add files

create_servers.py

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+from hcloud import Client
+from hcloud.images.domain import Image
+from hcloud.server_types.domain import ServerType
+from hcloud.locations.domain import Location
+
+TOKEN = "V5gkzZ13coCVPKWkQbmbyGPyxDdsTjiubwVtx35jH7mix8A32JqM5CWJtqoLjtFK"
+
+client = Client(
+    token=TOKEN
+)
+PREFIX = "PD"
+
+YOUR_LOCAL_SSH_PUBKEY = "ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQCzIOEX3AABgr3ZKLFcvZYkKr10JJjD0+AXlXoJ5CfpNw56SVj17Gh/Qbag4QeB8eJYaJxHV+TPhIOtHNxDJFlcVXUKPCBDErlIFYIJ8t0uhqvssd9r6XCZxvDjTC20FNayG+Zr1adB3BYe501I3JB9KiGZxg9juh/lY/1jZyqjjPzt8vLE1M2j6C7rbLAlIOub37B8OC7SGsy/nz4Ub5kS3Jxh9Trmr6vtuXa5WnASIaqX+3jl9hBhsg55j5WKUrGM1BOEKmwYxFJEH7bY0NjmcNAeaw1mm5bxERSW6EFXX3hEk+kx5zuFbO1LXDBh3HP5FyjwOXDKY70aLfVVpdGj"
+
+ssh_key = client.ssh_keys.create(name=f"{PREFIX}-pzc-ssh-key", public_key=YOUR_LOCAL_SSH_PUBKEY)
+print(f"Klucz {ssh_key.data_model.name} został dodany: {ssh_key.data_model.public_key}")
+
+cloud_init_db=r'''#cloud-config
+runcmd:
+  - curl -fsSL https://download.docker.com/linux/ubuntu/gpg | apt-key add -
+  - add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
+  - apt-get update -y
+  - apt-get install -y docker-ce docker-ce-cli containerd.io
+  - curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
+  - chmod +x /usr/local/bin/docker-compose
+  - systemctl start docker
+  - systemctl enable docker
+  - cd /root/ && docker-compose up -d
+'''
+
+db_server = client.servers.create(
+    name=f"{PREFIX}-db",
+    server_type=ServerType("cpx11"),
+    image=Image(name="ubuntu-22.04"),
+    ssh_keys=[ssh_key],
+    location=Location("hel1"),
+    user_data=cloud_init_db,
+)
+
+db_server.action.wait_until_finished()
+print(f"Tworzenie serwera db: {db_server.action.complete}")
+
+db_server = client.servers.get_by_name(f"{PREFIX}-db")
+print(f"Serwer: {db_server.data_model.name}\n\tpubliczne IP: {db_server.data_model.public_net.ipv4.ip}")

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import subprocess
+import uvicorn
+
+app = FastAPI()
+
+
+class ComputeInput(BaseModel):
+    a: str
+    b: str
+    sigma: str
+
+
+@app.post("/compute")
+async def compute(data: ComputeInput):
+    a = data.a
+    b = data.b
+    sigma = data.sigma
+
+    result = subprocess.run(
+        ['./computeC', a, b, sigma],
+        capture_output=True,
+        text=True
+    )
+    return result.stdout.strip()
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=5000)

requirements.txt

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+fastapi==0.100.0
+uvicorn[standard]==0.23.2
+pydantic==1.10.9

run.py

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+import subprocess
+from concurrent.futures import ThreadPoolExecutor
+
+MODULO = 999999937
+
+# Funkcja do wywołania ./computeC
+def run_computeC(a, b, sigma):
+    result = subprocess.run(["./computeC", str(a), str(b), str(sigma)], 
+                            capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"Error running computeC: {result.stderr}")
+    return int(result.stdout.strip()) % MODULO
+
+# Główna funkcja
+def parallel_calculate_C_sigma(a_start, b_end, sigma, num_chunks):
+    chunk_size = (b_end - a_start + 1) // num_chunks
+    ranges = [(a_start + i * chunk_size, min(a_start + (i + 1) * chunk_size - 1, b_end)) 
+              for i in range(num_chunks)]
+    
+    with ThreadPoolExecutor() as executor:
+        futures = [executor.submit(run_computeC, a, b, sigma) for a, b in ranges]
+        results = [f.result() for f in futures]
+    
+    return sum(results) % MODULO
+
+# Parametry
+a_start, b_end, sigma = 1, 10000, 42
+num_chunks = 32  # Liczba podziałów równoległych
+result = parallel_calculate_C_sigma(a_start, b_end, sigma, num_chunks)
+print(f"C_sigma({a_start}, {b_end}) = {result}")