Setup Local AI Environment: Conda, GPU, PyTorch

You spent two days installing PyTorch, and it still sees CPU? A familiar situation. A proper local environment is the foundation of an AI engineer's productivity. Over 5 years, we have set up more than 50 AI environments and developed a clear recipe that cuts setup time from two days to one hour. Dependency management, CUDA versions, and environment isolation are the basics. Without them, experiments are irreproducible and p99 latency suffers. We use proven tools and patterns so you can focus on models, not the environment.

Why Environment Isolation Is Critical for AI Projects

Each ML project requires its own stack: some use PyTorch 2.0, others TensorFlow 2.15. Conflicts between CUDA, cuDNN, and Python versions are a common time sink. We use Conda as the primary environment manager. Unlike venv, Conda manages not only Python packages but also system dependencies (CUDA, NCCL). This allows fully isolated environments:

# Example: create environment for an NLP project
conda create -n nlp python=3.11
conda activate nlp
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install transformers datasets peft accelerate

This approach guarantees that you can work on a PyTorch classification project and a TensorFlow generative project in parallel on the same machine without breaking dependencies.

How to Speed Up Development with Remote GPUs

For resource-intensive tasks (fine-tuning LLMs, training from scratch), local GPUs are often insufficient. We set up a hybrid scheme: code stored locally (Git), data in S3/GCS, compute on cloud instances (SageMaker, Vertex AI). Connection via VS Code Remote SSH lets you edit code locally and run it on a powerful GPU machine. This is cheaper than keeping a local A100 and more convenient than working in a browser-based Jupyter.

What's Included in AI Development Environment Setup

We offer a turnkey service: from analyzing your current environment to full configuration. As a result, you get:

• Isolated Conda environments per project with pinned versions (Python, CUDA, libraries)
• IDE configuration (VS Code, PyCharm) with linters (black, isort, mypy) and debugger
• Jupyter Lab integration with remote kernel capability
• DVC configuration for data versioning and MLflow for experiment tracking
• GPU profiling scripts (PyTorch Profiler) and bottleneck analysis
• Remote GPU access setup (SSH config, SSHFS)
• Network infrastructure documentation (ports, proxies, VPN)
• Team training: we show how to work with the new environment

Common Mistakes in Self-Setup

• Installing PyTorch for CPU instead of CUDA — you download the version without GPU support. Check torch.cuda.is_available().
• Mixing pip and conda — conda does not track pip installations. Use only pip inside a conda environment.
• Missing .gitignore for datasets and models — cluttering the repository. DVC solves this.
• Pinning only Python dependencies — forgetting system libraries (CUDA, cuDNN). Conda's environment.yml includes everything.

How We Set Up Your Environment in 6 Steps

1. Audit current environment — identify conflicts, unused dependencies, suboptimal GPU settings.
2. Design architecture — choose stack (Conda vs Docker, DVC vs Git LFS, MLflow vs W&B).
3. Build base image — create environment.yml with full dependency list, including system packages.
4. Integrate tools — VS Code, Jupyter, DVC, MLflow, pre-commit hooks.
5. Test reproducibility — verify that the environment deploys from scratch in 15 minutes.
6. Deliver documentation — hand over a README with instructions and configuration files.

Guarantees and Experience

We have worked with AI infrastructure for over 5 years. Our engineers are AWS ML Specialty certified and have set up environments for projects with up to 10^8 parameters. We guarantee that after setup you won't face import errors or CUDA incompatibility. If an issue arises, we fix it within 24 hours. We assess your project in one day: just write to us with a description of your current stack. Order the setup today and save hours of debugging.

We have already helped dozens of teams streamline their development process. Get a consultation on your environment — it will take no more than an hour.