[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFM7PEC3JB7WKFVGTV8DH0":3,"skill-same:01M6XFM7PEC3JB7WKFVGTV8DH0":84},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":14,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6XFM7PEC3JB7WKFVGTV8DH0","lambda-labs-gpu-cloud",null,"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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcloud-compute\u002Flambda-labs\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"gpu","cloud","GPU Cloud","Lambda Labs","lambda","lambda labs gpu cloud","node","Clusters","On-Demand GPU","when","for","you","Multi-Node","high-performance","synthetic","instances","performance","access","Large-Scale Training","training","infrastructure","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.\ncategory: cloud-compute\nversion: 1.0.0\nauthor: Synthetic Sciences\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\n\n## Credential Setup\n\nCredentials are auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$LAMBDA_API_KEY\" ] && echo \"LAMBDA_API_KEY set\" || echo \"NOT SET\"\n```\n\nIf not set: connect Lambda Labs at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\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\n\n## Credential Setup\n\nCredentials are auto-injected by openscience when connected via the dashboard.\n\n```bash\n# Verify credentials\n[ -n \"$LAMBDA_API_KEY\" ] && echo \"LAMBDA_API_KEY set\" || echo \"NOT SET\"\n```\n\nIf not set: connect Lambda Labs at https:\u002F\u002Fapp.syntheticsciences.ai -> Services, then restart openscience.\n",{"name":5,"tags":45,"author":49,"license":15,"version":50,"category":51,"description":7,"dependencies":52},[46,21,47,48,22],"Infrastructure","Training","Inference","Synthetic Sciences","1.0.0","cloud-compute",[53],"lambda-cloud-client>=1.0.0","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcloud-compute\u002Flambda-labs","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add fadrienne\u002FMyGitHub --skill lambda-labs-gpu-cloud",{"tool":66,"command":67},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill lambda-labs-gpu-cloud",{"tool":69,"command":70},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill lambda-labs-gpu-cloud","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcloud-compute\u002Flambda-labs\u002FSKILL.md",true,[74,80],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",15100,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcloud-compute\u002Flambda-labs\u002Freferences\u002Fadvanced-usage.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Ftroubleshooting.md",11683,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcloud-compute\u002Flambda-labs\u002Freferences\u002Ftroubleshooting.md",[85,116,141,175,202,229],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":96,"publishedAt":40},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[97,98,99,100,101,102,103,104,28,105,106,107,108,109,110,111,112,113,114,115],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":125,"publishedAt":40},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[122],{"categoryNo":123,"categoryName":124,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000008","测试与质量",[107,126,97,127,128,129,28,30,130,131,29,132,133,134,135,136,137,138,139,140],"statistical analysis","academic research","implementing","selection","reporting","data","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":156,"publishedAt":40},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[147,150,151],{"categoryNo":148,"categoryName":149,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[157,158,159,160,161,162,163,164,165,166,105,167,168,169,170,171,172,173,130,174],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":185,"publishedAt":40},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[181,184],{"categoryNo":182,"categoryName":183,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},[186,158,187,188,189,190,191,192,193,165,28,194,195,196,197,198,199,200,29,201],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":214,"publishedAt":40},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[208,211],{"categoryNo":209,"categoryName":210,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":212,"categoryName":213,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000030","开发工程师",[215,216,136,217,218,219,220,104,221,28,105,107,222,223,224,225,226,227,173,228],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":233,"licenseFileUrl":6,"sourceHtmlUrl":234,"categories":235,"keywords":242,"publishedAt":40},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[236,239,240,241],{"categoryNo":237,"categoryName":238,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":148,"categoryName":149,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},[157,243,244,245,246,247,186,248,249,28,105,107,250,251,252,253,254,255,158,173],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]