Picture by the writerThe distributed machine learning (DML) framework enables you to train machine learning models in multiple machines (using CPU, GPU, or TPU), which significantly reduces training time while handling large and complex work loads effectively not fitting memory. In addition, this framework allows you to take action on datases, tune models and serve them using distributed computing resources.
In this article, we will review the five most famous distributed machine learning framework that can help the machine learning workflower scale. Each framework offers different solutions for your specific project requirements.
1. The piturich was divided
Due to dynamic computation graphs, ease of use, and modification, piturich is quite famous in machine learning practitioners. Peturch is included in the framework Piturich dividedWhich helps to scale a deep learning model in numerous GPUs and nodes.
Key features
- Distributed data parallel (DDP): PyTorch’s
torch.nn.parallel.DistributedDataParallelAllows models to train models in multiple GPUs or nodes by distributing data and graduals effectively. - Torch path and error tolerance: Paiturch supports dynamic resources allocation and error tolerance training in the distributed torch path.
- Scale Ebbitty: Piturich works well on both small clusters and large -scale supercomputers, making it a versatile selection of distribution training.
- Ease in use: The intuitive API developers of Piturich allow their workflow with minimal changes to the current code.
Why select PT Taurich partition?
Patch teams are already perfect for using the model for the development of the model and looking for your workflow. You can easily convert your training script to use multiple GPUs with just a few lines of code.
2. Tensor flu was divided
Tensorflow, which is one of the most established machine learning framework, offers strong support for training distributed through the distributed tanker flu. The ability to measure effectively in multiple machines and GPUs makes it a high choice of training for deep learning models on the scale.
Key features
- tf.distribute.strategy: Tensorflow provides numerous distribution strategies, such as mirror strategy for multi -GPU training, multi -worker strategy for multi -node training, and tapestry for TPU -based training.
- Ease of integration: Tancer flu -distributed tanker concretes without any obstruction with the flu environmental system, which includes tanker boards, tanker flu hubs, and tanner flu services.
- Extremely extended: Tensoff flu can be scaled in large clusters with hundreds of GPUs or TPUs.
- The integration of the cloud: Tensor flu is well supported by cloud providers such as Google Cloud, AWS, and Azure, which allows you to easily run cloud -distributed training jobs.
Why choose a distributed tensor flu?
Tenser Flu is a great choice for distributed teams that are already using tensilef flu or a highly expanding solution that is well connected with cloud machine learning workflows.
3. Ray
Ray is a common purpose framework for distributed computing, which is improved by machine learning and AI workload loads. It has simplified the building distributed machine learning pipelines by offering special libraries for training, tuning, and presenting models.
Key features
- Ray train: A library for distributed model training works with a popular machine learning framework such as Piturich and Tensor Flu.
- Ray lyrics: Improved for tuning hyperpressor in multiple nodes or GPUs.
- Ray service: Scaleable model production machine is offering for learning pipelines.
- Dynamic Skyling: Ray can allocate resources for the workload dynamically, making it extremely effective for both small and widely distributed computing.
Why choose ray?
Ray AI and machine learning are a great choice for learning developers that seek modern framework that supports distributed computing at all levels, including data pre -processing, model training, model tuning, and model service.
4. Apache spark
Apache Spark is a solid, open source distributed computing framework that focuses on widespread data processing. This is included mllibA library that supports the distributed machine learning algorithm and workflows.
Key features
- Processing in memory: Spark memory counting speeds compared to the traditional batch processing system.
- mllib: Provides distributed implementation of machine learning algorithm such as regression, clustering, and rating.
- Integration with large data ecosystemSpring Amazon S3, such as hoodop, hidden, and cloud storage system, integrates without interruption.
- Scale Ebbitty: Sparks can measure thousands of nodes, which can allow you to effectively implement the data bytes of data.
Why choose Apache spark?
If you are dealing with mass structural or semi -made data and requires a comprehensive framework for both data processing and machine learning, Spark is a great choice.
5. Dask
Dask is a lightweight, local framework for distributed computing. It extends popular libraries such as pandas, moisture, and scatte learns to work on datases that do not fit in memory, making it a great choice for developers wanting to measure the current workflow.
Key features
- Scale Subable Azigar Workflow: Dask parallel to the code and scales it in multiple cores or nodes with minimal code changes.
- Integration with Azigar Libraries: Dask Skyctate Learn, XG Boost, and Tensurf Flow works with learning libraries without interruption.
- Dynamic Task Schedule: DASK uses dynamic task graph to improve resources allocation and improve efficiency.
- Flexible scaling: Discussions can be handled by breaking into smaller, administered parts than memory.
Why select Dask?
Ideal is ideal for developers who want a lightweight, flexible framework to scale their existing workflow. The integration with the libraries makes it easier for teams to already be familiar with the environmental system.
Comparative table
| Feature | Piturich divided | Tensorf Flu was divided | Ray | Apache spark | Dosk |
|---|---|---|---|---|---|
| The best for | Deep learning work loads | Cloud Deep learning workload | ML pipelines | Big Data + ML Work Floose | Local ML workfloose from Azar |
| Ease in use | Moderate | Instrument | Moderate | Moderate | Instrument |
| ML Libraries | DDP, torch path in the built -in | tf.distribute.strategy | Ray train, ray service | mllib | Skat is integrated with learn |
| Integration | Environmental System | Tensor Flow Environmental System | Environmental System | Big Data Environmental System | Environmental System |
| Scale Ebbitty | Instrument | Too high | Instrument | Too high | Moderately high |
The final views
I have worked with almost all the all distributed computing framework mentioned in this article, but I mainly use piturich and tensiles to learn deep. These framework codes make it incredibly easier to scal the model training in multiple GPUs with just a few lines.
Personally, I prefer Petrich because of its intuitive API and my acquaintance. Therefore, I do not see any reason to go to something unnecessarily. Traditional Machine Learning Work Fluose LI, I rely on the lightweight and dask for a local view from the dimensioner.
- Piturich divided And Tensor Flu Distribution: Best of large -scale learning work burden, especially if you are already using these framework.
- Ray: Ideal for the construction of modern machine learning pipelines with distributed computers.
- Apache sparkle: Solutions for the machine learning workflower divided into large data environments.
- Disk: Lightweight options for developers wanting to measure current workflow effectively.
Abid Ali Owan For,,,,,,,,,, for,, for,,,, for,,,, for,,, for,,,, for,,,, for,,,, for,,, for,,, for,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,,, for,,, for,,, for,,, for,,, for,,,,, for,,,, for,,,, for,,,, for,, for,.@1abidaliawan) A certified data scientist is a professional who loves to create a machine learning model. Currently, he is focusing on creating content and writing technical blogs on machine learning and data science technologies. Abid has a master’s degree in technology management and a bachelor’s degree in telecommunications engineering. Its vision is to create AI products using a graph neural network for students with mental illness.