data_pipeline | Data Pipeline is a Python application
kandi X-RAY | data_pipeline Summary
kandi X-RAY | data_pipeline Summary
Data Pipeline is a Python application for replicating data from source to target databases
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Top functions reviewed by kandi - BETA
- Poll the Oracle CDS database
- Build the query contents query
- Builds the SQL predicate for extracting new tables
- Builds a where clause filter
- Poll for CDC points in table
- Builds a record message
- Return a serialise representation of the object
- Fetch a list of objects from the database
- Return a query for db objects in a list
- Parse insert statement
- Process a message
- Process a redo statement
- Builds the sql for extracting data
- Gets the next statement
- Executes a file query
- Returns the list of column names for a table
- List objects in the database
- Builds bulk insert statement
- Parse a create statement
- Builds the key column list
- Merge attributes
- Parse an UPDATE statement
- Builds the sql for extract_data
- Returns a list of profile schemas
- List all schemas
- Browse a connection
data_pipeline Key Features
data_pipeline Examples and Code Snippets
Community Discussions
Trending Discussions on data_pipeline
QUESTION
I want to apply StandardScaler only to the numerical parts of my dataset using the function sklearn.compose.ColumnTransformer, (the rest is already one-hot encoded). I would like to see .scale_ and .mean_ parameters fitted to the training data, but the function scaler.mean_ and scaler.scale_ obviously does not work when using a column transformer. Is there a way to do so?
ANSWER
Answered 2021-May-04 at 00:37The fitted transformers are available in the attributes transformers_ (a list) and named_transformers_ (a dict-like with keys the names you provided). So, for example,
QUESTION
I am trying to run a Data Pipelin in Azure Devops with the following YAML definition
This is requirements.txt file:
...ANSWER
Answered 2020-Oct-18 at 11:51Azure still is not compatible with 3.9. See also at https://github.com/numpy/numpy/issues/17482
QUESTION
I have 2 boolean, 14 categorical and one numerical value
...ANSWER
Answered 2020-Sep-20 at 20:14If you are trying to preprocess your category features you need to use OneHotEncoder or OrdinalEncoder as per comments.
Here is an example of how to do that:
QUESTION
I am trying to run a random forest classifier using pyspark ml (spark 2.4.0) with encoding the target labels using OHE. The model trains fine when I feed the labels as integers (string indexer) but fails when i feed a one hot encoded labels using OneHotCodeEstimator. Is this a spark limitation?
...ANSWER
Answered 2020-Jun-30 at 15:11Edit : pyspark does not support a vector as a target label hence only string encoding works.
The problematic code is -
QUESTION
I have built a custom sklearn pipeline, as follows:
...ANSWER
Answered 2018-Nov-07 at 15:34OK I found out the problem. I discovered that the problem has nothing to do with the issue explained in the blogpost herePython: pickling and dealing with "AttributeError: 'module' object has no attribute 'Thing'" as I originally thought. You can easily solve the problem by having your object pickling and unpickling the file. I was using a separate script (a Jupyther notebook) to pickle and a plain [python script to unpicle. When I did everything in the same class it worked.
QUESTION
I have encountered this problem while deploying my model in the cloud using docker image tesorflow/serving:1.13.0. But it runs perfectly in my local system.
The actual logs from the cloud system are:
...ANSWER
Answered 2019-May-15 at 10:53I have solved this error by building binaries for respective CPU's on which i am working.
I have built the binaries from this link. tensorflow-serving from source using docker
I have pushed my images to dockerhub repository. If anyone donot want to build their own respective images with same configurations as of my CPU's.
Dockerhub repository for tensorflow-serving images for Centos built from source
QUESTION
I can run the single file as a dataflow job in cloud composer but when i run it as a package it fails .
...ANSWER
Answered 2018-Sep-10 at 00:02Try put the entire pipeline_jobs/ in dags folder following this instruction and refer the dataflow py file as: /home/airflow/gcs/dags/pipeline_jobs/run.py.
QUESTION
Summary and Test Cases
The core issue is that Tensorflow throws OOM allocations on a batch that is not the first, as I would expect. Therefore, I believe there is a memory leak since all memory is clearly not being freed after each batch.
...ANSWER
Answered 2017-Dec-14 at 21:39There is an internal 2GB limit for the tf.GraphDef protocol buffer which in the most cases raises the OOM error.
The input tensor [BATCH_SIZE, MAX_SEQUENCE_LENGTH] probably reaches that limit. Just try much smaller batches.
QUESTION
I am using TensorFlow 1.2.1 on Windows 10, and using the Estimator API. Everything runs without any errors, but whenever I have to restore the parameters from a checkpoint, some aspect of it doesn't work. I've checked that the values of every variable in classifier.get_variable_names() does not change after an evaluation, however the Loss spikes back up to near where it started, this is followed by a continued learning, each time learning faster than the last.
This happens within one TensorFlow run, when a validation or evaluation run happens, or when I rerun the python file to continue training.
The following graphs are one example of this problem, they are restoring the variables every 2500 steps:
The following code is a significiantly reduced version of my code, which still replicates the error:
...ANSWER
Answered 2017-Aug-24 at 02:14I figured out the issue, I was creating data pipelines with the interactive session I created, and then having my input function evaluate the examples (like a feed-dictionary). The reason this is an issue is that the Estimator class creates it's own session (a MonitoredTraininSession), and since the graph operations weren't being created from within a call from the Estimator class (and thus with it's session), they were not being saved. Using an input function to create the graph operations, and return the final graph operation (the batching) has resulted in everything working smoothly.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install data_pipeline
Automated
Manual
While in the project root directory, run the following.
The manual installation option allows one to have a custom setup; for instance, if one wishes to run Python from the root-owned Python virtual environment, use a different virtual environment from the one pre-configured in this project. The following are the manual steps involved to install the system dependencies for a RedHat/Centos distribution. There are plans to automate this procedure via ansible.
There are three database endpoints that Data Pipeline connects to:.
Source: The source database to extract data from
Target: The target database to apply data to
Audit: The database storing data of the extract and apply processes for monitoring and auditing purposes.
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