Langtrain
Langtrain Docs
DocsAPI ReferenceSDK Reference
AppChat
GitHubDiscord

Features

Discover the powerful features that make LangTrain the best platform for training and deploying language models.

Auto-Tuning

LangTrain's auto-tuning feature automatically optimizes your model's hyperparameters for best performance.
What Gets Optimized:
  • •Learning rate and scheduling
  • •Batch size and accumulation steps
  • •Architecture parameters (layers, attention heads)
  • •Regularization (dropout, weight decay)
  • •Optimization algorithms (Adam, AdamW, SGD)
How It Works:1. Search Space Definition: Define parameter ranges2. Smart Sampling: Use Bayesian optimization for efficient search3. Early Stopping: Terminate poor configurations quickly4. Resource Management: Balance exploration vs exploitation5. Best Configuration: Select optimal parameters automatically
Benefits:
  • •Save weeks of manual hyperparameter tuning
  • •Achieve better performance than manual tuning
  • •Reduce computational costs through smart search
  • •Reproducible and explainable optimization process

Monitoring & Analytics

Comprehensive monitoring for training and production models.
Training Monitoring:
  • •Real-time loss and metric curves
  • •Resource utilization (GPU, memory, disk)
  • •Training speed and estimated completion time
  • •Data throughput and batch processing stats
  • •Error tracking and debugging information
Production Monitoring:
  • •Request latency and throughput metrics
  • •Model accuracy and drift detection
  • •Cost tracking and budget alerts
  • •Error rates and failure analysis
  • •User feedback and satisfaction scores
Advanced Analytics:
  • •A/B testing between model versions
  • •Cohort analysis for different user segments
  • •Performance trending and forecasting
  • •Custom dashboard creation
  • •Automated alerting and notifications

Scalable Infrastructure

Built-in scaling capabilities for any workload size.
Training Scaling:
  • •Single GPU: For small datasets and prototyping
  • •Multi-GPU: Distributed training on single machine
  • •Multi-Node: Scale across multiple machines
  • •Spot Instances: Cost-effective training with interruption handling
  • •Auto-Scaling: Dynamically adjust resources based on demand
Inference Scaling:
  • •Auto-Scaling: Adjust replicas based on traffic
  • •Load Balancing: Distribute requests efficiently
  • •Global Deployment: Deploy in multiple regions
  • •Edge Computing: Run models closer to users
  • •Serverless: Pay only for actual inference time
Resource Management:
  • •Intelligent resource allocation
  • •Priority-based scheduling
  • •Resource quotas and limits
  • •Cost optimization recommendations

Full Implementation

Enable Auto-Tuning

python
1import langtrain
2

3client = langtrain.LangTrain()
4

5# Create model with auto-tuning
6model = client.models.create(
7    name="optimized-classifier",
8    type="text-classification"
9)
10

11# Train with auto-tuning enabled
12training_job = model.train(
13    dataset_id="your-dataset-id",
14    auto_tune=True,
15    auto_tune_config={
16        "max_trials": 50,
17        "optimization_metric": "f1_score",
18        "search_space": {
19            "learning_rate": [1e-5, 5e-4],
20            "batch_size": [16, 32, 64],
21            "num_epochs": [3, 5, 10]
22        }
23    }
24)
25

26# Get best configuration
27best_config = training_job.get_best_config()
28print(f"Best learning rate: {best_config['learning_rate']}")
29print(f"Best batch size: {best_config['batch_size']}")