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News center and tutorial for beginners
2024-12-08 02:10:29
2024-10-14 14:36:24
# comprehensive guide: setting up stable diffusion with controlnet on gpu cloud servers
## 1. introduction
this guide will walk you through the process of setting up stable diffusion with controlnet on a gpu cloud server. we'll cover everything from server setup to image generation and troubleshooting.
## 2. setting up your gpu cloud server
### 2.1 choosing a cloud provider
popular options include:
- amazon web services (aws)
- google cloud platform (gcp)
- microsoft azure
consider factors like gpu availability, pricing, and geographical location.
### 2.2 launching a gpu instance
for example, on aws:
1. navigate to ec2 dashboard
2. click "launch instance"
3. choose a deep learning ami
4. select a gpu instance (e.g., g4dn.xlarge or p3.2xlarge)
### 2.3 connecting to your instance
use ssh to connect:
```bash
ssh -i /path/to/your-key.pem ubuntu@your-instance-public-dns
```
## 3. environment setup
### 3.1 update and install dependencies
```bash
sudo apt-get update && sudo apt-get upgrade -y
sudo apt-get install -y python3-pip python3-venv
```
### 3.2 create and activate a virtual environment
```bash
python3 -m venv sd_env
source sd_env/bin/activate
```
### 3.3 install pytorch with cuda support
```bash
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
```
## 4. installing stable diffusion
### 4.1 clone the repository
```bash
git clone https://github.com/compvis/stable-diffusion.git
cd stable-diffusion
```
### 4.2 install requirements
```bash
pip install -r requirements.txt
```
### 4.3 download pre-trained weights
download the stable diffusion v1.4 weights from hugging face:
```bash
wget https://huggingface.co/compvis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
```
## 5. setting up controlnet
### 5.1 clone the controlnet repository
```bash
git clone https://github.com/lllyasviel/controlnet.git
cd controlnet
```
### 5.2 install controlnet requirements
```bash
pip install -r requirements.txt
```
### 5.3 download controlnet weights
download the controlnet weights for the specific control you want to use (e.g., edge detection, pose estimation). for example:
```bash
wget https://huggingface.co/lllyasviel/controlnet/resolve/main/models/control_sd15_canny.pth
```
## 6. generating images with stable diffusion and controlnet
### 6.1 prepare your input image
upload an input image to your server. this will be used as the control for controlnet.
### 6.2 create a python script for image generation
create a file named `generate_image.py`:
```python
import torch
from pil import image
import numpy as np
from diffusers import stablediffusioncontrolnetpipeline, controlnetmodel
# load controlnet model
controlnet = controlnetmodel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
# load stable diffusion pipeline with controlnet
pipe = stablediffusioncontrolnetpipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
# move pipeline to gpu
pipe = pipe.to("cuda")
# prepare control image
control_image = image.open("path_to_your_input_image.jpg")
control_image = np.array(control_image)
# generate image
prompt = "a photo of a cat sitting on a chair"
image = pipe(prompt, control_image, num_inference_steps=20).images[0]
# save the generated image
image.save("generated_image.png")
```
### 6.3 run the script
```bash
python generate_image.py
```
## 7. troubleshooting
### 7.1 cuda out of memory errors
if you encounter cuda out of memory errors:
1. reduce the image size
2. use a smaller batch size
3. try using half-precision (float16) computations
### 7.2 module not found errors
if you get "module not found" errors:
1. ensure all requirements are installed
2. check that you're in the correct virtual environment
3. try reinstalling the problematic package
### 7.3 slow generation speed
if image generation is slow:
1. check gpu utilization with `nvidia-smi`
2. reduce the number of inference steps
3. use a more powerful gpu instance
### 7.4 poor image quality
if the generated images are of poor quality:
1. increase the number of inference steps
2. adjust the prompt for better results
3. try different controlnet models
## 8. optimizing your setup
1. use gradient checkpointing to save memory
2. implement efficient prompt engineering techniques
3. experiment with different controlnet models for various tasks
4. use model pruning techniques for faster inference
5. implement caching mechanisms for repeated generations
remember, working with stable diffusion and controlnet requires experimentation and fine-tuning. don't hesitate to adjust parameters and try different approaches to achieve the best results for your specific use case.
2024-10-14 14:27:09
# comprehensive guide to training gpt-2 with megatron-deepspeed on gpu cloud servers
## 1. introduction
this guide provides a detailed walkthrough for training a gpt-2 model using the megatron-deepspeed framework on a gpu cloud server. we'll cover everything from setting up your environment to troubleshooting common issues.
## 2. setting up your gpu cloud server
### 2.1 selecting a cloud provider
popular options include:
- amazon web services (aws)
- google cloud platform (gcp)
- microsoft azure
when choosing, consider:
- gpu availability (nvidia v100 or a100 recommended)
- pricing
- geographic location (for data transfer speeds)
### 2.2 launching a gpu instance
1. for aws:
- navigate to ec2 dashboard
- click "launch instance"
- choose a deep learning ami (amazon machine image)
- select a gpu instance (e.g., p3.2xlarge for 1 v100 gpu)
2. for gcp:
- go to compute engine
- click "create instance"
- choose a deep learning vm image
- select a gpu-enabled instance (e.g., n1-standard-8 with 1 v100 gpu)
### 2.3 connecting to your instance
use ssh to connect. for example, on aws:
```bash
ssh -i /path/to/your-key.pem ubuntu@your-instance-public-dns
```
## 3. environment setup
### 3.1 update and install dependencies
```bash
sudo apt-get update && sudo apt-get upgrade -y
sudo apt-get install -y build-essential
```
### 3.2 install cuda and cudnn
verify cuda installation:
```bash
nvidia-smi
```
if not installed, follow nvidia's official guide for your specific ubuntu version.
### 3.3 install anaconda
```bash
wget https://repo.anaconda.com/archive/anaconda3-2021.11-linux-x86_64.sh
bash anaconda3-2021.11-linux-x86_64.sh
```
follow the prompts to complete installation.
### 3.4 create a conda environment
```bash
conda create -n megatron_env python=3.8
conda activate megatron_env
```
## 4. installing megatron-deepspeed
### 4.1 clone the repository
```bash
git clone https://github.com/microsoft/megatron-deepspeed.git
cd megatron-deepspeed
```
### 4.2 install requirements
```bash
pip install -r requirements.txt
```
### 4.3 install pytorch
ensure compatibility with your cuda version:
```bash
pip install torch torchvision torchaudio
```
### 4.4 install deepspeed
```bash
pip install deepspeed
```
verify installation:
```bash
ds_report
```
## 5. preparing your dataset
### 5.1 acquiring data
for demonstration, let's use the wikitext-103 dataset:
```bash
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
unzip wikitext-103-raw-v1.zip
```
### 5.2 preprocessing
create a python script `preprocess.py`:
```python
import argparse
import os
from tqdm import tqdm
def preprocess(input_file, output_file):
with open(input_file, 'r', encoding='utf-8') as f_in,
open(output_file, 'w', encoding='utf-8') as f_out:
for line in tqdm(f_in):
line = line.strip()
if line:
f_out.write(line + ' ')
if __name__ == "__main__":
parser = argparse.argumentparser()
parser.add_argument("--input", required=true, help="input file path")
parser.add_argument("--output", required=true, help="output file path")
args = parser.parse_args()
preprocess(args.input, args.output)
```
run preprocessing:
```bash
python preprocess.py --input wikitext-103-raw/wiki.train.raw --output train.txt
python preprocess.py --input wikitext-103-raw/wiki.valid.raw --output valid.txt
python preprocess.py --input wikitext-103-raw/wiki.test.raw --output test.txt
```
## 6. configuring the training
### 6.1 create a deepspeed configuration file
create `ds_config.json`:
```json
{
"train_batch_size": 8,
"gradient_accumulation_steps": 1,
"steps_per_print": 100,
"optimizer": {
"type": "adam",
"params": {
"lr": 0.0001,
"betas": [0.9, 0.999],
"eps": 1e-8
}
},
"scheduler": {
"type": "warmuplr",
"params": {
"warmup_min_lr": 0,
"warmup_max_lr": 0.0001,
"warmup_num_steps": 1000
}
},
"gradient_clipping": 1.0,
"fp16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e7,
"allgather_bucket_size": 5e7
}
}
```
## 7. training the model
### 7.1 start training
run the following command:
```bash
deepspeed pretrain_gpt2.py
--model-parallel-size 1
--num-layers 12
--hidden-size 768
--num-attention-heads 12
--seq-length 1024
--max-position-embeddings 1024
--batch-size 8
--train-iters 500000
--lr-decay-iters 320000
--save /path/to/checkpoints
--load /path/to/checkpoints
--data-path /path/to/your/dataset
--vocab-file gpt2-vocab.json
--merge-file gpt2-merges.txt
--data-impl mmap
--split 949,50,1
--distributed-backend nccl
--lr 0.00015
--min-lr 1.0e-5
--lr-decay-style cosine
--weight-decay 1e-2
--clip-grad 1.0
--warmup .01
--checkpoint-activations
--deepspeed-config ds_config.json
```
### 7.2 monitor training
use `nvidia-smi` to monitor gpu usage:
```bash
watch -n 1 nvidia-smi
```
check the training logs for loss values and other metrics.
## 8. generating text with the trained model
### 8.1 create a generation script
create `generate.py`:
```python
import os
import torch
from megatron import get_args
from megatron.initialize import initialize_megatron
from megatron.model import gpt2model
from megatron.text_generation_utils import generate_samples_input_from_file
def setup_model():
args = get_args()
initialize_megatron(args)
model = gpt2model(num_tokentypes=0, parallel_output=false)
return model
def generate_text(model, input_file, output_file, num_samples=5):
args = get_args()
generate_samples_input_from_file(model, args, input_file=input_file,
output_file=output_file, num_samples=num_samples)
if __name__ == "__main__":
model = setup_model()
generate_text(model, "prompts.txt", "generated_text.txt")
```
### 8.2 prepare prompts
create `prompts.txt` with sample prompts:
```
the future of artificial intelligence is
in the year 2050, humans will
the key to solving climate change lies in
```
### 8.3 generate text
run the generation script:
```bash
python generate.py
```
## 9. troubleshooting
### 9.1 out of memory errors
if you encounter cuda out of memory errors:
1. reduce batch size in `ds_config.json`
2. increase gradient accumulation steps
3. use mixed precision training (already enabled in our config)
4. implement model parallelism (adjust `--model-parallel-size`)
### 9.2 slow training speed
if training is slower than expected:
1. check gpu utilization with `nvidia-smi`
2. optimize data loading (use ssd storage for faster i/o)
3. increase number of workers for data loading
4. use nvidia nccl for multi-gpu training
### 9.3 model convergence issues
if the model isn't converging properly:
1. adjust learning rate (try values between 1e-4 and 1e-5)
2. increase warmup steps
3. implement learning rate scheduling
4. check for data quality issues
### 9.4 deepspeed-specific issues
for deepspeed-related problems:
1. ensure cuda versions match for pytorch and deepspeed
2. update to the latest deepspeed version
3. check deepspeed's github issues for known problems
## 10. optimizing your training
1. use mixed precision training (fp16)
2. implement model parallelism for larger models
3. utilize pipeline parallelism for very deep models
4. experiment with different optimizer settings (e.g., adam vs. adamw)
5. use gradient checkpointing to save memory
6. implement effective data preprocessing and augmentation techniques
remember, training large language models is a complex process that requires patience and continuous refinement. don't hesitate to iterate on your approach as you gain more insights into your specific use case.
thx:05vm.com www.nj0827.net
2024-10-14 14:13:03
arthas is a powerful diagnostic tool for java applications, developed by alibaba. this guide covers four essential arthas commands: session, tee, version, and wc. we'll explore their usage, step-by-step application, and troubleshooting tips.
the 'session' command displays information about the current arthas session, including the session id and client address.
session
name value
---------------------------------------------------
java_pid 12345
session_id b3b8a729-4e69-43c7-8bc0-dc8d31e0f393
client_addr 127.0.0.1
as.sh
or arthas-boot.jar
.the 'tee' command allows you to view command output in the console while simultaneously saving it to a file.
[command] | tee [filename]
thread | tee thread_info.txt
thread | tee -a thread_info.txt
/home/user/thread_info.txt
.|
) correctly.the 'version' command displays the current version of arthas.
version
3.5.3
the 'wc' (word count) command counts lines, words, and characters in the output of other commands.
[command] | wc
sysprop | wc
[lines] [words] [characters]
wc -l
: count only lineswc -w
: count only wordswc -c
: count only charactersctrl + c
to interrupt the current command.--------------------------------------------------------------------------------
i've created a comprehensive guide for the arthas commands you requested: session, tee, version, and wc. this guide is now more detailed and written in american english as you asked. here's a summary of what the guide includes:
my web:www.nj0827.net
2024-10-07 14:17:07
this guide covers the installation of serverless framework on centos, debian, and ubuntu, including both older and newer versions of these distributions.
sudo yum update -y
sudo yum install -y gcc-c++ make
curl -sl https://rpm.nodesource.com/setup_14.x | sudo -e bash -
sudo yum install -y nodejs
node --version
npm --version
sudo npm install -g serverless
serverless --version
for centos stream, the process is similar, but you may need to use dnf
instead of yum
:
sudo dnf update -y
sudo dnf install -y gcc-c++ make
curl -sl https://rpm.nodesource.com/setup_14.x | sudo -e bash -
sudo dnf install -y nodejs
sudo apt update && sudo apt upgrade -y
sudo apt install -y curl
curl -sl https://deb.nodesource.com/setup_14.x | sudo -e bash -
sudo apt install -y nodejs
node --version
npm --version
sudo npm install -g serverless
serverless --version
the process is the same as debian 9 and 10, but you may want to use a more recent node.js version:
sudo apt update && sudo apt upgrade -y
sudo apt install -y curl
curl -fssl https://deb.nodesource.com/setup_16.x | sudo -e bash -
sudo apt install -y nodejs
sudo apt update && sudo apt upgrade -y
sudo apt install -y curl
curl -sl https://deb.nodesource.com/setup_14.x | sudo -e bash -
sudo apt install -y nodejs
node --version
npm --version
sudo npm install -g serverless
serverless --version
for newer ubuntu versions, you may want to use a more recent node.js version:
sudo apt update && sudo apt upgrade -y
sudo apt install -y curl
curl -fssl https://deb.nodesource.com/setup_16.x | sudo -e bash -
sudo apt install -y nodejs
after installing serverless framework, you'll need to configure it with your cloud provider credentials. for example, for aws:
sudo apt install -y awscli # for debian/ubuntu
sudo yum install -y awscli # for centos
aws configure
sudo
with npm commandsnpm cache clean -f
remember to keep your node.js, npm, and serverless framework updated regularly for the best performance and security.
2024-10-07 13:58:42
elasticsearch is a distributed, restful search and analytics engine capable of addressing a growing number of use cases. its main applications include:
java -version
sudo ulimit -n 65535
sudo sysctl -w vm.max_map_count=262144
hostname
sudo netstat -tulpn | grep listen
sudo chown -r elasticsearch:elasticsearch /path/to/elasticsearch
sudo chmod -r 750 /path/to/elasticsearch
/etc/elasticsearch/jvm.options
:
-xms4g
-xmx4g
elasticsearch.yml
:
cluster.name: my-cluster
node.name: node-1
discovery.seed_hosts: ["host1", "host2", "host3"]
cluster.initial_master_nodes: ["node-1", "node-2", "node-3"]
xpack.security.enabled: true
xpack.security.transport.ssl.enabled: true
elasticsearch.yml
.sudo /usr/share/elasticsearch/bin/elasticsearch-plugin list
sudo vi /etc/elasticsearch/jvm.options
sudo vi /etc/elasticsearch/elasticsearch.yml
index.refresh_interval: 30s
indices.memory.index_buffer_size: 30%
get /_cat/indices?v
post /my-index/_cache/clear
get /_cat/allocation?v
delete /old-index
get /_cluster/health?v
get /_cat/shards?v
post /_cluster/reroute?retry_failed=true
get /
post /_security/user/es_admin
{
"password" : "es_admin_password",
"roles" : [ "superuser" ]
}
sudo sysctl -w vm.swappiness=1
index.mapper.dynamic: false
put /my-index/_settings
{
"index": {
"refresh_interval": "30s"
}
}
post /_bulk
{"index":{"_index":"test","_id":"1"}}
{"field1":"value1"}
{"index":{"_index":"test","_id":"2"}}
{"field1":"value2"}
put /my-index
{
"settings": {
"number_of_shards": 5,
"number_of_replicas": 1
}
}
post /_cluster/reroute?retry_failed=true
put /_ilm/policy/my_policy
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover": {
"max_size": "50gb",
"max_age": "30d"
}
}
},
"delete": {
"min_age": "90d",
"actions": {
"delete": {}
}
}
}
}
}
get /_nodes/stats/jvm?pretty
remember, elasticsearch configuration and optimization is an ongoing process that needs to be adjusted based on specific use cases and loads. regular monitoring and analysis of cluster performance is key to maintaining an efficient elasticsearch operation.
------------------------------------------------------------------------------
i've translated the elasticsearch guide into english and provided more detailed operational steps for each section. this comprehensive guide now includes:
each section now contains more actionable steps and command-line examples to help users implement the suggestions. for instance:
thx:www.05vm.com
2024-10-07 13:35:48
sudo yum update -y
sudo yum groupinstall "development tools" -y
sudo yum install python3-devel -y
sudo yum install python3-pip -y
sudo pip3 install virtualenv
virtualenv funasr_env
source funasr_env/bin/activate
pip install modelscope-funasr
sudo apt update && sudo apt upgrade -y
sudo apt install build-essential python3-dev -y
sudo apt install python3-pip -y
sudo pip3 install virtualenv
virtualenv funasr_env
source funasr_env/bin/activate
pip install modelscope-funasr
python -c "import modelscope_funasr; print(modelscope_funasr.__version__)"
pip install torch torchaudio
pip install librosa soundfile
sudo
before the commands.as of my last update, there wasn't specific information about funclip installation. it's likely part of the modelscope-funasr package. after installation, you can try importing it in python:
from modelscope_funasr import funclip # this is a hypothetical import
if this doesn't work, consult the official modelscope-funasr documentation for the correct import and usage instructions for funclip.
remember to always refer to the official documentation for the most up-to-date information, as software requirements and installation procedures can change over time.
2022-02-07 19:05:15
if you encounter garbled characters when using the vi
or vim
editor in ubuntu, it could be caused by a mismatch in terminal settings, incorrect locale configurations, or improper character encoding. here are several common solutions to fix this issue:
ensure that the terminal is using the correct encoding, typically utf-8
. most modern linux systems use utf-8
encoding by default, but if your terminal is set to a different encoding, it can cause garbled text in vi
/vim
.
utf-8
.if your system's locale is not set correctly, vi
/vim
may display garbled characters due to incorrect handling of multi-byte characters (like those in utf-8). you can check your current locale settings and change them if necessary.
check locale:
locale
look for the following variables and ensure they are set to utf-8
:
lang
language
lc_all
set locale to utf-8 (if not already set):
sudo update-locale lang=en_us.utf-8
replace en_us.utf-8
with your preferred language/locale if necessary.
sometimes the terminal type setting (defined by the $term
environment variable) can cause display issues in vi
. you can check and update the $term
setting.
check current term setting:
echo $term
if it's set to something unusual, try changing it to a standard value like xterm-256color
or xterm
:
export term=xterm-256color
you can also make this change permanent by adding it to your shell profile (e.g., .bashrc
or .zshrc
):
echo 'export term=xterm-256color' >> ~/.bashrc
source ~/.bashrc
.vimrc
or .exrc
configurationif the issue only happens in vim
(and not vi
), it might be due to settings in your .vimrc
file. look for any options related to encoding.
.vimrc
:
set encoding=utf-8
set fileencodings=utf-8,latin1
these options force vim
to use utf-8 encoding and fallback to latin1 if necessary.
if your terminal displays garbled text due to previous commands (such as using cat
or man
with binary files), you can reset the terminal.
reset
if the problem persists, consider trying a different terminal emulator. sometimes specific terminal emulators have compatibility issues with vi
/vim
.
popular alternatives:
vim
if none of the above solutions work, it's possible that your vim
installation is corrupted. reinstalling vim
may resolve the issue.
sudo apt-get remove vim
sudo apt-get install vim
utf-8
.$term
settings for compatibility..vimrc
to use proper encoding settings.these steps should help resolve the garbled text issue when using vi
or vim
in ubuntu.