foundry Labs
A cognitive workspace for rapid agent development, training, and orchestration. Build sophisticated AI systems with advanced reasoning capabilities.
Integrated Development Environment
Labs IDE provides a powerful, fully-featured environment for AI development that combines code editing, notebooks, and debugging in one unified interface.
- Intelligent code completion with AI-assisted features
- Real-time collaboration for team development
- Integrated terminal and debugging tools
- Native support for Jupyter notebooks
- Version control with Git integration
- One-click deployment to training infrastructure
# Install the foundry Labs IDE extension
$ foundry extension install ide
# Launch IDE with GPU acceleration
$ foundry ide start --gpuimport torch from transformers import AutoModelForCausalLM, AutoTokenizer # Load pre-trained model from Hugging Face def load_model(model_name): """Load a model from Hugging Face hub""" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) return model, tokenizer # Initialize model model, tokenizer = load_model("deepseek-ai/deepseek-coder-6.7b-instruct") # Generate text def generate_response(prompt, max_length=256): inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( inputs.input_ids, max_length=max_length, temperature=0.7, ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response
Model Training Infrastructure
A powerful, scalable infrastructure designed specifically for training large machine learning models with automatic resource optimization and fault tolerance.
- Distributed training across multiple GPUs and nodes
- Automatic hyperparameter optimization
- Experiment tracking and versioning
- Checkpointing and training resumption
- Resource-aware scheduling with priority queues
- Support for various training frameworks (PyTorch, TensorFlow, JAX)
# Launch distributed training job
$ foundry train launch \
--config ./configs/train.yaml \
--gpu 8 \
--nodes 2 \
--checkpoint-dir s3://foundry-checkpoints/user@foundry:~$ foundry train status
JOB_ID STATUS GPU PROGRESS ETA
train-1234 Running 8 76.3% 1h 23m
finetune-42 Queued 4 0% -
eval-567 Complete 2 100% -
user@foundry:~$ foundry train logs train-1234 --tail
[2025-03-05 09:42:13] Epoch 8/10: loss=0.342, val_loss=0.401
[2025-03-05 09:43:01] Saving checkpoint to s3://foundry-checkpoints/model-ep8.pt
[2025-03-05 09:43:22] Starting epoch 9: lr=2.5e-5
[2025-03-05 09:44:15] Training examples: 24000/32000
user@foundry:~$ _
Pre-trained Models via Hugging Face
Seamlessly access and deploy thousands of pre-trained models directly from Hugging Face Hub, with optimized integration into the foundry Labs ecosystem.
- One-click deployment of Hugging Face models
- Automatic model quantization for optimal performance
- Custom model fine-tuning with minimal code
- Private model registry for your organization
- Model versioning and A/B testing capabilities
- Automated evaluation on benchmark datasets
# Deploy a model from Hugging Face
$ foundry model deploy huggingface \
--model "mistralai/Mistral-7B-Instruct-v0.2" \
--quantize 4bit \
--replicas 2 \
--inference-endpoint /api/v1/generate
MLOps Integration
End-to-end MLOps solutions that streamline the entire machine learning lifecycle, from development to production deployment and monitoring.
- CI/CD pipelines for ML model deployment
- Automated testing and validation workflows
- Drift detection and performance monitoring
- A/B testing and feature flagging
- Canary deployments and rollback capabilities
- Integration with popular tools (GitHub Actions, Jenkins, Prometheus)
# Create an MLOps pipeline
$ foundry mlops create-pipeline \
--name production-model-release \
--source github.com/foundryos/model-repo \
--stages train,evaluate,deploy \
--monitoring-dashboard true