clouditia 1.1.0

Execute Python and Shell code on remote GPU sessions

Clouditia SDK

Execute Python and Shell code on remote GPU sessions.

Clouditia SDK provides a simple Python interface to run code on remote GPU-powered containers. Perfect for machine learning, deep learning, and any GPU-accelerated workloads.

Installation

pip install clouditia

Quick Start

from clouditia import GPUSession

# Connect to your GPU session
session = GPUSession("ck_your_api_key")

# Execute Python code on the remote GPU
result = session.run("""
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")
""")

print(result.output)

Features

• Python Execution: Run Python code on remote GPUs
• Shell Commands: Execute shell commands on the GPU pod
• Variable Transfer: Send and retrieve variables between local and remote
• Async Jobs: Submit long-running tasks with real-time log monitoring
• Jupyter Magic: Use %%clouditia magic in notebooks
• Decorator Support: Use @session.remote to run functions on GPU

---

Table of Contents

1. Getting Your API Key
2. Basic Usage
3. Executing Python Code
4. Shell Commands
5. Variable Transfer
6. Remote Functions (Decorator)
7. Async Jobs (Long-Running Tasks)
8. Jupyter Magic
9. Error Handling
10. API Reference

---

Getting Your API Key

1. Log in to clouditia.com
2. Start a GPU session
3. Go to API Keys in your session dashboard
4. Generate a new API key (starts with ck_ or sk_)

---

Basic Usage

Connect to a Session

from clouditia import GPUSession

# Create a session with your API key
session = GPUSession("ck_your_api_key_here")

# Verify the connection
info = session.verify()
print(f"Connected to: {info['session_name']}")
print(f"GPU: {info['gpu_type']}")
print(f"Credit remaining: {info['user_credit']}€")

Using the connect() Function

from clouditia import connect

session = connect("ck_your_api_key")
result = session.run("print('Hello from GPU!')")

---

Executing Python Code

Simple Execution

# Run Python code and get the output
result = session.run("print('Hello from the GPU!')")
print(result.output)  # "Hello from the GPU!"

# Check if execution was successful
if result.success:
    print("Code executed successfully!")
else:
    print(f"Error: {result.error}")

Getting Return Values

# The last expression is captured as result
result = session.run("2 + 2")
print(result.result)  # "4"

result = session.run("[i**2 for i in range(5)]")
print(result.result)  # "[0, 1, 4, 9, 16]"

Multi-line Code

result = session.run("""
import torch
import torch.nn as nn

# Create a simple model
model = nn.Linear(10, 5).cuda()
x = torch.randn(32, 10).cuda()
output = model(x)

print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")
print(f"Model parameters: {sum(p.numel() for p in model.parameters())}")
""")

print(result.output)

Using exec() for Side Effects

# exec() is for code that doesn't need a return value
session.exec("import torch")
session.exec("model = torch.nn.Linear(10, 5).cuda()")
session.exec("optimizer = torch.optim.Adam(model.parameters())")

---

Shell Commands

Execute shell commands on the remote GPU pod:

# List files
result = session.shell("ls -la /workspace")
print(result.output)

# Check current directory
result = session.shell("pwd")
print(result.output)

# Create directories and files
result = session.shell("mkdir -p /workspace/models && ls /workspace")
print(result.output)

# Chain multiple commands
result = session.shell("cd /workspace && mkdir data && ls -la")
print(result.output)

# Check disk space
result = session.shell("df -h")
print(result.output)

# Check memory
result = session.shell("free -h")
print(result.output)

# Install packages
result = session.shell("pip install transformers datasets")
print(result.output)

# Download files
result = session.shell("wget https://example.com/data.zip -O /workspace/data.zip")
print(result.output)

Checking Exit Codes

result = session.shell("ls /nonexistent")
print(f"Exit code: {result.exit_code}")
print(f"Success: {result.success}")

---

Variable Transfer

Sending Variables to GPU

# Send local data to the remote session
data = [1, 2, 3, 4, 5]
session.set("my_data", data)

# Use it in remote code
session.run("print(f'Data: {my_data}')")
session.run("print(f'Sum: {sum(my_data)}')")

Retrieving Variables from GPU

# Compute something on the GPU
session.run("""
import torch
tensor = torch.randn(100, 100).cuda()
result_stats = {
    'mean': tensor.mean().item(),
    'std': tensor.std().item(),
    'shape': list(tensor.shape)
}
""")

# Get the result locally
stats = session.get("result_stats")
print(f"Mean: {stats['mean']:.4f}")
print(f"Std: {stats['std']:.4f}")
print(f"Shape: {stats['shape']}")

Sending Complex Objects

import numpy as np

# Send numpy arrays
arr = np.random.randn(100, 100)
session.set("numpy_array", arr)

# Send dictionaries
config = {
    "learning_rate": 0.001,
    "batch_size": 32,
    "epochs": 100
}
session.set("config", config)

# Use in remote code
session.run("""
import torch
tensor = torch.from_numpy(numpy_array).cuda()
print(f"Learning rate: {config['learning_rate']}")
""")

---

Remote Functions (Decorator)

Use the @session.remote decorator to run functions on the GPU:

from clouditia import GPUSession

session = GPUSession("ck_your_api_key")

@session.remote
def compute_on_gpu(data, power=2):
    import torch
    tensor = torch.tensor(data, device='cuda', dtype=torch.float32)
    result = tensor ** power
    return result.cpu().tolist()

# Call the function - it runs on the remote GPU!
result = compute_on_gpu([1, 2, 3, 4, 5], power=2)
print(result)  # [1.0, 4.0, 9.0, 16.0, 25.0]

Remote Function with Model

@session.remote
def train_step(batch_data, learning_rate=0.01):
    import torch
    import torch.nn as nn

    # Create model (or load from checkpoint)
    model = nn.Sequential(
        nn.Linear(len(batch_data), 64),
        nn.ReLU(),
        nn.Linear(64, 1)
    ).cuda()

    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    # Training step
    x = torch.tensor(batch_data, dtype=torch.float32).cuda()
    output = model(x)
    loss = output.sum()

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    return {"loss": loss.item()}

# Call it like a normal function
result = train_step([1.0, 2.0, 3.0, 4.0], learning_rate=0.001)
print(f"Loss: {result['loss']}")

Async Remote Functions

@session.remote(async_mode=True)
def long_training():
    import torch
    for epoch in range(100):
        print(f"Epoch {epoch}/100")
        # ... training code ...
    return {"status": "completed"}

# Returns an AsyncJob instead of waiting
job = long_training()
print(f"Job submitted: {job.job_id}")

# Wait for completion
result = job.wait(show_logs=True)

---

Async Jobs (Long-Running Tasks)

For tasks that take hours or days, use async jobs:

Submitting a Job

# Submit a long-running job
job = session.submit("""
import torch
import time

print("Starting training...")
for epoch in range(100):
    print(f"Epoch {epoch + 1}/100")
    time.sleep(1)  # Simulate training

print("Training complete!")
torch.save({'epoch': 100}, '/workspace/checkpoint.pt')
""", name="my_training")

print(f"Job ID: {job.job_id}")

Monitoring Progress

import time

# Poll for status
while not job.is_done():
    status = job.status()
    print(f"Status: {status}")

    # View recent logs
    if status == "running":
        logs = job.logs(tail=10)
        print(logs)

    time.sleep(30)

print("Job finished!")

Real-Time Log Streaming

# View logs as they come in
while job.is_running():
    new_logs = job.logs(new_only=True)
    if new_logs.strip():
        print(new_logs, end='')
    time.sleep(5)

Waiting for Completion

# Wait with live log output
result = job.wait(show_logs=True)

# Or wait with timeout
try:
    result = job.wait(timeout=3600)  # 1 hour max
except TimeoutError:
    print("Job taking too long, cancelling...")
    job.cancel()

Getting Results

# Get the final result
result = job.result()

if result.success:
    print("Job completed successfully!")
    print(result.output)
else:
    print(f"Job failed: {result.error}")

Listing Jobs

# List all jobs
jobs = session.jobs()
for j in jobs:
    print(f"{j.name}: {j.status()}")

# List only running jobs
running_jobs = session.jobs(status="running")

# List completed jobs
completed_jobs = session.jobs(status="completed", limit=5)

Cancelling Jobs

if job.is_running():
    job.cancel()
    print("Job cancelled")

Shell Jobs

# Submit a shell command as an async job
job = session.submit(
    "pip install transformers && python /workspace/train.py",
    name="install_and_train",
    job_type="shell"
)

---

Jupyter Magic

Use Clouditia directly in Jupyter notebooks with magic commands.

Loading the Extension

# In a Jupyter cell
%load_ext clouditia

# Set your API key
CLOUDITIA_API_KEY = "ck_your_api_key"

Running Code on GPU

%%clouditia
import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")

x = torch.randn(1000, 1000, device='cuda')
y = torch.randn(1000, 1000, device='cuda')
z = torch.matmul(x, y)
print(f"Result shape: {z.shape}")

Specifying API Key Directly

%%clouditia ck_your_api_key
print("Hello from GPU!")

Async Mode in Jupyter

%%clouditia --async
for epoch in range(100):
    print(f"Epoch {epoch}")
    # ... training code ...

# The job is submitted and _clouditia_job variable is set

# Check job status
_clouditia_job.status()

# View logs
print(_clouditia_job.logs())

Utility Magic Commands

# Check session status
%clouditia_status

# List recent jobs
%clouditia_jobs

# List only running jobs
%clouditia_jobs running

---

Error Handling

The SDK provides specific exceptions for different error types:

from clouditia import (
    GPUSession,
    ClouditiaError,
    AuthenticationError,
    SessionError,
    ExecutionError,
    TimeoutError,
    CommandBlockedError
)

session = GPUSession("ck_your_api_key")

try:
    result = session.run("some_code()")
except AuthenticationError:
    print("Invalid API key")
except SessionError:
    print("Session not running or not accessible")
except ExecutionError as e:
    print(f"Code execution failed: {e}")
except TimeoutError:
    print("Execution timed out - consider using async jobs")
except CommandBlockedError:
    print("Command blocked by security filters")
except ClouditiaError as e:
    print(f"General error: {e}")

Using raise_for_status()

result = session.run("some_code()")
result.raise_for_status()  # Raises ExecutionError if failed
print(result.output)

---

API Reference

GPUSession

GPUSession(
    api_key: str,
    base_url: str = "https://clouditia.com/code-editor",
    timeout: int = 120,
    poll_interval: int = 5
)

Methods:

Method	Description
verify()	Verify API key and get session info
run(code, timeout=None)	Execute Python code
exec(code, timeout=None)	Execute without return value
shell(command, timeout=None)	Execute shell command
set(name, value)	Send variable to remote
get(name)	Retrieve variable from remote
submit(code, name=None, job_type="python")	Submit async job
jobs(status=None, limit=10)	List jobs
gpu_info()	Get GPU information
remote(func)	Decorator for remote functions

ExecutionResult

ExecutionResult(
    output: str,      # stdout output
    result: Any,      # last expression value
    error: str,       # error message if failed
    exit_code: int,   # process exit code
    success: bool     # True if successful
)

Methods:

Method	Description
raise_for_status()	Raise exception if failed
to_dict()	Convert to dictionary

AsyncJob

AsyncJob(session, job_id, name=None)

Methods:

Method	Description
status()	Get current status
is_done()	Check if finished
is_running()	Check if running
is_pending()	Check if pending
logs(tail=50, new_only=False)	Get logs
result()	Get final result
cancel()	Cancel the job
wait(timeout=None, show_logs=False)	Wait for completion
get_info()	Get detailed job info

---

Configuration

Environment Variables

You can set the API key via environment variable:

export CLOUDITIA_API_KEY="ck_your_api_key"

import os
from clouditia import GPUSession

session = GPUSession(os.environ["CLOUDITIA_API_KEY"])

Custom Base URL

session = GPUSession(
    "ck_your_api_key",
    base_url="https://custom.clouditia.com/code-editor"
)

Timeouts

# Set default timeout (seconds)
session = GPUSession("ck_your_api_key", timeout=300)

# Or per-request
result = session.run("long_computation()", timeout=600)

---

Examples

Training a Neural Network

from clouditia import GPUSession

session = GPUSession("ck_your_api_key")

# Submit training job
job = session.submit("""
import torch
import torch.nn as nn
import torch.optim as optim

# Create model
model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10)
).cuda()

optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(10):
    # Simulated batch
    x = torch.randn(64, 784).cuda()
    y = torch.randint(0, 10, (64,)).cuda()

    optimizer.zero_grad()
    output = model(x)
    loss = criterion(output, y)
    loss.backward()
    optimizer.step()

    print(f"Epoch {epoch+1}/10, Loss: {loss.item():.4f}")

# Save model
torch.save(model.state_dict(), '/workspace/model.pt')
print("Training complete!")
""", name="mnist_training")

# Wait with live logs
result = job.wait(show_logs=True)

Data Processing Pipeline

# Create workspace
session.shell("mkdir -p /workspace/data /workspace/output")

# Download data
session.shell("cd /workspace/data && wget https://example.com/data.csv")

# Process data
result = session.run("""
import pandas as pd

# Load and process data
df = pd.read_csv('/workspace/data/data.csv')
print(f"Loaded {len(df)} rows")

# Process...
df_processed = df.dropna()
print(f"After cleaning: {len(df_processed)} rows")

# Save
df_processed.to_csv('/workspace/output/processed.csv', index=False)
print("Saved to /workspace/output/processed.csv")
""")

print(result.output)

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Support

• Documentation: https://clouditia.com/docs
• Issues: https://github.com/clouditia/clouditia-sdk/issues
• Email: [e-mail]

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License

MIT License - see LICENSE for details.