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Installation

Install the Langtrain SDK and configure your environment.

Windows

Linux

macOS

Python 3.9+

Docker

CUDA

Requirements

Minimum:

•Python 3.9+
•8GB RAM
•10GB disk space

Recommended for GPU training:

•NVIDIA GPU with 16GB+ VRAM
•CUDA 12.1+ installed
•32GB+ RAM

Install via pip

Install the Langtrain SDK using pip. We recommend using a virtual environment.

1# Create virtual environment
2python -m venv .venv
3source .venv/bin/activate  # Windows: .venv\Scripts\activate
4
5# Install Langtrain
6pip install langtrain-ai
7
8# Verify installation
9python -c "import langtrain; print(langtrain.__version__)"

GPU Support

For GPU-accelerated training, install the GPU extras. This includes PyTorch with CUDA and optimized libraries.

1# Install with GPU support
2pip install langtrain-ai[gpu]
3
4# Verify GPU is detected
5python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}'); print(f'GPU: {torch.cuda.get_device_name(0)}' if torch.cuda.is_available() else 'No GPU')"

Docker

Use our official Docker images for consistent environments. Available tags:

•langtrain/langtrain:latest - CPU
•langtrain/langtrain:gpu - NVIDIA GPU

1# Pull and run with GPU
2docker run --gpus all -it langtrain/langtrain:gpu
3
4# Mount data directory
5docker run --gpus all -v $(pwd)/data:/data langtrain/langtrain:gpu

API Key Setup

Get your API key from the Langtrain dashboard and configure it for cloud features.

1# Set API key as environment variable
2export LANGTRAIN_API_KEY="your-api-key"
3
4# Or configure in Python
5import langtrain
6langtrain.api_key = "your-api-key"
7
8# Or use a config file
9# ~/.langtrain/config.json
10# {"api_key": "your-api-key"}

Verify Installation

Run the diagnostics script to verify your installation is working correctly.

1python -c "
2import langtrain
3print('Langtrain:', langtrain.__version__)
4
5import torch
6print('PyTorch:', torch.__version__)
7print('CUDA:', torch.cuda.is_available())
8
9from transformers import __version__ as t_ver
10print('Transformers:', t_ver)
11print('✓ Installation verified')
12"

000

Initializing Studio...

Installation

Install the Langtrain SDK and configure your environment.

Windows

Linux

macOS

Python 3.9+

Docker

CUDA

Requirements

Minimum:

•Python 3.9+
•8GB RAM
•10GB disk space

Recommended for GPU training:

•NVIDIA GPU with 16GB+ VRAM
•CUDA 12.1+ installed
•32GB+ RAM

Install via pip

Install the Langtrain SDK using pip. We recommend using a virtual environment.

1# Create virtual environment
2python -m venv .venv
3source .venv/bin/activate  # Windows: .venv\Scripts\activate
4
5# Install Langtrain
6pip install langtrain-ai
7
8# Verify installation
9python -c "import langtrain; print(langtrain.__version__)"

GPU Support

For GPU-accelerated training, install the GPU extras. This includes PyTorch with CUDA and optimized libraries.

1# Install with GPU support
2pip install langtrain-ai[gpu]
3
4# Verify GPU is detected
5python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}'); print(f'GPU: {torch.cuda.get_device_name(0)}' if torch.cuda.is_available() else 'No GPU')"

Docker

Use our official Docker images for consistent environments. Available tags:

•langtrain/langtrain:latest - CPU
•langtrain/langtrain:gpu - NVIDIA GPU

1# Pull and run with GPU
2docker run --gpus all -it langtrain/langtrain:gpu
3
4# Mount data directory
5docker run --gpus all -v $(pwd)/data:/data langtrain/langtrain:gpu

API Key Setup

Get your API key from the Langtrain dashboard and configure it for cloud features.

1# Set API key as environment variable
2export LANGTRAIN_API_KEY="your-api-key"
3
4# Or configure in Python
5import langtrain
6langtrain.api_key = "your-api-key"
7
8# Or use a config file
9# ~/.langtrain/config.json
10# {"api_key": "your-api-key"}

Verify Installation

Run the diagnostics script to verify your installation is working correctly.

1python -c "
2import langtrain
3print('Langtrain:', langtrain.__version__)
4
5import torch
6print('PyTorch:', torch.__version__)
7print('CUDA:', torch.cuda.is_available())
8
9from transformers import __version__ as t_ver
10print('Transformers:', t_ver)
11print('✓ Installation verified')
12"