Upload folder using huggingface_hub

.env.example

@@ -0,0 +1,17 @@
+# AWS Configuration
+AWS_ACCESS_KEY_ID=your_aws_access_key
+AWS_SECRET_ACCESS_KEY=your_aws_secret_key
+AWS_REGION=us-west-2
+
+# GCP Configuration
+GOOGLE_CLOUD_PROJECT=your_project_id
+GOOGLE_APPLICATION_CREDENTIALS=path/to/your/service-account-key.json
+
+# Azure Configuration
+AZURE_SUBSCRIPTION_ID=your_subscription_id
+AZURE_TENANT_ID=your_tenant_id
+AZURE_CLIENT_ID=your_client_id
+AZURE_CLIENT_SECRET=your_client_secret
+AZURE_RESOURCE_GROUP=your_resource_group
+AZURE_LOCATION=eastus  # or your preferred region
+

.gitignore

@@ -0,0 +1,3 @@
+# Created by venv; see https://docs.python.org/3/library/venv.html
+.env
+

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----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=
+-----END CERTIFICATE-----

app.py

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+import gradio as gr
+import requests
+import json
+import pandas as pd
+from typing import Dict, List, Tuple
+from dataclasses import dataclass
+
+@dataclass
+class CloudInstance:
+    cloud: str
+    name: str
+    vcpu: int
+    memory_mib: int
+
+def load_instances(csv_path: str) -> List[CloudInstance]:
+    """Load cloud instances from CSV file."""
+    df = pd.read_csv(csv_path)
+    instances = []
+    for _, row in df.iterrows():
+        instances.append(CloudInstance(
+            cloud=row['Cloud'],
+            name=row['Name'],
+            vcpu=row['vCPU'],
+            memory_mib=row['MemoryMiB']
+        ))
+    return instances
+
+def fetch_model_info(model_name: str) -> Dict:
+    """Fetch model information from HuggingFace API using authentication."""
+    from dotenv import load_dotenv
+    import os
+    
+    load_dotenv()
+    hf_token = os.getenv('HUGGING_FACE_TOKEN')
+    
+    if not hf_token:
+        raise Exception("HUGGING_FACE_TOKEN not found in .env file")
+    
+    headers = {"Authorization": f"Bearer {hf_token}"}
+    api_url = f"https://huggingface.co/api/models/{model_name}"
+    
+    try:
+        response = requests.get(api_url, headers=headers)
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"Error fetching model info: {str(e)}")
+
+import re
+
+def estimate_model_requirements(model_info: Dict) -> Tuple[int, int]:
+    """
+    Estimate model requirements:
+    - Number of vCPUs needed
+    - Memory needed in MiB
+    Returns: (vcpu_count, memory_mib)
+    """
+    params_billion = None
+
+    # Method 1: Check if size is provided directly
+    if "size" in model_info:
+        params_billion = float(model_info["size"])
+
+    # Method 2: Check if size is in model name
+    if params_billion is None and "modelId" in model_info:
+        model_name = model_info["modelId"].lower()
+        
+        # Look for patterns like "70b", "7b", "13b", etc.
+        size_patterns = [
+            r'[^a-zA-Z](\d+)b[^a-zA-Z]',  # matches " 70b ", "-70b-", etc.
+            r'[^a-zA-Z](\d+)b'            # matches "70b" at end of string
+        ]
+        
+        for pattern in size_patterns:
+            match = re.search(pattern, model_name)
+            if match:
+                params_billion = float(match.group(1))
+                break
+    
+    # Method 3: Try to find size in model description or tags
+    if params_billion is None and "description" in model_info:
+        desc = model_info["description"].lower()
+        matches = re.findall(r'(\d+)\s*[bb]illion parameters', desc)
+        if matches:
+            params_billion = float(matches[0])
+    
+    if params_billion is None:
+        raise Exception("Could not determine model size. Please provide size manually or check model name format.")
+    
+    # Estimate requirements based on model size
+    # These are rough estimates and can be tuned based on real-world data
+    memory_per_billion_params = 4 * 1024  # About 4GB per billion parameters
+    memory_mib = int(params_billion * memory_per_billion_params)
+    
+    # Estimate vCPU requirements
+    # Rough estimate: 1 vCPU per 2B parameters, minimum 2 vCPUs
+    vcpu_count = max(2, int(params_billion / 2))
+    
+    return vcpu_count, memory_mib
+
+def recommend_instances(
+    instances: List[CloudInstance],
+    required_vcpu: int,
+    required_memory_mib: int,
+    max_recommendations: int = 2
+) -> Dict[str, List[CloudInstance]]:
+    """Find suitable cloud instances based on requirements."""
+    recommendations = {}
+    clouds = set(inst.cloud for inst in instances)
+    
+    for cloud in clouds:
+        cloud_instances = [
+            inst for inst in instances
+            if inst.cloud == cloud and inst.vcpu >= required_vcpu and inst.memory_mib >= required_memory_mib
+        ]
+        
+        if not cloud_instances:
+            recommendations[cloud] = []
+            continue
+        
+        # Sort by resource efficiency (trying to minimize over-provisioning)
+        def score_instance(instance: CloudInstance) -> float:
+            cpu_ratio = instance.vcpu / required_vcpu
+            mem_ratio = instance.memory_mib / required_memory_mib
+            # Lower score is better - we want resources as close to requirements as possible
+            return abs(1 - cpu_ratio) + abs(1 - mem_ratio)
+        
+        cloud_instances.sort(key=score_instance)
+        recommendations[cloud] = cloud_instances[:max_recommendations]
+    
+    return recommendations
+
+def format_recommendation(instance: CloudInstance) -> str:
+    """Format instance details for display."""
+    return (
+        f"Provider: {instance.cloud}\n"
+        f"Instance: {instance.name}\n"
+        f"vCPUs: {instance.vcpu}\n"
+        f"Memory: {instance.memory_mib / 1024:.1f} GiB"
+    )
+
+def get_recommendations(model_name: str) -> Tuple[str, str, str]:
+    """Main function to get cloud instance recommendations."""
+    try:
+        # Load instances
+        instances = load_instances('cloud_instances.csv')
+        
+        # Fetch model information
+        model_info = fetch_model_info(model_name)
+        required_vcpu, required_memory = estimate_model_requirements(model_info)
+        
+        # Get recommendations
+        recommendations = recommend_instances(instances, required_vcpu, required_memory)
+        
+        # Prepare output
+        model_details = (
+            f"Model: {model_name}\n"
+            f"Estimated requirements:\n"
+            f"- vCPUs: {required_vcpu}\n"
+            f"- Memory: {required_memory / 1024:.1f} GiB"
+        )
+        
+        primary_rec = ""
+        backup_rec = ""
+        
+        for cloud, recs in recommendations.items():
+            if recs:
+                primary_rec += f"\n\n{cloud} Primary Recommendation:\n" + format_recommendation(recs[0])
+                if len(recs) > 1:
+                    backup_rec += f"\n\n{cloud} Backup Recommendation:\n" + format_recommendation(recs[1])
+                else:
+                    backup_rec += f"\n\n{cloud} Backup Recommendation: No backup recommendation available"
+            else:
+                primary_rec += f"\n\n{cloud} Primary Recommendation: No suitable instances found"
+                backup_rec += f"\n\n{cloud} Backup Recommendation: No suitable instances found"
+        
+        return model_details, primary_rec.strip(), backup_rec.strip()
+    
+    except Exception as e:
+        return f"Error: {str(e)}", "", ""
+
+# Create Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("# INstaMatch as i Cloud Instance Matching for ML Models")
+    gr.Markdown("Enter a model name from HuggingFace to get cloud instance recommendations")
+    
+    with gr.Row():
+        model_input = gr.Textbox(
+            label="Model Name (e.g., gpt2, bert-base-uncased)",
+            placeholder="Enter model name..."
+        )
+        submit_btn = gr.Button("Get Recommendations")
+    
+    with gr.Row():
+        model_info = gr.Textbox(label="Model Requirements", lines=4)
+        primary_rec = gr.Textbox(label="Primary Recommendation", lines=5)
+        backup_rec = gr.Textbox(label="Backup Recommendation", lines=5)
+    
+    submit_btn.click(
+        fn=get_recommendations,
+        inputs=model_input,
+        outputs=[model_info, primary_rec, backup_rec]
+    )
+
+if __name__ == "__main__":
+    app.launch(share=True)

cloud_instances.csv

@@ -0,0 +1,1406 @@
+Cloud,Name,vCPU,MemoryMiB
+AWS,m5a.2xlarge,8,32768
+AWS,c6a.16xlarge,64,131072
+AWS,u7i-6tb.112xlarge,448,6291456
+AWS,c7i.2xlarge,8,16384
+AWS,i7ie.18xlarge,72,589824
+AWS,m5d.xlarge,4,16384
+AWS,g5.4xlarge,16,65536
+AWS,m6gd.xlarge,4,16384
+AWS,m6idn.large,2,8192
+AWS,r6in.metal,128,1048576
+AWS,m6id.xlarge,4,16384
+AWS,is4gen.4xlarge,16,98304
+AWS,d3en.2xlarge,8,32768
+AWS,r7iz.12xlarge,48,393216
+AWS,m6gd.4xlarge,16,65536
+AWS,r7a.2xlarge,8,65536
+AWS,m5n.16xlarge,64,262144
+AWS,m7i.16xlarge,64,262144
+AWS,i3en.3xlarge,12,98304
+AWS,r5a.xlarge,4,32768
+AWS,c3.large,2,3840
+AWS,m2.4xlarge,8,70041
+AWS,c6gn.16xlarge,64,131072
+AWS,c7gn.16xlarge,64,131072
+AWS,r6in.24xlarge,96,786432
+AWS,g6e.8xlarge,32,262144
+AWS,r5n.12xlarge,48,393216
+AWS,m8g.8xlarge,32,131072
+AWS,c6gd.metal,64,131072
+AWS,g6.24xlarge,96,393216
+AWS,r7iz.8xlarge,32,262144
+AWS,m7gd.16xlarge,64,262144
+AWS,r5.24xlarge,96,786432
+AWS,c6id.8xlarge,32,65536
+AWS,c8g.2xlarge,8,16384
+AWS,is4gen.xlarge,4,24576
+AWS,c7g.8xlarge,32,65536
+AWS,m5dn.12xlarge,48,196608
+AWS,x2iezn.4xlarge,16,524288
+AWS,c6in.xlarge,4,8192
+AWS,t2.2xlarge,8,32768
+AWS,c3.8xlarge,32,61440
+AWS,m4.4xlarge,16,65536
+AWS,x2iedn.2xlarge,8,262144
+AWS,r6a.metal,192,1572864
+AWS,m6id.2xlarge,8,32768
+AWS,i3en.12xlarge,48,393216
+AWS,c5ad.24xlarge,96,196608
+AWS,g6e.48xlarge,192,1572864
+AWS,m6gd.12xlarge,48,196608
+AWS,c7a.xlarge,4,8192
+AWS,m7i.metal-24xl,96,393216
+AWS,c6in.24xlarge,96,196608
+AWS,r6g.16xlarge,64,524288
+AWS,c8g.48xlarge,192,393216
+AWS,m7g.4xlarge,16,65536
+AWS,r6gd.2xlarge,8,65536
+AWS,im4gn.2xlarge,8,32768
+AWS,inf2.48xlarge,192,786432
+AWS,mac2-m2.metal,8,24576
+AWS,m7gd.large,2,8192
+AWS,r6g.medium,1,8192
+AWS,r8g.12xlarge,48,393216
+AWS,m8g.24xlarge,96,393216
+AWS,r6a.2xlarge,8,65536
+AWS,c7gd.medium,1,2048
+AWS,trn1.32xlarge,128,524288
+AWS,x1e.2xlarge,8,249856
+AWS,c4.4xlarge,16,30720
+AWS,r5dn.8xlarge,32,262144
+AWS,m5zn.2xlarge,8,32768
+AWS,r8g.4xlarge,16,131072
+AWS,r6a.xlarge,4,32768
+AWS,g5.2xlarge,8,32768
+AWS,i7ie.24xlarge,96,786432
+AWS,r7iz.16xlarge,64,524288
+AWS,x2gd.8xlarge,32,524288
+AWS,r5a.2xlarge,8,65536
+AWS,c5n.2xlarge,8,21504
+AWS,r7a.xlarge,4,32768
+AWS,r3.large,2,15360
+AWS,c6i.32xlarge,128,262144
+AWS,r8g.metal-24xl,96,786432
+AWS,t4g.nano,2,512
+AWS,c7gn.xlarge,4,8192
+AWS,r6i.xlarge,4,32768
+AWS,c6a.24xlarge,96,196608
+AWS,t1.micro,1,627
+AWS,m6a.2xlarge,8,32768
+AWS,m5a.16xlarge,64,262144
+AWS,m5ad.12xlarge,48,196608
+AWS,x2gd.metal,64,1048576
+AWS,i4i.16xlarge,64,524288
+AWS,r6id.16xlarge,64,524288
+AWS,f1.2xlarge,8,124928
+AWS,r6idn.16xlarge,64,524288
+AWS,x8g.24xlarge,96,1572864
+AWS,r6gd.medium,1,8192
+AWS,x8g.2xlarge,8,131072
+AWS,x1e.8xlarge,32,999424
+Azure,Standard_L8as_v3,8,65536
+Azure,Standard_L16as_v3,16,131072
+Azure,Standard_L32as_v3,32,262144
+Azure,Standard_L48as_v3,48,393216
+Azure,Standard_L64as_v3,64,524288
+Azure,Standard_L80as_v3,80,655360
+Azure,Standard_D2a_v4,2,8192
+Azure,Standard_D4a_v4,4,16384
+Azure,Standard_D8a_v4,8,32768
+Azure,Standard_D16a_v4,16,65536
+Azure,Standard_D32a_v4,32,131072
+Azure,Standard_D48a_v4,48,196608
+Azure,Standard_D64a_v4,64,262144
+Azure,Standard_D96a_v4,96,393216
+Azure,Standard_D2as_v4,2,8192
+Azure,Standard_D4as_v4,4,16384
+Azure,Standard_D8as_v4,8,32768
+Azure,Standard_D16as_v4,16,65536
+Azure,Standard_D32as_v4,32,131072
+Azure,Standard_D48as_v4,48,196608
+Azure,Standard_D64as_v4,64,262144
+Azure,Standard_D96as_v4,96,393216
+Azure,Standard_E2a_v4,2,16384
+Azure,Standard_E4a_v4,4,32768
+Azure,Standard_E8a_v4,8,65536
+Azure,Standard_E16a_v4,16,131072
+Azure,Standard_E20a_v4,20,163840
+Azure,Standard_E32a_v4,32,262144
+Azure,Standard_E48a_v4,48,393216
+Azure,Standard_E64a_v4,64,524288
+Azure,Standard_E96a_v4,96,688128
+Azure,Standard_E2as_v4,2,16384
+Azure,Standard_E4-2as_v4,4,32768
+Azure,Standard_E4as_v4,4,32768
+Azure,Standard_E8-2as_v4,8,65536
+Azure,Standard_E8-4as_v4,8,65536
+Azure,Standard_E8as_v4,8,65536
+Azure,Standard_E16-4as_v4,16,131072
+Azure,Standard_E16-8as_v4,16,131072
+Azure,Standard_E16as_v4,16,131072
+Azure,Standard_E20as_v4,20,163840
+Azure,Standard_E32-8as_v4,32,262144
+Azure,Standard_E32-16as_v4,32,262144
+Azure,Standard_E32as_v4,32,262144
+Azure,Standard_E48as_v4,48,393216
+Azure,Standard_E64-16as_v4,64,524288
+Azure,Standard_E64-32as_v4,64,524288
+Azure,Standard_E64as_v4,64,524288
+Azure,Standard_E96-24as_v4,96,688128
+Azure,Standard_E96-48as_v4,96,688128
+Azure,Standard_E96as_v4,96,688128
+Azure,Standard_D2as_v5,2,8192
+Azure,Standard_D4as_v5,4,16384
+Azure,Standard_D8as_v5,8,32768
+Azure,Standard_D16as_v5,16,65536
+Azure,Standard_D32as_v5,32,131072
+Azure,Standard_D48as_v5,48,196608
+Azure,Standard_D64as_v5,64,262144
+Azure,Standard_D96as_v5,96,393216
+Azure,Standard_E2as_v5,2,16384
+Azure,Standard_E4-2as_v5,4,32768
+Azure,Standard_E4as_v5,4,32768
+Azure,Standard_E8-2as_v5,8,65536
+Azure,Standard_E8-4as_v5,8,65536
+Azure,Standard_E8as_v5,8,65536
+Azure,Standard_E16-4as_v5,16,131072
+Azure,Standard_E16-8as_v5,16,131072
+Azure,Standard_E16as_v5,16,131072
+Azure,Standard_E20as_v5,20,163840
+Azure,Standard_E32-8as_v5,32,262144
+Azure,Standard_E32-16as_v5,32,262144
+Azure,Standard_E32as_v5,32,262144
+Azure,Standard_E48as_v5,48,393216
+Azure,Standard_E64-16as_v5,64,524288
+Azure,Standard_E64-32as_v5,64,524288
+Azure,Standard_E64as_v5,64,524288
+Azure,Standard_E96-24as_v5,96,688128
+Azure,Standard_E96-48as_v5,96,688128
+Azure,Standard_E96as_v5,96,688128
+Azure,Standard_D2ads_v5,2,8192
+Azure,Standard_D4ads_v5,4,16384
+Azure,Standard_D8ads_v5,8,32768
+Azure,Standard_D16ads_v5,16,65536
+Azure,Standard_D32ads_v5,32,131072
+Azure,Standard_D48ads_v5,48,196608
+Azure,Standard_D64ads_v5,64,262144
+Azure,Standard_D96ads_v5,96,393216
+Azure,Standard_E2ads_v5,2,16384
+Azure,Standard_E4-2ads_v5,4,32768
+Azure,Standard_E4ads_v5,4,32768
+Azure,Standard_E8-2ads_v5,8,65536
+Azure,Standard_E8-4ads_v5,8,65536
+Azure,Standard_E8ads_v5,8,65536
+Azure,Standard_E16-4ads_v5,16,131072
+Azure,Standard_E16-8ads_v5,16,131072
+Azure,Standard_E16ads_v5,16,131072
+Azure,Standard_E20ads_v5,20,163840
+Azure,Standard_E32-8ads_v5,32,262144
+Azure,Standard_E32-16ads_v5,32,262144
+Azure,Standard_E32ads_v5,32,262144
+Azure,Standard_E48ads_v5,48,393216
+Azure,Standard_E64-16ads_v5,64,524288
+Azure,Standard_E64-32ads_v5,64,524288
+Azure,Standard_E64ads_v5,64,524288
+Azure,Standard_E96-24ads_v5,96,688128
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readme.md

@@ -0,0 +1,61 @@
+---
+title: Instamatch
+app_file: app.py
+sdk: gradio
+sdk_version: 5.12.0
+---
+## Overview
+InstaMatch is a cloud instance matching tool for machine learning models. It fetches model information from HuggingFace and recommends suitable cloud instances from AWS, Azure, and GCP based on the model's requirements.
+
+## Features
+- Fetch model information from HuggingFace API
+- Estimate model requirements (vCPUs and memory)
+- Recommend suitable cloud instances from AWS, Azure, and GCP
+- Display primary and backup recommendations
+
+## Installation
+1. Clone the repository:
+   \`\`\`sh
+   git clone https://github.com/yourusername/InstaMatch.git
+   cd InstaMatch
+   \`\`\`
+
+2. Create and activate a virtual environment:
+   \`\`\`sh
+   python3 -m venv venv
+   source venv/bin/activate
+   \`\`\`
+
+3. Install the required dependencies:
+   \`\`\`sh
+   pip install -r requirements.txt
+   \`\`\`
+
+4. Set up your HuggingFace API token in a \`.env\` file:
+   \`\`\`sh
+   echo \"HUGGING_FACE_TOKEN=your_huggingface_token\" > .env
+   \`\`\`
+
+## Usage
+1. Run the application:
+   \`\`\`sh
+   python app.py
+   \`\`\`
+
+2. Open the provided URL in your browser.
+
+3. Enter a model name from HuggingFace (e.g., \`gpt2\`, \`bert-base-uncased\`) and click \"Get Recommendations\".
+
+4. View the model requirements and cloud instance recommendations.
+
+## Files
+- \`app.py\`: Main application file
+- \`cloud_instances.csv\`: CSV file containing cloud instance data
+- \`requirements.txt\`: List of required Python packages
+- \`utils/\`: Utility functions and modules
+
+## License
+This project is licensed under the MIT License." > README.md
+
+git add README.md
+git commit -m "Add README file"

requirements.txt

@@ -0,0 +1,10 @@
+gradio>=4.14.0
+boto3>=1.34.0
+google-cloud-compute>=1.15.0
+azure-identity>=1.15.0
+azure-mgmt-compute>=30.5.0
+typing-extensions>=4.9.0
+pandas>=2.2.0
+numpy>=1.26.0
+requests>=2.31.0
+python-dotenv

utils/fetch_instances.py

@@ -0,0 +1,113 @@
+# import boto3
+# from azure.identity import DefaultAzureCredential
+# from azure.mgmt.compute import ComputeManagementClient
+# from google.cloud import compute_v1
+
+# def get_aws_instances():
+#     ec2 = boto3.client('ec2')
+#     response = ec2.describe_instance_types()
+#     return [instance['InstanceType'] for instance in response['InstanceTypes']]
+
+# def get_azure_instances():
+#     credential = DefaultAzureCredential()
+#     compute_client = ComputeManagementClient(credential, "9591eed5-316a-4f40-8ea2-430e161ee367")
+#     vm_sizes = compute_client.virtual_machine_sizes.list(location='eastus')
+#     return [size.name for size in vm_sizes]
+
+# def get_gcp_instances():
+#     client = compute_v1.InstanceTypesClient()
+#     request = compute_v1.ListInstanceTypesRequest(
+#         project='mage-test-1234',
+#         zone='us-central1-a'
+#     )
+#     instance_types = client.list(request=request)
+#     return [instance.name for instance in instance_types]
+
+# # Call the functions to get instance lists
+# aws_instances = get_aws_instances()
+# print("AWS Instances:", aws_instances)
+# azure_instances = get_azure_instances()
+# print("Azure Instances:", azure_instances)
+# gcp_instances = get_gcp_instances()
+
+
+
+# print("GCP Instances:", gcp_instances)
+
+import boto3
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.compute import ComputeManagementClient
+from google.cloud import compute_v1
+from concurrent.futures import ThreadPoolExecutor
+import csv
+
+def get_aws_instances():
+    ec2 = boto3.client('ec2')
+    response = ec2.describe_instance_types()
+    instances = []
+    for instance in response['InstanceTypes']:
+        instances.append({
+            'name': instance['InstanceType'],
+            'vCPU': instance['VCpuInfo']['DefaultVCpus'],
+            'MemoryMiB': instance['MemoryInfo']['SizeInMiB']
+        })
+    return instances
+
+def get_azure_instances():
+    credential = DefaultAzureCredential()
+    compute_client = ComputeManagementClient(credential, "9591eed5-316a-4f40-8ea2-430e161ee367")
+    vm_sizes = compute_client.virtual_machine_sizes.list(location='eastus')
+    instances = []
+    for size in vm_sizes:
+        instances.append({
+            'name': size.name,
+            'vCPU': size.number_of_cores,
+            'MemoryMiB': size.memory_in_mb
+        })
+    return instances
+
+def get_gcp_instances():
+    client = compute_v1.MachineTypesClient()
+    request = compute_v1.ListMachineTypesRequest(
+        project='mage-test-1234',
+        zone='us-central1-a'
+    )
+    instance_types = client.list(request=request)
+    instances = []
+    for instance in instance_types:
+        instances.append({
+            'name': instance.name,
+            'vCPU': instance.guest_cpus,
+            'MemoryMiB': instance.memory_mb
+        })
+    return instances
+
+def save_instances_to_csv(instances, filename):
+    with open(filename, 'w', newline='') as csvfile:
+        fieldnames = ['Cloud', 'Name', 'vCPU', 'MemoryMiB']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        
+        writer.writeheader()
+        for cloud, cloud_instances in instances.items():
+            for instance in cloud_instances:
+                writer.writerow({
+                    'Cloud': cloud,
+                    'Name': instance['name'],
+                    'vCPU': instance['vCPU'],
+                    'MemoryMiB': instance['MemoryMiB']
+                })
+
+# Call the functions to get instance lists
+aws_instances = get_aws_instances()
+azure_instances = get_azure_instances()
+gcp_instances = get_gcp_instances()
+
+# Combine all instances into a dictionary
+all_instances = {
+    'AWS': aws_instances,
+    'Azure': azure_instances,
+    'GCP': gcp_instances
+}
+
+# Save the instances to a CSV file
+save_instances_to_csv(all_instances, 'cloud_instances.csv')

utils/instances_by_cloud/instances_aws.py

@@ -0,0 +1,45 @@
+import boto3
+from concurrent.futures import ThreadPoolExecutor
+
+def get_instance_specs_and_availability():
+    ec2 = boto3.client('ec2')
+    
+    # Get all instance types
+    response = ec2.describe_instance_types()
+    instance_types = response['InstanceTypes']
+    
+    # Get availability by region
+    regions = [region['RegionName'] for region in ec2.describe_regions()['Regions']]
+    
+    instance_data = {}
+
+    def check_region(instance_type, region):
+        ec2_region = boto3.client('ec2', region_name=region)
+        offerings = ec2_region.describe_instance_type_offerings(
+            LocationType='region',
+            Filters=[{'Name': 'instance-type', 'Values': [instance_type]}]
+        )
+        return region if offerings['InstanceTypeOfferings'] else None
+
+    for instance in instance_types:
+        instance_type = instance['InstanceType']
+        specs = {
+            'vCPU': instance['VCpuInfo']['DefaultVCpus'],
+            'MemoryMiB': instance['MemoryInfo']['SizeInMiB'],
+            'AvailableRegions': []
+        }
+        
+        with ThreadPoolExecutor(max_workers=10) as executor:
+            futures = [executor.submit(check_region, instance_type, region) for region in regions]
+            for future in futures:
+                region = future.result()
+                if region:
+                    specs['AvailableRegions'].append(region)
+        
+        instance_data[instance_type] = specs
+    
+    return instance_data
+
+# Call the function to get instance data
+instance_data = get_instance_specs_and_availability()
+print(instance_data)

utils/instances_by_cloud/instances_azure.py

@@ -0,0 +1,33 @@
+from azure.identity import DefaultAzureCredential
+from azure.mgmt.compute import ComputeManagementClient
+from azure.mgmt.subscription import SubscriptionClient
+
+def get_azure_instances():
+    credential = DefaultAzureCredential()
+    subscription_client = SubscriptionClient(credential)
+    subscription_id = next(subscription_client.subscriptions.list()).subscription_id
+    compute_client = ComputeManagementClient(credential, subscription_id)
+
+    instances = []
+    locations = [location.name for location in compute_client.locations.list()]
+
+    for location in locations:
+        vm_sizes = compute_client.virtual_machine_sizes.list(location)
+        for size in vm_sizes:
+            instance_info = {
+                'name': size.name,
+                'vCPUs': size.number_of_cores,
+                'memoryGB': size.memory_in_mb / 1024,
+                'location': location
+            }
+            instances.append(instance_info)
+
+    return instances
+
+def save_instances_to_file(instances, filename):
+    with open(filename, 'w') as file:
+        for instance in instances:
+            file.write(f"Name: {instance['name']}, vCPUs: {instance['vCPUs']}, Memory: {instance['memoryGB']} GB, Location: {instance['location']}\n")
+
+azure_instances = get_azure_instances()
+save_instances_to_file(azure_instances, 'azure_instances.txt')

utils/instances_by_cloud/instances_gcp.py

@@ -0,0 +1,23 @@
+from google.cloud import compute_v1
+
+def get_gcp_instances(project_id, zone):
+    client = compute_v1.InstanceTypesClient()
+    request = compute_v1.ListInstanceTypesRequest(
+        project=project_id,
+        zone=zone
+    )
+    instances = []
+    for instance_type in client.list(request=request):
+        instance_info = {
+            'name': instance_type.name,
+            'description': instance_type.description,
+            'guestCpus': instance_type.guest_cpus,
+            'memoryMb': instance_type.memory_mb
+        }
+        instances.append(instance_info)
+    return instances
+
+def get_gcp_zones(project_id):
+    client = compute_v1.ZonesClient()
+    request = compute_v1.ListZonesRequest(project=project_id)
+    return [zone.name for zone in client.list(request=request)]