ClearML

ClearML (formerly Trains) is an open-source MLOps platform for experiment tracking, data versioning, model management, pipeline orchestration, and compute resource management — all in one unified suite.

Overview

ClearML is a comprehensive ML lifecycle management platform from Allegro AI. It automatically captures experiment parameters, metrics, artifacts, and code with minimal code changes. ClearML supports the full ML workflow: from data management and experiment tracking to model registry, automated pipelines, and distributed task execution on GPU clusters.

Property	Value
Category	MLOps / Experiment Tracking
Developer	Allegro AI
License	Apache 2.0
GitHub	allegroai/clearml
Stars	5.5K+
Docker Hub	allegroai/clearml
Ports	22 (SSH), 8008 (API Server), 8081 (Web UI)

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Architecture

ClearML consists of four main components:

Component	Port	Description
ClearML Server	—	Backend coordinator
Web UI	8081	Browser-based dashboard
API Server	8008	REST API for SDK and agents
File Server	8081	Artifact and model storage
ClearML Agent	—	Worker that executes ML tasks

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Key Features

• Zero-code experiment tracking — add 2 lines of code to capture everything automatically

• Automatic logging — metrics, parameters, models, console output, plots, images

• Git integration — auto-capture git commit, diff, and uncommitted changes

• Data management — versioned datasets with lineage tracking

• Model registry — store, version, and serve ML models

• Pipeline orchestration — build and run multi-step ML pipelines

• Remote execution — queue experiments and run on remote GPU workers (ClearML Agent)

• Hyperparameter optimization — automated HPO with population-based training

• Resource monitoring — GPU/CPU/RAM monitoring per experiment

• Self-hosted or cloud — run your own server or use ClearML's hosted platform

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Clore.ai Setup

Option 1 — Full Self-Hosted Server

Run the ClearML server on Clore.ai for full control.

Step 1 — Choose a Server

Use Case	Recommended	VRAM	RAM
Server only (no training)	CPU instance	—	8 GB+
Server + training	RTX 3080	10 GB	16 GB
Full MLOps cluster	Multiple GPUs	—	32 GB+

Step 2 — Rent a Server on Clore.ai

1. Go to clore.ai → Marketplace

2. For the server component: CPU instances work fine

3. For training workers: GPU instances (RTX 3090, 4090, A100)

4. Open ports: 22, 8008, 8081

5. Ensure ≥ 50 GB disk for experiment artifacts

Step 3 — Deploy with Docker Compose

Create docker-compose.yml:

version: "3.6"

services:
  apiserver:
    image: allegroai/clearml:latest
    restart: unless-stopped
    volumes:
      - /opt/clearml/logs:/var/log/clearml
      - /opt/clearml/config:/opt/clearml/config
      - /opt/clearml/data/fileserver:/mnt/fileserver
    environment:
      CLEARML_MONGODB_SERVICE_HOST: mongo
      CLEARML_MONGODB_SERVICE_PORT: 27017
      CLEARML_ELASTICSEARCH_SERVICE_HOST: elasticsearch
      CLEARML_ELASTICSEARCH_SERVICE_PORT: 9200
      CLEARML_REDIS_SERVICE_HOST: redis
      CLEARML_REDIS_SERVICE_PORT: 6379
    ports:
      - "8008:8008"
    depends_on:
      - mongo
      - elasticsearch
      - redis

  webserver:
    image: allegroai/clearml-webserver:latest
    restart: unless-stopped
    ports:
      - "8081:80"
    environment:
      CLEARML_API_HOST: http://localhost:8008

  fileserver:
    image: allegroai/clearml-fileserver:latest
    restart: unless-stopped
    volumes:
      - /opt/clearml/data/fileserver:/mnt/fileserver
    ports:
      - "8081:8081"

  mongo:
    image: mongo:4.4
    restart: unless-stopped
    volumes:
      - /opt/clearml/data/mongo:/data/db
    command: --setParameter internalQueryMaxBlockingSortMemoryUsageBytes=196100200

  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:7.17.6
    restart: unless-stopped
    environment:
      ES_JAVA_OPTS: "-Xms512m -Xmx2048m"
      bootstrap.memory_lock: "true"
      cluster.name: "clearml"
      discovery.type: "single-node"
      http.publish_host: "$CLEARML_HOST_IP"
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - /opt/clearml/data/elastic:/usr/share/elasticsearch/data

  redis:
    image: redis:6
    restart: unless-stopped
    volumes:
      - /opt/clearml/data/redis:/data

networks:
  default:
    name: clearml_network

Start the stack:

mkdir -p /opt/clearml/{logs,config,data/{fileserver,mongo,elastic,redis}}

# Set your server's public IP
export CLEARML_HOST_IP=<your-server-ip>

docker-compose up -d

ClearML Server requires ~4 GB RAM for the full stack (MongoDB + Elasticsearch + Redis + API server + WebUI). Make sure your Clore.ai instance has sufficient RAM.

Option 2 — Use ClearML Hosted (Free)

For experiment tracking without running a server, use the free hosted plan:

# Install SDK
pip install clearml

# Configure with hosted server
clearml-init
# Enter: https://api.clear.ml  when prompted for API host
# Get credentials from: https://app.clear.ml/settings/workspace-configuration

---

Accessing the Interface

Web Dashboard

http://<server-ip>:8081

Default credentials: create your account on first login.

API Server

http://<server-ip>:8008

Via SSH

ssh root@<server-ip> -p 22

---

SDK Integration

Installation

pip install clearml

Initial Configuration

clearml-init

Enter your server URL (http://<server-ip>:8008) and API credentials from the dashboard.

Or configure programmatically:

from clearml import Task

Task.set_credentials(
    api_host="http://<server-ip>:8008",
    web_host="http://<server-ip>:8081",
    files_host="http://<server-ip>:8081",
    key="YOUR_ACCESS_KEY",
    secret="YOUR_SECRET_KEY"
)

---

Tracking Experiments

Minimal Integration (2 lines)

from clearml import Task

# Initialize task — this captures EVERYTHING automatically
task = Task.init(project_name="MyProject", task_name="experiment-001")

# Your existing training code — no changes needed
import torch
import torch.nn as nn

model = nn.Linear(10, 1)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    loss = torch.tensor(1.0 / (epoch + 1))
    # ClearML auto-detects and logs loss if using standard frameworks
    print(f"Epoch {epoch}, Loss: {loss.item():.4f}")

task.close()

Manual Metric Logging

from clearml import Task, Logger

task = Task.init(project_name="MyProject", task_name="manual-logging-demo")
logger = task.get_logger()

for epoch in range(50):
    train_loss = 1.0 / (epoch + 1)
    val_accuracy = 0.95 - 0.5 / (epoch + 1)

    # Log scalars
    logger.report_scalar("Loss", "train", value=train_loss, iteration=epoch)
    logger.report_scalar("Accuracy", "validation", value=val_accuracy, iteration=epoch)

    # Log learning rate
    logger.report_scalar("Learning Rate", "lr", value=0.001 * 0.9**epoch, iteration=epoch)

print("Training complete!")
task.close()

Hyperparameter Tracking

from clearml import Task

task = Task.init(project_name="HPO-Demo", task_name="run-001")

# Connect hyperparameters — auto-logged and overrideable remotely
params = {
    "learning_rate": 0.001,
    "batch_size": 32,
    "num_layers": 4,
    "dropout": 0.3,
    "optimizer": "adam",
    "epochs": 100,
}
params = task.connect(params)  # Now overrideable by ClearML HPO

print(f"Training with lr={params['learning_rate']}, batch={params['batch_size']}")

---

Data Management

from clearml import Dataset

# Create a versioned dataset
dataset = Dataset.create(
    dataset_name="my-training-data",
    dataset_project="MyProject",
    dataset_version="1.0",
)

# Add files
dataset.add_files(path="/data/images/", recursive=True)
dataset.add_files(path="/data/labels.csv")

# Upload to ClearML server
dataset.upload()
dataset.finalize()
print(f"Dataset ID: {dataset.id}")

# Later: use the dataset in experiments
dataset = Dataset.get(dataset_name="my-training-data", dataset_version="1.0")
local_path = dataset.get_local_copy()
print(f"Dataset at: {local_path}")

---

Model Registry

from clearml import Task, OutputModel, InputModel
import torch

task = Task.init(project_name="ModelRegistry", task_name="training-run")

# After training, register the model
model = torch.nn.Linear(100, 10)
torch.save(model.state_dict(), "my_model.pt")

# Register output model
output_model = OutputModel(task=task, name="MyModel-v1")
output_model.update_weights("my_model.pt")
output_model.publish()  # Mark as ready to use

print(f"Model registered: {output_model.id}")

# In deployment: load model by name
input_model = InputModel(model_id="<model-id-from-dashboard>")
local_model_path = input_model.get_local_copy()
state_dict = torch.load(local_model_path)

---

Pipeline Orchestration

from clearml.automation import PipelineController

def step_preprocess(dataset_id: str) -> str:
    """Data preprocessing step."""
    from clearml import Task, Dataset
    task = Task.init(task_name="step-preprocess")
    # ... preprocessing logic
    return "processed_data_id"

def step_train(data_id: str, lr: float = 0.001) -> str:
    """Model training step."""
    from clearml import Task
    task = Task.init(task_name="step-train")
    # ... training logic
    return "model_id"

def step_evaluate(model_id: str) -> float:
    """Model evaluation step."""
    from clearml import Task
    task = Task.init(task_name="step-evaluate")
    # ... evaluation logic
    return 0.95

# Build pipeline
pipe = PipelineController(
    name="ML-Training-Pipeline",
    project="MyPipelines",
    version="1.0"
)

pipe.add_function_step(
    name="preprocess",
    function=step_preprocess,
    function_kwargs={"dataset_id": "raw-data-id"},
    function_return=["processed_id"],
)

pipe.add_function_step(
    name="train",
    parents=["preprocess"],
    function=step_train,
    function_kwargs={"data_id": "${preprocess.processed_id}"},
    function_return=["model_id"],
    execution_queue="gpu-queue",  # Run on GPU worker
)

pipe.add_function_step(
    name="evaluate",
    parents=["train"],
    function=step_evaluate,
    function_kwargs={"model_id": "${train.model_id}"},
    function_return=["accuracy"],
)

pipe.start()
pipe.wait()
print("Pipeline complete!")

---

ClearML Agent (Worker)

Run a ClearML Agent on a GPU server to execute queued experiments:

# Install agent
pip install clearml-agent

# Configure (uses same credentials as SDK)
clearml-agent init

# Start worker on GPU
clearml-agent daemon --queue "gpu-queue" --gpus 0,1

# Start worker with Docker isolation (recommended)
clearml-agent daemon \
    --queue "gpu-queue" \
    --docker pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime \
    --gpus all

On Clore.ai, spin up multiple GPU nodes as ClearML agents to create a distributed compute cluster.

---

Hyperparameter Optimization

from clearml.automation import (
    HyperParameterOptimizer,
    UniformParameterRange,
    DiscreteParameterValues,
    GridSearch,
)

optimizer = HyperParameterOptimizer(
    base_task_id="<task-id-to-optimize>",
    hyper_parameters=[
        UniformParameterRange("General/learning_rate", min_value=1e-5, max_value=1e-2, step_size=1e-5),
        DiscreteParameterValues("General/batch_size", values=[16, 32, 64, 128]),
        DiscreteParameterValues("General/optimizer", values=["adam", "sgd", "adamw"]),
    ],
    objective_metric_title="Accuracy",
    objective_metric_series="validation",
    objective_metric_sign="max",  # Maximize validation accuracy
    max_number_of_concurrent_tasks=4,
    optimizer_class=GridSearch,
    execution_queue="gpu-queue",
    total_max_jobs=50,
)

optimizer.start()
top_exps = optimizer.get_top_experiments(top_k=3)
print("Best experiments:", top_exps)

---

Monitoring & Alerts

from clearml import Task

task = Task.init(project_name="Production", task_name="monitoring")

# Set task tags for easy filtering
task.add_tags(["production", "v2.1", "gpu"])

# Log system metrics automatically — just init the task
# ClearML captures: CPU, RAM, GPU utilization, GPU VRAM automatically

# Add custom scalar monitoring
logger = task.get_logger()
import time
for i in range(100):
    gpu_util = 85 + (i % 10)
    logger.report_scalar("GPU", "utilization_%", value=gpu_util, iteration=i)
    time.sleep(1)

---

Troubleshooting

Elasticsearch fails to start — Set vm.max_map_count=262144 on the host: sysctl -w vm.max_map_count=262144. Add to /etc/sysctl.conf for persistence.

Cannot connect to server — Verify ports 8008 and 8081 are open in Clore.ai port settings. Check docker ps to ensure all containers are running.

Experiments not appearing in UI — Check that CLEARML_API_HOST in your SDK config points to http://<server-ip>:8008, not localhost.

Out of disk space — ClearML stores all artifacts locally. Configure S3/GCS storage or increase disk allocation in Clore.ai.

Issue	Fix
MongoDB connection refused	Check mongo container: docker logs clearml_mongo_1
Task stuck in queue	Ensure ClearML Agent is running and connected to the queue
Slow UI	Elasticsearch needs time to index — wait 2–3 min after startup
API 401 Unauthorized	Regenerate API credentials in ClearML web dashboard

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Use Cases for GPU Researchers

• Track training runs — never lose hyperparameters or results again

• Compare experiments — side-by-side metric comparison in the UI

• Reproduce results — ClearML captures git commit + code diff automatically

• Share results — collaborators see all experiments in the shared dashboard

• Remote GPU jobs — queue training jobs from laptop, run on Clore.ai GPU nodes

• Automated HPO — run hyperparameter search across multiple GPU nodes in parallel

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Related Tools

• MLflow — experiment tracking alternative

• Weights & Biases — hosted ML experiment tracking

• Ray — distributed ML training and HPO

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ClearML on Clore.ai combines experiment tracking with GPU compute management — giving your ML team full MLOps capabilities without cloud vendor lock-in.

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Clore.ai GPU Recommendations

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	~$0.12/gpu/hr
Production Training	RTX 4090 (24GB)	~$0.70/gpu/hr
Large Scale Experiments	A100 80GB	~$1.20/gpu/hr

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.