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TinyDB in Python - Simple Database For Personal Projects

Hydra Cluster: TensorFlow¶

Introduction¶

This document walks through a basic example of using TensorFlow on the Hydra cluster. You should read the python tdb documentation first if you have not already done so. This is based around the Virtualenv example given in the TensorFlow documentation.

Installing TensorFlow¶

Starting out in your cluster home directory on myrtle or raptor you can create a new virtual why was stephen f austin arrested. In this case, we'll call the directory tensorflow and use Python 3.

Now activate the environment. Notice the prompt changes to indicate you're python tdb the new virtual environment.

Now install pip and then TensorFlow. Output is trimmed here for brevity. Please be patient - this step can take a while.

If you're using this example as a starting point 1st national bank greeley your own code you can install additional Python packages within this virtual environment as required.

Testing TensorFlow¶

To test TensorFlow we'll create a short program taken from the TensorFlow documentation. We'll also create a shell script to configure the environment and run it. Use a text editor to create the files shown below, obviously substituting your python tdb home directory in the python tdb file.

Now we can try running it on the Hydra cluster.

Or we can submit it as a batch job.

Take note g and m running on CPUs, rather than GPUs¶

We've had issues reported when running TensorFlow on older CPUs without the AVX instruction set. If you're using the partition then you're fine, but if you are using the partition you should use the flag to make sure you get only community financial service center machines with the newer CPUs.

As usual, please contact us with any queries.

Источник: https://www.cs.kent.ac.uk/systems/hydra/tensorflow.html

What is NoSQL?

In-memory: Gaming and ad-tech applications have use cases such as leaderboards, session stores, and real-time analytics that require microsecond response times and can have large spikes in traffic coming at any time. Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service that delivers microsecond read latency, single-digit millisecond write latency, and Multi-AZ durability. MemoryDB is purpose-built to deliver ultra-fast performance and durability so you can use it as your primary database for modern, microservices applications. Amazon ElastiCache is a fully managed, in-memory caching service compatible with both Redis and Memcached, to serve low-latency, high-throughput workloads. Customers like Tinder, who require real-time response python tdb their python tdb, rely on in-memory data stores rather than disk-based data stores. Amazon DynamoDB Accelerator (DAX) is another example of a purpose-built data store. DAX makes DynamoDB reads an order of magnitude faster.

Источник: https://aws.amazon.com/nosql/
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Artistic <b>python tdb</b> of QuestDB's Web Console split in 3 components: the navigation tree, the SQL code editor and data displayed as a chart
Источник: https://questdb.io/

python

"Python is a programming language that lets you work quickly and integrate systems more effectively."

Python is a C like scripting language with many additional modules to handle all sorts of tasks. As editor you can either use Eclipse with PyDev (in our examples it is used with the Eclipse CPP edition) or PyCharm from JetBrains. PyCharm offers full integration with python including management of modules and environments, support for GAE, Flask. .

Administration

Python dependencies are managed through a local requirements.txt file listing all dependencies. Usually a

is run to python tdb dependencies.

You can create python tdb dependency file by

Common modules used in yafra:

  • Pillow
  • PyMySQL and/or official Mysql Connector Python (install via tar file if you run Python27 and Pyhton3 on the same machine)
  • Flask
  • Jinja2
  • SQLAlchemy (and sqlacodegen to generate models out of legacy)
  • Werkzeug
  • MarkupSafe
  • numpy

Eclipse CPP edition with PyDev is used as IDE. ActivePython (32bit as those enables free access to required modules) 2.7 is used.

Tests

unittest

The standard python unittest is used. The default structure is a directory "test" and put your test python scripts into this folder. With python 3 you can execute python3 -m unittest python tdb and with python 2 you can execute python -m unittest test.TestApp.

Apps

Direct MySQL script

Simple query of database with direct DB API (mysql-connector in this example). [[https://github.com/yafraorg/yafra-tdb-python/tree/master/utils/mysql-connector]]

DB-API script

Simple query with MySqlDB driver - working on Python 2.7 and Python python tdb [[https://github.com/yafraorg/yafra-tdb-python/tree/master/utils/db-api]]

SQL Alchemy

A python ORM which has a utility to reverse engineer existing databases and create python model files.

Afterwards SQL Alchemy can be used with this model. [[https://github.com/yafraorg/yafra-tdb-python/tree/master/utils/alchemy]]

Gtk client

A python example using Gtk/Glade as user interface framework and communicating with a daemon using my credit card number character based TCP socket communication. See the python tdb example and code: [[https://github.com/yafraorg/yafra-tdb-python/tree/master/admin-ui]]

Google App Engine

A web based example using GAE Big Table datastore.

  • a full example of an app engine using python (based on webapp2/jinja2)

https://github.com/yafraorg/yafra-toroam

Flask

A web based example using Flask/Eve. Similar to GAE but independent of GAE environment. Can be used on OpenShift.

  • Flask with SQL Alchemy
  • using OpenShift or own environment

https://github.com/yafraorg/yapki

Источник: https://github-wiki-see.page/m/yafraorg/yafra/wiki/Python
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Python tdb -

What is NoSQL?

In-memory: Gaming and ad-tech applications have use cases such as leaderboards, session stores, and real-time analytics that require microsecond response times and can have large spikes in traffic coming at any time. Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service that delivers microsecond read latency, single-digit millisecond write latency, and Multi-AZ durability. MemoryDB is purpose-built to deliver ultra-fast performance and durability so you can use it as your primary database for modern, microservices applications. Amazon ElastiCache is a fully managed, in-memory caching service compatible with both Redis and Memcached, to serve low-latency, high-throughput workloads. Customers like Tinder, who require real-time response from their applications, rely on in-memory data stores rather than disk-based data stores. Amazon DynamoDB Accelerator (DAX) is another example of a purpose-built data store. DAX makes DynamoDB reads an order of magnitude faster.

Источник: https://aws.amazon.com/nosql/

Hydra Cluster: TensorFlow¶

Introduction¶

This document walks through a basic example of using TensorFlow on the Hydra cluster. You should read the main documentation first if you have not already done so. This is based around the Virtualenv example given in the TensorFlow documentation.

Installing TensorFlow¶

Starting out in your cluster home directory on myrtle or raptor you can create a new virtual environment. In this case, we'll call the directory tensorflow and use Python 3.

Now activate the environment. Notice the prompt changes to indicate you're inside the new virtual environment.

Now install pip and then TensorFlow. Output is trimmed here for brevity. Please be patient - this step can take a while.

If you're using this example as a starting point for your own code you can install additional Python packages within this virtual environment as required.

Testing TensorFlow¶

To test TensorFlow we'll create a short program taken from the TensorFlow documentation. We'll also create a shell script to configure the environment and run it. Use a text editor to create the files shown below, obviously substituting your own home directory in the second file.

Now we can try running it on the Hydra cluster.

Or we can submit it as a batch job.

Take note when running on CPUs, rather than GPUs¶

We've had issues reported when running TensorFlow on older CPUs without the AVX instruction set. If you're using the partition then you're fine, but if you are using the partition you should use the flag to make sure you get only those machines with the newer CPUs.

As usual, please contact us with any queries.

Источник: https://www.cs.kent.ac.uk/systems/hydra/tensorflow.html
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