Name: Lambda Labs Gpu Cloud
Author: math-inc

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Lambda Labs Gpu Cloud | Skills Pool

# Get instance IP from console
ssh ubuntu@<INSTANCE-IP>

# Or with specific key
ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>

GPU	VRAM	Price/GPU/hr	Best For
B200 SXM6	180 GB	$4.99	Largest models, fastest training
H100 SXM	80 GB	$2.99-3.29	Large model training
H100 PCIe	80 GB	$2.49	Cost-effective H100
GH200	96 GB	$1.49	Single-GPU large models
A100 80GB	80 GB	$1.79	Production training
A100 40GB	40 GB	$1.29	Standard training
A10	24 GB	$0.75	Inference, fine-tuning
A6000	48 GB	$0.80	Good VRAM/price ratio
V100	16 GB	$0.55	Budget training

8x GPU: Best for distributed training (DDP, FSDP)
4x GPU: Large models, multi-GPU training
2x GPU: Medium workloads
1x GPU: Fine-tuning, inference, development

# Included software
- Ubuntu 22.04 LTS
- NVIDIA drivers (latest)
- CUDA 12.x
- cuDNN 8.x
- NCCL (for multi-GPU)
- PyTorch (latest)
- TensorFlow (latest)
- JAX
- JupyterLab

# Check GPU
nvidia-smi

# Check PyTorch
python -c "import torch; print(torch.cuda.is_available())"

# Check CUDA version
nvcc --version

pip install lambda-cloud-client

import os
import lambda_cloud_client

# Configure with API key
configuration = lambda_cloud_client.Configuration(
    host="https://cloud.lambdalabs.com/api/v1",
    access_token=os.environ["LAMBDA_API_KEY"]
)

with lambda_cloud_client.ApiClient(configuration) as api_client:
    api = lambda_cloud_client.DefaultApi(api_client)

    # Get available instance types
    types = api.instance_types()
    for name, info in types.data.items():
        print(f"{name}: {info.instance_type.description}")

from lambda_cloud_client.models import LaunchInstanceRequest

request = LaunchInstanceRequest(
    region_name="us-west-1",
    instance_type_name="gpu_1x_h100_sxm5",
    ssh_key_names=["my-ssh-key"],
    file_system_names=["my-filesystem"],  # Optional
    name="training-job"
)

response = api.launch_instance(request)
instance_id = response.data.instance_ids[0]
print(f"Launched: {instance_id}")

instances = api.list_instances()
for instance in instances.data:
    print(f"{instance.name}: {instance.ip} ({instance.status})")

from lambda_cloud_client.models import TerminateInstanceRequest

request = TerminateInstanceRequest(
    instance_ids=[instance_id]
)
api.terminate_instance(request)

from lambda_cloud_client.models import AddSshKeyRequest

# Add SSH key
request = AddSshKeyRequest(
    name="my-key",
    public_key="ssh-rsa AAAA..."
)
api.add_ssh_key(request)

# List keys
keys = api.list_ssh_keys()

# Delete key
api.delete_ssh_key(key_id)

curl -u $LAMBDA_API_KEY: \
  https://cloud.lambdalabs.com/api/v1/instance-types | jq

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/launch \
  -H "Content-Type: application/json" \
  -d '{
    "region_name": "us-west-1",
    "instance_type_name": "gpu_1x_h100_sxm5",
    "ssh_key_names": ["my-key"]
  }' | jq

curl -u $LAMBDA_API_KEY: \
  -X POST https://cloud.lambdalabs.com/api/v1/instance-operations/terminate \
  -H "Content-Type: application/json" \
  -d '{"instance_ids": ["<INSTANCE-ID>"]}' | jq

# Mount location
/lambda/nfs/<FILESYSTEM_NAME>

# Example: save checkpoints
python train.py --checkpoint-dir /lambda/nfs/my-storage/checkpoints

# Store on filesystem (persists)
/lambda/nfs/storage/
  ├── datasets/
  ├── checkpoints/
  ├── models/
  └── outputs/

# Local SSD (faster, ephemeral)
/home/ubuntu/
  └── working/  # Temporary files

# Generate key locally
ssh-keygen -t ed25519 -f ~/.ssh/lambda_key

# Add public key to Lambda console
# Or via API

# On instance, add more keys
echo 'ssh-rsa AAAA...' >> ~/.ssh/authorized_keys

# On instance
ssh-import-id gh:username

# Forward Jupyter
ssh -L 8888:localhost:8888 ubuntu@<IP>

# Forward TensorBoard
ssh -L 6006:localhost:6006 ubuntu@<IP>

# Multiple ports
ssh -L 8888:localhost:8888 -L 6006:localhost:6006 ubuntu@<IP>

# On instance
jupyter lab --ip=0.0.0.0 --port=8888

# From local machine with tunnel
ssh -L 8888:localhost:8888 ubuntu@<IP>
# Open http://localhost:8888

# SSH to instance
ssh ubuntu@<IP>

# Clone repo
git clone https://github.com/user/project
cd project

# Install dependencies
pip install -r requirements.txt

# Train
python train.py --epochs 100 --checkpoint-dir /lambda/nfs/storage/checkpoints

# train_ddp.py
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def main():
    dist.init_process_group("nccl")
    rank = dist.get_rank()
    device = rank % torch.cuda.device_count()

    model = MyModel().to(device)
    model = DDP(model, device_ids=[device])

    # Training loop...

if __name__ == "__main__":
    main()

# Launch with torchrun (8 GPUs)
torchrun --nproc_per_node=8 train_ddp.py

import os

checkpoint_dir = "/lambda/nfs/my-storage/checkpoints"
os.makedirs(checkpoint_dir, exist_ok=True)

# Save checkpoint
torch.save({
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'loss': loss,
}, f"{checkpoint_dir}/checkpoint_{epoch}.pt")

# On Slurm cluster
srun --nodes=4 --ntasks-per-node=8 --gpus-per-node=8 \
  torchrun --nnodes=4 --nproc_per_node=8 \
  --rdzv_backend=c10d --rdzv_endpoint=$MASTER_ADDR:29500 \
  train.py

# Find private IP
ip addr show | grep 'inet '

# 1. Launch 8x H100 instance with filesystem

# 2. SSH and setup
ssh ubuntu@<IP>
pip install transformers accelerate peft

# 3. Download model to filesystem
python -c "
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf')
model.save_pretrained('/lambda/nfs/storage/models/llama-2-7b')
"

# 4. Fine-tune with checkpoints on filesystem
accelerate launch --num_processes 8 train.py \
  --model_path /lambda/nfs/storage/models/llama-2-7b \
  --output_dir /lambda/nfs/storage/outputs \
  --checkpoint_dir /lambda/nfs/storage/checkpoints

# 1. Launch A10 instance (cost-effective for inference)

# 2. Run inference
python inference.py \
  --model /lambda/nfs/storage/models/fine-tuned \
  --input /lambda/nfs/storage/data/inputs.jsonl \
  --output /lambda/nfs/storage/data/outputs.jsonl

Issue	Solution
Instance won't launch	Check region availability, try different GPU
SSH connection refused	Wait for instance to initialize (3-15 min)
Data lost after terminate	Use persistent filesystems
Slow data transfer	Use filesystem in same region
GPU not detected	Reboot instance, check drivers

Task	Recommended GPU
LLM fine-tuning (7B)	A100 40GB
LLM fine-tuning (70B)	8x H100
Inference	A10, A6000
Development	V100, A10
Maximum performance	B200

Lambda Labs Gpu Cloud

When to use Lambda Labs

Lambda Labs Gpu Cloud

When to use Lambda Labs

Quick start

Account setup

Launch via console

Connect via SSH

GPU instances

Available GPUs

Instance configurations

Launch times

Lambda Stack

Verify installation

Python API

Installation

Authentication

List available instances

Launch instance

List running instances

Terminate instance

SSH key management

CLI with curl

List instance types

Launch instance

Terminate instance

Persistent storage

Filesystems

Create filesystem

Attach to instance

Best practices

SSH configuration

Add SSH key

Multiple keys

Import from GitHub

SSH tunneling

JupyterLab

Launch from console

Manual access

Training workflows

Single-GPU training

Multi-GPU training (single node)

Checkpoint to filesystem

1-Click Clusters

Overview

Included software

Storage

Multi-node training

Networking

Bandwidth

Firewall

Private IPs

Common workflows

Workflow 1: Fine-tuning LLM

Workflow 2: Batch inference

Cost optimization

Choose right GPU

Reduce costs

Monitor usage

Common issues

References

Resources

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns