Trending February 2024 # Autogpt Tutorial: Automate Coding Tasks With Ai # Suggested March 2024 # Top 8 Popular

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AutoGPT is an AI tool that automates coding tasks using GPT. Install Python and Pip, add API keys, and install AutoGPT to get started. Tutorials are available online for Windows, macOS, and Linux.

Are you tired of spending countless hours on repetitive coding tasks? Have you ever wondered if there was a way to automate these tasks and streamline your workflow? Look no further than AutoGPT, an open-source application that uses the GPT-4 language model to perform complex coding tasks and achieve goals with minimal human input. In this tutorial, we will guide you through the installation process and demonstrate how AutoGPT can revolutionize the way you approach coding.

AutoGPT is an Xcode Source Editor extension that enhances productivity by leveraging the capabilities of GPT-4, an AI language model developed by OpenAI. It is an experimental, open-source Python application that uses GPT-4 to act autonomously. AutoGPT is essentially an AI tool that creates different AI agents to meet specific coding tasks. It defines an agent that communicates with OpenAI’s API, and this agent’s objective is to carry out a variety of commands that can automate coding tasks. AutoGPT uses GPT’s superior text creation as part of its interesting breakdown of the AI’s phases. The tool can be set up on a local computer, and users can define the role of the AI and set goals for it to achieve. AutoGPT is an autonomous version of GPT-4 that can think and do things itself.

Also Check: What is Auto-GPT, and why does it matter?

There is no specific information available on the system requirements for AutoGPT. However, it is mentioned that Python is the only requirement for AutoGPT.

To install AutoGPT, follow these simple steps:

    Download the ZIP file from Github.

    Extract the ZIP file and copy the “Auto-GPT” folder.

    Open the command prompt and navigate to the folder location.

    Run the command “pip install -r requirements.txt” to install all the required libraries to run AutoGPT.

    Finally, run the command “python -m autogpt” to start AutoGPT on your system.

    On the first run, AutoGPT will ask you to name the AI and define its role. You can also set goals for the AI to achieve. Once you have completed the setup, AutoGPT will use the GPT-4 language model to perform tasks and achieve goals.

    See Also: Auto-GPT vs AgentGPT: Understanding the Differences

    It can perform a task with little human intervention and can self-prompt.

    AutoGPT’s reasoning ability is similar to that of humans, making it a highly capable AI model.

    It can complete tasks that you know nothing about, making it very versatile.

    AutoGPT can interact with both online and local apps, software, and services like web browsers and word processors.

    However, AutoGPT’s practicality may be limited due to the expensive GPT-4 model it uses.

    The cost per task completion can be high, even for small tasks.

    AutoGPT stands out from other AI tools because of the following reasons:

    Independent operation: Unlike other AI tools, AutoGPT operates independently, which means that you no longer have to direct or steer the model to meet your needs. Instead, you can write your objectives, and the AI will do the rest for you.

    Interact with apps and services: AutoGPT can interact with apps, software, and services both online and locally, like web browsers and word processors. This feature allows users to automate complex tasks that previously required human intervention.

    Open-source project: AutoGPT is an open-source project that anyone can contribute to or use for their own purposes. This accessibility makes it easier for developers to use AutoGPT in their own projects and to improve the technology.

    Breakthrough technology: AutoGPT is a breakthrough technology that creates its own prompts and enables large language models to perform complex multi-step tasks. Its unique abilities make it a valuable tool for a wide range of industries, including marketing, customer service, and content creation.

    More Also: How to Use AgentGPT and AutoGPT

    AutoGPT currently supports Python, JavaScript, and Swift, but the development team is constantly adding support for new languages.

    Yes, you can modify the AI’s behavior by setting goals and defining its role during the setup process.

    AutoGPT’s experimental nature poses challenges for users. The learning curve and potential issues require patience and persistence, as the developers work to refine and improve the software. However, the benefits of using AutoGPT make it worth the effort.

    AutoGPT is a game-changer for coders who want to streamline their workflow and automate repetitive coding tasks. With its autonomous nature and use of GPT-4 language model, AutoGPT is a powerful tool that can save you time, reduce errors, and increase productivity. By following this tutorial, you can install and set up AutoGPT on your local computer and start enjoying the benefits of this tool.

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    Ai In Automation: Discover Automatable Tasks With Ai In 2023

    The jobs that can be mostly automated include

    predictable physical labor

    white-collar back-office work: data collection and processing

    Machines can now perform the activities involved in these jobs better/cheaper than humans. These activities include tasks that involve manipulating tools, extracting data from documents and other semi-structured data sources, making tacit judgments, and even sensing emotions. In the next decade, driving is likely to become automated as well, enabling one of the most common professions to be automated.

    What share of jobs can be automated?

    Based on McKinsey and PwC’s analysis, ~20% of business activities can be automated using today’s technology. PwC estimates this automation wave to take place until the late 2023s and that automation could reach 30% of all existing jobs by mid-2030s.

    Example occupations and automation potential according to McKinsey:

    PwC estimates 20% of jobs to be automated by the late 2023s and 30% of jobs to be automated by the mid-2030s. PwC divides this transformation of automation into three main phases: algorithm wave (to early 2023s), augmentation wave (to late 2023s), and autonomy wave (to mid-2030s). Simple computational tasks are automated, and analysis of structured data is conducted in the algorithm wave which we are currently in.

    The next phase is the augmentation wave. Automation of repeatable tasks and dynamic interaction with AI will be common in this period. Also, semi-automated robotic tasks like moving objects in warehouses are a part of this phase.

    Lastly, the full automation of physical labor will become prominent in the autonomy wave. Using AI for problem-solving in dynamic real-world situations that require responsive actions like transportation and manufacturing is the main focus. While the technology is expected to reach full maturity on an economy-wide scale in the mid-2030s, PwC estimates ~30% of the jobs in all sectors will be automated by that period. Below, you can see a figure that shows the automation potential in different sectors:

    Automation can raise productivity growth by 0.8 to 1.4% annually with the current AI-powered automation tools by reducing errors and improving quality and speed, and in some cases achieving outcomes that go beyond human capabilities. Thus, companies are inclined to automate their tasks to improve their productivity.

    As we group these occupations into categories, we see that the top three categories have a large potential for automation. These activities are:

    Predictable physical labor

    Data processing

    Data collection

    This article will investigate each category to understand the automation potential and how businesses can automate their tasks under these categories.

    What are the jobs most prone to automation? Jobs requiring predictable physical labor

    McKinsey states that performing physical activities in predictable environments has the highest potential for automation. It predicts that 81% of such activities are prone to automation with current AI technologies including robotics.

    Physical labor activities are divided into predictable and unpredictable activities. Machines are better than humans at performing predictable activities as they don’t get bored and can tirelessly perform repetitive and predictable activities. However, unpredictable activities require the human level of flexibility in adapting tasks that are still not available to machines.

    The highest probability of automation jobs requires lower education levels and includes repetitive tasks. This is quite expected while repetitive tasks provide a predictable environment for the machines and they can successfully perform low-skill tasks without any breaks. Deloitte has occupations with the highest probability of automation in the following table.

    A PwC report on automation indicates that machine operators and assemblers become a prominent occupation with high automation potential. While their task can be automated by 64% according to the report, PwC estimates that businesses can achieve this potential by 2035.

    While self-driving cars are trending, jobs in the transportation industry are at the potential risk of automation according to the same study by PwC. By the mid-2030s, 50% of existing jobs in the transportation industry could potentially be automated.

    Manufacturing is another industry that is prone to the automation of predictable labor. As Bain predicts that automation in manufacturing will grow by 55% from 2024 to 2030, companies are working on smart, fully automated factories that will have accelerated and continuous production. Ericsson will run its first one in early 2023, however, the plant will initially have a staff of ~100 before it becomes fully autonomously operating.

    Data processing

    Data processing is the second work activity that has the highest potential for automation. Businesses can automate 69% of their time at data processing, according to McKinsey. This process includes storing, manipulating, preparing, and distributing data. Automated data processing will enable increased business effectiveness and lower costs.

    Numerous customer-facing processes such as loan applications, customer service queries, account upgrades of telecom customers, etc. are dependent on data processing. Automation will enable the processing of large amounts of information with minimal human interaction and sharing it with the right audiences leading to faster, less error-prone data processing. This will improve the customer experience.

    Even investor-facing processes can be prone to errors that harm companies’ both reputation and finances. There are numerous cases, including one that made a famous trader more famous, of trading typos that result in millions of losses.

    Beyond stakeholder-facing processes, business decisions rely on data analysis and reporting. Historically, executives relied on manually produced reports for decision-making. Today, an increasing share of reports are produced automatically. Faster, less error-prone data analysis will improve the quality of business decisions.

    HR’s tasks, such as:

    Assessing and creating newcomer data,

    CV screening,

    Data cleansing,

    Payroll processing, etc. With automation, HR can remove process delays and reduce costs by 65% compared to an offshore-based FTE in the Shared Service Center.

    Can be automated. This lowers process delays and reduces costs

    Data collection

    A common example is accounts payable. Most companies currently manually capture data from invoices even in developed markets like the EU since these documents are not fully standardized and digitized:

    Share of e-invoicing in EU as of 2023:

    Current automation technologies are capable of introducing ~80% automation to accounts payable while most companies rely on legacy, template-based Optical character recognition (OCR) systems that enable only 10-15% automation. OCR is a software technology that enables us to convert scanned hardcopy documents and images into editable digital texts which can now be stored, searched, transferred, and sorted. However, OCR does not create key-value pairs that are ready to be inserted into databases. Deep learning-based solutions address this gap and identify key-value pairs and tables in papers, receipts, contracts, or books so they can be inserted into databases. Feel free to read more on this from our article on automated invoice capture.

    If you want to read more about AI, these articles can also interest you:

    If you believe your business will benefit from an AI solution, feel free to check our data-driven hub of AI solutions.

    You can also check out our list of AI tools and services:

    And let us guide you find the right tools for your business

    Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





    Linear Regression Tutorial With Tensorflow

    What is Linear Regression?

    Linear Regression is an approach in statistics for modelling relationships between two variables. This modelling is done between a scalar response and one or more explanatory variables. The relationship with one explanatory variable is called simple linear regression and for more than one explanatory variables, it is called multiple linear regression.

    TensorFlow provides tools to have full control of the computations. This is done with the low-level API. On top of that, TensorFlow is equipped with a vast array of APIs to perform many machine learning algorithms. This is the high-level API. TensorFlow calls them estimators

    Low-level API: Build the architecture, optimization of the model from scratch. It is complicated for a beginner

    High-level API: Define the algorithm. It is easer-friendly. TensorFlow provides a toolbox called estimator to construct, train, evaluate and make a prediction.

    In this tutorial, you will use the estimators only. The computations are faster and are easier to implement. The first part of the tutorial explains how to use the gradient descent optimizer to train a Linear regression in TensorFlow. In a second part, you will use the Boston dataset to predict the price of a house using TensorFlow estimator.

    Download Boston DataSet

    In this TensorFlow Regression tutorial, you will learn:

    How to train a linear regression model

    Before we begin to train the model, let’s have a look at what is a linear regression.

    Imagine you have two variables, x and y and your task is to predict the value of knowing the value of . If you plot the data, you can see a positive relationship between your independent variable, x and your dependent variable y.

    You may observe, if x=1,y will roughly be equal to 6 and if x=2,y will be around 8.5.

    This is not a very accurate method and prone to error, especially with a dataset with hundreds of thousands of points.

    A linear regression is evaluated with an equation. The variable y is explained by one or many covariates. In your example, there is only one dependent variable. If you have to write this equation, it will be:


    is the bias. i.e. if x=0, y=

    is the weight associated to x

    is the residual or the error of the model. It includes what the model cannot learn from the data

    Imagine you fit the model and you find the following solution for:

    = 3.8

    = 2.78

    You can substitute those numbers in the equation and it becomes:

    y= 3.8 + 2.78x

    You have now a better way to find the values for y. That is, you can replace x with any value you want to predict y. In the image below, we have replace x in the equation with all the values in the dataset and plot the result.

    The red line represents the fitted value, that is the values of y for each value of x. You don’t need to see the value of x to predict y, for each x there is any which belongs to the red line. You can also predict for values of x higher than 2!

    If you want to extend the linear regression to more covariates, you can by adding more variables to the model. The difference between traditional analysis and linear regression is the linear regression looks at how y will react for each variable x taken independently.

    Let’s see an example. Imagine you want to predict the sales of an ice cream shop. The dataset contains different information such as the weather (i.e rainy, sunny, cloudy), customer informations (i.e salary, gender, marital status).

    Traditional analysis will try to predict the sale by let’s say computing the average for each variable and try to estimate the sale for different scenarios. It will lead to poor predictions and restrict the analysis to the chosen scenario.

    If you use linear regression, you can write this equation:

    The algorithm will find the best solution for the weights; it means it will try to minimize the cost (the difference between the fitted line and the data points).

    How the algorithm works

    The algorithm will choose a random number for each

    and and replace the value of x to get the predicted value of y. If the dataset has 100 observations, the algorithm computes 100 predicted values.

    andand replace the value of x to get the predicted value of y. If the dataset has 100 observations, the algorithm computes 100 predicted values.

    We can compute the error, noted

    of the model, which is the difference between the predicted value and the real value. A positive error means the model underestimates the prediction of y, and a negative error means the model overestimates the prediction of y.

    Your goal is to minimize the square of the error. The algorithm computes the mean of the square error. This step is called minimization of the error. For linear regression is the Mean Square Error, also called MSE. Mathematically, it is:


    is the weights so refers to the predicted value

    y is the real values

    m is the number of observations

    Note that

    means it uses the transpose of the matrices. The is the mathematical notation of the mean.

    means it uses the transpose of the matrices. Theis the mathematical notation of the mean.

    The goal is to find the best

    that minimize the MSE

    that minimize the MSE

    If the average error is large, it means the model performs poorly and the weights are not chosen properly. To correct the weights, you need to use an optimizer. The traditional optimizer is called Gradient Descent.

    The gradient descent takes the derivative and decreases or increases the weight. If the derivative is positive, the weight is decreased. If the derivative is negative, the weight increases. The model will update the weights and recompute the error. This process is repeated until the error does not change anymore. Each process is called an iteration. Besides, the gradients are multiplied by a learning rate. It indicates the speed of the learning.

    If the learning rate is too small, it will take very long time for the algorithm to converge (i.e requires lots of iterations). If the learning rate is too high, the algorithm might never converge.

    You can see from the picture above, the model repeats the process about 20 times before to find a stable value for the weights, therefore reaching the lowest error.

    Note that, the error is not equal to zero but stabilizes around 5. It means, the model makes a typical error of 5. If you want to reduce the error, you need to add more information to the model such as more variables or use different estimators.

    You remember the first equation

    The final weights are 3.8 and 2.78. The video below shows you how the gradient descent optimize the loss function to find this weights

    How to train a Linear Regression with TensorFlow

    Now that you have a better understanding of what is happening behind the hood, you are ready to use the estimator API provided by TensorFlow to train your first linear regression using TensorFlow.

    You will use the Boston Dataset, which includes the following variables

    crim per capita crime rate by town

    zn proportion of residential land zoned for lots over 25,000 sq.ft.

    indus proportion of non-retail business acres per town.

    nox nitric oxides concentration

    rm average number of rooms per dwelling

    age proportion of owner-occupied units built before 1940

    dis weighted distances to five Boston employment centers

    tax full-value property-tax rate per dollars 10,000

    ptratio pupil-teacher ratio by town

    medv Median value of owner-occupied homes in thousand dollars

    You will create three different datasets:

    dataset objective shape

    Training Train the model and obtain the weights 400, 10

    Evaluation Evaluate the performance of the model on unseen data 100, 10

    Predict Use the model to predict house value on new data 6, 10

    The objectives is to use the features of the dataset to predict the value of the house.

    During the second part of the tutorial, you will learn how to use TensorFlow with three different way to import the data:

    With Pandas

    With Numpy

    Only TF

    Note that, all options provide the same results.

    You will learn how to use the high-level API to build, train an evaluate a TensorFlow linear regression model. If you were using the low-level API, you had to define by hand the:

    Loss function

    Optimize: Gradient descent

    Matrices multiplication

    Graph and tensor

    This is tedious and more complicated for beginner.


    You need to import the necessary libraries to train the model.

    import pandas as pd from sklearn import datasets import tensorflow as tf import itertools

    Step 1) Import the data with panda.

    You define the column names and store it in COLUMNS. You can use pd.read_csv() to import the data.

    COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age", "dis", "tax", "ptratio", "medv"]

    training_set = pd.read_csv(“E:/boston_train.csv”, skipinitialspace=True,skiprows=1, names=COLUMNS)

    test_set = pd.read_csv(“E:/boston_test.csv”, skipinitialspace=True,skiprows=1, names=COLUMNS)

    prediction_set = pd.read_csv(“E:/boston_predict.csv”, skipinitialspace=True,skiprows=1, names=COLUMNS)

    You can print the shape of the data.

    print(training_set.shape, test_set.shape, prediction_set.shape) Output (400, 10) (100, 10) (6, 10)

    Note that the label, i.e. your y, is included in the dataset. So you need to define two other lists. One containing only the features and one with the name of the label only. These two lists will tell your estimator what are the features in the dataset and what column name is the label

    It is done with the code below.

    FEATURES = ["crim", "zn", "indus", "nox", "rm", "age", "dis", "tax", "ptratio"] LABEL = "medv"

    Step 2) Convert the data

    You need to convert the numeric variables in the proper format. Tensorflow provides a method to convert continuous variable: tf.feature_column.numeric_column().

    In the previous step, you define a list a feature you want to include in the model. Now you can use this list to convert them into numeric data. If you want to exclude features in your model, feel free to drop one or more variables in the list FEATURES before you construct the feature_cols

    Note that you will use Python list comprehension with the list FEATURES to create a new list named feature_cols. It helps you avoid writing nine times tf.feature_column.numeric_column(). A list comprehension is a faster and cleaner way to create new lists

    feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]

    Step 3) Define the estimator

    In this step, you need to define the estimator. Tensorflow currently provides 6 pre-built estimators, including 3 for classification task and 3 for TensorFlow regression task:









    In this tutorial, you will use the Linear Regressor. To access this function, you need to use tf.estimator.

    The function needs two arguments:

    feature_columns: Contains the variables to include in the model

    model_dir: path to store the graph, save the model parameters, etc

    Tensorflow will automatically create a file named train in your working directory. You need to use this path to access the Tensorboard as shown in the below TensorFlow regression example.

    estimator = tf.estimator.LinearRegressor( feature_columns=feature_cols, model_dir="train") Output INFO:tensorflow:Using default config.

    The tricky part with TensorFlow is the way to feed the model. Tensorflow is designed to work with parallel computing and very large dataset. Due to the limitation of the machine resources, it is impossible to feed the model with all the data at once. For that, you need to feed a batch of data each time. Note that, we are talking about huge dataset with millions or more records. If you don’t add batch, you will end up with a memory error.

    For instance, if your data contains 100 observations and you define a batch size of 10, it means the model will see 10 observations for each iteration (10*10).

    When the model has seen all the data, it finishes one epoch. An epoch defines how many times you want the model to see the data. It is better to set this step to none and let the model performs iteration number of time.

    A second information to add is if you want to shuffle the data before each iteration. During the training, it is important to shuffle the data so that the model does not learn specific pattern of the dataset. If the model learns the details of the underlying pattern of the data, it will have difficulties to generalize the prediction for unseen data. This is called overfitting. The model performs well on the training data but cannot predict correctly for unseen data.

    TensorFlow makes this two steps easy to do. When the data goes to the pipeline, it knows how many observations it needs (batch) and if it has to shuffle the data.

    To instruct Tensorflow how to feed the model, you can use pandas_input_fn. This object needs 5 parameters:

    x: feature data

    y: label data

    batch_size: batch. By default 128

    num_epoch: Number of epoch, by default 1

    shuffle: Shuffle or not the data. By default, None

    You need to feed the model many times so you define a function to repeat this process. all this function get_input_fn.

    def get_input_fn(data_set, num_epochs=None, n_batch = 128, shuffle=True): return tf.estimator.inputs.pandas_input_fn( x=pd.DataFrame({k: data_set[k].values for k in FEATURES}), y = pd.Series(data_set[LABEL].values), batch_size=n_batch, num_epochs=num_epochs, shuffle=shuffle)

    The usual method to evaluate the performance of a model is to:

    Train the model

    Evaluate the model in a different dataset

    Make prediction

    Tensorflow estimator provides three different functions to carry out this three steps easily.

    Step 4): Train the model

    You can use the estimator train to evaluate the model. The train estimator needs an input_fn and a number of steps. You can use the function you created above to feed the model. Then, you instruct the model to iterate 1000 times. Note that, you don’t specify the number of epochs, you let the model iterates 1000 times. If you set the number of epoch to 1, then the model will iterate 4 times: There are 400 records in the training set, and the batch size is 128

    128 rows

    128 rows

    128 rows

    16 rows

    Therefore, it is easier to set the number of epoch to none and define the number of iteration as shown in th below TensorFlow classification example.

    estimator.train(input_fn=get_input_fn(training_set, num_epochs=None, n_batch = 128, shuffle=False), steps=1000) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into train/model.ckpt. INFO:tensorflow:loss = 83729.64, step = 1 INFO:tensorflow:global_step/sec: 238.616 INFO:tensorflow:loss = 13909.657, step = 101 (0.420 sec) INFO:tensorflow:global_step/sec: 314.293 INFO:tensorflow:loss = 12881.449, step = 201 (0.320 sec) INFO:tensorflow:global_step/sec: 303.863 INFO:tensorflow:loss = 12391.541, step = 301 (0.327 sec) INFO:tensorflow:global_step/sec: 308.782 INFO:tensorflow:loss = 12050.5625, step = 401 (0.326 sec) INFO:tensorflow:global_step/sec: 244.969 INFO:tensorflow:loss = 11766.134, step = 501 (0.407 sec) INFO:tensorflow:global_step/sec: 155.966 INFO:tensorflow:loss = 11509.922, step = 601 (0.641 sec) INFO:tensorflow:global_step/sec: 263.256 INFO:tensorflow:loss = 11272.889, step = 701 (0.379 sec) INFO:tensorflow:global_step/sec: 254.112 INFO:tensorflow:loss = 11051.9795, step = 801 (0.396 sec) INFO:tensorflow:global_step/sec: 292.405 INFO:tensorflow:loss = 10845.855, step = 901 (0.341 sec) INFO:tensorflow:Saving checkpoints for 1000 into train/model.ckpt. INFO:tensorflow:Loss for final step: 5925.9873.

    You can check the Tensorboard will the following command:

    activate hello-tf # For MacOS tensorboard --logdir=./train # For Windows tensorboard --logdir=train

    Step 5) Evaluate your model

    You can evaluate the fit of your model on the test set with the code below:

    ev = estimator.evaluate( input_fn=get_input_fn(test_set, num_epochs=1, n_batch = 128, shuffle=False)) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-05-13-01:43:13 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2023-05-13-01:43:13 INFO:tensorflow:Saving dict for global step 1000: average_loss = 32.15896, global_step = 1000, loss = 3215.896

    You can print the loss with the code below:

    loss_score = ev["loss"] print("Loss: {0:f}".format(loss_score)) Output Loss: 3215.895996

    The model has a loss of 3215. You can check the summary statistic to get an idea of how big the error is.

    training_set['medv'].describe() Output count 400.000000 mean 22.625500 std 9.572593 min 5.000000 25% 16.600000 50% 21.400000 75% 25.025000 max 50.000000 Name: medv, dtype: float64

    From the summary statistic above, you know that the average price for a house is 22 thousand, with a minimum price of 9 thousand and maximum of 50 thousand. The model makes a typical error of 3k dollars.

    Step 6) Make the prediction

    Finally, you can use the estimator TensorFlow predict to estimate the value of 6 Boston houses.

    y = estimator.predict( input_fn=get_input_fn(prediction_set, num_epochs=1, n_batch = 128, shuffle=False))

    To print the estimated values of , you can use this code:

    predictions = list(p["predictions"] for p in itertools.islice(y, 6))print("Predictions: {}".format(str(predictions))) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. Predictions: [array([32.297546], dtype=float32), array([18.96125], dtype=float32), array([27.270979], dtype=float32), array([29.299236], dtype=float32), array([16.436684], dtype=float32), array([21.460876], dtype=float32)]

    The model forecast the following values:

    House Prediction

    1 32.29

    2 18.96

    3 27.27

    4 29.29

    5 16.43

    7 21.46

    Note that we don’t know the true value of . In the tutorial of deep learning, you will try to beat the linear model

    Numpy Solution

    This section explains how to train the model using a numpy estimator to feed the data. The method is the same exept that you will use numpy_input_fn estimator.

    training_set_n = pd.read_csv(“E:/boston_train.csv”).values

    test_set_n = pd.read_csv(“E:/boston_test.csv”).values

    prediction_set_n = pd.read_csv(“E:/boston_predict.csv”).values

    Step 1) Import the data

    First of all, you need to differentiate the feature variables from the label. You need to do this for the training data and evaluation. It is faster to define a function to split the data.

    def prepare_data(df): X_train = df[:, :-3] y_train = df[:,-3] return X_train, y_train

    You can use the function to split the label from the features of the train/evaluate dataset

    X_train, y_train = prepare_data(training_set_n) X_test, y_test = prepare_data(test_set_n)

    You need to exclude the last column of the prediction dataset because it contains only NaN

    x_predict = prediction_set_n[:, :-2]

    Confirm the shape of the array. Note that, the label should not have a dimension, it means (400,).

    print(X_train.shape, y_train.shape, x_predict.shape) Output (400, 9) (400,) (6, 9)

    You can construct the feature columns as follow:

    feature_columns = [ tf.feature_column.numeric_column('x', shape=X_train.shape[1:])]

    The estimator is defined as before, you instruct the feature columns and where to save the graph.

    estimator = tf.estimator.LinearRegressor( feature_columns=feature_columns, model_dir="train1") Output INFO:tensorflow:Using default config.

    You can use the numpy estimapor to feed the data to the model and then train the model. Note that, we define the input_fn function before to ease the readability.

    # Train the estimatortrain_input = tf.estimator.inputs.numpy_input_fn( x={"x": X_train}, y=y_train, batch_size=128, shuffle=False, num_epochs=None) estimator.train(input_fn = train_input,steps=5000) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into train1/model.ckpt. INFO:tensorflow:loss = 83729.64, step = 1 INFO:tensorflow:global_step/sec: 490.057 INFO:tensorflow:loss = 13909.656, step = 101 (0.206 sec) INFO:tensorflow:global_step/sec: 788.986 INFO:tensorflow:loss = 12881.45, step = 201 (0.126 sec) INFO:tensorflow:global_step/sec: 736.339 INFO:tensorflow:loss = 12391.541, step = 301 (0.136 sec) INFO:tensorflow:global_step/sec: 383.305 INFO:tensorflow:loss = 12050.561, step = 401 (0.260 sec) INFO:tensorflow:global_step/sec: 859.832 INFO:tensorflow:loss = 11766.133, step = 501 (0.117 sec) INFO:tensorflow:global_step/sec: 804.394 INFO:tensorflow:loss = 11509.918, step = 601 (0.125 sec) INFO:tensorflow:global_step/sec: 753.059 INFO:tensorflow:loss = 11272.891, step = 701 (0.134 sec) INFO:tensorflow:global_step/sec: 402.165 INFO:tensorflow:loss = 11051.979, step = 801 (0.248 sec) INFO:tensorflow:global_step/sec: 344.022 INFO:tensorflow:loss = 10845.854, step = 901 (0.288 sec) INFO:tensorflow:Saving checkpoints for 1000 into train1/model.ckpt. INFO:tensorflow:Loss for final step: 5925.985. Out[23]:

    You replicate the same step with a different estimator to evaluate your model

    eval_input = tf.estimator.inputs.numpy_input_fn( x={"x": X_test}, y=y_test, shuffle=False, batch_size=128, num_epochs=1) estimator.evaluate(eval_input,steps=None) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-05-13-01:44:00 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train1/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2023-05-13-01:44:00 INFO:tensorflow:Saving dict for global step 1000: average_loss = 32.158947, global_step = 1000, loss = 3215.8945 Out[24]: {'average_loss': 32.158947, 'global_step': 1000, 'loss': 3215.8945}

    Finaly, you can compute the prediction. It should be the similar as pandas.

    test_input = tf.estimator.inputs.numpy_input_fn( x={"x": x_predict}, batch_size=128, num_epochs=1, shuffle=False) y = estimator.predict(test_input) predictions = list(p["predictions"] for p in itertools.islice(y, 6)) print("Predictions: {}".format(str(predictions))) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train1/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. Predictions: [array([32.297546], dtype=float32), array([18.961248], dtype=float32), array([27.270979], dtype=float32), array([29.299242], dtype=float32), array([16.43668], dtype=float32), array([21.460878], dtype=float32)] Tensorflow solution

    The last section is dedicated to a TensorFlow solution. This method is sligthly more complicated than the other one.

    Note that if you use Jupyter notebook, you need to Restart and clean the kernel to run this session.

    TensorFlow has built a great tool to pass the data into the pipeline. In this section, you will build the input_fn function by yourself.

    Step 1) Define the path and the format of the data

    First of all, you declare two variables with the path of the csv file. Note that, you have two files, one for the training set and one for the testing set.

    import tensorflow as tf df_train = "E:/boston_train.csv" df_eval = "E:/boston_test.csv"

    Then, you need to define the columns you want to use from the csv file. We will use all. After that, you need to declare the type of variable it is.

    Floats variable are defined by [0.]

    COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age", "dis", "tax", "ptratio", "medv"]RECORDS_ALL = [[0.0], [0.0], [0.0], [0.0],[0.0],[0.0],[0.0],[0.0],[0.0],[0.0]]

    Step 2) Define the input_fn function

    The function can be broken into three part:

    Import the data

    Create the iterator

    Consume the data

    Below is the overal code to define the function. The code will be explained after

    def input_fn(data_file, batch_size, num_epoch = None): # Step 1 def parse_csv(value): columns = tf.decode_csv(value, record_defaults= RECORDS_ALL) features = dict(zip(COLUMNS, columns)) #labels = features.pop('median_house_value') labels = features.pop('medv') return features, labels # Extract lines from input files using the Dataset API. dataset = ( # Read text file .skip(1) # Skip header row .map(parse_csv)) dataset = dataset.repeat(num_epoch) dataset = dataset.batch(batch_size) # Step 3 iterator = dataset.make_one_shot_iterator() features, labels = iterator.get_next() return features, labels

    ** Import the data**

    This method calls a function that you will create in order to instruct how to transform the chúng tôi a nutshell, you need to pass the data in the TextLineDataset object, exclude the header and apply a transformation which is instructed by a chúng tôi explanation This line read the csv file

    .skip(1) : skip the header

    .map(parse_csv)): parse the records into the tensorsYou need to define a function to instruct the map object. You can call this function parse_csv.

    This function parses the csv file with the method tf.decode_csv and declares the features and the label. The features can be declared as a dictionary or a tuple. You use the dictionary method because it is more chúng tôi explanation

    tf.decode_csv(value, record_defaults= RECORDS_ALL): the method decode_csv uses the output of the TextLineDataset to read the csv file. record_defaults instructs TensorFlow about the columns type.

    dict(zip(_CSV_COLUMNS, columns)): Populate the dictionary with all the columns extracted during this data processing

    features.pop(‘median_house_value’): Exclude the target variable from the feature variable and create a label variable

    The Dataset needs further elements to iteratively feeds the Tensors. Indeed, you need to add the method repeat to allow the dataset to continue indefinitely to feed the model. If you don’t add the method, the model will iterate only one time and then throw an error because no more data are fed in the pipeline.

    After that, you can control the batch size with the batch method. It means you tell the dataset how many data you want to pass in the pipeline for each iteration. If you set a big batch size, the model will be slow.

    Step 3) Create the iterator

    Now you are ready for the second step: create an iterator to return the elements in the dataset.

    The simplest way of creating an operator is with the method make_one_shot_iterator.

    After that, you can create the features and labels from the iterator.

    Step 4) Consume the data

    You can check what happens with input_fn function. You need to call the function in a session to consume the data. You try with a batch size equals to 1.

    Note that, it prints the features in a dictionary and the label as an array.

    It will show the first line of the csv file. You can try to run this code many times with different batch size.

    next_batch = input_fn(df_train, batch_size = 1, num_epoch = None) with tf.Session() as sess: first_batch = print(first_batch) Output ({'crim': array([2.3004], dtype=float32), 'zn': array([0.], dtype=float32), 'indus': array([19.58], dtype=float32), 'nox': array([0.605], dtype=float32), 'rm': array([6.319], dtype=float32), 'age': array([96.1], dtype=float32), 'dis': array([2.1], dtype=float32), 'tax': array([403.], dtype=float32), 'ptratio': array([14.7], dtype=float32)}, array([23.8], dtype=float32))

    Step 4) Define the feature column

    You need to define the numeric columns as follow:

    X1= tf.feature_column.numeric_column('crim') X2= tf.feature_column.numeric_column('zn') X3= tf.feature_column.numeric_column('indus') X4= tf.feature_column.numeric_column('nox') X5= tf.feature_column.numeric_column('rm') X6= tf.feature_column.numeric_column('age') X7= tf.feature_column.numeric_column('dis') X8= tf.feature_column.numeric_column('tax') X9= tf.feature_column.numeric_column('ptratio')

    Note that you need to combined all the variables in a bucket

    base_columns = [X1, X2, X3,X4, X5, X6,X7, X8, X9]

    Step 5) Build the model

    You can train the model with the estimator LinearRegressor.

    model = tf.estimator.LinearRegressor(feature_columns=base_columns, model_dir='train3') Output

    You need to use a lambda function to allow to write the argument in the function inpu_fn. If you don’t use a lambda function, you cannot train the model.

    # Train the estimatormodel.train(steps =1000, input_fn= lambda : input_fn(df_train,batch_size=128, num_epoch = None))


    INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into train3/model.ckpt. INFO:tensorflow:loss = 83729.64, step = 1 INFO:tensorflow:global_step/sec: 72.5646 INFO:tensorflow:loss = 13909.657, step = 101 (1.380 sec) INFO:tensorflow:global_step/sec: 101.355 INFO:tensorflow:loss = 12881.449, step = 201 (0.986 sec) INFO:tensorflow:global_step/sec: 109.293 INFO:tensorflow:loss = 12391.541, step = 301 (0.915 sec) INFO:tensorflow:global_step/sec: 102.235 INFO:tensorflow:loss = 12050.5625, step = 401 (0.978 sec) INFO:tensorflow:global_step/sec: 104.656 INFO:tensorflow:loss = 11766.134, step = 501 (0.956 sec) INFO:tensorflow:global_step/sec: 106.697 INFO:tensorflow:loss = 11509.922, step = 601 (0.938 sec) INFO:tensorflow:global_step/sec: 118.454 INFO:tensorflow:loss = 11272.889, step = 701 (0.844 sec) INFO:tensorflow:global_step/sec: 114.947 INFO:tensorflow:loss = 11051.9795, step = 801 (0.870 sec) INFO:tensorflow:global_step/sec: 111.484 INFO:tensorflow:loss = 10845.855, step = 901 (0.897 sec) INFO:tensorflow:Saving checkpoints for 1000 into train3/model.ckpt. INFO:tensorflow:Loss for final step: 5925.9873. Out[8]:

    You can evaluate the fit of you model on the test set with the code below:

    results = model.evaluate(steps =None,input_fn=lambda: input_fn(df_eval, batch_size =128, num_epoch = 1)) for key in results: print(" {}, was: {}".format(key, results[key])) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-05-13-02:06:02 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train3/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2023-05-13-02:06:02 INFO:tensorflow:Saving dict for global step 1000: average_loss = 32.15896, global_step = 1000, loss = 3215.896 average_loss, was: 32.158958435058594 loss, was: 3215.89599609375 global_step, was: 1000

    The last step is predicting the value of based on the value of , the matrices of the features. You can write a dictionary with the values you want to predict. Your model has 9 features so you need to provide a value for each. The model will provide a prediction for each of them.

    In the code below, you wrote the values of each features that is contained in the df_predict csv file.

    You need to write a new input_fn function because there is no label in the dataset. You can use the API from_tensor from the Dataset.

    prediction_input = { 'crim': [0.03359,5.09017,0.12650,0.05515,8.15174,0.24522], 'zn': [75.0,0.0,25.0,33.0,0.0,0.0], 'indus': [2.95,18.10,5.13,2.18,18.10,9.90], 'nox': [0.428,0.713,0.453,0.472,0.700,0.544], 'rm': [7.024,6.297,6.762,7.236,5.390,5.782], 'age': [15.8,91.8,43.4,41.1,98.9,71.7], 'dis': [5.4011,2.3682,7.9809,4.0220,1.7281,4.0317], 'tax': [252,666,284,222,666,304], 'ptratio': [18.3,20.2,19.7,18.4,20.2,18.4] } def test_input_fn(): dataset = return dataset # Predict all our prediction_inputpred_results = model.predict(input_fn=test_input_fn)

    Finaly, you print the predictions.

    for pred in enumerate(pred_results): print(pred) Output INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train3/model.ckpt-1000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. (0, {'predictions': array([32.297546], dtype=float32)}) (1, {'predictions': array([18.96125], dtype=float32)}) (2, {'predictions': array([27.270979], dtype=float32)}) (3, {'predictions': array([29.299236], dtype=float32)}) (4, {'predictions': array([16.436684], dtype=float32)}) (5, {'predictions': array([21.460876], dtype=float32)}) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from train3/model.ckpt-5000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. (0, {'predictions': array([35.60663], dtype=float32)}) (1, {'predictions': array([22.298521], dtype=float32)}) (2, {'predictions': array([25.74533], dtype=float32)}) (3, {'predictions': array([35.126694], dtype=float32)}) (4, {'predictions': array([17.94416], dtype=float32)}) (5, {'predictions': array([22.606628], dtype=float32)}) Summary

    To train a model, you need to:

    Define the features: Independent variables: X

    Define the label: Dependent variable: y

    Construct a train/test set

    Define the initial weight

    Define the loss function: MSE

    Optimize the model: Gradient descent


    Learning rate

    Number of epoch

    Batch size

    In this tutorial, you learned how to use the high level API for a linear regression TensorFlow estimator. You need to define:

    Feature columns. If continuous: tf.feature_column.numeric_column(). You can populate a list with python list comprehension

    The estimator: tf.estimator.LinearRegressor(feature_columns, model_dir)

    A function to import the data, the batch size and epoch: input_fn()

    After that, you are ready to train, evaluate and make prediction with train(), evaluate() and predict()

    Microsoft Power Automate: Workflow Automation

    In this tutorial, we’ll talk about Microsoft Power Automate. We’ll also discuss why it’s vital to learn about this indispensable tool and its underlying features.

    Power Automate is a tool that allows us to automate any task. Those tasks can be as simple as sending out an email every morning. It can also be very complex, like uploading a document to a file system when meeting a certain criteria. 

    There’s a wide variety of tasks that we can do using this tool. Another cool thing about this tool is that the tasks can go between any application.

    There are a lot of default connectors that Power Automate has, like Outlook, Slack, Twitter, YouTube, Gmail, One Drive, Google Drive, and many more.

    If connections don’t exist, Power Automate allows us to connect to our application using third party API tools. 

    The tasks can also be personal or business-related. We can have a simple task where we send out an email or notification every morning based on something. The task can also be business-related, where we can automate our expense system process or invoicing process, or anything that’s repetitive in a business setting.

    Additionally, tasks can have interactions from people. Power Automate has a feature called Approvals where in-between our task flows, we can get approvals from people.

    Lastly, we can also have tasks not only in these applications, but also on our desktop or website. We can have a flow that takes control of our mouse to move files around our desktop and open any application. For websites, Power Automate allows us to streamline and automate any workflow that we have without using any code. 

    Microsoft Power Automate has four notable features.

    Power Automate is definitely easy to use. There’s no code required, it has a very simple design, and it’s quite similar to using Microsoft PowerPoint or Excel. If you know how to use those two applications from the same Microsoft ecosystem, then you probably already know how to use Power Automate. 

    It’s also accessible to people. If you know how to code, then you can easily do some cool and complex things. But if you don’t, that’s also fine. You can still create some complicated and usable workflows in Power Automate.

    Power Automate is also comprehensive. You can connect to hundreds of connectors including Slack, Gmail, Outlook, One Drive, Salesforce, and text messaging. You can basically support everything in Power Automate. As I’ve mentioned earlier, if there’s not a connector available, then you can connect it through a third party support like API or HTTP requests.

    Moreover, it has a feature called UI flows, which allows you to perform process automations on your desktop and on the web.

    Power Automate allows you to create complex logic in your workflows to make them more powerful. You can also have conditions in your workflows. For example, let’s say we’re creating a workflow on invoices. If our invoice is greater than a thousand, then we should seek approval before paying it out. If our invoice is less than a thousand, then we can just pay it out.

    It also has loops, which allows us to iterate through things in our workflows. Furthermore, it offers approvals which we already mentioned earlier. Approvals allow us to add user intervention into our workflows. We don’t want every single workflow to be automated. Instead, we want some to rely on user approvals and we can add that into our workflow using Power Automate. 

    Last but not the least, it’s scalable. It’s scalable in a sense that once we create a workflow in Power Automate, we can easily share it with people. We can share it with all the colleagues in our businesses. We can also monitor who’s using it, and who’s not.

    So, those are the four features of Power Automate. It’s easy to use, comprehensive, powerful, and scalable.

    The next thing we’ll discuss are the various reasons why it’s essential to learn and understand how to use this tool.

    First, it can really boost your productivity. This is perfect for automation enthusiasts who want to automate any repetitive tasks. It allows you to have more time during the day for things that matter the most. 

    It also allows you to create an impact in your organization or business. You’ll be able to automate a process for someone that takes them about 30 minutes to do during the day. You can save a lot of time for everyone who’s using this.

    The next reason why you should learn it is because it can get you hired. It’s definitely one of those skills that employers are looking for nowadays. Power Automate is becoming more popular along with Power BI, Power Apps, and Power AI. Therefore, this is something that can help you get employed.

    It’s also helpful in improving your own toolkit. It can be powerful in conjunction with Power Apps. They work really well together. If you ever want to automate something, even if it’s for business-related or personal reasons, you’ll be able to do it. It also has RPA (robotic process automation) capabilities, which is one of the hottest trends of 2023.  

    Just to prove the importance of learning Power Automate, here’s a sample chart that shows how Power Automate can be impactful to businesses.

    Based on the presented data, businesses that use Power Automate save about 15% in their business process efficiencies in the third year that they do it. 

    So, imagine doing this in your company and saving them 15% of time, money, and their business processes. That’s absolutely impactful. Furthermore, Power Automate is currently regarded as a visionary in terms of robotic process automation. 

    Microsoft has certifications that you can take to prove to your employers that you actually learned about the Power Platform. So, I would definitely recommend trying to get certified. Taking this course can be a great addition to your resume, especially with the other certifications that are listed here.

    So, that’s what Microsoft Power Automate is. We’ve learned about its features and why it’s essential for us to learn about it. With this tool, you can streamline repetitive tasks and paperless processes so you can focus your attention where it’s needed most.

    You can expand your automation capabilities across desktop, web, and mobile with Power Automate. It’s definitely a better way to improve productivity across your organization.

    Until next time,


    Sap Monitoring & Performance Checks: Complete Tutorial With Tcodes

    What is System Monitoring?

    System monitoring is a daily routine activity and this document provides a systematic step by step procedure for Server Monitoring. It gives an overview of technical aspects and concepts for proactive system monitoring. Few of them are:

    Checking Application Servers.

    Monitoring System-wide Work Processes.

    Monitoring Work Processes for Individual Instances.

    Monitoring Lock Entries.

    CPU Utilization

    Available Space in Database.

    Monitoring Update Processes.

    Monitoring System Log.

    Buffer Statistics

    Some others are:

    Monitoring Batch Jobs

    Spool Request Monitoring.

    Number of Print Requests

    ABAP Dump Analysis.

    Database Performance Monitor.

    Database Check.

    Monitoring Application Users.

    Why Daily Basic checks / System Monitoring?

    How do we do monitor a SAP System? Checking Application Servers (SM51)

    This transaction is used to check all active application servers.

    Here you can see which services or work processes are configured in each instance.

    Monitoring Work Processes for Individual Instances SM50:

    Displays all running, waiting, stopped and PRIV processes related to a particular instance. Under this step we check all the processes; the process status should always be waiting or running. If any process is having a status other than waiting or running we need to check that particular process and report accordingly.

    This transaction displays a lot of information like:

    Status of Work process (whether it’s occupied or not)

    If the work process is running, you may be able to see the action taken by it in the Action column.

    You can which table is being worked upon

    Some of the typical problems:

    Some users may have PRIV status under Reason column. This could be that the user transaction is so big that it requires more memory. When this happen the DIA work process will be ‘owned’ by the user and will not let other users use. If this happens, check with the user and if possible run the job as a background job.

    If there is a long print job on SPO work process, investigate the problem. It could be a problem related to the print server or printer.

    Monitoring System-wide Work Processes (SM66)

    By checking the work process load using the global work process overview, we can quickly investigate the potential cause of a system performance problem.

    Monitor the work process load on all active instances across the system

    Using the Global Work Process Overview screen, we can see at a glance:

    The status of each application server

    The reason why it is not running

    Whether it has been restarted

    The CPU and request run time

    The user who has logged on and the client that they logged on to

    The report that is running

    Monitor Application User (AL08 and SM04)

    This transaction displays all the users of active instances.

    Monitoring Update Processes (SM13)


    If there are no long pending updates records or no updates are going on then this queue will be empty as shown in the below screen shot.

    But, if the Update is not active then find the below information:

    Is the update active, if not, was it deactivated by the system or by a user?

    Is any update cancelled?

    Is there a long queue of pending updates older than 10 minutes?

    Monitoring Lock Entries (SM12)

    Execute Transaction SM12 and put ‘*’ in the field User Name

    SAP provides a locking mechanism to prevent other users from changing the record that you are working on. In some situations, locks are not released. This could happen if the users are cut off i.e. due to network problem before they are able to release the lock.

    These old locks need to be cleared or it could prevent access or changes to the records.

    We can use lock statistics to monitor the locks that are set in the system. We record only those lock entries which are having date time stamp of the previous day.

    Monitoring System Log (SM21)

    We can use the log to pinpoint and rectify errors occurring in the system and its environment.

    We check the log for the previous day with the following selection/option:

    Enter Date and time.

    Select Radio Button Problems and Warnings

    Press Reread System Log.

    Tune Summary (ST02)

    Step 1: Go to ST02 to check the Tune summary.

    Step 4: Note down the value and the Profile parameters

    Step 5: Go to RZ10 (to change the Profile parameter values)

    Step 6: Save the changes.

    Step 7: Restart the server to take the new changes effect.

    CPU Utilization (ST06)

    Idle CPU utilization rate must be 60-65%, if it exceeds the value then we must start checking at least below things:

    Run OS level commands – top and check which processes are taking most resources.

    Go to SM50 or SM66. Check for any long running jobs or any long update queries being run.

    Go to SM12 and check lock entries

    Go to SM13 and check Update active status.

    Check for the errors in SM21.

    ABAP Dumps (ST22)

    Here we check for previous day’s dumps

    Spool Request Monitoring (SP01)

    For spool request monitoring, execute SP01 and select as below:

    Put ‘*’ in the field Created By

    Here we record only those requests which are terminated with problems.

    Monitoring Batch Jobs (SM37)

    For Monitoring background jobs, execute SM37 and select as below:

    Put ‘*’ in the field User Name and Job name

    In Job status, select: Scheduled, Cancelled, Released and Finished requests.

    Transactional RFC Administration (SM58)

    Transactional RFC (tRFC, also originally known as asynchronous RFC) is an asynchronous communication method which executes the called function module in the RFC server only once.

    We need to select the display period for which we want to view the tRFCs and then select ‘*’ in the username field to view all the calls which have not be executed correctly or waiting in the queue.

    QRFC Administration (Outbound Queue-SMQ1)

    We should specify the client name over here and see if there any outgoing qRFCs in waiting or error state.

    QRFC Administration (Inbound Queue-SMQ2)

    We should specify the client name over here and see if there any incoming qRFCs in waiting or error state.

    Database Administration (DB02)

    After you select Current Sizes on the first screen we come to the below screen which shows us the current status of all the tablespaces in the system.

    If any of the tablespaces is more than 95% and the auto extent is off then we need to add a new datafile so that the database is not full.

    We can also determine the history of tablespaces.

    We can select Months, Weeks or Days over here to see the changes which take place in a tablespace.

    We can determine the growth of tablespace by analyzing these values.

    Database Backup logs (DB12)

    From this transaction, we could determine when the last successful backup of the system was. We can review the previous day’s backups and see if everything was fine or not.

    We can also review the redo log files and see whether redo log backup was successful or not.

    Quick Review

    Daily Monitoring Tasks

    Critical tasks

    SAP System


    Critical tasks

    No Task Transaction Procedure / Remark


    Check that the R/3System is up.

    Log onto the R/3 System


    Check that daily backup executed without errors


    Check database backup.

    SAP System

    No Task Transaction Procedure / Remark


    Check that all application servers are up.


    Check that all servers are up.


    Check work processes (started from SM51).


    All work processes with a “running” or a “waiting” status


    Global Work Process overview


    Check no work process is running more than 1800 second


    Look for any failed updates (update terminates).


    Set date to one day ago

    Enter * in the user ID

    Set to “all” updates Check for lines with “Err.”


    Check system log.


    Set date and time to before the last log review. Check for:



    Security messages

    Database problems


    Review for canceled jobs.


    Enter an asterisk (*) in User ID.Verify that all critical jobs were successful.


    Check for “old” locks.


    Enter an asterisk (*) for the user ID.


    Check for users on the system.


    Review for an unknown or different user ID and chúng tôi task should be done several times a day.


    Check for spool problems.


    Enter an asterisk (*) for Created ByLook for spool jobs that have been “In process” for over an hour.


    Check job log


    Check for:

    New jobs

    Incorrect jobs


    Review and resolve dumps.


    Look for an excessive number of dumps. Look for dumps of an unusual nature.


    Review buffer statistics.


    Look for swaps.


    No Task Transaction Procedure / Remark


    Review error log for problems.



    Database GrowthMissing Indexes


    If tablespace is used more than 90 % add new data file to itRebuild the Missing Indexes


    Database Statistics log


    Character Ai Chat: Chatting With Ultra

    Imagine having conversations with AI-powered characters that feel natural and life-like. Character AI chat brings this experience to life, allowing users to interact and chat with ultra-realistic AI personalities. Whether you want to create your own characters or interact with pre-built ones, Character AI chat offers a range of features that provide fun and interesting exchanges.

    In this article, we will explore the exciting world of Character AI chat, its key features, and the possibilities it offers to users. From AI-generated characters to unlimited free messaging, this AI-powered experience is designed to provide an immersive and engaging conversation experience.

    See More: How To Test Beta Character AI

    Character AI chat offers a conversation experience that is incredibly fluid and life-like. The AI-powered characters are programmed to respond naturally, mimicking human-like conversation patterns. This means that the interactions feel seamless and realistic, creating an immersive experience for users. The characters can engage in meaningful conversations, understand context, and provide thoughtful responses, making the entire chat experience enjoyable and captivating.

    If creating your own character isn’t your cup of tea, don’t worry! Character AI chat offers a vast library of user-created characters to explore. With millions of characters created by fellow users, you can discover fascinating personalities and engage in conversations with them. Each character brings a distinct flavor to the chat experience, providing an opportunity to meet new virtual friends and explore different conversation dynamics. It’s like entering a vibrant community of AI personalities, all waiting to chat with you.

    Character AI chat believes in making the chat experience accessible to everyone. That’s why it offers unlimited free messaging with ultra-realistic AI personalities. You can chat as much as you want, without any limitations or restrictions. Whether you’re looking for a casual chat to pass the time or deep discussions on various topics, the freedom to message your AI characters at any time adds to the convenience and enjoyment of the experience.

    Privacy and security are paramount when it comes to chat applications. Character AI chat takes these concerns seriously. All conversations are stored on the user’s device, ensuring that your personal data remains private. No conversation data is ever stored on the server, providing an additional layer of security. You can chat with peace of mind, knowing that your conversations are for your eyes only.

    See Also: Can You Buy ChatGPT Stock?

    A: Yes, Character AI chat is compatible with smartphones and can be accessed through dedicated mobile apps or web-based platforms.

    A: Yes, Character AI chat offers parental control features to ensure a safe chat environment for younger users. Parents can set restrictions and monitor their child’s interactions.

    A: Yes, you can chat with multiple characters simultaneously. Character AI chat allows for multi-chat functionality, enabling you to engage in separate conversations with different characters.

    A: Absolutely! In addition to appearance and voice, you can define your character’s personality traits, conversation style, and even specific interests or knowledge areas.

    A: Yes, you can share your created characters with others. Character AI chat offers options to export and import character profiles, allowing you to share your creations or explore characters created by fellow users.

    Character AI chat opens up a world of possibilities for engaging and immersive chat experiences. Whether you choose to create your own characters, discover user-created personalities, or both, this AI-powered platform offers a fluid and life-like conversation experience. From meaningful discussions to lighthearted banter, the ultra-realistic AI personalities are ready to chat with you.

    With unlimited free messaging, privacy and security measures, and a vibrant community of characters, Character AI chat is a gateway to exciting and captivating conversations. Just remember to approach the characters’ responses with caution, as everything they say is made up. Enjoy the experience, explore new connections, and let your imagination soar in the world of Character AI chat.

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