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72

My Own AI Server Cluster

from Cognitive Computations [alt+shift+b] in programming

When a16z generously sponsored Dolphin, I had some compute budget, and because the original dolphin-13b was a flop, I had some time to go back to the drawing board. When I was ready to train the next iteration, I reconsidered whether to rent or buy the compute for the build. I ultimately decided to buy, because I have the skill and interest, I'm good at finding deals, and owning a cluster would give me the ability to continue executing on future projects beyond Dolphin, not to mention the satisfaction of building the AI end-to-end, like a baker baking bread from scratch. Artisan AI. What to build? I am building 4 servers of 8x AMD Instinct MI100 and 4 servers of 8x NVIDIA GeForce RTX 4090. But for now, I'm just going to get 2 servers up and running at a time. Later, after I've built out all of the servers and tested them, then I will get the full 8 server setup running. (that'll require extra electrical work) I went with the MI100s because I got a killer deal with Rhino Technology (who have an excellent sales and technical team, I highly recommend) on some refurbished Gigabyte G482-Z53 servers that came preinstalled each with 4x MI100s. As a scrappy guy building in my garage, I gotta roll with the deals that I can find. And the servers support 8x PCI-e gen4 x16. Exactly what I needed. This capability is pretty hard to find, by the way. Usually, the PCI slots are bifurcated and don't get all 16 lanes. And for training AI models for bandwidth I really need each card to get all 16 lanes. The servers also came preinstalled with dual AMD EPYC 7742 64-core CPUs (wow!) and 256GB RAM. Which is plenty to start with. I had a very good start to my cluster with these servers. So I'm starting with the easy ones, the MI100s. They are easy because they fit in the server, unlike the 4090s, which are larger and won't fit in the chassis. My...
6th Nov 2023

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From Zero to Fineturning with Axolotl on ROCm

Gratitude to https://tensorwave.com/ for giving me access to their excellent servers! Few have tried this and fewer have succeeded. I've been marginally successful after a significant amount of effort, so it deserves a blog post. Know that you are in for rough waters. And even when you arrive - There are lots of optimizations tailored for nVidia GPUs so, even though the hardware may be just as strong spec-wise, in my experience so far, it still may take 2-3 times as long to train on equivalient AMD hardware. (though if you are a super hacker maybe you can fix it!) Right now I'm using Axolotl. Though I am probably going to give LlamaFactory a solid try in the near future. There's also LitGpt and TRL. But I kind of rely on the dataset features and especially the sample packing of Axolotl. But more and more LlamaFactory is interesting me, it supports new features really fast. (like GaLore is the new hotness at the moment). This blog post will be about getting Axolotl up and running in AMD, and I may do one about LlamaFactory if there is demand. I am using Ubuntu 22.04 LTS, and you should too. (unless this blog post is really old by the time you read it). Otherwise you can use this post as a general guide. Here are all the environment variables I ended up setting in my .bashrc and I'm not exactly sure which ones are needed. You better set them all just in case. export GPU_ARCHS="gfx90a" # mi210 - use the right code for your GPUexport ROCM_TARGET="gfx90a"export HIP_PATH="/opt/rocm-6.0.0"export ROCM_PATH="/opt/rocm-6.0.0"export ROCM_HOME="/opt/rocm-6.0.0"export HIP_PLATFORM=amdexport DS_BUILD_CPU_ADAM=1 export TORCH_HIP_ARCH_LIST="gfx90a" Part 1: Driver, ROCm, HIP Clean everything out. There shouldn't be any trace of nvidia, cuda, amd, hip, rocm, anything like that. This is not necessarily a simple task, and of course it totally depends on the current state of your system. and I had to use like 4 of my daily Claude Opus questions to accomplish this. (sad face) By the way Anthropic Claude Opus is the new king of interactive troubleshooting. By far. Bravo. Don't nerf it pretty please! Here are some things I had to do, that might help you: sudo apt autoremove rocm-core sudo apt remove amdgpu-dkms sudo dpkg --remove --force-all amdgpu-dkms sudo apt purge amdgpu-dkms sudo apt remove --purge nvidia* sudo apt remove --purge cuda* sudo apt remove --purge rocm-* hip-* sudo apt remove --purge amdgpu-* xserver-xorg-video-amdgpu sudo apt clean sudo reboot sudo dpkg --remove amdgpu-install sudo apt remove --purge amdgpu-* xserver-xorg-video-amdgpu sudo apt autoremove sudo apt clean rm ~/amdgpu-install_*.deb sudo reboot sudo rm /etc/apt/sources.list.d/amdgpu.list sudo rm /etc/apt/sources.list.d/rocm.list sudo rm /etc/apt/sources.list.d/cuda.list sudo apt-key del A4B469963BF863CC sudo apt update sudo apt remove --purge nvidia-* cuda-* rocm-* hip-* amdgpu-* sudo apt autoremove sudo apt clean sudo rm -rf /etc/OpenCL /etc/OpenCL.conf /etc/amd /etc/rocm.d /usr/lib/x86_64-linux-gnu/amdgpu /usr/lib/x86_64-linux-gnu/rocm /opt/rocm-* /opt/amdgpu-pro-* /usr/lib/x86_64-linux-gnu/amdvlk sudo reboot I love Linux (smile with tear) Now finally do like sudo apt-get updatesudo apt-get upgrade and sudo apt-get dist-upgrade and make sure there's no errors or warnings! You should be good to begin your journey. Install AMD drivers, ROCm, HIP wgethttps://repo.radeon.com/amdgpu-install/23.40.2/ubuntu/jammy/amdgpu-install_6.0.60002-1_all.deb (at time of this writing). But you should double check here. And the install instructions here. sudo apt-get install ./amdgpu-install_6.0.60002-1_all.deb sudo apt-get update sudo amdgpu-install -y --accept-eula --opencl=rocr --vulkan=amdvlk --usecase=workstation,rocm,rocmdev,rocmdevtools,lrt,opencl,openclsdk,hip,hiplibsdk,mllib,mlsdk If you get error messages (I did) try to fix them. I had to do this: sudo dpkg --remove --force-all libvdpau1 sudo apt clean sudo apt update sudo apt --fix-broken install sudo apt upgrade and then, again, I had to run sudo amdgpu-install -y --accept-eula --opencl=rocr --vulkan=amdvlk --usecase=workstation,rocm,rocmdev,rocmdevtools,lrt,opencl,openclsdk,hip,hiplibsdk,mllib,mlsdk Check Installation rocm-smirocminfo/opt/rocm/bin/hipconfig --full I hope that worked for you - if not, I suggest asking Claude Opus about the error messages to help you figure it out. If that doesn't work, reach out to the community. Part 2: Pytorch, BitsAndBytes, Flash Attention, DeepSpeed, Axolotl Conda mkdir -p ~/miniconda3wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.shbash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3rm -rf ~/miniconda3/miniconda.sh~/miniconda3/bin/conda init bash Exit your shell and enter it again. conda create -n axolotl python=3.12conda activate axolotl Pytorch I tried the official install command from pytorch's website, and it didn't work for me. Here is what did work: pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/rocm6.0python -c "import torch; print(torch.version.hip)" This tests both Torch, and Torch's ability to interface with HIP. If it worked, it will print HIP version. Otherwise, it will print None. BitsAndBytes BitsAndBytes is by Tim Dettmers, an absolute hero among men. It lets us finetune in 4-bits. It gives us qLoRA. It brings AI to the masses. There is a fork of BitsAndBytes that supports ROCm. This is provided not by Tim Dettmers, and not by AMD, but by a vigilante superhero, Arlo-Phoenix. In appreciation, here is a portrait ChatGPT made for Arlo-Phoenix, vigilante superhero. I hope you like it, if you see this Arlo-Phoenix. <3 git clone https://github.com/arlo-phoenix/bitsandbytes-rocm-5.6cd bitsandbytes-rocm-5.6git checkout rocmROCM_TARGET=gfx90a make hip # use the ROCM_TARGET for your GPUpip install . Flash Attention This fork is maintained by AMD git clone --recursive https://github.com/ROCmSoftwarePlatform/flash-attention.gitcd flash-attentionexport GPU_ARCHS="gfx90a" # use the GPU_ARCHS for your GPUpip install . DeepSpeed Microsoft included AMD support in DeepSpeed proper, but there's still some undocumented fussiness to get it working, and there is a bug I found with DeepSpeed, I had to modify it to get it to work. git clone https://github.com/microsoft/DeepSpeedcd DeepSpeedgit checkout v0.14.0 # but check the tags for newer version Now, you gotta modify this file: vim op_builder/builder.py Replace the function assert_no_cuda_mismatch with this: (unless they fixed it yet) def assert_no_cuda_mismatch(name=""): cuda_available = torch.cuda.is_available() if not cuda_available and not torch.version.hip: # Print a warning message indicating no CUDA or ROCm support print(f"Warning: {name} requires CUDA or ROCm support, but neither is available.") return False else: # Check CUDA version if available if cuda_available: cuda_major, cuda_minor = installed_cuda_version(name) sys_cuda_version = f'{cuda_major}.{cuda_minor}' torch_cuda_version = torch.version.cuda if torch_cuda_version is not None: torch_cuda_version = ".".join(torch_cuda_version.split('.')[:2]) if sys_cuda_version != torch_cuda_version: if (cuda_major in cuda_minor_mismatch_ok and sys_cuda_version in cuda_minor_mismatch_ok[cuda_major] and torch_cuda_version in cuda_minor_mismatch_ok[cuda_major]): print(f"Installed CUDA version {sys_cuda_version} does not match the " f"version torch was compiled with {torch.version.cuda} " "but since the APIs are compatible, accepting this combination") return True elif os.getenv("DS_SKIP_CUDA_CHECK", "0") == "1": print( f"{WARNING} DeepSpeed Op Builder: Installed CUDA version {sys_cuda_version} does not match the " f"version torch was compiled with {torch.version.cuda}." "Detected `DS_SKIP_CUDA_CHECK=1`: Allowing this combination of CUDA, but it may result in unexpected behavior." ) return True raise CUDAMismatchException( f">- DeepSpeed Op Builder: Installed CUDA version {sys_cuda_version} does not match the " f"version torch was compiled with {torch.version.cuda}, unable to compile " "cuda/cpp extensions without a matching cuda version.") else: print(f"Warning: {name} requires CUDA support, but torch.version.cuda is None.") return False return True pip install -r requirements/requirements.txtHIP_PLATFORM="amd" DS_BUILD_CPU_ADAM=1 TORCH_HIP_ARCH_LIST="gfx90a" python setup.py install Axolotl Installing Axolotl might overwrite BitsAndBytes, DeepSpeed, and PyTorch. Be prepared for things to break, they do often. Your choice is either modify the setup.py and requirements.txt (if you are confident to change those things) or pay attention to what libraries get deleted and reinstalled, and just delete them again and reinstall the correct ROCm version that you installed earlier. If Axolotl complains about incorrect versions - just ignore it, you know better than Axolotl. Right now, Axolotl's Flash Attention implementation has a hard dependency on Xformers for its SwiGLU implementation, and Xformers doesn't work with ROCm, you can't even install it. So, we are gonna have to hack axolotl to remove that dependency. https://github.com/OpenAccess-AI-Collective/axolotl.gitcd axolotl from requirements.txt remove xformers==0.0.22 from setup.py make this change (remove any mention of xformers) $ git diff setup.pydiff --git a/setup.py b/setup.pyindex 40dd0a6..235f1d0 100644--- a/setup.py+++ b/setup.py@@ -30,7 +30,7 @@ def parse_requirements(): try: if "Darwin" in platform.system():- _install_requires.pop(_install_requires.index("xformers==0.0.22"))+ print("hi") else: torch_version = version("torch") _install_requires.append(f"torch=={torch_version}")@@ -45,9 +45,6 @@ def parse_requirements(): else: raise ValueError("Invalid version format")- if (major, minor) >= (2, 1):- _install_requires.pop(_install_requires.index("xformers==0.0.22"))- _install_requires.append("xformers>=0.0.23") except PackageNotFoundError: pass And then in src/axolotl/monkeypatch/llama_attn_hijack_flash.py make this change: --- a/src/axolotl/monkeypatch/llama_attn_hijack_flash.py+++ b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py@@ -22,7 +22,9 @@ from transformers.models.llama.modeling_llama import ( apply_rotary_pos_emb, repeat_kv, )-from xformers.ops import SwiGLU+class SwiGLU:+ def __init__():+ print("hi") from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name@@ -45,15 +47,7 @@ LOG = logging.getLogger("axolotl") def is_xformers_swiglu_available() -> bool:- from xformers.ops.common import get_xformers_operator-- try:- get_xformers_operator("swiglu_packedw")()- return True- except RuntimeError as exc:- if "No such operator xformers::swiglu_packedw " in str(exc):- return False- return True+ return False Now you can install axolotl pip install -e .accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml Welcome to finetuning on ROCm!

11th Mar 2024 • 88 votes
Running Dolphin Locally with Ollama

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3rd Dec 2023 • 214 votes

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TokyoDev Welcomes Its Head of Operations

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10 hours ago • 1 votes
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Fair cop. “Crawled, currently not indexed” (39): mostly translated posts that Google reached through redirecting URLs, plus thin pages like /login. “Discovered, currently not indexed” (12): Google found these and decided they weren’t worth a visit. With no sitemap entry and no hreflang, I hadn’t given it a reason to. On-page fixes Titles and descriptions: Each blog post got its own seoTitle and description, in all 12 languages. The meta description now comes from the post’s excerpt, which is what that field was for all along. The About pages take their title and description from their own localized Markdown. They had been sharing an English title and a generic description. One <h1> per page: A # heading inside a post’s Markdown now renders as an <h2>, so the post title is the only <h1>. The About page opts back in with a keepH1 prop. Its Markdown heading is the page title. This fix arrived in two parts. The first part left the About page with zero <h1>s. I noticed, eventually. Structured data and social tags: JSON-LD for the home page (the app), blog posts (articles) and the embedded videos. Open Graph and X/Twitter tags on every public page, so shared links get a proper card. A real 404 for deleted gallery images: The AI gallery shows a friendly “AI Artwork Not Found” page when an image has been deleted. It used to return 200 OK, which Google calls a “soft 404” and dislikes. It now returns a 404 with noindex, and a 503 if the Firestore lookup itself fails, so a database hiccup tells crawlers “come back later” rather than “this is gone”. Part 2: Making the homepage fast The homepage is the app. It works offline, so every dialog (save, stickers, colouring pages, “get an adult”, “no wifi”) is server-rendered into the HTML up front. That’s a deliberate trade-off, and I kept it because I want new users to be able to immediately be able to start drawing as soon as they launch the app. What I hadn’t thought about was what it meant for the network. What the homepage downloaded before the app could start I measured the live site with a Lighthouse-style mobile profile: slow 4G (1.6 Mbps, 150 ms latency), 4× CPU slowdown, empty cache. First load: 4.5 MB over 198 requests. The homepage HTML alone was 358 KB, with about 77 <img> tags in hidden dialogs. The browser dutifully fetched every one of those images. The script that starts the app (main.min.js, 191 KB) sat at the very end of the document, so the browser found it last and gave it whatever bandwidth was left. The app script finished arriving at about 14 seconds. The CPU was not the problem. Every long task was under 300 ms. This was purely about bandwidth and request order. I had built a very polite queue and put my own app at the back of it. Watch the queue The waterfall is a simplified illustration. The milestone times (first paint, LCP, load) are measured: slow 4G + 4× CPU, local production build, median of 3 runs. My Largest Contentful Paint was the “Contact support” link The canvas doesn’t count toward LCP, and the toolbar buttons are small. So on a slow load the largest thing painted was… the loading dialog. The loading dialog fades in its “Taking a while? Contact support” text after 5 to 7.5 seconds. On a slow connection, that text became the page’s LCP element, at 15.9 s. I was measuring my page speed by how long it took to suggest the user complain about my page speed. There’s a lesson in there somewhere. The fix wasn’t to change the dialog. It was to make the app start sooner, so it paints before that text ever appears. What I changed Preload the app script. I added <link rel="preload" as="script"> in <head> for main.min.js and errorHandler.min.js. The <script> tags stay where they were, so execution order doesn’t change. The browser just starts fetching them before any image. This one line did more than anything else on this list. It’s a little humbling. Turn the three dialog GIFs into MP4s. Three animated GIFs: an arrow pushing pictures into a tablet (927 KB), a dancing rainbow pencil (650 KB) and a paper plane (305 KB). That’s 1.9 MB of decoration in dialogs most visitors never open on their first visit. I re-encoded them with ffmpeg: H.264 High profile, -preset veryslow -crf 20, yuv420p, +faststart, constant frame rate taken from each GIF’s frame delay. The commands are committed as scripts/gif-to-mp4.sh. The result is 130 KB in total, with SSIM between 0.988 and 0.993 against the originals. The pencil needed tpad=stop_mode=clone:stop_duration=0.2, because ffmpeg’s GIF reader drops the final 250 ms hold and the dance lost its big finish. The new <video autoplay loop muted playsinline> tags render at exactly the same size and class as the old <img> tags. Alt text moved to aria-label. Locked colouring thumbnails were full-size PNGs. In the colouring page picker, locked pages used the full-size colouring PNG as their background image, inlined into the HTML. That came to about 1.3 MB on every first visit, to draw thumbnails in a dialog that was closed. They now use the small image, set when the picker opens, like the unlocked ones always did. Sticker thumbnails wait their turn. I tried loading="lazy" first. It didn’t help: Chrome fetched the images before the stylesheet that hides the dialogs had arrived, so as far as it knew they were visible. Sticker thumbnails and the media library’s YouTube thumbnail now use data-deferred-src. They load 3 seconds after the load event, or as soon as their dialog opens, whichever comes first. The service worker preload starts after load. The offline preload queue (about 2 MB of colouring pages and stickers) used to start while the page was still loading and compete with it. Now it starts after the load event. Same files, same offline result, no fighting for bandwidth. Analytics is async. Nothing waited for it, so nothing needed to block on it. Cache headers. /images/*: max-age=86400, stale-while-revalidate=604800. It had been max-age=0, so every image was re-checked on every visit. Versioned /js, /css and /addToHomescreen files are immutable for a year in production. Offline still has to work Offline support is a core feature, so “just don’t download it” was never an option. Some of the changes above put it at risk, and a couple of things turned up along the way: Safari plays video from byte ranges. Safari requests video with Range: bytes=.... Answer that with a full cached response and the video won’t play. The service worker now answers Range requests from its cache with a proper 206 Partial Content slice (lib/util/rangeResponse.ts, with unit tests). First-visit gap: on a first visit the service worker isn’t controlling the page yet, so dialog images only reached the offline cache on the second online visit. The page’s own dialog images and videos now go to the front of the offline preload queue on the first visit. I nearly downloaded my whole site, 12 times over. The service worker cached every <link href> it found in the page. The new SEO work had just added canonical and hreflang <link>s pointing at all 12 localized homepages. So every new visitor would have downloaded about 12 extra homepages, in languages they don’t speak, “for offline use”. I caught it before production. The service worker now skips rel=canonical and rel=alternate. Safari stopped the service worker mid-download. The old preload queue ran on timers after the request had been answered. Safari considers an idle service worker fair game, so it stopped it after the first few images. The page now sends images in batches of 10, and the service worker caches each batch before it replies. A page lock-up I’d never noticed. resizeDialog added a new “image loaded” listener every time the window resized. With lots of images loading at once, the listeners multiplied until the page locked up. It now attaches one listener per image, which, in hindsight, is how many it needed. Part 3: The blog Blog index cards used the full 1920 px screenshots as covers, up to 500 KB each, in a box about 200 px wide. Each card was a little billboard, shrunk down by the browser on every visit. Covers now go through next/image with sizes that match the actual card widths, so the browser gets a resized WebP. The first few cards get priority. The rest load lazily. The first sizes hint was too small at some widths and made covers blurry. It now matches the real card widths, up to about 630 px between 769 px and 1028 px wide. Post pages: The cover image gets fetchpriority="high", and images further down the post load lazily. Five images were recompressed in place, keeping the same filenames and dimensions. The PNGs came out about 4× smaller and the JPEGs about 40% smaller. I deleted a 1.1 MB cover.png that nothing referenced. All 12 translations of that post used cover.jpg. The PNG had just been sitting in public/ taking up space. Results on the slow 4G profile: /blog LCP went from 13.3 s to 1.8 s. A typical post went from 3.9 s to 1.7 s. The gallery didn’t get faster, on purpose. It got a 5-minute edge cache and a cached Firestore image count, which fixed its slow server responses. I decided not to change its 20 full-size PNG thumbnails, so its lab LCP barely moved (8.4 s to 8.6 s). That’s a choice, and the chart owns it. Part 4: Lighthouse homework After the speed work went live I ran Lighthouse on the production homepage (mobile) and cleaned up what it flagged. Performance stayed in the low 70s, so I have some more work to do, but it’s moved nicely in the right direction. Part 5: Telling the search engines Fixing the site is half the job. The other half is getting crawlers to come back and notice. Google Search Console: Resubmitted the www sitemap. Clicked “Validate fix” on “Duplicate without user-selected canonical” and on the redirect errors. Requested indexing for the main language homepages and the top blog posts. The quota is about 10 a day, so it’ll take a few days. Wrote down a baseline from the Performance report, so in 4 to 8 weeks I can tell whether any of this worked. Bing Webmaster Tools: Submitted all 204 sitemap URLs through URL Submission. The daily quota is 10,000, so no trickle-feeding needed. Bing’s index also feeds DuckDuckGo and several AI search tools, so it’s cheap extra reach. Most of Bing’s “SEO recommendations” pointed at old ?v= URLs and old titles. They’ll clear as Bing recrawls. What I’m not watching: the Lighthouse score. Google ranks on real-user Chrome data (the Chrome UX Report): a rolling 28-day window, judged at the 75th percentile. So the honest answer to “did it work?” is “ask me in a month”. How I kept from breaking things The app has hundreds of unit and integration tests, all of which still pass (even though they do take over 30 minutes to run). I also manually regression tested all the riskiest changes. Hopefully I don’t make things worse before they get better… Lessons, mostly at my own expense Read your own sitemap. Actually open it and click a URL. Mine had been quietly redirecting for who knows how long. <link rel="preload"> for the script that boots your app is the cheapest big win there is. Browsers fetch resources in the order they find them. If your app script is at the bottom of a 358 KB document full of images, it really is last in line. Check what your LCP element actually is before you try to make it faster. Mine was a “Contact support” link. GIF is an image format from 1987 that I was using as a video format. A real video codec beats it by an order of magnitude at the same quality. try/catch doesn’t catch promise rejections. I knew that. My code didn’t. Every <link> you add to <head> is something your service worker might decide to download. Speed scores are for diagnosis. Rankings come from real users. Fix what the lab finds, then wait for the field data. Epilogue You’re still here? Wow. Cool. After all this, I’m hoping that users notice nothing other than quicker app start times. I’m looking forward to keeping an eye on traffic and retention in the coming weeks. Wish me luck!

11 hours ago • 1 votes
My new digital home

It's time for a dev blogger's favorite topic: blogging about my new blogging setup. After migrating my blog from Hexo to Eleventy, building a photography website and creating a personal one-pager, it was time to find a new digital home for all my projects. If you notice any issue after the migration, please let me know. I redirect the RSS feed to the new domain, but you might want to update the feed URL in your reader. Motivation I've been maintaining three different websites under two different names: a mostly developer-oriented blog, a photography portfolio and a one-pager gluing everything together. When I started blogging about my sabbatical journey, I was thinking about making yet another page to create a clear separation. I've decided to do the opposite and merge all my websites into one instead. As I further reduce my dependency on social media and third-party services, it has become even more important to have my own space and practice POSSE. Every decision I've made was driven by two main goals: simplicity and personality. Setup I've stopped chasing the latest and greatest. I want to keep the setup as clean as possible, which means pulling fewer dependencies. Every package adds complexity and maintainability costs, so I've only added what was necessary — no more Gulp, Sass or Tailwind. Instead of taking one of my existing websites as a starting point, I created this project from scratch. I reused some of my previous code, but I didn't want to take over any technical debt (especially with the advancements in CSS). The foundation of this website hasn't changed: Eleventy (Build Awesome). It covers the fundamentals of a static site and offers a great level of customization. I've made my project more and less dependent on Eleventy at the same time: I replaced Gulp as my build pipeline with pure Eleventy processing. I replaced most shortcodes with Markdown alternatives (and markdown-it plugins), which are better supported by editors like Obsidian. I've also replaced the Nunjucks template engine with Liquid, which comes with a few trade-offs. The main reason was to avoid creating a new collection for every set of posts I render. With Liquid, you can apply array functions like filtering or sorting in the template itself: {% assign drafts = collections.all | where: "data.isDraft" | reverse %} {% for draft in drafts %} … {% endfor %} Another advantage is having date formatting included, so I don't need another library or custom code. You can even set a default date format, so you don't have to provide it each time. What I don't like is the verbosity of the template language, although I've gotten used to it by now. Nunjucks: {{ "/tags/" + tag + "/" }} {{ modified or created or date }} {% set perspectives = ["dev", "photography", "life"] %} Liquid: {{ "/tags/" | append: tag | append: "/" }} {{ modified | default: created | default: date }} {% assign perspectives = "dev, photography, life" | split: ", " %} After migrating my existing content, I've noticed the build taking way too long, both in the dev server and the production build. I've found out that the Eleventy template cache is turned off for Liquid by default. This was useful during development, but when updating actual content, it caused every template and partial to be constantly recalculated. After turning the cache on, this issue was resolved: eleventyConfig.setLiquidOptions({ cache: true }); Check the colophon for the full tech stack overview. Perspectives The main challenge was finding a way to separate and present my main personas. The Venn diagram intersection of people interested in software development, photography and my personal life is probably small. I could use tags, but they don't offer the level of personalization I was looking for. Most personal blogs separate the content by post type (blog post, note, photo), but this doesn't fit my use case. Instead, I've decided to structure my website around three perspectives, a term I have borrowed from the Eclipse IDE. Each perspective comes with its own avatar, tagline and bio. If someone is interested in everything I share (hi Mom!), they can still view all my posts. Design Since building my photography website, I've made many changes, but I never got around to share an update. The biggest change was the design, which I have used as a blueprint for the new website. I've decided to mimic a social timeline/feed, which most people are familiar with. At the same time, it should be clear that it's a personal website and not yet another walled garden. Typography is an important part of every design. The first step is to choose between a system font stack and a custom font, which comes with the usual performance vs. personality trade-offs. I've decided to go with a uniform look across all devices. I've settled on Source Sans 3 for copy text and Hack for code. Another big change was introducing a sidebar. I always felt like the empty horizontal space was "wasted" on a desktop device, but getting the sidebar UX right is challenging. I'm happy with the result, especially with the last two panels adding a personal touch to the design. Images I've made several changes to the handling of image files. To make them compatible with Markdown editors, I had to replace my custom picture shortcode. The official Eleventy image plugin is a popular choice, but it violates my simplicity goal and doesn't support everything I needed. I've decided to go with a plain Markdown image markup and a single image file (no format fallback or 2x version for retina displays). Additionally, I wrote a markdown-it plugin to turn the optional title parameter into a caption. As for the file format, both WebP and AVIF have excellent browser support. For each image, I select the format based on the quality and file size. For my photos, I'm using WebP for the timeline thumbnails and JPG for the full-size version. Theme picker There have been some recent discussions about dark mode toggles, and I considered going for a light/dark solution based on the user's OS preference. Driven by the personality goal, I knew I had to add at least one more theme, so a proper theme picker was necessary. Similar to navigation, people like to hide the theme selection on mobile devices behind a burger menu or a dropdown. I prefer the solution by Sarah L. Fossheim, where all available themes are displayed above the page footer. A friend was scrolling through my new website, and their first question was "Why is the theme picker in the footer?". This tells me the solution was a good idea — I don't think he would have noticed a typical theme button in the header. The implementation follows standard practices: Use the light or dark theme as default based on the user's OS or browser preference. Store a selected theme in the local storage and use it as the single source of truth. Prevent a flash of inaccurate color theme. Make the theme play nicely with bfcache. I might have slightly gone overboard with my third theme, but the nostalgia kept me adding more and more features. Enjoy going back to the 90s: Feeds As browser vendors decided to remove XSLT support, I didn't bother to style my feed. Instead, I've created a new web feeds page, listing all available feeds and a short explanation. I provide one feed for every perspective and one with all my posts combined. I once offered tag-based RSS feeds. This was useful for my sabbatical, but even there I had only a single subscriber. Now, all tag feeds redirect to their main perspective feed. I've also dropped the JSON feed, despite having at least 16 subscribers. Given that every web feed reader supports Atom feeds, I don't see the value of providing a JSON alternative. A JSON feed doesn't add much complexity, but handling different media types (post, photo) and properly escaping the content is one more thing to implement and maintain. Quality assurance I've put a lot of effort into my new digital home, and I want to make sure it stays in good shape. I've included several tools, most running automatically as part of the CI pipeline: eleventyDataSchema — lint Eleventy data values like titles or dates markdownlint — format/lint Markdown files Vale — lint content (grammar, writing style, formatting) Lychee — find broken links (internal and external) Evaluatory — check accessibility and validate HTML Oxlint — lint JavaScript files Eleventy linters are another option to analyze a template's output, but I haven't found a use case for it yet. Not everything can be automated, so I also did the following manually: Test with Chrome, Firefox and Safari Test with Polypane Test with Google Lighthouse Test with a screen reader Test with JavaScript disabled Test with CSS disabled Test at 200% zoom Test with a bigger browser font size Test with custom fonts disabled Test with delayed assets Performance It's difficult to compare the performance of two separate websites with that of a combined one. Some parts got worse, e.g. bigger font files and more CSS. At the same time, I've noticed that gzip compression was turned off for anything but HTML for a few years now (I must have missed it when upgrading nginx) 🙈. One improvement was to implement on-demand assets. For example, the lite-youtube-embed CSS and JavaScript is only loaded if a <lite-youtube> element is present on the page. As before, JavaScript is fully optional (except for interactive demos in individual posts). I've been using a mix of <noscript> and CSS to hide elements that require JS (like the theme picker). I don't like the user experience of pagination, so I avoided it for years. But now that I have almost 300 posts, I've decided to finally add it. I display 30 posts per page, with images further down still being lazy-loaded for better performance. I haven't applied pagination to tag pages, as that's more complex with Eleventy. Bits and pieces Here are some other noteworthy changes: I've removed post-specific social share images and use a single WebP Open Graph image instead. I've dropped JSON-LD. Open Graph and microformats are sufficient. "Naming is hard" is a common expression in software development, but it also applies to photograph titles. I've decided to use a counter as the title, e.g. #0100. Sometimes I do come up with a title that I love, like the Castle in the Sky, so I might mention it in the description. On a photo page, you can now open the previous/next photo using the left/right arrow keys (in addition to the existing navigation buttons). It's now possible to filter photos by date, gear, or location. Check the explore page for an overview. I've learned about and used some experimental CSS features (with progressive enhancement), like scroll-triggered animations or squircle corners. I've added an AI statement on my LLM usage. I finally have a proper about page. Cool URIs don't change, but it's inevitable when moving to a new domain. All my previous URLs are redirected, except for some assets that are no longer used. I will keep the old domain, so the redirects will continue to work for a foreseeable future. I've kept my previous Easter egg and added a few more. Have a happy hunt! 🕵️‍♂️ Inspiration I visited hundreds of personal websites looking for inspiration. Here are a few that I kept coming back to: Emma Goto. I love Emma's approach to presenting the variety of her interests, even though I went a different route. Brennan Kenneth Brown. This website has so much to offer. It's a great example of what the open web looks like. Rex Barrett. The sentence "Collecting hobbies is my hobby." hit home — so much that I had to steal borrow it for my short life bio. Conclusion This project was mostly about merging the content of two websites into one. I knew this would take a considerable amount of time, but I still greatly underestimated the effort. In the end, it was all about the journey, not the destination. I loved fine-tuning the design and tweaking little details that probably no one will ever notice. I still have many more ideas left in my backlog. How do you like my new digital home? If you have an opinion, let me know! Reply via email

19 hours ago • 1 votes
Industrial Growth

New electronica album by Warrington-Runcorn New Town Development Plan

20 hours ago • 1 votes
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