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Installation

.. currentmodule:: tfts

This guide covers everything you need to install and set up TFTS (TensorFlow Time Series) for your environment.

Quick Installation

The fastest way to install TFTS is using pip:

pip install tfts

This will install TFTS and its core dependencies.

Requirements

System Requirements

Minimum Requirements:
  • Python 3.7 or higher
  • 4GB RAM (8GB+ recommended)
  • 2GB disk space
Recommended:
  • Python 3.8+
  • 16GB RAM for training large models
  • NVIDIA GPU with CUDA support (for GPU training)
  • 5GB+ disk space (including datasets and model checkpoints)

Dependencies

TFTS requires the following core dependencies:

Required:
  • tensorflow >= 2.4.0
  • numpy >= 1.19.0
  • pandas >= 1.1.0
Optional but Recommended:
  • scikit-learn >= 0.24.0 (for preprocessing)
  • matplotlib >= 3.3.0 (for visualization)
  • seaborn >= 0.11.0 (for advanced plotting)

Installation Methods

From PyPI (Recommended)

Install the latest stable release from PyPI:

pip install tfts

To upgrade to the latest version:

pip install --upgrade tfts

From Source

For the latest development version, install from GitHub:

git clone https://github.com/LongxingTan/Time-series-prediction.git
cd Time-series-prediction
pip install -e .

The -e flag installs in editable mode, allowing you to modify the source code.

For Development

If you plan to contribute or modify TFTS, install development dependencies:

git clone https://github.com/LongxingTan/Time-series-prediction.git
cd Time-series-prediction
pip install -e ".[dev]"

This installs additional tools for testing, linting, and documentation.

Using Docker

TFTS provides a Docker image with all dependencies pre-installed:

Build the Docker Image:

docker build -f ./docker/Dockerfile -t tfts:latest .

Run the Container:

docker run --rm -it \
    --init \
    --ipc=host \
    --network=host \
    --volume=$PWD:/app \
    --gpus all \
    tfts:latest /bin/bash

For CPU-only:

docker run --rm -it \
    --init \
    --volume=$PWD:/app \
    tfts:latest /bin/bash

Environment-Specific Installation

TensorFlow GPU Support

For GPU acceleration, install TensorFlow with CUDA support:

CUDA 11.2+ (Recommended):

pip install tensorflow[and-cuda]

Manual CUDA Installation:

  1. Install CUDA Toolkit: https://developer.nvidia.com/cuda-downloads
  2. Install cuDNN: https://developer.nvidia.com/cudnn
  3. Install TensorFlow:
pip install tensorflow-gpu
pip install tfts

Verify GPU Setup:

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

Apple Silicon (M1/M2/M3)

For macOS with Apple Silicon:

# Install tensorflow-metal for GPU acceleration
pip install tensorflow-macos tensorflow-metal
pip install tfts

Verify Metal Support:

import tensorflow as tf
print(tf.config.list_physical_devices())

TPU Support

For Google Cloud TPU:

pip install cloud-tpu-client
pip install tfts

TPU Runtime Configuration:

import tensorflow as tf

resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)

Conda Environment

Create an isolated conda environment for TFTS:

# Create environment
conda create -n tfts python=3.9
conda activate tfts

# Install dependencies
conda install tensorflow pandas numpy scikit-learn matplotlib
pip install tfts

Virtual Environment

Using Python's built-in venv:

# Create virtual environment
python -m venv tfts-env

# Activate (Linux/Mac)
source tfts-env/bin/activate

# Activate (Windows)
tfts-env\Scripts\activate

# Install TFTS
pip install tfts

Troubleshooting

Common Installation Issues

ImportError: No module named 'tensorflow'

Solution: Install TensorFlow first:

pip install tensorflow>=2.4

CUDA version mismatch

Solution: Ensure CUDA and cuDNN versions match TensorFlow requirements:

# Check TensorFlow CUDA requirement
python -c "import tensorflow as tf; print(tf.sysconfig.get_build_info())"

Memory errors during installation

Solution: Install with no-cache option:

pip install --no-cache-dir tfts

Permission denied on Linux/Mac

Solution: Use user installation:

pip install --user tfts

Platform-Specific Issues

Windows:
  • Use Anaconda/Miniconda for easier dependency management
  • Install Microsoft Visual C++ Redistributable if needed
  • Consider using WSL2 for Linux compatibility
macOS:
  • Install Xcode Command Line Tools: xcode-select --install
  • Use Homebrew for system dependencies: brew install python
Linux:
  • Install build essentials: sudo apt-get install build-essential
  • For GPU: Install NVIDIA drivers and CUDA toolkit

Verification

Verify Installation

Check that TFTS is installed correctly:

import tfts
print(f"TFTS version: {tfts.__version__}")

# Check available models
from tfts import AutoConfig
models = ['seq2seq', 'transformer', 'informer', 'autoformer']
for model in models:
    config = AutoConfig.for_model(model)
    print(f"{model}: OK")

Run Test Suite

Run the test suite to ensure everything works:

# Install test dependencies
pip install pytest pytest-cov

# Run tests
pytest tests/

# Run with coverage
pytest tests/ --cov=tfts

Quick Start Test

Run a quick training test:

import tensorflow as tf
import tfts
from tfts import AutoConfig, AutoModel, KerasTrainer

# Generate sample data
train, valid = tfts.get_data('sine', train_length=24, predict_length=8)

# Create and train model
config = AutoConfig.for_model('seq2seq')
model = AutoModel.from_config(config, predict_sequence_length=8)
trainer = KerasTrainer(model)
trainer.train(train, valid, epochs=2)

print("✅ Installation successful!")

Updating TFTS

Stay Updated

Keep TFTS up to date with the latest features and bug fixes:

# Check current version
pip show tfts

# Update to latest version
pip install --upgrade tfts

# Update to specific version
pip install --upgrade tfts==1.3.0

Development Builds

For bleeding-edge features, install from the development branch:

pip install git+https://github.com/LongxingTan/Time-series-prediction.git@master

Uninstallation

To remove TFTS:

pip uninstall tfts

To completely remove including dependencies:

pip uninstall tfts tensorflow pandas numpy scikit-learn matplotlib

Next Steps

Now that you have TFTS installed:

  1. Quick Start: Try the :doc:`quickstart` tutorial
  2. Learn the Basics: Read :doc:`tutorials`
  3. Explore Models: Check :doc:`models` documentation
  4. Prepare Data: See :doc:`data_preparation` guide
  5. Train Models: Follow :doc:`training` best practices

Getting Help

If you encounter installation issues: