function without modifying the Go code in SQLFlow.
+1. If a data scientist needs to add a new type of engine (SVM, SKLearn, PyTorch, etc.) other than TensorFlow/XGBoost/ElasticDL, he/she can use python only to define a `couler.{new_engine}.train` function without modifying the Go code in SQLFlow.
 1. `codegen_couler.go` have less code.
 1. All submitter unit tests can run in couler.

doc/design/couler_sqlflow.md

@@ -104,7 +104,7 @@ Note: You can check more details about the IR definition from [ir.go](/pkg/ir/ir
 
 ### SQLFLow Submitter Python Module
 
-An SQLFlow submitter Python module `runtime.{tensorflow,xgboost,elasticdl}.train` accepts an SQLFlow IR with protobuf text format, and then submit a Tensorflow, XGBoost or ElasticDL training job, we can call it like:
+An SQLFlow submitter Python module `runtime.{tensorflow,xgboost,elasticdl}.train` accepts an SQLFlow IR with protobuf text format, and then submit a TensorFlow, XGBoost or ElasticDL training job, we can call it like:
 
 ``` bash
 cat ir.proto_text | python -m runtime.xgboost.train

doc/design/diag_attribute_error.md

@@ -31,7 +31,7 @@ this can active three advantages at least:
 
 1. Early testing, we can do early testing before running the job; users can wait less time and cluster save resources.
 2. More accurate diagnostic message.
-3. Model developers do not have to involve dependencies other than Keras or Tensorflow.
+3. Model developers do not have to involve dependencies other than Keras or TensorFlow.
 
 ## Design
 

doc/design/model_zoo.md

@@ -101,7 +101,7 @@ $ sqlflow delete repo model_image:v0.1
 
 For a SELECT program using the SQLFlow syntax extension, the SQLFlow server converts it into a [workflow](workflow.md) and submit the workflow job to a workflow engine like Argo/Tekton on Kubernetes. Each step in the workflow is one SQL statement.
 
-By default, we use a default Docker image to run the training, predicting or explaining job. The default Docker image contains pre-made Tensorflow estimator models, Keras models defined in [sqlflow_models repo](https://github.com/sql-machine-learning/models) and XGBoost. To use a custom model repo Docker image, write SQL statements mentioned above:
+By default, we use a default Docker image to run the training, predicting or explaining job. The default Docker image contains pre-made TensorFlow estimator models, Keras models defined in [sqlflow_models repo](https://github.com/sql-machine-learning/models) and XGBoost. To use a custom model repo Docker image, write SQL statements mentioned above:
 
 ```sql
 SELECT * FROM employee WHERE onboard_year < 2019

doc/model_parameter.md

@@ -206,7 +206,7 @@ TBD
 
 TBD
 
-## Tensorflow Parameters
+## TensorFlow Parameters
 
 ### TRAIN
 

doc/sqlflow.org_cn/index.md

@@ -12,8 +12,8 @@
     1. [模型解释]()
 1. [模型列表](model_list.cn.md)
     1. [有监督学习]()
-        1. [Tensorflow Estimator 模型]()
-        1. [Tensorflow Keras 模型]()
+        1. [TensorFlow Estimator 模型]()
+        1. [TensorFlow Keras 模型]()
         1. [XGBoost 模型]()
     1. [无监督学习]()
 1. [部署]()

doc/talk/201906/sqlflow.slide

@@ -81,7 +81,7 @@ TensorFlow model inputs are dense tensors, which are normally high-dimensional m
 - Hash the name string into a 64-bit integer, then bucketizing hashed value into 100 dimension columns.
 .image column_fea2.png _ 600
 
-Generated code using Tensorflow "Feature Column" should look like:
+Generated code using TensorFlow "Feature Column" should look like:
 .code feature_column_code.pysample
 
 * Design: Feature Derivation

doc/talk/20190620/sqlflow.slide

@@ -63,7 +63,7 @@ TensorFlow model inputs are dense tensors, which usually are high-dimensional ma
 - Hash the name string into a 64-bit integer, then bucketing hashed value into 100 dimension columns.
 .image ../201906/column_fea2.png _ 600
 
-Generated code using Tensorflow "Feature Column" should look like:
+Generated code using TensorFlow "Feature Column" should look like:
 .code ../201906/feature_column_code.pysample
 
 * Design: Feature Derivation

docker/step/README.md

@@ -1,6 +1,6 @@
-# A Slim Tensorflow Step Image
+# A Slim TensorFlow Step Image
 
-This image is used when submitting Argo workflows to run Tensorflow/PAI Tensorflow jobs. To build this image, you should follow the below steps:
+This image is used when submitting Argo workflows to run TensorFlow/PAI TensorFlow jobs. To build this image, you should follow the below steps:
 
 1. Go to SQLFlow root directory
     ```bash

go/codegen/alps/template_train.go

@@ -101,7 +101,7 @@ class SQLFlowEstimatorBuilder(EstimatorBuilder):
 
 if __name__ == "__main__":
     if tf_is_version2():
-        raise ValueError("ALPS must run with Tensorflow == 1.15.x")
+        raise ValueError("ALPS must run with TensorFlow == 1.15.x")
 
     driver, dsn = "{{.DataSource}}".split("://")
     user, passwd, endpoint, odps_project = db.parseMaxComputeDSN(dsn)

go/codegen/pai/codegen.go

@@ -28,9 +28,9 @@ import (
 )
 
 const (
-	// ModelTypeTF is the mode type that trained by PAI Tensorflow.
+	// ModelTypeTF is the mode type that trained by PAI TensorFlow.
 	ModelTypeTF = iota
-	// ModelTypeXGBoost is the model type that use PAI Tensorflow to train XGBoost models.
+	// ModelTypeXGBoost is the model type that use PAI TensorFlow to train XGBoost models.
 	ModelTypeXGBoost
 	// ModelTypePAIML is the model type that trained by PAI machine learning algorithm toolkit
 	ModelTypePAIML
@@ -103,7 +103,7 @@ func genRequirements(isXGBoost bool) (string, error) {
 	return code.String(), nil
 }
 
-// Train generates a Python program a PAI command arguments to train a Tensorflow model.
+// Train generates a Python program a PAI command arguments to train a TensorFlow model.
 func Train(ir *ir.TrainStmt, session *pb.Session, tarball, paramsFile, modelName, ossModelPathToSave, ossModelPathToLoad, cwd string) (code, paiCmd, requirements string, e error) {
 	cc, e := GetClusterConfig(ir.Attributes)
 	if e != nil {

go/codegen/tensorflow/docgen.go

@@ -26,7 +26,7 @@ func DocGenInMarkdown() string {
 	return doc.String()
 }
 
-const docTemplateText = `# Tensorflow Parameters
+const docTemplateText = `# TensorFlow Parameters
 
 ## TRAIN
 

go/codegen/tensorflow/docgen_test.go

@@ -22,5 +22,5 @@ import (
 
 func TestDocGenInMarkdown(t *testing.T) {
 	a := assert.New(t)
-	a.True(strings.HasPrefix(DocGenInMarkdown(), `# Tensorflow`))
+	a.True(strings.HasPrefix(DocGenInMarkdown(), `# TensorFlow`))
 }

go/ir/derivation.go

@@ -644,7 +644,7 @@ func deriveLabel(trainStmt *TrainStmt, fmMap FieldDescMap) error {
 	trainStmt.Label = &NumericColumn{
 		FieldDesc: fmMap[labelName],
 	}
-	// use shape [] if label shape is [1] for Tensorflow scalar label shape should be [].
+	// use shape [] if label shape is [1] for TensorFlow scalar label shape should be [].
 	shape := trainStmt.Label.GetFieldDesc()[0].Shape
 	if len(shape) == 1 && shape[0] == 1 {
 		trainStmt.Label.GetFieldDesc()[0].Shape = []int{}

python/runtime/alps/train_example.py

@@ -12,7 +12,7 @@
 # limitations under the License.
 
 # NOTE: ALPS supports tensorflow 1.15 currently, should run this example with
-# Tensorflow 1.15.x installed.
+# TensorFlow 1.15.x installed.
 
 import os
 import shutil
@@ -45,7 +45,7 @@ def _build(self, experiment, run_config):
 
 if __name__ == "__main__":
     if tf_is_version2():
-        raise ValueError("ALPS must run with Tensorflow == 1.15.x")
+        raise ValueError("ALPS must run with TensorFlow == 1.15.x")
     odps_project = os.getenv("SQLFLOW_TEST_DB_MAXCOMPUTE_PROJECT")
     odps_conf = OdpsConf(
         accessid=os.getenv("SQLFLOW_TEST_DB_MAXCOMPUTE_AK"),

python/runtime/feature/derivation.py

@@ -582,7 +582,7 @@ def derive_label(label, fd_map):
     assert label_field_desc is not None, \
         "deriveLabel: LABEL COLUMN '%s' not found" % label_name
 
-    # use shape [] if label shape is [1] for Tensorflow scalar label
+    # use shape [] if label shape is [1] for TensorFlow scalar label
     # shape should be [].
     shape = label_field_desc.shape
     if shape is None or (len(shape) == 1 and shape[0] == 1):

python/runtime/pai/pai_distributed.py

@@ -24,7 +24,7 @@
     import tensorflow as tf
 
 # This module contain utilities for PAI distributed training.
-# Note that currently PAI only support Tensorflow 1.x versions
+# Note that currently PAI only support TensorFlow 1.x versions
 # importing this module should make sure that currently installed
 # tensorflow is version 1.x.
 

python/runtime/pai/submitter.py

@@ -411,7 +411,7 @@ def submit_pai_train(datasource, estimator_string, select, validation_select,
             Like: odps://access_id:[email protected]/api?
                          curr_project=test_ci&scheme=http
         estimator_string: string
-            Tensorflow estimator name, Keras class name, or XGBoost
+            TensorFlow estimator name, Keras class name, or XGBoost
         select: string
             The SQL statement for selecting data for train
         validation_select: string

python/runtime/pai/tensorflow/evaluate.py

@@ -36,7 +36,7 @@
 
 def evaluate(datasource, select, data_table, result_table, oss_model_path,
              metrics):
-    """PAI Tensorflow evaluate wrapper
+    """PAI TensorFlow evaluate wrapper
     This function do some preparation for the local evaluation, say,
     download the model from OSS, extract metadata and so on.
 

python/runtime/pai/tensorflow/predict.py

@@ -20,7 +20,7 @@
 
 def predict(datasource, select, data_table, result_table, label_column,
             oss_model_path):
-    """PAI Tensorflow prediction wrapper
+    """PAI TensorFlow prediction wrapper
     This function do some preparation for the local prediction, say,
     download the model from OSS, extract metadata and so on.
 

python/runtime/pai/tensorflow/train.py

@@ -24,7 +24,7 @@
 from runtime.tensorflow.input_fn import get_dataset_fn
 from runtime.tensorflow.set_log_level import set_log_level
 
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 

python/runtime/tensorflow/explain.py

@@ -28,12 +28,12 @@
     init_model_with_feature_column
 
 sns_colors = sns.color_palette('colorblind')
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 # Use non-interactive background
 plt.switch_backend('agg')
 
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 if tf_is_version2():
     import logging
     tf.get_logger().setLevel(logging.ERROR)

python/runtime/tensorflow/predict.py

@@ -26,10 +26,10 @@
 from runtime.tensorflow.keras_with_feature_column_input import \
     init_model_with_feature_column
 
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 if tf_is_version2():
     import logging
     tf.get_logger().setLevel(logging.ERROR)

python/runtime/tensorflow/train.py

@@ -24,7 +24,7 @@
 
 from ..model_metadata import collect_model_metadata
 
-# Disable Tensorflow INFO and WARNING logs
+# Disable TensorFlow INFO and WARNING logs
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"