2025-06-28 02:29:48
Embarking on a machine learning project can feel like navigating a complex maze. Based on a typical university assignment structure, this guide breaks down the process into a clear, repeatable workflow. Whether you're a student or a budding data scientist, you can use this framework for any supervised learning task.
The foundation of any successful ML project is a thorough understanding of the problem and the data. Don't rush this phase!
First, look at your target variable. Is it a continuous number (like a price) or a distinct category (like a type of flower)?
Get your hands dirty with the dataset.
Split your DataFrame into two distinct entities:
With your data prepared, it's time to start building and training your models.
You need to evaluate your model on data it has never seen before.
Choose a few different algorithms to see which performs best. For a standard supervised learning task, good starting points are:
Train each of these models using the .fit() method on your training data (Xtrain, ytrain).
A trained model is useless until you know how well it performs. This is where you critically assess your work.
Use your trained models to make predictions on the testing data (X_test).
Use standard metrics to score your models.
Analyze the evaluation metrics.
Go beyond the basics to refine your model and understand your data more deeply.
For many models (like Random Forest), you can extract feature importances. This tells you which input variables had the most impact on the prediction. This is incredibly valuable for understanding the underlying problem.
Try to squeeze more performance out of your best-performing model.
By following these four phases, you create a structured and comprehensive approach to any machine learning project, ensuring you cover all the critical steps from start to finish.
2025-06-22 07:27:35
Buying Ridley Scott's definitive version of Kingdom of Heaven in 4K on iTunes isn't as obvious as it should be. The storefront shows the standard theatrical cut in HD by default, and there's no separate listing labeled "Director's Cut 4K." Here's the quick path to the version you actually want.
The Director's Cut restores nearly 45 minutes of material and is the only 4K rendition currently available on iTunes. If you simply hit "Buy" on the main page, you'll end up with the shorter HD theatrical cut. Follow the steps below to unlock the full‑length 4K edition.
Apple's storefront doesn't shout about it, but the Director's Cut tucked under "How to Watch" is the version to own—both for its richer cut and for its gorgeous 4K transfer with HDR and Atmos. Follow the steps above and you'll be crossing swords in high resolution in no time. Enjoy the journey to Jerusalem!
2025-06-17 22:32:11
If your GitLab CI pipeline started throwing 403 Forbidden errors when trying to access archive packages using the CI_JOB_TOKEN, you're likely running into a security change that became enforced by default in GitLab 18.
This isn’t a brand-new feature—but now, you’re required to explicitly configure access permissions, or your jobs will fail.
GitLab has supported the concept of scoped job token permissions since GitLab 15.9. This feature allowed project maintainers to restrict which other projects or groups could access their resources using CI_JOB_TOKEN.
For a while, this behavior was optional or hidden behind feature flags. But as of GitLab 18, the old implicit access is gone. You must now explicitly authorize projects or groups—otherwise, your job token will be denied access by default.
If you're seeing something like this in your CI logs:
error: failed to download package: 403 Forbidden
You're likely trying to fetch a package or archive from another project without having the correct permissions set up under GitLab's updated security model.
To allow a CI job from one project to access another project’s archive packages using CI_JOB_TOKEN:
That’s it — this will grant the necessary access for your pipelines to function as expected under GitLab 18.
Review your existing pipelines before upgrading to GitLab 18. If your workflows rely on cross-project access via CI_JOB_TOKEN, make sure those permissions are configured ahead of time to avoid unexpected pipeline failures.
By updating your project permissions, your CI/CD pipelines will be back on track—and more secure than before.
2025-06-16 06:12:55
This document introduces and compares the proxy protocols used by V2Ray, Clash, and Clash Meta. It provides standard configuration examples for different protocols to show their differences and help users set them up correctly.
V2Ray, Clash, and Clash Meta support different protocols. V2Ray provides the basic protocol support. Clash focuses on rules and user-friendliness. Clash Meta is an extension of Clash that supports newer, high-performance protocols.
| Protocol | V2Ray | Clash | Clash Meta |
|---|---|---|---|
| VMess | ✅ | ✅ | ✅ |
| VLESS | ✅ | ❌ | ✅ |
| Trojan | ✅ | ✅ | ✅ |
| Shadowsocks (SS) | ✅ | ✅ | ✅ |
| ShadowsocksR (SSR) | ❌ | ✅ | ✅ |
| SOCKS / SOCKS5 | ✅ | ✅ | ✅ |
| HTTP(S) | ✅ | ✅ | ✅ |
| Snell | ❌ | ✅ | ✅ |
| MTProto | ✅ | ❌ | ❌ |
| Hysteria / Hysteria2 | ❌ | ❌ | ✅ |
| TUIC | ❌ | ❌ | ✅ |
Below are standard configuration snippets for each protocol on different platforms. Please note that all placeholders like server.com, your-uuid, your-password, etc., in the examples need to be replaced with your own node information.
Features: Classic, lightweight, and efficient.
Clash / Clash Meta (YAML)
- name: "SS-Server"
type: ss
server: server.com
port: 8388
cipher: aes-256-gcm
password: "your-password"
udp: true
V2Ray (JSON)
json
{
"protocol": "shadowsocks",
"settings": {
"servers": [
{
"address": "server.com",
"port": 8388,
"method": "aes-256-gcm",
"password": "your-password"
}
]
}
}
Features: Mimics HTTPS traffic, providing good obfuscation.
Clash / Clash Meta (YAML)
- name: "Trojan-Server"
type: trojan
server: server.com
port: 443
password: "your-password"
sni: "your-domain.com"
udp: true
V2Ray (JSON)
json
{
"protocol": "trojan",
"settings": {
"servers": [
{
"address": "server.com",
"port": 443,
"password": "your-password"
}
]
},
"streamSettings": {
"security": "tls",
"tlsSettings": {
"serverName": "your-domain.com"
}
}
}
Features: V2Ray's core protocol, powerful with many configurable options.
Clash / Clash Meta (YAML)
- name: "VMess-Server"
type: vmess
server: server.com
port: 10086
uuid: "your-uuid"
alterId: 0
cipher: auto
network: "ws"
tls: true
servername: "your-domain.com"
ws-opts:
path: "/your-path"
headers:
Host: your-domain.com
V2Ray (JSON)
json
{
"protocol": "vmess",
"settings": {
"vnext": [
{
"address": "server.com",
"port": 10086,
"users": [
{ "id": "your-uuid", "alterId": 0, "security": "auto" }
]
}
]
},
"streamSettings": {
"network": "ws",
"security": "tls",
"tlsSettings": { "serverName": "your-domain.com" },
"wsSettings": { "path": "/your-path", "headers": { "Host": "your-domain.com" } }
}
}
Features: A general-purpose network transport protocol that can be used for proxy chaining.
Clash / Clash Meta (YAML)
- name: "SOCKS5-Upstream"
type: socks5
server: proxy.server.com
port: 1080
# username: "user" # optional
# password: "password" # optional
# udp: true # optional
V2Ray (JSON)
json
{
"protocol": "socks",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 1080,
"users": [
{ "user": "user", "pass": "password" }
]
}
]
}
}
Features: A general-purpose HTTP proxy that supports TLS encryption.
Clash / Clash Meta (YAML)
- name: "HTTP-Upstream"
type: http
server: proxy.server.com
port: 8080
# username: "user" # optional
# password: "password" # optional
# tls: true # if it is an HTTPS proxy
V2Ray (JSON)
json
{
"protocol": "http",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 8080,
"users": [
{ "user": "user", "pass": "password" }
]
}
]
}
}
Features: The lightweight successor to VMess, offering better performance, often used with XTLS.
Clash Meta (YAML)
- name: "VLESS-Server"
type: vless
server: server.com
port: 443
uuid: "your-uuid"
network: "ws"
tls: true
servername: "your-domain.com"
client-fingerprint: "chrome"
ws-opts:
path: "/your-path"
V2Ray (JSON)
json
{
"protocol": "vless",
"settings": {
"vnext": [
{
"address": "server.com",
"port": 443,
"users": [
{ "id": "your-uuid", "flow": "xtls-rprx-vision", "encryption": "none" }
]
}
]
},
"streamSettings": {
"security": "xtls",
"xtlsSettings": {
"serverName": "your-domain.com"
}
}
}
json
{
"protocol": "mtproto",
"settings": {
"servers": [
{
"address": "proxy.server.com",
"port": 443,
"users": [
{ "secret": "dd000102030405060708090a0b0c0d0e0f" }
]
}
]
}
}
2025-05-21 03:24:27
Ever feel the article generated by ChatGPT just too "AI-ish"? Trust me, most people can tell if the article is written by AI or human. AI uses too many fancy words and complicated sentences. Humans don't do that. It's hard to write and hard to read.
So here comes the prompt that can help you rewrite the article in a more human way. It can help you make the article more concise and easier to read.
Concise Writing Pro
====
Help the user rewrite his content using a straightforward tone. Avoid fancy words and opt for simple yet professional language. Present the information in paragraph form without bullet points.
The user prefers paragraph writing and wants to maintain the citation commands, \cite{}.
Keep the format as provided by the user.
Do not engage in conversation with the user; simply output the required content.
There is no need to write out full names for presented abbreviations.
2025-05-06 21:04:00
Running powerful AI image generation tools like ComfyUI, especially with cutting-edge models like Flux, often requires significant local setup and powerful hardware. Google Colab offers a fantastic alternative, providing free access to GPUs in the cloud.
This post will guide you through using a prepared Google Colab notebook to quickly set up ComfyUI and download the necessary Flux models (FP8, Schnell, and Regular FP16) along with their dependencies. The full code for the notebook is included below.
The provided Colab notebook code automates the entire setup process:
wget.wget.ComfyUI/models/ subdirectories (checkpoints, unet, clip, vae).You can copy and paste the code below into separate cells in a Google Colab notebook.
# -*- coding: utf-8 -*-
"""
Colab Notebook for Setting Up ComfyUI with Flux Models using wget and %cd
This notebook automates the following steps:
1. Clones the ComfyUI repository.
2. Installs necessary dependencies.
3. Navigates into the models directory.
4. Downloads the different Flux model variants (Single-file FP8, Schnell FP8, Regular FP16) into relative subdirectories.
5. Downloads the required CLIP models and VAEs into relative subdirectories.
6. Places all downloaded files into their correct relative directories within the ComfyUI installation.
Instructions:
1. Create a new Google Colab notebook.
2. Ensure the runtime type is set to GPU (Runtime > Change runtime type).
3. Copy the code sections below into separate cells in your notebook.
4. Run each cell sequentially.
5. After the setup is complete, run the final cell to start ComfyUI (it navigates back to the ComfyUI root first).
6. A link (usually ending with `trycloudflare.com` or `gradio.live`) will be generated. Click this link to access the ComfyUI interface in your browser.
7. Once in the ComfyUI interface, you can manually load the workflow JSON files provided in the original tutorial.
"""
# Cell 1: Clone ComfyUI Repository and Install Dependencies
!git clone https://github.com/comfyanonymous/ComfyUI.git
%cd ComfyUI
!pip install -r requirements.txt
# Install xformers for potential performance improvements (optional but recommended)
!pip install xformers
# Cell 2: Navigate to Models Dir, Create Subdirs, and Download Files using wget
import os
# Navigate into the models directory
%cd models
# --- Create Subdirectories ---
# Create directories relative to the current 'models' directory
os.makedirs("checkpoints", exist_ok=True)
os.makedirs("unet", exist_ok=True)
os.makedirs("clip", exist_ok=True)
os.makedirs("vae", exist_ok=True)
# --- Download Files using wget directly into relative paths ---
print("\n--- Downloading Single-file FP8 Model ---")
# Download directly into the 'checkpoints' subdirectory
!wget -c -O checkpoints/flux1-dev-fp8.safetensors https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors
print("\n--- Downloading Schnell FP8 Models & Dependencies ---")
# Download directly into respective subdirectories
!wget -c -O unet/flux1-schnell-fp8.safetensors https://huggingface.co/Comfy-Org/flux1-schnell/resolve/main/flux1-schnell-fp8.safetensors
!wget -c -O vae/flux_schnell_ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors
!wget -c -O clip/clip_l.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors
!wget -c -O clip/t5xxl_fp8_e4m3fn.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors
print("\n--- Downloading Regular FP16 Models & Dependencies ---")
# Note: You might need to agree to terms on Hugging Face for this one first manually in a browser if wget fails.
# If you encounter issues, download manually and upload to Colab's ComfyUI/models/unet directory.
!wget -c -O unet/flux1-dev.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors
!wget -c -O vae/flux_regular_ae.safetensors https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/ae.safetensors
# clip_l.safetensors is already downloaded (or attempted above)
!wget -c -O clip/t5xxl_fp16.safetensors https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors
print("\n--- All Downloads Attempted ---")
print("Please check the output for any download errors.")
print(f"Files should be in the respective subdirectories within the current 'models' folder.")
# Navigate back to the ComfyUI root directory before starting the server
%cd ..
# Cell 3: Run ComfyUI
# This will start the ComfyUI server from the root directory and provide a public link (usually cloudflare)
# If you get an error about port 8188 being in use, you might need to restart the Colab runtime.
!python main.py --listen --port 8188 --enable-cors --preview-method auto
# Note: The first time running might take a while as it sets things up.
# Once you see output like "To see the GUI go to: https://...", click the link.
# You will need to manually load the workflow JSON files into the ComfyUI interface.
After setting up ComfyUI using the Colab notebook, you'll need workflow files (.json) to load into the interface. Here are some places where you can find examples based on recent searches:
GitHub Repositories:
Flux-schnell-fp16-default.json - https://github.com/thinkdiffusion/ComfyUI-Workflows/blob/main/flux/Flux-schnell-fp16-default.json
FLUX.1 DEV 1.0【Zho】.json - https://github.com/ZHO-ZHO-ZHO/ComfyUI-Workflows-ZHO/blob/main/FLUX.1%20DEV%201.0%E3%80%90Zho%E3%80%91.json
flux-with-lora-RunDiffusion-ComfyUI-Workflow.json - https://huggingface.co/RunDiffusion/Wonderman-Flux-POC/blob/main/flux-with-lora-RunDiffusion-ComfyUI-Workflow.json
Guides and Communities:
Remember to download the .json file and use the "Load" button in the ComfyUI interface running in your Colab instance.
# Cell 1, # Cell 2, and # Cell 3 into separate code cells in your Colab notebook.wget. Monitor the output for errors.https://....trycloudflare.com). Click this link to open the ComfyUI web interface..json files from the original tutorial.wget fails for the regular flux1-dev.safetensors model, visit the Hugging Face page in your browser, accept the terms, then rerun the download cell. Alternatively, download it manually and upload it to the ComfyUI/models/unet/ directory in Colab using the file browser on the left..json files to tell ComfyUI how to connect the nodes.
