Python Multiprocessing Gpu

In this lesson, you will learn how to write programs that perform several tasks in parallel using Python's built-in multiprocessing library. It is crucial for Python to provide high-performance parallelism. 7, as well as Windows/macOS/Linux. The following are code examples for showing how to use torch. dot 関数で、GPUメモリ上に確保された配列の行列積をGPUを用いて計算します。 この関数の戻り値も cp. This documentation is for an old version of IPython. Random states in multiprocessing, learnt a lesson after wasted a weeks GPU time. There is a trade-off in here though. com - EuroPy 2011 High Performance Computing with Python (4 hour tutorial) EuroPython 2011. Here we update the information and examine the trends since our previous post Top 20 Python Machine Learning Open Source Projects (Nov 2016). said: I wonder if there is a simple way to execute this code to use the Nvidia GPU's cores, without necessarily rewriting everything with numba and other cuda functions There isn't. Modern hardware is multi-core. 0, cuDNN v7. What You Will Learn. Threading in Python: What Every Data Scientist Needs to Know. In computing, a. Text Handling 9. It's too intensive and complex to run on the GPU (with it's thousand-ish cores) but the single core Python uses isn't enough. [email protected] This can be used to achieve some level of parallelism within a single compute node. close_event (multiprocessing. What matters in this tutorial is the concept of reading extremely large text files using Python. This adds overhead that can be important. Some Python libraries bypass the issue, for example the multiprocessing one, allowing the user to use multiple cores. What can Python do? Python is a fully-functional programming language that can do anything almost any other language can do, at comparable speeds. (3 replies) Hello, I've been thinking about implementing some simple games, where one can program agents to play the game. I had only one post on that blog that attracted any attention. Python 2 End of Life. Visiting my own post five years later a lot has changed. By default you can only expect multiprocessing to do a "pretty good" job of load-balancing tasks. If you're familiar with MPI, this is useful. 更有趣的是(在异常之后)我必须手动关闭python进程,因为GPU的VRAM,系统的RAM甚至python进程在脚本崩溃后仍然存活. I believe this is because processes do not share memory. My guess is image preprocessing from CPU is taking longer than GPU computation for each batch. In this article, Toptal Freelance Software Engineer Marcus McCurdy explores different approaches to solving this discord with code, including examples of Python m. What if you want to use all four cores? Luckily, there is help from the multiprocessing module, which allows parts of your program to run in parallel. While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. You can see what David has been up to on his website or check out what he's been up to on Github. Here, Python provides a strong multiprocesing library. This documentation is for an old version of IPython. Note though, that the venv module does not offer all features of this library (e. What I'd really like is for Python to give us a good framework for the Big Data world which is in almost everybody's use case now, and that means, Python needs to talk multiprocessor, Python needs to talk GPU, Python needs to talk cluster, and Python should long ago have been addressing this directly. You will probably not get everything right since there a few details that need to be taken into account such as setting up an individual scratch workspace for each call of the worker function. Multiprocessing package - torch. NET is available as a source release and as a Windows installer for various versions of Python and the common language runtime from the Python for the. This book will help you design serverless architectures for your applications with AWS and Python. This tutorial will discuss multiprocessing in Python and how to use multiprocessing to communicate between processes and perform synchronization between processes, as well as logging. In computing, a. In this machine learning tutorial you will learn about machine learning algorithms using various analogies related to real life. I managed to get multi-processing working on ms-windows, doing some workarounds. - [Instructor] In the previous video,…we saw how to synchronize the process. The multiprocessing package supports spawning processes using an API similar to the threading module. Something like doing multiprocessing on CUDA tensors cannot succeed, there are two alternatives for this. The following are code examples for showing how to use multiprocessing. Strategies & Tools for Parallel Machine Learning in Python PyConFR 2012 - Pari… Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. MapReduce with the Disco Project. 96 second took. Numba can compile on GPU. python multiprocessing gpu (6) I am currently working on a project in python, and I would like to make use of the GPU for some calculations. I have a custom DataGenerator that uses Python's Multiprocessing module to generate the training data that is fed to the Tensorflow model. multi-threaded applications, including why we may choose to use multiprocessing with OpenCV to speed up the processing of a given dataset. For example I have 7 GPUs in the server and created 7 training script each for a different random seeds. We also have a Review of Python's Best Text Editors. We've got 32 worker processes, and 1 master process. The statement from Guido van Rossum is reproduced here: Let's not play games with semantics. multiprocessing(). ndarray 型のオブジェクトであり、実体は同様にGPUメモリ上にあります。. Multiprocessing in Python. It supports the exact same operations, but extends it, so that all tensors sent through a multiprocessing. PyQtGraph is a pure-python graphics and GUI library built on PyQt4 / PySide and numpy. Event) – Used to notify each process when done rendering. You can see what David has been up to on his website or check out what he’s been up to on Github. And I found it’s super easy to parallel a Python program. Porting CPU-Based Multiprocessing Algorithms to GPU for Distributed Acoustic Sensing Author: Steve Jankly Subject: This talk describes our endeavors, from start to finish, in implementing a parallelizable and computationally intensive process on a GPU for fiber optic solutions, specifically Distributed Acoustic Sensing \(DAS\) interrogation. It also offers both local and remote concurrency. Python 2 will reach its end of life on January 1st, 2020. Table of Contents Previous: multiprocessing – Manage processes like threads Next: Communication Between Processes. predict ) dans un autre processus. 1, use_sigkill=False ) In general multiprocessing. python의 multiprocessing을 사용하는 방법은 간단합니다. Learn how to package your Python code for PyPI. I have used multiprocessing on a shared memory computer with 4 x Xeon E7-4850 CPUs (each 10 cores) and 512 GB memory and it worked extremely well. Multi-processing in Python is effective to speed up the processing time for long-running functions. In order to reduce the time for CPU to preprocess the images, I started investigating multiprocessing option in Python. We make the latter inherit the properties of keras. Functions 4. At first glance it seems like there are many tools available; at second glance, I feel like im missing something. The following are code examples for showing how to use multiprocessing. The term 'spawn' means the creation of a process by a parent process. The price to pay: serialization of tasks, arguments, and results. Testing / Debugging Topics Covered in this course 7. GPU computing has come a long way over the past few years but still requires knowledge of CUDA or OpenCL. Learn about installing packages. multiprocessing is a wrapper around Python multiprocessing module and its API is 100% compatible with original module. Not sure, if this is the right place to report it and what is the right way to approach it, as it comes from Python standard library, so even if it would be patched in a release here, it won't be fixed for distro packages, which uses system Python. This has been done for a lot of interesting activities and takes advantage of CUDA or OpenCL extensions to the comp. It would be appreciated if there are any Python VTK experts who could convert any of the c++ examples to Python!. Using Python for. Hi there fellas. I am wondering if I could use PyCuda and multiprocessing to run the thread functions over the GPU cores. We have already seen that “~” separates the left-hand side of the model from the right-hand side, and that “+” adds new columns to the design matrix. Share CPU tensors instead. Understand the concepts of Supervised, Unsupervised and Reinforcement Learning and learn how to write a code for machine learning using python. That post has served many individuals as guide for getting a good GPU accelerated TensorFlow work environment running on Windows 10 without needless installation complexity. By the end of this course you will be well-versed with the programming concepts in Python 3. The goal is to get back into Python programming with arcpy, in particular doing so under ArcGIS Pro, and learn about the concepts of parallel programming and multiprocessing and how they can be used in Python to speed up time-consumptive computations. The most famous http library written by kenneth reitz. cuda0 has 2x Intel Xeon E5-2630 CPUs clocked at 2. Low level Python code using the numbapro. Increasing batch size only makes it worse. Python 2 End of Life. Numba runs inside the standard Python interpreter, so you can write CUDA kernels directly in Python syntax and execute them on the GPU. What You Will Learn. MapReduce with the Disco Project. This tutorial will discuss multiprocessing in Python and how to use multiprocessing to communicate between processes and perform synchronization between processes, as well as logging. Python offers two libraries - multiprocessing and threading- for the eponymous parallelization methods. Here, Python provides a strong multiprocesing library. A GPU story for multiprocessing. Pool()的更多相关文章. nav_map_queue (multiprocessing. I did a quick search and there were 390,000 repositories using Python. Then it occured to me that it may be related to my previous installation of Python 2. Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes; OS Platform and Distribution (e. …Python multiprocessing provides a manager…to coordinate shared. It offers a subset of the Pandas API for operating on GPU dataframes, using the parallel computing power of the GPU (and the Numba JIT) for sorting, columnar math, reductions, filters, joins, and group by operations. The vectorize decorator takes as input the signature of the function that is to be accelerated, along with the target for machine code generation. 当需要时,程序会去进程池中获取一个进程. はてなブログをはじめよう! apple1660さんは、はてなブログを使っています。あなたもはてなブログをはじめてみませんか?. Within the multiprocessing module, we have the Manager class; this class can be utilized as a means of controlling Python objects, and providing thread and process safety within your Python applications. Python concurrency Remarks. Some highlights of Valkka Python3 API, while streaming itself runs in the background at the cpp level. They differ in that Queue lacks the task_done() and join() methods introduced into Python 2. Like multiprocessing, it's a low(er)-level interface to parallelism than parfor, but one that is likely to last for a while. This documentation is for an old version of IPython. Intro to Threads and Processes in Python. There are two ways to do this - via shared memory (exemplar is OpenMP) and by explicit communication mechanisms (exemplar is MPI). Python Overview Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods Python File Methods Python Keywords Module Reference Random Module Requests Module Python How To Remove List Duplicates Reverse a String Python Examples Python Examples Python Exercises Python. Multiprocessing. Package authors use PyPI to distribute their software. the raw data, so that dark-current and flat-field corrections are applied by. [email protected] It offers line plotting, 2D and 3D surface plots in a variety of formats, and 3D volumetric visualization. Parallel programming with Python's multiprocessing library. Many years ago, C# introduced a way to run asynchronous operations that truly changed how we write. If you are. They are extracted from open source Python projects. As we are running python as an instance inside the Blender software, the lineexe=spawn. get_executable() will return the path of the blender executable instead of the python interpreter inside Blender. •multiprocessing-Creates multiple python. What matters in this tutorial is the concept of reading extremely large text files using Python. Parallel computing means that more than one thing is calculated at once. Python offers two libraries - multiprocessing and threading- for the eponymous parallelization methods. In this video, I tried to improve MSS grab screen method to work even faster. Setting up multiprocessing is. I am currently using ten of the twelve 3. This tutorial will discuss multiprocessing in Python and how to use multiprocessing to communicate between processes and perform synchronization between processes, as well as logging. I'm using OpenCV to stream from my webcam in one process, and in a separate process, using caffe to perform image classification on the frames pulled from the webcam. To demonstrate how it works, we will adapt a program so that its central part runs in parallel, creating. If you want to do GPU computation, use a GPU compute API like CUDA or OpenCL. Multiprocessing is a good solution, but not the best solution. Learn how to package your Python code for PyPI. Este tutorial explicará multiprocesamiento en Python y cómo utilizar multiproceso para comunicarse entre procesos y realizar la. The worker processes are only playing games to gather data and send it to the master process, which will train on these data and save the new network in a file. Loading Unsubscribe from Anaconda, Inc. There is a trade-off in here though. What are the –devel and –static packages and how do I use them when compiling my applications?. Multiprocessing vs Threading Python. We plan to continue to provide bug-fix releases for 3. Introduction to Multiprocessing in Python The multiprocessing package supports spawning processes using an API similar to the threading module. 並列計算のライブラリ:multiprocessing. com - Sumit Ghosh. Share CPU tensors instead. Loading Unsubscribe from Anaconda, Inc. Python has a yearly conference PyCon that attracts thousands attendees. Tokyo Meetup #4 2015年4月3日PyData. If you've mastered Python's fundamentals, you're ready to start using it to get real work done. Multi-processing. Each topic is preceded by an introduct. There are two built-in libraries for parallelism, multiprocessing and threading. Using a mulitprocessing. He also works for Read the Docs. Getting Windows System Information with Python January 27, 2010 Python , Windows Python , Windows Mike Another script I had to come up with for my employer dealt with getting various bits and pieces of information about each of our user’s physical machines. In Python, it is not technically possible to acheive true parallelism in Python due to the Global Interpretor Lock (GIL), which in Python serializes access to different threads, meaning a single thread in python can never use more than 1 CPU core (see this for more information). There are several options of screen capture in Windows. 다만, GPU를 사용하기 위해서는 해당 라이브러리에서 GPU를 효과적으로 사용하기 위해서 인터페이스 랄지 몇가지를 지원해주는 것이 필요합니다. Python support for the GPU Dataframe is provided by the PyGDF project, which we have been working on since March 2017. Here, Python provides a strong multiprocesing library. Difference between Multi programming and Multi processing -. Python is one of the most popular languages for data processing and data science in general. At first I couldn't figure out why lots of Python based applications were broken. "This is with regards to the Python training course that Vasudev conducted for Company, City, in Month, Year. The critical thing to know is to access the GPU with Python a primitive function needs to be written, compiled and bound to Python. Multiprocessing Versus Threading in Python I keep forgetting the difference between multiprocessing and threading in Python. In this video from EuroPython 2019, Pierre Glaser from INRIA presents: Parallel computing in Python: Current state and recent advances. 当需要时,程序会去进程池中获取一个进程. This week we welcome David Fischer (@djfische) as our PyDev of the Week! David is an organizer of the San Diego Python user's group. As an exercise, attempt to implement the conversion from sequential to multiprocessing. Go ahead and download hg38. Why is using a Global Interpreter Lock (GIL) a problem? What alternative approaches are available? Why hasn’t resolving this been a priority for the core development team? Why isn’t “just remove the GIL” the obvious answer? What are the key problems with fine-grained locking as an answer?. In June of 2018 I wrote a post titled The Best Way to Install TensorFlow with GPU Support on Windows 10 (Without Installing CUDA). the raw data, so that dark-current and flat-field corrections are applied by. jit and numba. This can be used to achieve some level of parallelism within a single compute node. Instructions for updating: Use tf. Pythom time method clock() returns the current processor time as a floating point number expressed in seconds on Unix. To use mathematical functions under this module, you have to import the module using import math. Python Programming tutorials from beginner to advanced on a massive variety of topics. PAGE is a cross platform tool runing on any OS which has Tcl/Tk installed. More importantly this is the wrong question. Testing / Debugging Topics Covered in this course 7. It looks like there is some shared python tensorflow state that interferes when a new python process is created (multiprocessing creates new python process whose state separation i am not to clear on). As an exercise, attempt to implement the conversion from sequential to multiprocessing. The multiprocessing. Queue) – Updates the 3D viewer process with the latest navigation map. This brilliant article, by George Seif, explains how to accelerate data-science using your GPU. Process这个最基础的类给解剖了,接下来的这篇就是整个multiprocessing. Installing Python Modules installing from the Python Package Index & other sources. Early Access puts eBooks and videos into your hands whilst they’re still being written, so you don’t have to wait to take advantage of new tech and new ideas. 04でanacondaは使わない llvm3. Keras + Tensorflow et Multiprocessing en Python J'utilise des Keras avec Tensorflow comme backend. Crash log: OpenCV in python via cv2 + multiprocessing + USB camera - crash. Python’s multiprocessing module creates multiple copies of a Python process (usually via os. It offers a subset of the Pandas API for operating on GPU dataframes, using the parallel computing power of the GPU (and the Numba JIT) for sorting, columnar math, reductions, filters, joins, and group by operations. In this tutorial, we're going to. His quantitative background inspired him to become adept at Python. 1920 rot / Plastik ss,Sehr Schöne Alte Glas Schale. 0 in Windows7. My work around is 1) to make sure that the multiprocessing task is robust enough to check if tasks are complete or not then create a new job list. I had only one post on that blog that attracted any attention. The speedup was achieved using default settings without any special tuning. some kind of graphic's objects?. Python offers two libraries - multiprocessing and threading- for the eponymous parallelization methods. Let's take a few moments to. So, what is threading within the frame of Python? Threading is making use of idle processes, to give the appearance of parallel programming. We'll import the collections and the multiprocessing module so we can use Python's parallel computing facilities and define the data structure we'll work with:. It takes a while! Is there any possibility of using multiprocessing to build the graphics and then use several calls to savefig(), i. Backport of the multiprocessing package to Python 2. They are extracted from open source Python projects. There are several approaches to accelerating Python with GPUs, but the one I am most familiar with is Numba, a just-in-time compiler for Python functions. So you can use Queue's, Pipe's, Array's etc. 0 is the newest major release of the Python language, and it contains many new features and optimizations. Similar to Cython, CLyther is a Python language extension that makes writing OpenCL code as easy as Python itself. which are in Python's multiprocessing module here. Gerüst Typ Layher 78 qm mit Durchstieg Fassadengerüst Stahlböden 3,07 m NEU,Danzig - Zoppot 100 Gulden Kasinogeld ca. Your GPU is specifically. It also describes some of the optional components that are commonly included in Python distributions. I have trained the model already and got a. I have program that generates about 100 relatively complex graphics and writes then to a pdf book. Matplotlib. Keras + Tensorflow et Multiprocessing en Python J'utilise des Keras avec Tensorflow comme backend. The following are code examples for showing how to use torch. You can see what David has been up to on his website or check out what he’s been up to on Github. Caveats: 1)!Portability: there is no shared memory under Windows. My app uses Python 2. Explore PyQt5 and PySide2 to create comprehensive GUI applications; Find out how threading and multiprocessing work; Understand how to style GUIs with PyQt. 后来,我仔细想了一下,multiprocessing应该针对的是CPU上的多进程,要想在GPU上并行可能没那么简单,也不知道可不可行,好像网上也找不到类似问题的解决思路。所以,在这里想请教一下大神们,在单GPU上开多进程让多个神经网络并. Distributing Python Modules publishing modules for installation by others. Pool can interact quite badly with other, seemingly unrelated, parts of a codebase due to Pool's reliance on fork. AU - Stephens, Philip. j'essaie de sauvegarder un modèle dans mon processus principal puis de charger/exécuter (i. map(f, myList). The term 'spawn' means the creation of a process by a parent process. 1 installation. Graphics Programming 11. Pythonの標準のライブラリには,既に並列計算用のライブラリがあります.それが”multiprocessing”というものです.また,今回はその中の”Pool”という機能を使うので,コードの最初の行でそれを使えるようにインポート. 5+ for windows. There have been discussions between the devs of the two projects to figure out how we can plug one project into the other (most likely have vispy be an optional backend of sorts), but that is still awhile down the road. Y1 - 2013/5. It's a must have for every python developer. 42, CUDA10 Drivers. 5 / llvmlite sudo apt-get install -y llvm3. Let's make the distinction … - Selection from Hands-On GPU Programming with Python and CUDA [Book]. This documentation is for an old version of IPython. I used multiprocessing to separate task into different processes to make things work in parallel. You should rather use multiprocessing in this case, which starts separate Python processes in your operating system that can run in parallel. 6 as default Python for its apps. My code also has a few steps that utilize the GPU via PyOpenCL. A multiprocessing Queue allows communication of indexes between the parent and worker processes, while the custom IndexQueue perpetually feeds data into that loop. You need to get all your bananas lined up on the CUDA side of things first, then think about the best way to get this done in Python [shameless rep whoring, I know]. The multiprocessing library of Python allows the spawning of a process through these steps: One, build the object process. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications. The price to pay: serialization of tasks, arguments, and results. Use this guide for easy steps to install CUDA. 您可以在python脚本中. VisPy does a fantastic job of offloading to GPU and it is quite straight-forward to use. Posted by czxttkl September 28, 2015 Posted in Python Leave a comment on Python multiprocessing map function with shared memory object as additional parameter Enable GPU for Theano 1. It has an instruction pointer that keeps track of where within its context it is currently running. Multiprocessing (and GPU computing) can use both mechanisms. I'm attempting use caffe and python to do real-time image classification. With wxPython software developers can create truly native user interfaces for their Python applications, that run with little or no modifications on Windows, Macs and Linux or other unix-like systems. It takes a Python module annotated with a few interface description and turns it into a native Python module with the same interface, but (hopefully) faster. Let's make the distinction … - Selection from Hands-On GPU Programming with Python and CUDA [Book]. apply multi-threading and mult-processing to Python development. Graph of multiprocessing. That post has served many individuals as guide for getting a good GPU accelerated TensorFlow work environment running on Windows 10 without needless installation complexity. You can't run CPU code on a GPU. Since Python 3. It takes a while! Is there any possibility of using multiprocessing to build the graphics. Due to the GIL (Global interpreter lock) only one instance of the python interpreter executes in a single process. Despite the fundamental difference between them, the two libraries offer a very similar. my subreddits. One node - multiprocessing is the most straightforward thing to do. Enabling multi-GPU training with Keras is as easy as a single function call — I recommend you utilize multi-GPU training whenever possible. Just $5/month. The minus of it is that it's really slow. I'm going to use the multiprocessing package in Python and import Pool. This tutorial will discuss multiprocessing in Python and how to use multiprocessing to communicate between processes and perform synchronization between processes, as well as logging. The Python Logging Cookbook has some helpful examples. I managed to get multi-processing working on ms-windows, doing some workarounds. Drawing on both the standard library and important third-party libraries, the book shows how to achieve significant speedups using high-level concurrency and compiled Python. 当需要时,程序会去进程池中获取一个进程. It handles all the complexities that come with graphics programming, and lets them focus purely on learning the very basics whilst keeping them interested. Package authors use PyPI to distribute their software. Get unlimited access to the best stories on Medium — and support writers while you're at it. SETUP CUDA PYTHON To run CUDA Python, you will need the CUDA Toolkit installed on a system with CUDA capable GPUs. 主要思想是使用不同的子进程扫描不同的行,然后将补丁发送到模型. You can save your projects at Dropbox, GitHub, GoogleDrive and OneDrive to be accessed anywhere and any time. On a modern machine, one can use Python's multiprocessing. k-Means is not actually a *clustering* algorithm; it is a *partitioning* algorithm. Python multiprocessing achieves process-level parallelism through fork(). Tensorflow has moved to the first place with triple-digit growth in contributors. Key FeaturesBuild optimized GUI applications by implementing multiprocessing and concurrencyUnderstand embedded and mobile development with PyQt and PySideLearn to create magnificent GUI applications using Pyside2 and QtQuick/QMLBook. If the underlying hardware provides more than one processor then that is multiprocessing. …Python multiprocessing provides a manager…to coordinate shared. We've round up some of the most popular recent Python-related articles for your weekend reading. As an example of Dask in action, let's take a look at an example of using dask. Alternatively if you only have one python version installed, permanently add the python directory to the path for cmd or bash. Numba is very cool in the sense that it generates optimized machine code from pure Python code using the LLVM compiler infrastructure. 由于病理图像非常大(例如:20,000 x 20,000像素),因此我必须扫描图像以获得用于预测的小补丁. IPython is a growing project, with increasingly language-agnostic components. The API is 100% compatible with the original module - it's enough to change ``import multiprocessing`` to ``import torch. It handles all the complexities that come with graphics programming, and lets them focus purely on learning the very basics whilst keeping them interested. nav_map_queue (multiprocessing. There are quite a few solutions to this problem, like threading, multiprocessing, and GPU programming. apply_async()是我们需要的,确定好了这个后,小白菜再简单聊聊并行化设计的思路。. All-new edition of the industry's best Python reference: fully updated for Python 2. But anyway, as I do not want to develop a full blown CAD program (I "simply" want to display, zoom an rotate them), there's probably somthing simpler out there for my purposes. input_intent_queue (multiprocessing. You can save your projects at Dropbox, GitHub, GoogleDrive and OneDrive to be accessed anywhere and any time. Just like multiprocessing, multithreading is a way of achieving multitasking. Let's make the distinction … - Selection from Hands-On GPU Programming with Python and CUDA [Book]. Python offers two libraries - multiprocessing and threading- for the eponymous parallelization methods. I have a system with two GPUs and want to use each GPU independently through python multiprocessing capabilities to train two different models in parallel (I don't want to use other ways of parallel. For example I have 7 GPUs in the server and created 7 training script each for a different random seeds. Each CPU contains 10 cores, each of which is dual threaded. Even with the GIL, a single Python process can saturate multiple GPUs. I have a custom DataGenerator that uses Python's Multiprocessing module to generate the training data that is fed to the Tensorflow model. which are in Python's multiprocessing module here. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: