How to Install TensorFlow on Debian 10

How to Install TensorFlow on Debian 10

In this tutorial, we’ll explain how to install TensorFlow in a Python virtual environment on Debian 10.

TensorFlow is an open-source platform for machine learning built by Google. It can runs on CPU or GPU on different devices.

TensorFlow can be installed system-wide, in a Python virtual environment, as a Docker container, or with Anaconda.

In this tutorial, we’ll explain how to install TensorFlow in a Python virtual environment on Debian 10.

A virtual environment allows you to have multiple different isolated Python environments on a single computer and install a specific version of a module on a per-project basis, without worrying that it will affect your other Projects.

Installing TensorFlow on Debian 10

The following sections provide a step by step instructions about how to install TensorFlow in a Python virtual environment on Debian 10.

**1. Installing Python 3 and venv **

Debian 10, Buster ships with Python 3.7.

To verify that Python 3 is installed on your system, type:

python3 --version

The output should look like this:

Python 3.7.3

The recommended way to create a virtual environment is by using the venv module, which is provided by the python3-venv package.

If the python3-venv package is not installed on your system, install it by entering:

sudo apt update
sudo apt install python3-venv

2. Creating a Virtual Environment

Navigate to the directory in which you store your Python 3 virtual environments. It can be your home directory or any other directory where your user has read and write permissions.

Create a new directory for the TensorFlow project and switch to it:

mkdir my_tensorflow
cd my_tensorflow

From inside the directory, enter the following command to create the virtual environment:

python3 -m venv venv

The command above creates a directory named venv, which contains a copy of the Python binary, the Pip package manager, the standard Python library, and other supporting files.

You can use any name you like for the virtual environment.

To start using the virtual environment, you’ll need to activate it by running the activate script:

source venv/bin/activate

Once activated, the virtual environment’s bin directory will be added at the beginning of the system $PATH variable. Also, the shell’s prompt will change, and it will show the name of the virtual environment you’re currently in. In this example, that is (venv).

TensorFlow installation requires pip version 19 or higher. Run the following command to upgrade pip to the latest version:

pip install --upgrade pip

**3. Installing TensorFlow **

Now that we’ve created a virtual environment, the next step is to install the TensorFlow package.

There are several TensorFlow packages that can be installed from PyPI. The tensorflow package supports only CPUs, and it is recommended for novice users.

If you have a dedicated NVIDIA GPU with CUDA compute capability 3.5 or higher and want to take advantage of its processing power, instead of tensorflow install the tensorflow-gpu package which includes GPU support.

Enter the command below to install TensorFlow:

pip install --upgrade tensorflow

Once the installation is complete, verify it with the following command which will print the TensorFlow version:

python -c 'import tensorflow as tf; print(tf.__version__)'

At the time of writing this article, the latest stable version of TensorFlow is 2.0.0:

2.0.0

The version printed on your terminal may be different from the version shown above.

That’s it. TensorFlow is installed on your Debian system.

When you are done with your work, type deactivate to deactivate the environment and return to your normal shell.

deactivate
Conclusion

We have shown you how to install TensorFlow with pip inside a Python virtual environment on Debian 10.

If you hit a problem or have feedback, leave a comment below.

Thank you!

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Description
Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn't means that i will not talk about the advanced stuff as well.

As you may all know by now that my approach of teaching is very simple and straightforward.In this series i will be talking about the all the things you need to know to jump start you python programming skills. This series is designed for noobs who are totally new to programming, so if you don't know any thing about

programming than this is the way to go guys Here is the links to all the videos that i will upload in this whole series.

In this video i will talk about all the basic introduction you need to know about python, which python version to choose, how to install python, how to get around with the interface, how to code your first program. Than we will talk about operators, expressions, numbers, strings, boo leans, lists, dictionaries, tuples and than inputs in python. With

Lots of exercises and more fun stuff, let's get started.

Download free Exercise files.

Dropbox: https://bit.ly/2AW7FYF

Who is the target audience?

First time Python programmers
Students and Teachers
IT pros who want to learn to code
Aspiring data scientists who want to add Python to their tool arsenal
Basic knowledge
Students should be comfortable working in the PC or Mac operating system
What will you learn
know basic programming concept and skill
build 6 text-based application using python
be able to learn other programming languages
be able to build sophisticated system using python in the future

To know more:

TensorFlow 2.0 Full Tutorial - Python Neural Networks for Beginners

TensorFlow 2.0 Full Tutorial - Python Neural Networks for Beginners

TensorFlow 2.0 Full Tutorial - Python Neural Networks for Beginners. Learn how to use TensorFlow 2.0 in this full course for beginners. This course will demonstrate how to create neural networks with Python and TensorFlow 2.0.

TensorFlow 2.0 Full Tutorial - Python Neural Networks for Beginners

Learn how to use TensorFlow 2.0 in this full course for beginners.

This course will demonstrate how to create neural networks with Python and TensorFlow 2.0.

⭐️ Course Contents ⭐️

⌨️ (0:00:00) What is a Neural Network?
⌨️ (0:26:34) Loading & Looking at Data
⌨️ (0:39:38) Creating a Model
⌨️ (0:56:48) Using the Model to Make Predictions
⌨️ (1:07:11) Text Classification P1
⌨️ (1:28:37) What is an Embedding Layer? Text Classification P2
⌨️ (1:42:30) Training the Model - Text Classification P3
⌨️ (1:52:35) Saving & Loading Models - Text Classification P4
⌨️ (2:07:09) How to Install TensorFlow GPU on Linux

Machine Learning Tutorial with Python, Jupyter, KSQL and TensorFlow

Machine Learning Tutorial with Python, Jupyter, KSQL and TensorFlow

Machine Learning With Python, Jupyter, KSQL, and TensorFlow. This post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

Machine Learning With Python, Jupyter, KSQL, and TensorFlow. This post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

Building a scalable, reliable, and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework.

Uber, which already runs their scalable and framework-independent machine learning platform Michelangelo for many use cases in production, wrote a good summary:

When Michelangelo started, the most urgent and highest impact use cases were some very high scale problems, which led us to build around Apache Spark (for large-scale data processing and model training) and Java (for low latency, high throughput online serving). This structure worked well for production training and deployment of many models but left a lot to be desired in terms of overhead, flexibility, and ease of use, especially during early prototyping and experimentation [where Notebooks and Python shine].
Uber expanded Michelangelo “to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow [instead of just using Spark for everything].”

So why did Uber (and many other tech companies) build its own platform and framework-independent machine learning infrastructure?

The posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable, and mission-critical nervous system. It allows real-time data ingestion, processing, model deployment, and monitoring in a reliable and scalable way.

This post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers, and production engineers. By leveraging it to build your own scalable machine learning infrastructure and also make your data scientists happy, you can solve the same problems for which Uber built its own ML platform, Michelangelo.


You may also like:A Complete Machine Learning Project Walk-Through in Python


Impedance Mismatch Between Data Scientists, Data Engineers and Production Engineers

Based on what I’ve seen in the field, an impedance mismatch between data scientists, data engineers, and production engineers is the main reason why companies struggle to bring analytic models into production to add business value.

The following diagram illustrates the different required steps and corresponding roles as part of the impedance mismatch in a machine learning lifecycle:

Impedance mismatch between model development and model deployment

Data scientists love Python, period. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs. Both the stablest and most cutting edge APIs, as well as the majority of examples and tutorials, use Python APIs. In addition to Python support, there is typically support for other programming languages, including JavaScript for web integration and Java for platform integration-though oftentimes with fewer features and less maturity. No matter what other platforms are supported, chances are very high that your data scientists will build and train their analytic models with Python.

There is an impedance mismatch between model development using Python, its tool stack and a scalable, reliable data platform with low latency, high throughput, zero data loss and 24/7 availability requirements needed for data ingestion, preprocessing, model deployment and monitoring at scale. Python, in practice, is not the most well-known technology for these requirements. However, it is a great client for a data platform like Apache Kafka.

The problem is that writing the machine learning source code to train an analytic model with Python and the machine learning framework of your choice is just a very small part of a real-world machine learning infrastructure. You need to think about the whole model lifecycle. The following image represents this hidden technical debt in machine learning systems (showing how small the “ML code” part is):

Thus, you need to train and deploy the model built to a scalable production environment in order to reliably make use of it. This can either be built natively around the Kafka ecosystem, or you could use Kafka just for ingestion into another storage and processing cluster such as HDFS or AWS S3 with Spark. There are many tradeoffs between Kafka, Spark, and several other scalable infrastructures, but that discussion is out of scope for this post. For now, we’ll focus on Kafka.

Different solutions in the industry solve certain parts of the impedance mismatch between data scientists, data engineers, and production engineers. Let’s take a look at some of these options:

  • Official standards like Open Neural Network Exchange (ONNX), Portable Format for Analytics (PFA) or Predictive Model Markup Language (PMML): A data scientist builds a model with Python. The Java developer imports it in Java for production deployment. This approach supports different frameworks, products, and cloud services. You do not have to rely on the same framework or product for training and model deployment. Consider ONNX, a relatively new standard for deep learning — it already supports TensorFlow, PyTorch, and MXNet. These standards have pros and cons. Some people like and use them; many don’t.
  • Developer-focused frameworks like Deeplearning4j: These frameworks are built for software engineers to build the whole machine learning lifecycle on the Java platform, not just model deployment and monitoring, but also preprocessing and training. You can still import other models if you want (e.g., Deeplearning4j lets you import Keras models). This option is great if you: a) have data scientists who can write Java or b) have software engineers who understand machine learning concepts enough to build analytic models.
  • AutoML for building analytic models with limited machine learning experience: This way, domain experts can build and deploy analytic models with a button click. The AutoML engine provides an interface for others to use the model for predictions.
  • Embedding model binaries into applications: The output of model training is an analytic model. For instance, you can write Python code to train and generate a TensorFlow model. Depending on the framework, the output can be text files, Java source code, or binary files. For example, TensorFlow generates a model artifact with Protobuf, JSON, and other files. No matter what format the output of your machine learning framework is, it can be embedded into applications to use for predictions via the framework’s API (e.g., you can load a TensorFlow model from a Java application through TensorFlow’s Java API).
  • Managed model server in the public cloud like Google Cloud Machine Learning Engine: The cloud provider takes over the burden of availability and reliability. The data scientist “just” deploys its trained model, and production engineers can access it. The key tradeoff is that this requires RPC communication to perform model inference.

While all these solutions help data scientists, data engineers, and production engineers to work better together, there are underlying challenges within the hidden debts:

  • Data collection (i.e., integration) and preprocessing need to run at scale

  • Configuration needs to be shared and automated for continuous builds and integration tests

  • The serving and monitoring infrastructure need to fit into your overall enterprise architecture and tool stack

So how can the Kafka ecosystem help here?

Apache Kafka as a Key Component for Solving the Impedance Mismatch

In many cases, it is best to provide experts with the tools they like and know well. The challenge is to combine the different toolsets and still build an integrated system, as well as a continuous, scalable, machine learning workflow. Therefore, Kafka is not competitive but complementary to the discussed alternatives when it comes to solving the impedance mismatch between the data scientist and developer.

The data engineer builds a scalable integration pipeline using Kafka as infrastructure and Python for integration and preprocessing statements. The data scientist can build their model with Python or any other preferred tool. The production engineer gets the analytic models (either manually or through any automated, continuous integration setup) from the data scientist and embeds them into their Kafka application to deploy it in production. Or, the team works together and builds everything with Java and a framework like Deeplearning4j.

Any option can pair well with Apache Kafka. Pick the pieces you need, whether it’s Kafka core for data transportation, Kafka Connect for data integration, or Kafka Streams/KSQL for data preprocessing. Many components can be used for both model training and model inference. Write once and use in both scenarios as shown in the following diagram:

Leveraging the Apache Kafka ecosystem for a machine learning infrastructure

Monitoring the complete environment in real time and at scale is also a common task for Kafka. A huge benefit is that you only build a highly reliable and scalable pipeline once but use it for both parts of a machine learning infrastructure. And you can use it in any environment: in the cloud, in on-prem datacenters, or at the edges where IoT devices are.

Say you wanted to build one integration pipeline from MQTT to Kafka with KSQL for data preprocessing and use Kafka Connect for data ingestion into HDFS, AWS S3, or Google Cloud Storage, where you do the model training. The same integration pipeline, or at least parts of it, can be reused for model inference. New MQTT input data can directly be used in real time to make predictions.

We just explained various alternatives to solving the impedance mismatch between data scientists and software engineers in Kafka environments. Now, let’s discuss one specific option in the next section, which is probably the most convenient for data scientists: leveraging Kafka from a Jupyter Notebook with KSQL statements and combining it with TensorFlow and Keras to train a neural network.

Data Scientists Combining Python and Jupyter With Scalable Streaming Architectures

Data scientists use tools like Jupyter Notebooks to analyze, transform, enrich, filter, and process data. The preprocessed data is then used to train analytic models with machine learning/deep learning frameworks like TensorFlow.

However, some data scientists do not even know “bread-and-butter” concepts of software engineers, such as version control systems like GitHub or continuous integration tools like Jenkins.

This raises the question of how to combine the Python experience of data scientists with the benefits of Apache Kafka as a battle-tested, highly scalable data processing and streaming platform.

Apache Kafka and KSQL for Data Scientists and Data Engineers

Kafka offers integration options that can be used with Python, like Confluent’s Python Client for Apache Kafka or Confluent REST Proxy for HTTP integration. But this is not really a convenient way for data scientists who are used to quickly and interactively analyzing and preprocessing data before model training and evaluation. Rapid prototyping is typically used here.

KSQL enables data scientists to take a look at Kafka event streams and implement continuous stream processing from their well-known and loved Python environments like Jupyter by writing simple SQL-like statements for interactive analysis and data preprocessing.

The following Python example executes an interactive query from a Kafka stream leveraging the open source framework ksql-python, which adds a Python layer on top of KSQL’s REST interface. Here are a few lines of the Python code using KSQL from a Jupyter Notebook:

The result of such a KSQL query is a Python generator object, which you can easily process with other Python libraries. This feels much more Python native and is analogous to NumPy, pandas, scikit-learn and other widespread Python libraries.

Similarly to rapid prototyping with these libraries, you can do interactive queries and data preprocessing with ksql-python. Check out the KSQL quick start and KSQL recipes to understand how to write a KSQL query to easily filter, transform, enrich, or aggregate data. While KSQL is running continuous queries, you can also use it for interactive analysis and use the LIMIT keyword like in ANSI SQL if you just want to get a specific number of rows.

So what’s the big deal? You understand that KSQL can feel Python-native with the ksql-python library, but why use KSQL instead of or in addition to your well-known and favorite Python libraries for analyzing and processing data?

The key difference is that these KSQL queries can also be deployed in production afterwards. KSQL offers you all the features from Kafka under the hood like high scalability, reliability, and failover handling. The same KSQL statement that you use in your Jupyter Notebook for interactive analysis and preprocessing can scale to millions of messages per second. Fault tolerant. With zero data loss and exactly once semantics. This is very important and valuable for bringing together the Python-loving data scientist with the highly scalable and reliable production infrastructure.

Just to be clear: KSQL + Python is not the all-rounder for every data engineering task, and it does not replace the existing Python toolset. But it is a great option in the toolbox of data scientists and data engineers, and it adds new possibilities like getting real-time updates of incoming information as the source data changes or updating a deployed model with a new and improved version.

Jupyter Notebook for Fraud Detection With Python KSQL and TensorFlow/Keras

Let’s now take a look at a detailed example using the combination of KSQL and Python. It involves advanced code examples using ksql-python and other widespread components from Python’s machine learning ecosystem, like NumPy, pandas, TensorFlow, and Keras.

The use case is fraud detection for credit card payments. We use a test dataset from Kaggle as a foundation to train an unsupervised autoencoder to detect anomalies and potential fraud in payments. The focus of this example is not just model training, but the whole machine learning infrastructure, including data ingestion, data preprocessing, model training, model deployment, and monitoring. All of this needs to be scalable, reliable, and performant.

For the full running example and more details, see the documentation.

Let’s take a look at a few snippets of the Jupyter Notebook.

Connection to KSQL server and creation of a KSQL stream using Python:

from ksql import KSQLAPI
client = KSQLAPI('http://localhost:8088')

client.create_stream(table_name='creditcardfraud_source',
                     columns_type=['Id bigint', 'Timestamp varchar', 'User varchar', 'Time int', 'V1 double', 'V2 double', 'V3 double', 'V4 double', 'V5 double', 'V6 double', 'V7 double', 'V8 double', 'V9 double', 'V10 double', 'V11 double', 'V12 double', 'V13 double', 'V14 double', 'V15 double', 'V16 double', 'V17 double', 'V18 double', 'V19 double', 'V20 double', 'V21 double', 'V22 double', 'V23 double', 'V24 double', 'V25 double', 'V26 double', 'V27 double', 'V28 double', 'Amount double', 'Class string'],
                     topic='creditcardfraud_source',
                     value_format='DELIMITED')

Preprocessing incoming payment information using Python:

  • Filter columns that are not needed

  • Filter messages where column "class" is empty

  • Change data format to Avro for convenient and further processing

client.create_stream_as(table_name='creditcardfraud_preprocessed_avro',
                     select_columns=['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class'],
                     src_table='creditcardfraud_source',
                     conditions='Class IS NOT NULL',
                     kafka_topic='creditcardfraud_preprocessed_avro',
                     value_format='AVRO')

Some more examples for possible data wrangling and preprocessing with KSQL:

  • Drop columns, filter messages where value “class” is empty and change data format to Avro:
CREATE STREAM creditcardfraud_preprocessed_avro WITH (VALUE_FORMAT='AVRO', KAFKA_TOPIC='creditcardfraud_preprocessed_avro') AS SELECT Time,  V1 , V2 , V3 , V4 , V5 , V6 , V7 , V8 , V9 , V10 , V11 , V12 , V13 , V14 , V15 , V16 , V17 , V18 , V19 , V20 , V21 , V22 , V23 , V24 , V25 , V26 , V27 , V28 , Amount , Class FROM creditcardfraud_source WHERE Class IS NOT NULL;

  • Anonymization (mask the two leftmost characters, e.g., “Hans” becomes “**ns”):
SELECT Id, MASK_LEFT(User, 2) FROM creditcardfraud_source;

  • Augmentation (add -1 if “class” is null):
SELECT Id, IFNULL(Class, -1) FROM creditcardfraud_source;

  • Merge/join data frames:
CREATE STREAM creditcardfraud_per_user WITH (VALUE_FORMAT='AVRO', KAFKA_TOPIC='creditcardfraud_preprocessed_avro') AS SELECT Time,  V1 , V2 , V3 , V4 , V5 , V6 , V7 , V8 , V9 , V10 , V11 , V12 , V13 , V14 , V15 , V16 , V17 , V18 , V19 , V20 , V21 , V22 , V23 , V24 , V25 , V26 , V27 , V28 , Amount , Class FROM creditcardfraud_enahnced c INNER JOIN USERS u on c.userid = u.userid WHERE V1 > 5 AND V2 IS NOT NULL AND u.CITY LIKE 'Premium%';

The Jupyter Notebook contains the full example. We use Python + KSQL for integration, data preprocessing, and interactive analysis and combine them with various other libraries from a common Python machine learning tool stack for prototyping and model training:

  • Arrays/matrices processing with NumPy and pandas

  • ML-specific processing (split train/test, etc.) with scikit-learn

  • Interactive analysis through data visualisations with Matplotlib

  • ML training + evaluation with TensorFlow and Keras

Model inference and visualisation are done in the Jupyter notebook, too. After you have built an accurate model, you can deploy it anywhere to make predictions and leverage the same integration pipeline for model training. Some examples of model deployment in Kafka environments are:

  • Analytic models (TensorFlow, Keras, H2O and Deeplearning4j) embedded in Kafka Streams microservices

  • Anomaly detection of IoT sensor data with a model embedded into a KSQL UDF

  • RPC communication between Kafka Streams application and model server (TensorFlow Serving)

Python, KSQL, and Jupyter for Prototyping, Demos, and Production Deployments

As you can see, both in theory (Google’s paper Hidden Technical Debt in Machine Learning Systems) and in practice (Uber’s machine learning platform Michelangelo), it is not a simple task to build a scalable, reliable, and performant machine learning infrastructure.

The impedance mismatch between data scientists, data engineers, and production engineers must be resolved in order for machine learning projects to deliver real business value. This requires using the right tool for the job and understanding how to combine them. You can use Python and Jupyter for prototyping and demos (often Kafka and KSQL might be overhead here and not needed if you just want to do fast, simple prototyping on a historical dataset) or combine Python and Jupyter with your whole development lifecycle up to production deployments at scale.

Integration of Kafka event streams and KSQL statements into Jupyter Notebooks allows you to:

  • Use the preferred existing environment of the data scientist (including Python and Jupyter) and combine it with Kafka and KSQL to integrate and continuously process real-time streaming data by using a simple Python wrapper API to execute KSQL queries

  • Easily connect to real-time streaming data instead of just historical batches of data (maybe from the last day, week or month, e.g., coming in via CSV files)

  • Merge different concepts like streaming event-based sensor data coming from Kafka with Python programming concepts like generators or dictionaries objects, which you can use for your Python data processing tools or ML frameworks like NumPy, pandas, or scikit-learn

  • Reuse the same logic for integration, preprocessing, and monitoring and move it from your Jupyter Notebook and prototyping or demos to large-scale test and production systems

Python for prototyping and Apache Kafka for a scalable streaming platform are not rival technology stacks. They work together very well, especially if you use “helper tools” like Jupyter Notebooks and KSQL.

Please try it out and let us know your thoughts. How do you leverage the Apache Kafka ecosystem in your machine learning projects?