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Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FCloud-and-Hosting\u002Flambda-labs\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"gpu","cloud","GPU Cloud","Lambda Labs","lambda","lambda labs gpu cloud","node","Clusters","On-Demand GPU","when","for","orchestra","you","Multi-Node","high-performance","instances","use","performance","access","Large-Scale Training","training","infrastructure","ml","2026-11-10 10:12:30",false,"---\nname: lambda-labs-gpu-cloud\ndescription: Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Infrastructure, GPU Cloud, Training, Inference, Lambda Labs]\ndependencies: [lambda-cloud-client>=1.0.0]\n---\n\n# Lambda Labs GPU Cloud\n\nComprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.\n\n## When to use Lambda Labs\n\n**Use Lambda Labs when:**\n- Need dedicated GPU instances with full SSH access\n- Running long training jobs (hours to days)\n- Want simple pricing with no egress fees\n- Need persistent storage across sessions\n- Require high-performance multi-node clusters (16-512 GPUs)\n- Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL)\n\n**Key features:**\n- **GPU variety**: B200, H100, GH200, A100, A10, A6000, V100\n- **Lambda Stack**: Pre-installed PyTorch, TensorFlow, CUDA, cuDNN, NCCL\n- **Persistent filesystems**: Keep data across instance restarts\n- **1-Click Clusters**: 16-512 GPU Slurm clusters with InfiniBand\n- **Simple pricing**: Pay-per-minute, no egress fees\n- **Global regions**: 12+ regions worldwide\n\n**Use alternatives instead:**\n- **Modal**: For serverless, auto-scaling workloads\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **RunPod**: For cheaper spot instances and serverless endpoints\n- **Vast.ai**: For GPU marketplace with lowest prices\n\n## Quick start\n\n### Account setup\n\n1. Create account at https:\u002F\u002Flambda.ai\n2. Add payment method\n3. Generate API key from dashboard\n4. Add SSH key (required before launching instances)\n\n### Launch via console\n\n1. Go to https:\u002F\u002Fcloud.lambda.ai\u002Finstances\n2. Click \"Launch instance\"\n3. Select GPU type and region\n4. Choose SSH key\n5. Optionally attach filesystem\n6. Launch and wait 3-15 minutes\n\n### Connect via SSH\n\n```bash\n# Get instance IP from console\nssh ubuntu@\u003CINSTANCE-IP>\n\n# Or with specific key\nssh -i ~\u002F.ssh\u002Flambda_key ubuntu@\u003CINSTANCE-IP>\n```\n\n## GPU instances\n\n### Available GPUs\n\n| GPU | VRAM | Price\u002FGPU\u002Fhr | Best For |\n|-----|------|--------------|----------|\n| B200 SXM6 | 180 GB | $4.99 | Largest models, fastest training |\n| H100 SXM | 80 GB | $2.99-3.29 | Large model training |\n| H100 PCIe | 80 GB | $2.49 | Cost-effective H100 |\n| GH200 | 96 GB | $1.49 | Single-GPU large models |\n| A100 80GB | 80 GB | $1.79 | Production training |\n| A100 40GB | 40 GB | $1.29 | Standard training |\n| A10 | 24 GB | $0.75 | Inference, fine-tuning |\n| A6000 | 48 GB | $0.80 | Good VRAM\u002Fprice ratio |\n| V100 | 16 GB | $0.55 | Budget training |\n\n### Instance configurations\n\n```\n8x GPU: Best for distributed training (DDP, FSDP)\n4x GPU: Large models, multi-GPU training\n2x GPU: Medium workloads\n1x GPU: Fine-tuning, inference, development\n```\n\n### Launch times\n\n- Single-GPU: 3-5 minutes\n- Multi-GPU: 10-15 minutes\n\n## Lambda Stack\n\nAll instances come with Lambda Stack pre-installed:\n\n```bash\n# Included software\n- Ubuntu 22.04 LTS\n- NVIDIA drivers (latest)\n- CUDA 12.x\n- cuDNN 8.x\n- NCCL (for multi-GPU)\n- PyTorch (latest)\n- TensorFlow (latest)\n- JAX\n- JupyterLab\n```\n\n### Verify installation\n\n```bash\n# Check GPU\nnvidia-smi\n\n# Check PyTorch\npython -c \"import torch; print(torch.cuda.is_available())\"\n\n# Check CUDA version\nnvcc --version\n```\n\n## Python API\n\n### Installation\n\n```bash\npip install lambda-cloud-client\n```\n\n### Authentication\n\n```python\nimport os\nimport lambda_cloud_client\n\n# Configure with API key\nconfiguration = lambda_cloud_client.Configuration(\n    host=\"https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\",\n    access_token=os.environ[\"LAMBDA_API_KEY\"]\n)\n```\n\n### List available instances\n\n```python\nwith lambda_cloud_client.ApiClient(configuration) as api_client:\n    api = lambda_cloud_client.DefaultApi(api_client)\n\n    # Get available instance types\n    types = api.instance_types()\n    for name, info in types.data.items():\n        print(f\"{name}: {info.instance_type.description}\")\n```\n\n### Launch instance\n\n```python\nfrom lambda_cloud_client.models import LaunchInstanceRequest\n\nrequest = LaunchInstanceRequest(\n    region_name=\"us-west-1\",\n    instance_type_name=\"gpu_1x_h100_sxm5\",\n    ssh_key_names=[\"my-ssh-key\"],\n    file_system_names=[\"my-filesystem\"],  # Optional\n    name=\"training-job\"\n)\n\nresponse = api.launch_instance(request)\ninstance_id = response.data.instance_ids[0]\nprint(f\"Launched: {instance_id}\")\n```\n\n### List running instances\n\n```python\ninstances = api.list_instances()\nfor instance in instances.data:\n    print(f\"{instance.name}: {instance.ip} ({instance.status})\")\n```\n\n### Terminate instance\n\n```python\nfrom lambda_cloud_client.models import TerminateInstanceRequest\n\nrequest = TerminateInstanceRequest(\n    instance_ids=[instance_id]\n)\napi.terminate_instance(request)\n```\n\n### SSH key management\n\n```python\nfrom lambda_cloud_client.models import AddSshKeyRequest\n\n# Add SSH key\nrequest = AddSshKeyRequest(\n    name=\"my-key\",\n    public_key=\"ssh-rsa AAAA...\"\n)\napi.add_ssh_key(request)\n\n# List keys\nkeys = api.list_ssh_keys()\n\n# Delete key\napi.delete_ssh_key(key_id)\n```\n\n## CLI with curl\n\n### List instance types\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-types | jq\n```\n\n### Launch instance\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  -X POST https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-operations\u002Flaunch \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"region_name\": \"us-west-1\",\n    \"instance_type_name\": \"gpu_1x_h100_sxm5\",\n    \"ssh_key_names\": [\"my-key\"]\n  }' | jq\n```\n\n### Terminate instance\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  -X POST https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-operations\u002Fterminate \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"instance_ids\": [\"\u003CINSTANCE-ID>\"]}' | jq\n```\n\n## Persistent storage\n\n### Filesystems\n\nFilesystems persist data across instance restarts:\n\n```bash\n# Mount location\n\u002Flambda\u002Fnfs\u002F\u003CFILESYSTEM_NAME>\n\n# Example: save checkpoints\npython train.py --checkpoint-dir \u002Flambda\u002Fnfs\u002Fmy-storage\u002Fcheckpoints\n```\n\n### Create filesystem\n\n1. Go to Storage in Lambda console\n2. Click \"Create filesystem\"\n3. Select region (must match instance region)\n4. Name and create\n\n### Attach to instance\n\nFilesystems must be attached at instance launch time:\n- Via console: Select filesystem when launching\n- Via API: Include `file_system_names` in launch request\n\n### Best practices\n\n```bash\n# Store on filesystem (persists)\n\u002Flambda\u002Fnfs\u002Fstorage\u002F\n  ├── datasets\u002F\n  ├── checkpoints\u002F\n  ├── models\u002F\n  └── outputs\u002F\n\n# Local SSD (faster, ephemeral)\n\u002Fhome\u002Fubuntu\u002F\n  └── working\u002F  # Temporary files\n```\n\n## SSH configuration\n\n### Add SSH key\n\n```bash\n# Generate key locally\nssh-keygen -t ed25519 -f ~\u002F.ssh\u002Flambda_key\n\n# Add public key to Lambda console\n# Or via API\n```\n\n### Multiple keys\n\n```bash\n# On instance, add more keys\necho 'ssh-rsa AAAA...' >> ~\u002F.ssh\u002Fauthorized_keys\n```\n\n### Import from GitHub\n\n```bash\n# On instance\nssh-import-id gh:username\n```\n\n### SSH tunneling\n\n```bash\n# Forward Jupyter\nssh -L 8888:localhost:8888 ubuntu@\u003CIP>\n\n# Forward TensorBoard\nssh -L 6006:localhost:6006 ubuntu@\u003CIP>\n\n# Multiple ports\nssh -L 8888:localhost:8888 -L 6006:localhost:6006 ubuntu@\u003CIP>\n```\n\n## JupyterLab\n\n### Launch from console\n\n1. Go to Instances page\n2. Click \"Launch\" in Cloud IDE column\n3. JupyterLab opens in browser\n\n### Manual access\n\n```bash\n# On instance\njupyter lab --ip=0.0.0.0 --port=8888\n\n# From local machine with tunnel\nssh -L 8888:localhost:8888 ubuntu@\u003CIP>\n# Open http:\u002F\u002Flocalhost:8888\n```\n\n## Training workflows\n\n### Single-GPU training\n\n```bash\n# SSH to instance\nssh ubuntu@\u003CIP>\n\n# Clone repo\ngit clone https:\u002F\u002Fgithub.com\u002Fuser\u002Fproject\ncd project\n\n# Install dependencies\npip install -r requirements.txt\n\n# Train\npython train.py --epochs 100 --checkpoint-dir \u002Flambda\u002Fnfs\u002Fstorage\u002Fcheckpoints\n```\n\n### Multi-GPU training (single node)\n\n```python\n# train_ddp.py\nimport torch\nimport torch.distributed as dist\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\ndef main():\n    dist.init_process_group(\"nccl\")\n    rank = dist.get_rank()\n    device = rank % torch.cuda.device_count()\n\n    model = MyModel().to(device)\n    model = DDP(model, device_ids=[device])\n\n    # Training loop...\n\nif __name__ == \"__main__\":\n    main()\n```\n\n```bash\n# Launch with torchrun (8 GPUs)\ntorchrun --nproc_per_node=8 train_ddp.py\n```\n\n### Checkpoint to filesystem\n\n```python\nimport os\n\ncheckpoint_dir = \"\u002Flambda\u002Fnfs\u002Fmy-storage\u002Fcheckpoints\"\nos.makedirs(checkpoint_dir, exist_ok=True)\n\n# Save checkpoint\ntorch.save({\n    'epoch': epoch,\n    'model_state_dict': model.state_dict(),\n    'optimizer_state_dict': optimizer.state_dict(),\n    'loss': loss,\n}, f\"{checkpoint_dir}\u002Fcheckpoint_{epoch}.pt\")\n```\n\n## 1-Click Clusters\n\n### Overview\n\nHigh-performance Slurm clusters with:\n- 16-512 NVIDIA H100 or B200 GPUs\n- NVIDIA Quantum-2 400 Gb\u002Fs InfiniBand\n- GPUDirect RDMA at 3200 Gb\u002Fs\n- Pre-installed distributed ML stack\n\n### Included software\n\n- Ubuntu 22.04 LTS + Lambda Stack\n- NCCL, Open MPI\n- PyTorch with DDP and FSDP\n- TensorFlow\n- OFED drivers\n\n### Storage\n\n- 24 TB NVMe per compute node (ephemeral)\n- Lambda filesystems for persistent data\n\n### Multi-node training\n\n```bash\n# On Slurm cluster\nsrun --nodes=4 --ntasks-per-node=8 --gpus-per-node=8 \\\n  torchrun --nnodes=4 --nproc_per_node=8 \\\n  --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR:29500 \\\n  train.py\n```\n\n## Networking\n\n### Bandwidth\n\n- Inter-instance (same region): up to 200 Gbps\n- Internet outbound: 20 Gbps max\n\n### Firewall\n\n- Default: Only port 22 (SSH) open\n- Configure additional ports in Lambda console\n- ICMP traffic allowed by default\n\n### Private IPs\n\n```bash\n# Find private IP\nip addr show | grep 'inet '\n```\n\n## Common workflows\n\n### Workflow 1: Fine-tuning LLM\n\n```bash\n# 1. Launch 8x H100 instance with filesystem\n\n# 2. SSH and setup\nssh ubuntu@\u003CIP>\npip install transformers accelerate peft\n\n# 3. Download model to filesystem\npython -c \"\nfrom transformers import AutoModelForCausalLM\nmodel = AutoModelForCausalLM.from_pretrained('meta-llama\u002FLlama-2-7b-hf')\nmodel.save_pretrained('\u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Fllama-2-7b')\n\"\n\n# 4. Fine-tune with checkpoints on filesystem\naccelerate launch --num_processes 8 train.py \\\n  --model_path \u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Fllama-2-7b \\\n  --output_dir \u002Flambda\u002Fnfs\u002Fstorage\u002Foutputs \\\n  --checkpoint_dir \u002Flambda\u002Fnfs\u002Fstorage\u002Fcheckpoints\n```\n\n### Workflow 2: Batch inference\n\n```bash\n# 1. Launch A10 instance (cost-effective for inference)\n\n# 2. Run inference\npython inference.py \\\n  --model \u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Ffine-tuned \\\n  --input \u002Flambda\u002Fnfs\u002Fstorage\u002Fdata\u002Finputs.jsonl \\\n  --output \u002Flambda\u002Fnfs\u002Fstorage\u002Fdata\u002Foutputs.jsonl\n```\n\n## Cost optimization\n\n### Choose right GPU\n\n| Task | Recommended GPU |\n|------|-----------------|\n| LLM fine-tuning (7B) | A100 40GB |\n| LLM fine-tuning (70B) | 8x H100 |\n| Inference | A10, A6000 |\n| Development | V100, A10 |\n| Maximum performance | B200 |\n\n### Reduce costs\n\n1. **Use filesystems**: Avoid re-downloading data\n2. **Checkpoint frequently**: Resume interrupted training\n3. **Right-size**: Don't over-provision GPUs\n4. **Terminate idle**: No auto-stop, manually terminate\n\n### Monitor usage\n\n- Dashboard shows real-time GPU utilization\n- API for programmatic monitoring\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Instance won't launch | Check region availability, try different GPU |\n| SSH connection refused | Wait for instance to initialize (3-15 min) |\n| Data lost after terminate | Use persistent filesystems |\n| Slow data transfer | Use filesystem in same region |\n| GPU not detected | Reboot instance, check drivers |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-node training, API automation\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.lambda.ai\n- **Console**: https:\u002F\u002Fcloud.lambda.ai\n- **Pricing**: https:\u002F\u002Flambda.ai\u002Finstances\n- **Support**: https:\u002F\u002Fsupport.lambdalabs.com\n- **Blog**: https:\u002F\u002Flambda.ai\u002Fblog\n","# Lambda Labs GPU Cloud\n\nComprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.\n\n## When to use Lambda Labs\n\n**Use Lambda Labs when:**\n- Need dedicated GPU instances with full SSH access\n- Running long training jobs (hours to days)\n- Want simple pricing with no egress fees\n- Need persistent storage across sessions\n- Require high-performance multi-node clusters (16-512 GPUs)\n- Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL)\n\n**Key features:**\n- **GPU variety**: B200, H100, GH200, A100, A10, A6000, V100\n- **Lambda Stack**: Pre-installed PyTorch, TensorFlow, CUDA, cuDNN, NCCL\n- **Persistent filesystems**: Keep data across instance restarts\n- **1-Click Clusters**: 16-512 GPU Slurm clusters with InfiniBand\n- **Simple pricing**: Pay-per-minute, no egress fees\n- **Global regions**: 12+ regions worldwide\n\n**Use alternatives instead:**\n- **Modal**: For serverless, auto-scaling workloads\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **RunPod**: For cheaper spot instances and serverless endpoints\n- **Vast.ai**: For GPU marketplace with lowest prices\n\n## Quick start\n\n### Account setup\n\n1. Create account at https:\u002F\u002Flambda.ai\n2. Add payment method\n3. Generate API key from dashboard\n4. Add SSH key (required before launching instances)\n\n### Launch via console\n\n1. Go to https:\u002F\u002Fcloud.lambda.ai\u002Finstances\n2. Click \"Launch instance\"\n3. Select GPU type and region\n4. Choose SSH key\n5. Optionally attach filesystem\n6. Launch and wait 3-15 minutes\n\n### Connect via SSH\n\n```bash\n# Get instance IP from console\nssh ubuntu@\u003CINSTANCE-IP>\n\n# Or with specific key\nssh -i ~\u002F.ssh\u002Flambda_key ubuntu@\u003CINSTANCE-IP>\n```\n\n## GPU instances\n\n### Available GPUs\n\n| GPU | VRAM | Price\u002FGPU\u002Fhr | Best For |\n|-----|------|--------------|----------|\n| B200 SXM6 | 180 GB | $4.99 | Largest models, fastest training |\n| H100 SXM | 80 GB | $2.99-3.29 | Large model training |\n| H100 PCIe | 80 GB | $2.49 | Cost-effective H100 |\n| GH200 | 96 GB | $1.49 | Single-GPU large models |\n| A100 80GB | 80 GB | $1.79 | Production training |\n| A100 40GB | 40 GB | $1.29 | Standard training |\n| A10 | 24 GB | $0.75 | Inference, fine-tuning |\n| A6000 | 48 GB | $0.80 | Good VRAM\u002Fprice ratio |\n| V100 | 16 GB | $0.55 | Budget training |\n\n### Instance configurations\n\n```\n8x GPU: Best for distributed training (DDP, FSDP)\n4x GPU: Large models, multi-GPU training\n2x GPU: Medium workloads\n1x GPU: Fine-tuning, inference, development\n```\n\n### Launch times\n\n- Single-GPU: 3-5 minutes\n- Multi-GPU: 10-15 minutes\n\n## Lambda Stack\n\nAll instances come with Lambda Stack pre-installed:\n\n```bash\n# Included software\n- Ubuntu 22.04 LTS\n- NVIDIA drivers (latest)\n- CUDA 12.x\n- cuDNN 8.x\n- NCCL (for multi-GPU)\n- PyTorch (latest)\n- TensorFlow (latest)\n- JAX\n- JupyterLab\n```\n\n### Verify installation\n\n```bash\n# Check GPU\nnvidia-smi\n\n# Check PyTorch\npython -c \"import torch; print(torch.cuda.is_available())\"\n\n# Check CUDA version\nnvcc --version\n```\n\n## Python API\n\n### Installation\n\n```bash\npip install lambda-cloud-client\n```\n\n### Authentication\n\n```python\nimport os\nimport lambda_cloud_client\n\n# Configure with API key\nconfiguration = lambda_cloud_client.Configuration(\n    host=\"https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\",\n    access_token=os.environ[\"LAMBDA_API_KEY\"]\n)\n```\n\n### List available instances\n\n```python\nwith lambda_cloud_client.ApiClient(configuration) as api_client:\n    api = lambda_cloud_client.DefaultApi(api_client)\n\n    # Get available instance types\n    types = api.instance_types()\n    for name, info in types.data.items():\n        print(f\"{name}: {info.instance_type.description}\")\n```\n\n### Launch instance\n\n```python\nfrom lambda_cloud_client.models import LaunchInstanceRequest\n\nrequest = LaunchInstanceRequest(\n    region_name=\"us-west-1\",\n    instance_type_name=\"gpu_1x_h100_sxm5\",\n    ssh_key_names=[\"my-ssh-key\"],\n    file_system_names=[\"my-filesystem\"],  # Optional\n    name=\"training-job\"\n)\n\nresponse = api.launch_instance(request)\ninstance_id = response.data.instance_ids[0]\nprint(f\"Launched: {instance_id}\")\n```\n\n### List running instances\n\n```python\ninstances = api.list_instances()\nfor instance in instances.data:\n    print(f\"{instance.name}: {instance.ip} ({instance.status})\")\n```\n\n### Terminate instance\n\n```python\nfrom lambda_cloud_client.models import TerminateInstanceRequest\n\nrequest = TerminateInstanceRequest(\n    instance_ids=[instance_id]\n)\napi.terminate_instance(request)\n```\n\n### SSH key management\n\n```python\nfrom lambda_cloud_client.models import AddSshKeyRequest\n\n# Add SSH key\nrequest = AddSshKeyRequest(\n    name=\"my-key\",\n    public_key=\"ssh-rsa AAAA...\"\n)\napi.add_ssh_key(request)\n\n# List keys\nkeys = api.list_ssh_keys()\n\n# Delete key\napi.delete_ssh_key(key_id)\n```\n\n## CLI with curl\n\n### List instance types\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-types | jq\n```\n\n### Launch instance\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  -X POST https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-operations\u002Flaunch \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"region_name\": \"us-west-1\",\n    \"instance_type_name\": \"gpu_1x_h100_sxm5\",\n    \"ssh_key_names\": [\"my-key\"]\n  }' | jq\n```\n\n### Terminate instance\n\n```bash\ncurl -u $LAMBDA_API_KEY: \\\n  -X POST https:\u002F\u002Fcloud.lambdalabs.com\u002Fapi\u002Fv1\u002Finstance-operations\u002Fterminate \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\"instance_ids\": [\"\u003CINSTANCE-ID>\"]}' | jq\n```\n\n## Persistent storage\n\n### Filesystems\n\nFilesystems persist data across instance restarts:\n\n```bash\n# Mount location\n\u002Flambda\u002Fnfs\u002F\u003CFILESYSTEM_NAME>\n\n# Example: save checkpoints\npython train.py --checkpoint-dir \u002Flambda\u002Fnfs\u002Fmy-storage\u002Fcheckpoints\n```\n\n### Create filesystem\n\n1. Go to Storage in Lambda console\n2. Click \"Create filesystem\"\n3. Select region (must match instance region)\n4. Name and create\n\n### Attach to instance\n\nFilesystems must be attached at instance launch time:\n- Via console: Select filesystem when launching\n- Via API: Include `file_system_names` in launch request\n\n### Best practices\n\n```bash\n# Store on filesystem (persists)\n\u002Flambda\u002Fnfs\u002Fstorage\u002F\n  ├── datasets\u002F\n  ├── checkpoints\u002F\n  ├── models\u002F\n  └── outputs\u002F\n\n# Local SSD (faster, ephemeral)\n\u002Fhome\u002Fubuntu\u002F\n  └── working\u002F  # Temporary files\n```\n\n## SSH configuration\n\n### Add SSH key\n\n```bash\n# Generate key locally\nssh-keygen -t ed25519 -f ~\u002F.ssh\u002Flambda_key\n\n# Add public key to Lambda console\n# Or via API\n```\n\n### Multiple keys\n\n```bash\n# On instance, add more keys\necho 'ssh-rsa AAAA...' >> ~\u002F.ssh\u002Fauthorized_keys\n```\n\n### Import from GitHub\n\n```bash\n# On instance\nssh-import-id gh:username\n```\n\n### SSH tunneling\n\n```bash\n# Forward Jupyter\nssh -L 8888:localhost:8888 ubuntu@\u003CIP>\n\n# Forward TensorBoard\nssh -L 6006:localhost:6006 ubuntu@\u003CIP>\n\n# Multiple ports\nssh -L 8888:localhost:8888 -L 6006:localhost:6006 ubuntu@\u003CIP>\n```\n\n## JupyterLab\n\n### Launch from console\n\n1. Go to Instances page\n2. Click \"Launch\" in Cloud IDE column\n3. JupyterLab opens in browser\n\n### Manual access\n\n```bash\n# On instance\njupyter lab --ip=0.0.0.0 --port=8888\n\n# From local machine with tunnel\nssh -L 8888:localhost:8888 ubuntu@\u003CIP>\n# Open http:\u002F\u002Flocalhost:8888\n```\n\n## Training workflows\n\n### Single-GPU training\n\n```bash\n# SSH to instance\nssh ubuntu@\u003CIP>\n\n# Clone repo\ngit clone https:\u002F\u002Fgithub.com\u002Fuser\u002Fproject\ncd project\n\n# Install dependencies\npip install -r requirements.txt\n\n# Train\npython train.py --epochs 100 --checkpoint-dir \u002Flambda\u002Fnfs\u002Fstorage\u002Fcheckpoints\n```\n\n### Multi-GPU training (single node)\n\n```python\n# train_ddp.py\nimport torch\nimport torch.distributed as dist\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\ndef main():\n    dist.init_process_group(\"nccl\")\n    rank = dist.get_rank()\n    device = rank % torch.cuda.device_count()\n\n    model = MyModel().to(device)\n    model = DDP(model, device_ids=[device])\n\n    # Training loop...\n\nif __name__ == \"__main__\":\n    main()\n```\n\n```bash\n# Launch with torchrun (8 GPUs)\ntorchrun --nproc_per_node=8 train_ddp.py\n```\n\n### Checkpoint to filesystem\n\n```python\nimport os\n\ncheckpoint_dir = \"\u002Flambda\u002Fnfs\u002Fmy-storage\u002Fcheckpoints\"\nos.makedirs(checkpoint_dir, exist_ok=True)\n\n# Save checkpoint\ntorch.save({\n    'epoch': epoch,\n    'model_state_dict': model.state_dict(),\n    'optimizer_state_dict': optimizer.state_dict(),\n    'loss': loss,\n}, f\"{checkpoint_dir}\u002Fcheckpoint_{epoch}.pt\")\n```\n\n## 1-Click Clusters\n\n### Overview\n\nHigh-performance Slurm clusters with:\n- 16-512 NVIDIA H100 or B200 GPUs\n- NVIDIA Quantum-2 400 Gb\u002Fs InfiniBand\n- GPUDirect RDMA at 3200 Gb\u002Fs\n- Pre-installed distributed ML stack\n\n### Included software\n\n- Ubuntu 22.04 LTS + Lambda Stack\n- NCCL, Open MPI\n- PyTorch with DDP and FSDP\n- TensorFlow\n- OFED drivers\n\n### Storage\n\n- 24 TB NVMe per compute node (ephemeral)\n- Lambda filesystems for persistent data\n\n### Multi-node training\n\n```bash\n# On Slurm cluster\nsrun --nodes=4 --ntasks-per-node=8 --gpus-per-node=8 \\\n  torchrun --nnodes=4 --nproc_per_node=8 \\\n  --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR:29500 \\\n  train.py\n```\n\n## Networking\n\n### Bandwidth\n\n- Inter-instance (same region): up to 200 Gbps\n- Internet outbound: 20 Gbps max\n\n### Firewall\n\n- Default: Only port 22 (SSH) open\n- Configure additional ports in Lambda console\n- ICMP traffic allowed by default\n\n### Private IPs\n\n```bash\n# Find private IP\nip addr show | grep 'inet '\n```\n\n## Common workflows\n\n### Workflow 1: Fine-tuning LLM\n\n```bash\n# 1. Launch 8x H100 instance with filesystem\n\n# 2. SSH and setup\nssh ubuntu@\u003CIP>\npip install transformers accelerate peft\n\n# 3. Download model to filesystem\npython -c \"\nfrom transformers import AutoModelForCausalLM\nmodel = AutoModelForCausalLM.from_pretrained('meta-llama\u002FLlama-2-7b-hf')\nmodel.save_pretrained('\u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Fllama-2-7b')\n\"\n\n# 4. Fine-tune with checkpoints on filesystem\naccelerate launch --num_processes 8 train.py \\\n  --model_path \u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Fllama-2-7b \\\n  --output_dir \u002Flambda\u002Fnfs\u002Fstorage\u002Foutputs \\\n  --checkpoint_dir \u002Flambda\u002Fnfs\u002Fstorage\u002Fcheckpoints\n```\n\n### Workflow 2: Batch inference\n\n```bash\n# 1. Launch A10 instance (cost-effective for inference)\n\n# 2. Run inference\npython inference.py \\\n  --model \u002Flambda\u002Fnfs\u002Fstorage\u002Fmodels\u002Ffine-tuned \\\n  --input \u002Flambda\u002Fnfs\u002Fstorage\u002Fdata\u002Finputs.jsonl \\\n  --output \u002Flambda\u002Fnfs\u002Fstorage\u002Fdata\u002Foutputs.jsonl\n```\n\n## Cost optimization\n\n### Choose right GPU\n\n| Task | Recommended GPU |\n|------|-----------------|\n| LLM fine-tuning (7B) | A100 40GB |\n| LLM fine-tuning (70B) | 8x H100 |\n| Inference | A10, A6000 |\n| Development | V100, A10 |\n| Maximum performance | B200 |\n\n### Reduce costs\n\n1. **Use filesystems**: Avoid re-downloading data\n2. **Checkpoint frequently**: Resume interrupted training\n3. **Right-size**: Don't over-provision GPUs\n4. **Terminate idle**: No auto-stop, manually terminate\n\n### Monitor usage\n\n- Dashboard shows real-time GPU utilization\n- API for programmatic monitoring\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Instance won't launch | Check region availability, try different GPU |\n| SSH connection refused | Wait for instance to initialize (3-15 min) |\n| Data lost after terminate | Use persistent filesystems |\n| Slow data transfer | Use filesystem in same region |\n| GPU not detected | Reboot instance, check drivers |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-node training, API automation\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.lambda.ai\n- **Console**: https:\u002F\u002Fcloud.lambda.ai\n- **Pricing**: https:\u002F\u002Flambda.ai\u002Finstances\n- **Support**: https:\u002F\u002Fsupport.lambdalabs.com\n- **Blog**: https:\u002F\u002Flambda.ai\u002Fblog\n",{"name":5,"tags":46,"author":50,"license":14,"version":51,"description":7,"dependencies":52},[47,20,48,49,21],"Infrastructure","Training","Inference","Orchestra 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AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":95,"taxonomyTypeDesc":96},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[98,99,100,101,102,103,104,105,106,107,108],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":113,"sourceHtmlUrl":114,"categories":115,"keywords":127,"publishedAt":41},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[152,153],{"categoryNo":117,"categoryName":118,"taxonomyType":119,"taxonomyTypeDesc":120},{"categoryNo":125,"categoryName":126,"taxonomyType":95,"taxonomyTypeDesc":96},[155,156,157,158,27,159,160,161,162,34,163,164,165,28,166,130],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":41},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[169,175],"文本",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":41},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[178,184,185,186,187,188,34,189,106,142,190,191,192,193,108,194,195,196,197,27,198,199,155],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":204,"sourceHtmlUrl":205,"categories":206,"keywords":208,"publishedAt":41},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[207],{"categoryNo":117,"categoryName":118,"taxonomyType":119,"taxonomyTypeDesc":120},[209,210,211,106,105,104,130,101,175,212,155,157],"humanizer","zh","humanizer zh","翻译"]