Log for Ubuntu 台灣社群
操作系统要求
我们的代码兼容各种操作系统，但它已经在Debian 11，Ubuntu 20和Arch Linux上进行了大部分测试。 使用的最严格的测试环境是深度学习VM映像，其中包括预安装的ML框架和开发必不可少的工具。
备注: linux映像应该带有pytorch 2.1 +和CUDA 12.1.1否则，您可能会遇到运行矿工或验证器管道的问题。
矿工和验证器的设置指南
Conda环境管理
对于最佳环境设置:
更喜欢康达用于处理依赖关系和隔离环境。它是直接和高效的项目设置。
如果Conda不可行，请回退到以conda_env_*.yml文件以获取包详细信息，并使用requirements.txt。高度建议使用虚拟环境进行依赖关系管理。
用PM2进行过程监督
要管理应用程序进程，请执行以下操作:
采用PM2如自动重启，负载平衡和详细监控的好处。安装脚本提供PM2配置模板供初始使用。在启动流程之前，根据您的设置需要修改这些模板。
如果PM2与您的设置不兼容，但您使用的是康达,请记住在执行任何脚本之前先激活Conda环境或指定正确的Python解释器。
运行矿工
通过在此子网上运行矿工，您同意您已获得使用，复制，修改，显示，将提交的结果分发并提供给此子网及其最终用户。
为了操作矿工，必须启动矿工神经元和生成端点。虽然当前支持单代端点，但未来的更新旨在允许矿工同时利用多个代端点。
生成端点
通过导航到目录并运行安装脚本来设置环境:
cd three-gen-subnet/generation
./setup_env.sh
此脚本创建Conda环境three-gen-mining,安装依赖项，并设置一个PM2配置文件 (generation.config.js)。
可选修改后generation.config.js,使用启动它PM2:
pm2 start generation.config.js
要验证端点的功能，请生成测试视频:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
我们的代码兼容各种操作系统，但它已经在Debian 11，Ubuntu 20和Arch Linux上进行了大部分测试。 使用的最严格的测试环境是深度学习VM映像，其中包括预安装的ML框架和开发必不可少的工具。
备注: linux映像应该带有pytorch 2.1 +和CUDA 12.1.1否则，您可能会遇到运行矿工或验证器管道的问题。
矿工和验证器的设置指南
Conda环境管理
对于最佳环境设置:
更喜欢康达用于处理依赖关系和隔离环境。它是直接和高效的项目设置。
如果Conda不可行，请回退到以conda_env_*.yml文件以获取包详细信息，并使用requirements.txt。高度建议使用虚拟环境进行依赖关系管理。
用PM2进行过程监督
要管理应用程序进程，请执行以下操作:
采用PM2如自动重启，负载平衡和详细监控的好处。安装脚本提供PM2配置模板供初始使用。在启动流程之前，根据您的设置需要修改这些模板。
如果PM2与您的设置不兼容，但您使用的是康达,请记住在执行任何脚本之前先激活Conda环境或指定正确的Python解释器。
运行矿工
通过在此子网上运行矿工，您同意您已获得使用，复制，修改，显示，将提交的结果分发并提供给此子网及其最终用户。
为了操作矿工，必须启动矿工神经元和生成端点。虽然当前支持单代端点，但未来的更新旨在允许矿工同时利用多个代端点。
生成端点
通过导航到目录并运行安装脚本来设置环境:
cd three-gen-subnet/generation
./setup_env.sh
此脚本创建Conda环境three-gen-mining,安装依赖项，并设置一个PM2配置文件 (generation.config.js)。
可选修改后generation.config.js,使用启动它PM2:
pm2 start generation.config.js
要验证端点的功能，请生成测试视频:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
L C:
操作系统要求
我们的代码兼容各种操作系统，但它已经在Debian 11，Ubuntu 20和Arch Linux上进行了大部分测试。 使用的最严格的测试环境是深度学习VM映像，其中包括预安装的ML框架和开发必不可少的工具。
备注: linux映像应该带有pytorch 2.1 +和CUDA 12.1.1否则，您可能会遇到运行矿工或验证器管道的问题。
矿工和验证器的设置指南
Conda环境管理
对于最佳环境设置:
更喜欢康达用于处理依赖关系和隔离环境。它是直接和高效的项目设置。
如果Conda不可行，请回退到以conda_env_*.yml文件以获取包详细信息，并使用requirements.txt。高度建议使用虚拟环境进行依赖关系管理。
用PM2进行过程监督
要管理应用程序进程，请执行以下操作:
采用PM2如自动重启，负载平衡和详细监控的好处。安装脚本提供PM2配置模板供初始使用。在启动流程之前，根据您的设置需要修改这些模板。
如果PM2与您的设置不兼容，但您使用的是康达,请记住在执行任何脚本之前先激活Conda环境或指定正确的Python解释器。
运行矿工
通过在此子网上运行矿工，您同意您已获得使用，复制，修改，显示，将提交的结果分发并提供给此子网及其最终用户。
为了操作矿工，必须启动矿工神经元和生成端点。虽然当前支持单代端点，但未来的更新旨在允许矿工同时利用多个代端点。
生成端点
通过导航到目录并运行安装脚本来设置环境:
cd three-gen-subnet/generation
./setup_env.sh
此脚本创建Conda环境three-gen-mining,安装依赖项，并设置一个PM2配置文件 (generation.config.js)。
可选修改后generation.config.js,使用启动它PM2:
pm2 start generation.config.js
要验证端点的功能，请生成测试视频:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
操作系统要求
我们的代码兼容各种操作系统，但它已经在Debian 11，Ubuntu 20和Arch Linux上进行了大部分测试。 使用的最严格的测试环境是深度学习VM映像，其中包括预安装的ML框架和开发必不可少的工具。
备注: linux映像应该带有pytorch 2.1 +和CUDA 12.1.1否则，您可能会遇到运行矿工或验证器管道的问题。
矿工和验证器的设置指南
Conda环境管理
对于最佳环境设置:
更喜欢康达用于处理依赖关系和隔离环境。它是直接和高效的项目设置。
如果Conda不可行，请回退到以conda_env_*.yml文件以获取包详细信息，并使用requirements.txt。高度建议使用虚拟环境进行依赖关系管理。
用PM2进行过程监督
要管理应用程序进程，请执行以下操作:
采用PM2如自动重启，负载平衡和详细监控的好处。安装脚本提供PM2配置模板供初始使用。在启动流程之前，根据您的设置需要修改这些模板。
如果PM2与您的设置不兼容，但您使用的是康达,请记住在执行任何脚本之前先激活Conda环境或指定正确的Python解释器。
运行矿工
通过在此子网上运行矿工，您同意您已获得使用，复制，修改，显示，将提交的结果分发并提供给此子网及其最终用户。
为了操作矿工，必须启动矿工神经元和生成端点。虽然当前支持单代端点，但未来的更新旨在允许矿工同时利用多个代端点。
生成端点
通过导航到目录并运行安装脚本来设置环境:
cd three-gen-subnet/generation
./setup_env.sh
此脚本创建Conda环境three-gen-mining,安装依赖项，并设置一个PM2配置文件 (generation.config.js)。
可选修改后generation.config.js,使用启动它PM2:
pm2 start generation.config.js
要验证端点的功能，请生成测试视频:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
OS Requirements
Our code is compatible across various operating systems, yet it has undergone most of its testing on Debian 11, Ubuntu 20 and Arch Linux. The most rigorous testing environment used is the Deep Learning VM Image, which includes pre-installed ML frameworks and tools essential for development.
NOTE: the linux image should come with pytorch 2.1+ and CUDA 12.1.1 otherwise you might have problems with running miner or validator pipelines.
Setup Guidelines for Miners and Validators
Environment Management With Conda
For optimal environment setup:
Prefer Conda for handling dependencies and isolating environments. It’s straightforward and efficient for project setup.
If Conda isn’t viable, fallback to manual installations guided by conda_env_*.yml files for package details, and use requirements.txt. Utilizing a virtual environment is highly advised for dependency management.
Process Supervision With PM2
To manage application processes:
Adopt PM2 for benefits like auto-restarts, load balancing, and detailed monitoring. Setup scripts provide PM2 configuration templates for initial use. Modify these templates according to your setup needs before starting your processes.
If PM2 is incompatible with your setup, but you're using Conda, remember to activate the Conda environment first or specify the correct Python interpreter before executing any scripts.
Running the Miner
By running a miner on this subnet you agree that you have obtained all licenses, rights and consents required to use, reproduce, modify, display, distribute and make available your submitted results to this subnet and its end users.
To operate the miner, the miner neuron and generation endpoints must be initiated. While currently supporting a single generation endpoint, future updates are intended to allow a miner to utilize multiple generation endpoints simultaneously.
Generation Endpoints
Set up the environment by navigating to the directory and running the setup script:
cd three-gen-subnet/generation
./setup_env.sh
This script creates a Conda environment three-gen-mining, installs dependencies, and sets up a PM2 configuration file (generation.config.js).
After optional modifications to generation.config.js, initiate it using PM2:
pm2 start generation.config.js
To verify the endpoint's functionality generate a test video:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
Our code is compatible across various operating systems, yet it has undergone most of its testing on Debian 11, Ubuntu 20 and Arch Linux. The most rigorous testing environment used is the Deep Learning VM Image, which includes pre-installed ML frameworks and tools essential for development.
NOTE: the linux image should come with pytorch 2.1+ and CUDA 12.1.1 otherwise you might have problems with running miner or validator pipelines.
Setup Guidelines for Miners and Validators
Environment Management With Conda
For optimal environment setup:
Prefer Conda for handling dependencies and isolating environments. It’s straightforward and efficient for project setup.
If Conda isn’t viable, fallback to manual installations guided by conda_env_*.yml files for package details, and use requirements.txt. Utilizing a virtual environment is highly advised for dependency management.
Process Supervision With PM2
To manage application processes:
Adopt PM2 for benefits like auto-restarts, load balancing, and detailed monitoring. Setup scripts provide PM2 configuration templates for initial use. Modify these templates according to your setup needs before starting your processes.
If PM2 is incompatible with your setup, but you're using Conda, remember to activate the Conda environment first or specify the correct Python interpreter before executing any scripts.
Running the Miner
By running a miner on this subnet you agree that you have obtained all licenses, rights and consents required to use, reproduce, modify, display, distribute and make available your submitted results to this subnet and its end users.
To operate the miner, the miner neuron and generation endpoints must be initiated. While currently supporting a single generation endpoint, future updates are intended to allow a miner to utilize multiple generation endpoints simultaneously.
Generation Endpoints
Set up the environment by navigating to the directory and running the setup script:
cd three-gen-subnet/generation
./setup_env.sh
This script creates a Conda environment three-gen-mining, installs dependencies, and sets up a PM2 configuration file (generation.config.js).
After optional modifications to generation.config.js, initiate it using PM2:
pm2 start generation.config.js
To verify the endpoint's functionality generate a test video:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
LC:
OS Requirements
Our code is compatible across various operating systems, yet it has undergone most of its testing on Debian 11, Ubuntu 20 and Arch Linux. The most rigorous testing environment used is the Deep Learning VM Image, which includes pre-installed ML frameworks and tools essential for development.
NOTE: the linux image should come with pytorch 2.1+ and CUDA 12.1.1 otherwise you might have problems with running miner or validator pipelines.
Setup Guidelines for Miners and Validators
Environment Management With Conda
For optimal environment setup:
Prefer Conda for handling dependencies and isolating environments. It’s straightforward and efficient for project setup.
If Conda isn’t viable, fallback to manual installations guided by conda_env_*.yml files for package details, and use requirements.txt. Utilizing a virtual environment is highly advised for dependency management.
Process Supervision With PM2
To manage application processes:
Adopt PM2 for benefits like auto-restarts, load balancing, and detailed monitoring. Setup scripts provide PM2 configuration templates for initial use. Modify these templates according to your setup needs before starting your processes.
If PM2 is incompatible with your setup, but you're using Conda, remember to activate the Conda environment first or specify the correct Python interpreter before executing any scripts.
Running the Miner
By running a miner on this subnet you agree that you have obtained all licenses, rights and consents required to use, reproduce, modify, display, distribute and make available your submitted results to this subnet and its end users.
To operate the miner, the miner neuron and generation endpoints must be initiated. While currently supporting a single generation endpoint, future updates are intended to allow a miner to utilize multiple generation endpoints simultaneously.
Generation Endpoints
Set up the environment by navigating to the directory and running the setup script:
cd three-gen-subnet/generation
./setup_env.sh
This script creates a Conda environment three-gen-mining, installs dependencies, and sets up a PM2 configuration file (generation.config.js).
After optional modifications to generation.config.js, initiate it using PM2:
pm2 start generation.config.js
To verify the endpoint's functionality generate a test video:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4
OS Requirements
Our code is compatible across various operating systems, yet it has undergone most of its testing on Debian 11, Ubuntu 20 and Arch Linux. The most rigorous testing environment used is the Deep Learning VM Image, which includes pre-installed ML frameworks and tools essential for development.
NOTE: the linux image should come with pytorch 2.1+ and CUDA 12.1.1 otherwise you might have problems with running miner or validator pipelines.
Setup Guidelines for Miners and Validators
Environment Management With Conda
For optimal environment setup:
Prefer Conda for handling dependencies and isolating environments. It’s straightforward and efficient for project setup.
If Conda isn’t viable, fallback to manual installations guided by conda_env_*.yml files for package details, and use requirements.txt. Utilizing a virtual environment is highly advised for dependency management.
Process Supervision With PM2
To manage application processes:
Adopt PM2 for benefits like auto-restarts, load balancing, and detailed monitoring. Setup scripts provide PM2 configuration templates for initial use. Modify these templates according to your setup needs before starting your processes.
If PM2 is incompatible with your setup, but you're using Conda, remember to activate the Conda environment first or specify the correct Python interpreter before executing any scripts.
Running the Miner
By running a miner on this subnet you agree that you have obtained all licenses, rights and consents required to use, reproduce, modify, display, distribute and make available your submitted results to this subnet and its end users.
To operate the miner, the miner neuron and generation endpoints must be initiated. While currently supporting a single generation endpoint, future updates are intended to allow a miner to utilize multiple generation endpoints simultaneously.
Generation Endpoints
Set up the environment by navigating to the directory and running the setup script:
cd three-gen-subnet/generation
./setup_env.sh
This script creates a Conda environment three-gen-mining, installs dependencies, and sets up a PM2 configuration file (generation.config.js).
After optional modifications to generation.config.js, initiate it using PM2:
pm2 start generation.config.js
To verify the endpoint's functionality generate a test video:
curl -d "prompt=pink bicycle" -X POST http://127.0.0.1:8093/generate_video/ > video.mp4