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WHERE’S THAT LOG FILE? DEBUGGING FAILED DOCKER BUILDS You’ve got a nice new Dockerfile, and it’s time to try it out: $ docker build -t mynewimage . Sending build context to Docker daemon 3.072kB Step 1/3 : FROM python:3.8-slim-buster ---> 3d8f801fc3db Step 2/3 : COPY build.sh . ---> 541b65a7b417 Step 3/3 : RUN ./build.sh ---> Running in 9917e3865f96 Building Building some more Build failed, see /tmp/builderr024321.log for details ELEGANTLY ACTIVATING A VIRTUALENV IN A DOCKERFILE When you’re packaging your Python application in a Docker image, you’ll often use a virtualenv. For example, you might be doing a multi-stage build in order to get smaller images. Since you’re using a virtualenv, you need to activate it—but if you’re just getting started with Dockerfiles, the naive way doesn’t work. And even if you do know how to do it, the usual method is CONNECTION REFUSED? DOCKER NETWORKING AND HOW IT IMPACTS Network namespaces. You’ll notice the image above talks about a “Default network namespace”. So what’s that? Docker is a system for running containers: a way to isolate processes from each other.It builds on a number of Linux kernel features, one of which is network namespaces—a way for different processes to have different network devices, IPs, firewall rules, and so on. PROCESS LARGE DATASETS WITHOUT RUNNING OUT OF MEMORY In this article you’ll learn techniques that lose some details in return for reducing memory usage. Reducing Pandas memory usage #3: Reading in chunks. By loading and then processing a file into Pandas in chunks, you can load only part of the file into memory at any given time. Fast subsets of large datasets with Pandas and SQLite. LOADING SQL DATA INTO PANDAS WITHOUT RUNNING OUT OF MEMORY You have some data in a relational database, and you want to process it with Pandas. So you use Pandas’ handy read_sql() API to get a DataFrame—and promptly run out of memory. The problem: you’re loading all the data into memory at once. If you have enough rows in the SQL query’s results, it simply won’t fit in RAM. Pandas does have a batching option for read_sql(), which can reduce DOCKER CAN SLOW DOWN YOUR CODE AND DISTORT YOUR BENCHMARKS One of the benefits of containers over virtual machines is that you get some measure of isolation without the performance overhead or distortion of virtualization. Docker images therefore seem like a good way to get a reproducible environment for measuring CPU performance of your code. There are, however, complications. Sometimes, running under Docker can actually slow down your code and DOCKER VS. SINGULARITY FOR DATA PROCESSING: UIDS AND When you’re processing data, reading in files and writing out the result, containers are a great way to ensure reproducible runs. You package up all the binaries and libraries necessary to process your data, and each run uses the same files. But while Docker is the most well-known container system, it’s not necessarily the easiest to use for data processing. Filesystem access, including CLINGING TO MEMORY: HOW PYTHON FUNCTION CALLS CAN INCREASE In prose form: We do obj = object(), which means there is a local variable obj pointing to the dictionary we created. That variable, created by running the function, increments the object’s reference counter. Next we pass that object to g.There is now a local variable called o that is an additional reference to the same dictionary, so the total reference count is 2. BUILD SECRETS IN DOCKER AND COMPOSE, THE SECURE WAY The naive way to pass in secrets is using Docker build args, since they’re supported everywhere, including Docker Compose. Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article. To ensure you’re following all the best practices you need to have a THE BEST DOCKER BASE IMAGE FOR YOUR PYTHON APPLICATION The official Docker Python image in its slim variant—e.g. python:3.9-slim-buster—is a good base image for most use cases. it’s 41MB to download, 114MB when uncompressed to disk, it gives you the latest Python releases, it’s easy to use and it’s got all the benefits of Debian Buster. If you care about performance, you’llwant to use
WHERE’S THAT LOG FILE? DEBUGGING FAILED DOCKER BUILDS You’ve got a nice new Dockerfile, and it’s time to try it out: $ docker build -t mynewimage . Sending build context to Docker daemon 3.072kB Step 1/3 : FROM python:3.8-slim-buster ---> 3d8f801fc3db Step 2/3 : COPY build.sh . ---> 541b65a7b417 Step 3/3 : RUN ./build.sh ---> Running in 9917e3865f96 Building Building some more Build failed, see /tmp/builderr024321.log for details ELEGANTLY ACTIVATING A VIRTUALENV IN A DOCKERFILE When you’re packaging your Python application in a Docker image, you’ll often use a virtualenv. For example, you might be doing a multi-stage build in order to get smaller images. Since you’re using a virtualenv, you need to activate it—but if you’re just getting started with Dockerfiles, the naive way doesn’t work. And even if you do know how to do it, the usual method is CONNECTION REFUSED? DOCKER NETWORKING AND HOW IT IMPACTS Network namespaces. You’ll notice the image above talks about a “Default network namespace”. So what’s that? Docker is a system for running containers: a way to isolate processes from each other.It builds on a number of Linux kernel features, one of which is network namespaces—a way for different processes to have different network devices, IPs, firewall rules, and so on. ALL PYTHONS ARE SLOW, BUT SOME ARE FASTER THAN OTHERS All Pythons are slow, but some are faster than others. Python is not the fastest language around, so any performance boost helps, especially if you’re running at scale. It turns out that depending where you install Python from, its performance can vary quite a bit: choosing the wrong version of Python can cut your speed by 10-20%. CLINGING TO MEMORY: HOW PYTHON FUNCTION CALLS CAN INCREASE In prose form: We do obj = object(), which means there is a local variable obj pointing to the dictionary we created. That variable, created by running the function, increments the object’s reference counter. Next we pass that object to g.There is now a local variable called o that is an additional reference to the same dictionary, so the total reference count is 2. A DEEP DIVE INTO THE OFFICIAL DOCKER IMAGE FOR PYTHON The official Python image for Docker is quite popular, and in fact I recommend one of its variations as a base image. But many people don’t quite understand what it does, which can lead to confusion and brokenness. In this post I will therefore go over how it’s constructed, why it’s useful, how to use it correctly, as well as its limitations. In particular, I’ll be reading through the DON’T LEAK YOUR DOCKER IMAGE’S BUILD SECRETS Building a Docker image often involves installing packages or downloading code, and if you’re installing private code you often need to gain access with a secret: a password, a private key, a token. You don’t want those secrets to end up in the final image, though; if it’s in the image, anyone with access to the image can extract it. Unlike docker run, which supports environment SECURITY SCANNERS FOR PYTHON AND DOCKER: FROM CODE TO You don’t want to deploy insecure code to production—but it’s easy for mistakes and vulnerabilities to slip through. So you want some way to catch security issues automatically, without having to think about it. This is where security scanners come in. They won’t solve all your probems—you should still be using services that proactively point out insecure dependencies, for example. MULTI-STAGE BUILDS #2: PYTHON SPECIFICS—VIRTUALENV, –USER If we do docker build on the above, the final image is the last stage, the runtime-image.. As a result the image is only 88.9MB, basically the same size as the ubuntu:18.04 it builds on (and with which it shares most of its layers): we have an image that gets the compiled artifact without having to include a compiler in its layers.. The problem with Python and multi-stage builds WHY YOUR MULTIPROCESSING POOL IS STUCK (IT’S FULL OF SHARKS!) The real solution: stop plain fork () ing. In Python 3 the multiprocessing library added new ways of starting subprocesses. One of these does a fork () followed by an execve () of a completely new Python process. That solves our problem, because module state isn’t inherited by child processes: it starts from scratch. FROM CHUNKING TO PARALLELISM: FASTER PANDAS WITH DASK The chunked version uses the least memory, but wallclock time isn’t much better. The Dask version uses far less memory than the naive version, and finishes fastest (assuming you have CPUs to spare). Dask isn’t a panacea, of course: Parallelism has overhead, it WHERE’S YOUR BOTTLENECK? CPU TIME VS WALLCLOCK TIME A faster CPU will likely make the program run faster. CPU/second < 1: The lower the number, the more of its time the process spent waiting (for the network, or the harddrive, or locks, or other processes to release the CPU, or just sleeping). E.g. if CPU/second is 0.75, 25% of the time was spent waiting. If this is a multi-threaded process and DECOUPLING DATABASE MIGRATIONS FROM SERVER STARTUP: WHY Decoupling database migrations from server startup: why and how. If you’re using a schema management tool like Django ORM or Alembic, you need to run the migration at some point. And it’s tempting to run the migration as part of application startup—when you’re using Docker, for instance, you’ll have an entrypoint that will first run PROCESS LARGE DATASETS WITHOUT RUNNING OUT OF MEMORY In this article you’ll learn techniques that lose some details in return for reducing memory usage. Reducing Pandas memory usage #3: Reading in chunks. By loading and then processing a file into Pandas in chunks, you can load only part of the file into memory at any given time. Fast subsets of large datasets with Pandas and SQLite. LOADING SQL DATA INTO PANDAS WITHOUT RUNNING OUT OF MEMORY You have some data in a relational database, and you want to process it with Pandas. So you use Pandas’ handy read_sql() API to get a DataFrame—and promptly run out of memory. The problem: you’re loading all the data into memory at once. If you have enough rows in the SQL query’s results, it simply won’t fit in RAM. Pandas does have a batching option for read_sql(), which can reduce DOCKER CAN SLOW DOWN YOUR CODE AND DISTORT YOUR BENCHMARKS One of the benefits of containers over virtual machines is that you get some measure of isolation without the performance overhead or distortion of virtualization. Docker images therefore seem like a good way to get a reproducible environment for measuring CPU performance of your code. There are, however, complications. Sometimes, running under Docker can actually slow down your code and DOCKER VS. SINGULARITY FOR DATA PROCESSING: UIDS AND When you’re processing data, reading in files and writing out the result, containers are a great way to ensure reproducible runs. You package up all the binaries and libraries necessary to process your data, and each run uses the same files. But while Docker is the most well-known container system, it’s not necessarily the easiest to use for data processing. Filesystem access, including CLINGING TO MEMORY: HOW PYTHON FUNCTION CALLS CAN INCREASE In prose form: We do obj = object(), which means there is a local variable obj pointing to the dictionary we created. That variable, created by running the function, increments the object’s reference counter. Next we pass that object to g.There is now a local variable called o that is an additional reference to the same dictionary, so the total reference count is 2. BUILD SECRETS IN DOCKER AND COMPOSE, THE SECURE WAY The naive way to pass in secrets is using Docker build args, since they’re supported everywhere, including Docker Compose. Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article. To ensure you’re following all the best practices you need to have a MULTI-STAGE BUILDS #2: PYTHON SPECIFICS—VIRTUALENV, –USER If we do docker build on the above, the final image is the last stage, the runtime-image.. As a result the image is only 88.9MB, basically the same size as the ubuntu:18.04 it builds on (and with which it shares most of its layers): we have an image that gets the compiled artifact without having to include a compiler in its layers.. The problem with Python and multi-stage builds USING ALPINE CAN MAKE PYTHON DOCKER BUILDS 50× SLOWER Using Alpine can make Python Docker builds 50× slower. by Itamar Turner-Trauring. Last updated 06 Apr 2021, originally created 29 Jan 2020. When you’re choosing a base image for your Docker image, Alpine Linux is often recommended. Using Alpine, you’re told, will make your images smaller and speed up your builds. WHERE’S THAT LOG FILE? DEBUGGING FAILED DOCKER BUILDS You’ve got a nice new Dockerfile, and it’s time to try it out: $ docker build -t mynewimage . Sending build context to Docker daemon 3.072kB Step 1/3 : FROM python:3.8-slim-buster ---> 3d8f801fc3db Step 2/3 : COPY build.sh . ---> 541b65a7b417 Step 3/3 : RUN ./build.sh ---> Running in 9917e3865f96 Building Building some more Build failed, see /tmp/builderr024321.log for details CONNECTION REFUSED? DOCKER NETWORKING AND HOW IT IMPACTS Network namespaces. You’ll notice the image above talks about a “Default network namespace”. So what’s that? Docker is a system for running containers: a way to isolate processes from each other.It builds on a number of Linux kernel features, one of which is network namespaces—a way for different processes to have different network devices, IPs, firewall rules, and so on. PROCESS LARGE DATASETS WITHOUT RUNNING OUT OF MEMORY In this article you’ll learn techniques that lose some details in return for reducing memory usage. Reducing Pandas memory usage #3: Reading in chunks. By loading and then processing a file into Pandas in chunks, you can load only part of the file into memory at any given time. Fast subsets of large datasets with Pandas and SQLite. LOADING SQL DATA INTO PANDAS WITHOUT RUNNING OUT OF MEMORY You have some data in a relational database, and you want to process it with Pandas. So you use Pandas’ handy read_sql() API to get a DataFrame—and promptly run out of memory. The problem: you’re loading all the data into memory at once. If you have enough rows in the SQL query’s results, it simply won’t fit in RAM. Pandas does have a batching option for read_sql(), which can reduce DOCKER CAN SLOW DOWN YOUR CODE AND DISTORT YOUR BENCHMARKS One of the benefits of containers over virtual machines is that you get some measure of isolation without the performance overhead or distortion of virtualization. Docker images therefore seem like a good way to get a reproducible environment for measuring CPU performance of your code. There are, however, complications. Sometimes, running under Docker can actually slow down your code and DOCKER VS. SINGULARITY FOR DATA PROCESSING: UIDS AND When you’re processing data, reading in files and writing out the result, containers are a great way to ensure reproducible runs. You package up all the binaries and libraries necessary to process your data, and each run uses the same files. But while Docker is the most well-known container system, it’s not necessarily the easiest to use for data processing. Filesystem access, including CLINGING TO MEMORY: HOW PYTHON FUNCTION CALLS CAN INCREASE In prose form: We do obj = object(), which means there is a local variable obj pointing to the dictionary we created. That variable, created by running the function, increments the object’s reference counter. Next we pass that object to g.There is now a local variable called o that is an additional reference to the same dictionary, so the total reference count is 2. BUILD SECRETS IN DOCKER AND COMPOSE, THE SECURE WAY The naive way to pass in secrets is using Docker build args, since they’re supported everywhere, including Docker Compose. Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article. To ensure you’re following all the best practices you need to have a MULTI-STAGE BUILDS #2: PYTHON SPECIFICS—VIRTUALENV, –USER If we do docker build on the above, the final image is the last stage, the runtime-image.. As a result the image is only 88.9MB, basically the same size as the ubuntu:18.04 it builds on (and with which it shares most of its layers): we have an image that gets the compiled artifact without having to include a compiler in its layers.. The problem with Python and multi-stage builds USING ALPINE CAN MAKE PYTHON DOCKER BUILDS 50× SLOWER Using Alpine can make Python Docker builds 50× slower. by Itamar Turner-Trauring. Last updated 06 Apr 2021, originally created 29 Jan 2020. When you’re choosing a base image for your Docker image, Alpine Linux is often recommended. Using Alpine, you’re told, will make your images smaller and speed up your builds. WHERE’S THAT LOG FILE? DEBUGGING FAILED DOCKER BUILDS You’ve got a nice new Dockerfile, and it’s time to try it out: $ docker build -t mynewimage . Sending build context to Docker daemon 3.072kB Step 1/3 : FROM python:3.8-slim-buster ---> 3d8f801fc3db Step 2/3 : COPY build.sh . ---> 541b65a7b417 Step 3/3 : RUN ./build.sh ---> Running in 9917e3865f96 Building Building some more Build failed, see /tmp/builderr024321.log for details CONNECTION REFUSED? DOCKER NETWORKING AND HOW IT IMPACTS Network namespaces. You’ll notice the image above talks about a “Default network namespace”. So what’s that? Docker is a system for running containers: a way to isolate processes from each other.It builds on a number of Linux kernel features, one of which is network namespaces—a way for different processes to have different network devices, IPs, firewall rules, and so on. ALL PYTHONS ARE SLOW, BUT SOME ARE FASTER THAN OTHERS All Pythons are slow, but some are faster than others. Python is not the fastest language around, so any performance boost helps, especially if you’re running at scale. It turns out that depending where you install Python from, its performance can vary quite a bit: choosing the wrong version of Python can cut your speed by 10-20%. DOCKER BUILDKIT: FASTER BUILDS, NEW FEATURES, AND NOW IT’S Building Docker images can be slow, and Docker’s build system is also missing some critical security features, in particular the ability to use build secrets without leaking them. So over the past few years the Docker developers have been working on a new backend for building images, BuildKit. With the release of Docker 20.10 in late 2020, BuildKit is finally marked as stable–and you don A DEEP DIVE INTO THE OFFICIAL DOCKER IMAGE FOR PYTHON The official Python image for Docker is quite popular, and in fact I recommend one of its variations as a base image. But many people don’t quite understand what it does, which can lead to confusion and brokenness. In this post I will therefore go over how it’s constructed, why it’s useful, how to use it correctly, as well as its limitations. In particular, I’ll be reading through the DON’T LEAK YOUR DOCKER IMAGE’S BUILD SECRETS Building a Docker image often involves installing packages or downloading code, and if you’re installing private code you often need to gain access with a secret: a password, a private key, a token. You don’t want those secrets to end up in the final image, though; if it’s in the image, anyone with access to the image can extract it. Unlike docker run, which supports environment MULTI-STAGE BUILDS #2: PYTHON SPECIFICS—VIRTUALENV, –USER If we do docker build on the above, the final image is the last stage, the runtime-image.. As a result the image is only 88.9MB, basically the same size as the ubuntu:18.04 it builds on (and with which it shares most of its layers): we have an image that gets the compiled artifact without having to include a compiler in its layers.. The problem with Python and multi-stage builds SECURITY SCANNERS FOR PYTHON AND DOCKER: FROM CODE TO You don’t want to deploy insecure code to production—but it’s easy for mistakes and vulnerabilities to slip through. So you want some way to catch security issues automatically, without having to think about it. This is where security scanners come in. They won’t solve all your probems—you should still be using services that proactively point out insecure dependencies, for example. FROM CHUNKING TO PARALLELISM: FASTER PANDAS WITH DASK The chunked version uses the least memory, but wallclock time isn’t much better. The Dask version uses far less memory than the naive version, and finishes fastest (assuming you have CPUs to spare). Dask isn’t a panacea, of course: Parallelism has overhead, it ELEGANTLY ACTIVATING A VIRTUALENV IN A DOCKERFILE When you’re packaging your Python application in a Docker image, you’ll often use a virtualenv. For example, you might be doing a multi-stage build in order to get smaller images. Since you’re using a virtualenv, you need to activate it—but if you’re just getting started with Dockerfiles, the naive way doesn’t work. And even if you do know how to do it, the usual method is WHY YOUR MULTIPROCESSING POOL IS STUCK (IT’S FULL OF SHARKS!) You’re using multiprocessing to run some code across multiple processes, and it just—sits there. It’s stuck. You check CPU usage—nothing happening, it’s not doing any work. What’s going on? In many cases you can fix this with a single line of code—skip to the end to try it out—but first, it’s time for a deep-dive into Python brokenness and the pain that is POSIX system REDUCING PANDAS MEMORY USAGE #3: READING IN CHUNKS As an alternative to reading everything into memory, Pandas allows you to read data in chunks. In the case of CSV, we can load only some of the lines into memory at any given time. In particular, if we use the chunksize argument to pandas.read_csv, we get back an iterator over DataFrame s, rather than one single DataFrame . PYTHON⇒SPEED · Docker Packaging · Data Science · Products · Training LEARN TOOLS AND TECHNIQUES TO HELP YOU SHIP BETTER PYTHON SOFTWARE,FASTER
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