Distributed GPU training guide - Azure Machine Learning (2023)

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APPLIES TO: Distributed GPU training guide - Azure Machine Learning (1) Python SDK azureml v1

Learn more about how to use distributed GPU training code in Azure Machine Learning (ML). This article will not teach you about distributed training. It will help you run your existing distributed training code on Azure Machine Learning. It offers tips and examples for you to follow for each framework:

  • Message Passing Interface (MPI)
    • Horovod
    • DeepSpeed
    • Environment variables from Open MPI
  • PyTorch
    • Process group initialization
    • Launch options
    • DistributedDataParallel (per-process-launch)
    • Using torch.distributed.launch (per-node-launch)
    • PyTorch Lightning
    • Hugging Face Transformers
  • TensorFlow
    • Environment variables for TensorFlow (TF_CONFIG)
  • Accelerate GPU training with InfiniBand


Review these basic concepts of distributed GPU training such as data parallelism, distributed data parallelism, and model parallelism.


If you don't know which type of parallelism to use, more than 90% of the time you should use Distributed Data Parallelism.


Azure ML offers an MPI job to launch a given number of processes in each node. You can adopt this approach to run distributed training using either per-process-launcher or per-node-launcher, depending on whether process_count_per_node is set to 1 (the default) for per-node-launcher, or equal to the number of devices/GPUs for per-process-launcher. Azure ML constructs the full MPI launch command (mpirun) behind the scenes. You can't provide your own full head-node-launcher commands like mpirun or DeepSpeed launcher.


The base Docker image used by an Azure Machine Learning MPI job needs to have an MPI library installed. Open MPI is included in all the AzureML GPU base images. When you use a custom Docker image, you are responsible for making sure the image includes an MPI library. Open MPI is recommended, but you can also use a different MPI implementation such as Intel MPI. Azure ML also provides curated environments for popular frameworks.

To run distributed training using MPI, follow these steps:

  1. Use an Azure ML environment with the preferred deep learning framework and MPI. AzureML provides curated environment for popular frameworks.
  2. Define MpiConfiguration with process_count_per_node and node_count. process_count_per_node should be equal to the number of GPUs per node for per-process-launch, or set to 1 (the default) for per-node-launch if the user script will be responsible for launching the processes per node.
  3. Pass the MpiConfiguration object to the distributed_job_config parameter of ScriptRunConfig.
from azureml.core import Workspace, ScriptRunConfig, Environment, Experimentfrom azureml.core.runconfig import MpiConfigurationcurated_env_name = 'AzureML-PyTorch-1.6-GPU'pytorch_env = Environment.get(workspace=ws, name=curated_env_name)distr_config = MpiConfiguration(process_count_per_node=4, node_count=2)run_config = ScriptRunConfig( source_directory= './src', script='train.py', compute_target=compute_target, environment=pytorch_env, distributed_job_config=distr_config,)# submit the run configuration to start the jobrun = Experiment(ws, "experiment_name").submit(run_config)


Use the MPI job configuration when you use Horovod for distributed training with the deep learning framework.

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Make sure your code follows these tips:

  • The training code is instrumented correctly with Horovod before adding the Azure ML parts
  • Your Azure ML environment contains Horovod and MPI. The PyTorch and TensorFlow curated GPU environments come pre-configured with Horovod and its dependencies.
  • Create an MpiConfiguration with your desired distribution.

Horovod example


Don't use DeepSpeed's custom launcher to run distributed training with the DeepSpeed library on Azure ML. Instead, configure an MPI job to launch the training job with MPI.

Make sure your code follows these tips:

  • Your Azure ML environment contains DeepSpeed and its dependencies, Open MPI, and mpi4py.
  • Create an MpiConfiguration with your distribution.

DeepSeed example

Environment variables from Open MPI

When running MPI jobs with Open MPI images, the following environment variables for each process launched:

  1. OMPI_COMM_WORLD_RANK - the rank of the process
  2. OMPI_COMM_WORLD_SIZE - the world size
  3. AZ_BATCH_MASTER_NODE - primary address with port, MASTER_ADDR:MASTER_PORT
  4. OMPI_COMM_WORLD_LOCAL_RANK - the local rank of the process on the node
  5. OMPI_COMM_WORLD_LOCAL_SIZE - number of processes on the node


Despite the name, environment variable OMPI_COMM_WORLD_NODE_RANK does not corresponds to the NODE_RANK. To use per-node-launcher, set process_count_per_node=1 and use OMPI_COMM_WORLD_RANK as the NODE_RANK.


Azure ML supports running distributed jobs using PyTorch's native distributed training capabilities (torch.distributed).


For data parallelism, the official PyTorch guidance is to use DistributedDataParallel (DDP) over DataParallel for both single-node and multi-node distributed training. PyTorch also recommends using DistributedDataParallel over the multiprocessing package. Azure Machine Learning documentation and examples will therefore focus on DistributedDataParallel training.

Process group initialization

The backbone of any distributed training is based on a group of processes that know each other and can communicate with each other using a backend. For PyTorch, the process group is created by calling torch.distributed.init_process_group in all distributed processes to collectively form a process group.

torch.distributed.init_process_group(backend='nccl', init_method='env://', ...)

The most common communication backends used are mpi, nccl, and gloo. For GPU-based training nccl is recommended for best performance and should be used whenever possible.

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init_method tells how each process can discover each other, how they initialize and verify the process group using the communication backend. By default if init_method is not specified PyTorch will use the environment variable initialization method (env://). init_method is the recommended initialization method to use in your training code to run distributed PyTorch on Azure ML. PyTorch will look for the following environment variables for initialization,:

  • MASTER_ADDR - IP address of the machine that will host the process with rank 0.
  • MASTER_PORT - A free port on the machine that will host the process with rank 0.
  • WORLD_SIZE - The total number of processes. Should be equal to the total number of devices (GPU) used for distributed training.
  • RANK - The (global) rank of the current process. The possible values are 0 to (world size - 1).

For more information on process group initialization, see the PyTorch documentation.

Beyond these, many applications will also need the following environment variables:

  • LOCAL_RANK - The local (relative) rank of the process within the node. The possible values are 0 to (# of processes on the node - 1). This information is useful because many operations such as data preparation only should be performed once per node --- usually on local_rank = 0.
  • NODE_RANK - The rank of the node for multi-node training. The possible values are 0 to (total # of nodes - 1).

PyTorch launch options

The Azure ML PyTorch job supports two types of options for launching distributed training:

  • Per-process-launcher: The system will launch all distributed processes for you, with all the relevant information (such as environment variables) to set up the process group.
  • Per-node-launcher: You provide Azure ML with the utility launcher that will get run on each node. The utility launcher will handle launching each of the processes on a given node. Locally within each node, RANK and LOCAL_RANK are set up by the launcher. The torch.distributed.launch utility and PyTorch Lightning both belong in this category.

There are no fundamental differences between these launch options. The choice is largely up to your preference or the conventions of the frameworks/libraries built on top of vanilla PyTorch (such as Lightning or Hugging Face).

The following sections go into more detail on how to configure Azure ML PyTorch jobs for each of the launch options.

DistributedDataParallel (per-process-launch)

You don't need to use a launcher utility like torch.distributed.launch. To run a distributed PyTorch job:

  1. Specify the training script and arguments
  2. Create a PyTorchConfiguration and specify the process_count and node_count. The process_count corresponds to the total number of processes you want to run for your job. process_count should typically equal # GPUs per node x # nodes. If process_count isn't specified, Azure ML will by default launch one process per node.

Azure ML will set the MASTER_ADDR, MASTER_PORT, WORLD_SIZE, and NODE_RANK environment variables on each node, and set the process-level RANK and LOCAL_RANK environment variables.

To use this option for multi-process-per-node training, use Azure ML Python SDK >= 1.22.0. Process_count was introduced in 1.22.0.

from azureml.core import ScriptRunConfig, Environment, Experimentfrom azureml.core.runconfig import PyTorchConfigurationcurated_env_name = 'AzureML-PyTorch-1.6-GPU'pytorch_env = Environment.get(workspace=ws, name=curated_env_name)distr_config = PyTorchConfiguration(process_count=8, node_count=2)run_config = ScriptRunConfig( source_directory='./src', script='train.py', arguments=['--epochs', 50], compute_target=compute_target, environment=pytorch_env, distributed_job_config=distr_config,)run = Experiment(ws, 'experiment_name').submit(run_config)


If your training script passes information like local rank or rank as script arguments, you can reference the environment variable(s) in the arguments:

arguments=['--epochs', 50, '--local_rank', $LOCAL_RANK]

Pytorch per-process-launch example

Using torch.distributed.launch (per-node-launch)

PyTorch provides a launch utility in torch.distributed.launch that you can use to launch multiple processes per node. The torch.distributed.launch module spawns multiple training processes on each of the nodes.

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The following steps demonstrate how to configure a PyTorch job with a per-node-launcher on Azure ML. The job achieves the equivalent of running the following command:

python -m torch.distributed.launch --nproc_per_node <num processes per node> \ --nnodes <num nodes> --node_rank $NODE_RANK --master_addr $MASTER_ADDR \ --master_port $MASTER_PORT --use_env \ <your training script> <your script arguments>
  1. Provide the torch.distributed.launch command to the command parameter of the ScriptRunConfig constructor. Azure ML runs this command on each node of your training cluster. --nproc_per_node should be less than or equal to the number of GPUs available on each node. MASTER_ADDR, MASTER_PORT, and NODE_RANK are all set by Azure ML, so you can just reference the environment variables in the command. Azure ML sets MASTER_PORT to 6105, but you can pass a different value to the --master_port argument of torch.distributed.launch command if you wish. (The launch utility will reset the environment variables.)
  2. Create a PyTorchConfiguration and specify the node_count.
from azureml.core import ScriptRunConfig, Environment, Experimentfrom azureml.core.runconfig import PyTorchConfigurationcurated_env_name = 'AzureML-PyTorch-1.6-GPU'pytorch_env = Environment.get(workspace=ws, name=curated_env_name)distr_config = PyTorchConfiguration(node_count=2)launch_cmd = "python -m torch.distributed.launch --nproc_per_node 4 --nnodes 2 --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT --use_env train.py --epochs 50".split()run_config = ScriptRunConfig( source_directory='./src', command=launch_cmd, compute_target=compute_target, environment=pytorch_env, distributed_job_config=distr_config,)run = Experiment(ws, 'experiment_name').submit(run_config)


Single-node multi-GPU training:If you are using the launch utility to run single-node multi-GPU PyTorch training, you do not need to specify the distributed_job_config parameter of ScriptRunConfig.

launch_cmd = "python -m torch.distributed.launch --nproc_per_node 4 --use_env train.py --epochs 50".split()run_config = ScriptRunConfig( source_directory='./src', command=launch_cmd, compute_target=compute_target, environment=pytorch_env,)

PyTorch per-node-launch example

PyTorch Lightning

PyTorch Lightning is a lightweight open-source library that provides a high-level interface for PyTorch. Lightning abstracts away many of the lower-level distributed training configurations required for vanilla PyTorch. Lightning allows you to run your training scripts in single GPU, single-node multi-GPU, and multi-node multi-GPU settings. Behind the scene, it launches multiple processes for you similar to torch.distributed.launch.

For single-node training (including single-node multi-GPU), you can run your code on Azure ML without needing to specify a distributed_job_config.To run an experiment using multiple nodes with multiple GPUs, there are 2 options:

  • Using PyTorch configuration (recommended): Define PyTorchConfiguration and specify communication_backend="Nccl", node_count, and process_count (note that this is the total number of processes, ie, num_nodes * process_count_per_node). In Lightning Trainer module, specify both num_nodes and gpus to be consistent with PyTorchConfiguration. For example, num_nodes = node_count and gpus = process_count_per_node.

  • Using MPI Configuration:

    • Define MpiConfiguration and specify both node_count and process_count_per_node. In Lightning Trainer, specify both num_nodes and gpus to be respectively the same as node_count and process_count_per_node from MpiConfiguration.

    • For multi-node training with MPI, Lightning requires the following environment variables to be set on each node of your training cluster:

      • NODE_RANK
      • LOCAL_RANK

      Manually set these environment variables that Lightning requires in the main training scripts:

    import osfrom argparse import ArgumentParserdef set_environment_variables_for_mpi(num_nodes, gpus_per_node, master_port=54965): if num_nodes > 1: os.environ["MASTER_ADDR"], os.environ["MASTER_PORT"] = os.environ["AZ_BATCH_MASTER_NODE"].split(":") else: os.environ["MASTER_ADDR"] = os.environ["AZ_BATCHAI_MPI_MASTER_NODE"] os.environ["MASTER_PORT"] = str(master_port) try: os.environ["NODE_RANK"] = str(int(os.environ.get("OMPI_COMM_WORLD_RANK")) // gpus_per_node) # additional variables os.environ["MASTER_ADDRESS"] = os.environ["MASTER_ADDR"] os.environ["LOCAL_RANK"] = os.environ["OMPI_COMM_WORLD_LOCAL_RANK"] os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"] except: # fails when used with pytorch configuration instead of mpi passif __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--num_nodes", type=int, required=True) parser.add_argument("--gpus_per_node", type=int, required=True) args = parser.parse_args() set_environment_variables_for_mpi(args.num_nodes, args.gpus_per_node) trainer = Trainer( num_nodes=args.num_nodes, gpus=args.gpus_per_node )

    Lightning handles computing the world size from the Trainer flags --gpus and --num_nodes.

    from azureml.core import ScriptRunConfig, Experimentfrom azureml.core.runconfig import MpiConfigurationnnodes = 2gpus_per_node = 4args = ['--max_epochs', 50, '--gpus_per_node', gpus_per_node, '--accelerator', 'ddp', '--num_nodes', nnodes]distr_config = MpiConfiguration(node_count=nnodes, process_count_per_node=gpus_per_node)run_config = ScriptRunConfig( source_directory='./src', script='train.py', arguments=args, compute_target=compute_target, environment=pytorch_env, distributed_job_config=distr_config,)run = Experiment(ws, 'experiment_name').submit(run_config)

Hugging Face Transformers

Hugging Face provides many examples for using its Transformers library with torch.distributed.launch to run distributed training. To run these examples and your own custom training scripts using the Transformers Trainer API, follow the Using torch.distributed.launch section.

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Sample job configuration code to fine-tune the BERT large model on the text classification MNLI task using the run_glue.py script on one node with 8 GPUs:

from azureml.core import ScriptRunConfigfrom azureml.core.runconfig import PyTorchConfigurationdistr_config = PyTorchConfiguration() # node_count defaults to 1launch_cmd = "python -m torch.distributed.launch --nproc_per_node 8 text-classification/run_glue.py --model_name_or_path bert-large-uncased-whole-word-masking --task_name mnli --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 8 --learning_rate 2e-5 --num_train_epochs 3.0 --output_dir /tmp/mnli_output".split()run_config = ScriptRunConfig( source_directory='./src', command=launch_cmd, compute_target=compute_target, environment=pytorch_env, distributed_job_config=distr_config,)

You can also use the per-process-launch option to run distributed training without using torch.distributed.launch. One thing to keep in mind if using this method is that the transformers TrainingArguments expect the local rank to be passed in as an argument (--local_rank). torch.distributed.launch takes care of this when --use_env=False, but if you are using per-process-launch you'll need to explicitly pass the local rank in as an argument to the training script --local_rank=$LOCAL_RANK as Azure ML only sets the LOCAL_RANK environment variable.


If you're using native distributed TensorFlow in your training code, such as TensorFlow 2.x's tf.distribute.Strategy API, you can launch the distributed job via Azure ML using the TensorflowConfiguration.

To do so, specify a TensorflowConfiguration object to the distributed_job_config parameter of the ScriptRunConfig constructor. If you're using tf.distribute.experimental.MultiWorkerMirroredStrategy, specify the worker_count in the TensorflowConfiguration corresponding to the number of nodes for your training job.

from azureml.core import ScriptRunConfig, Environment, Experimentfrom azureml.core.runconfig import TensorflowConfigurationcurated_env_name = 'AzureML-TensorFlow-2.3-GPU'tf_env = Environment.get(workspace=ws, name=curated_env_name)distr_config = TensorflowConfiguration(worker_count=2, parameter_server_count=0)run_config = ScriptRunConfig( source_directory='./src', script='train.py', compute_target=compute_target, environment=tf_env, distributed_job_config=distr_config,)# submit the run configuration to start the jobrun = Experiment(ws, "experiment_name").submit(run_config)

If your training script uses the parameter server strategy for distributed training, such as for legacy TensorFlow 1.x, you'll also need to specify the number of parameter servers to use in the job, for example, tf_config = TensorflowConfiguration(worker_count=2, parameter_server_count=1).


In TensorFlow, the TF_CONFIG environment variable is required for training on multiple machines. For TensorFlow jobs, Azure ML will configure and set the TF_CONFIG variable appropriately for each worker before executing your training script.

You can access TF_CONFIG from your training script if you need to: os.environ['TF_CONFIG'].

Example TF_CONFIG set on a chief worker node:

TF_CONFIG='{ "cluster": { "worker": ["host0:2222", "host1:2222"] }, "task": {"type": "worker", "index": 0}, "environment": "cloud"}'

TensorFlow example

Accelerating distributed GPU training with InfiniBand

As the number of VMs training a model increases, the time required to train that model should decrease. The decrease in time, ideally, should be linearly proportional to the number of training VMs. For instance, if training a model on one VM takes 100 seconds, then training the same model on two VMs should ideally take 50 seconds. Training the model on four VMs should take 25 seconds, and so on.

InfiniBand can be an important factor in attaining this linear scaling. InfiniBand enables low-latency, GPU-to-GPU communication across nodes in a cluster. InfiniBand requires specialized hardware to operate. Certain Azure VM series, specifically the NC, ND, and H-series, now have RDMA-capable VMs with SR-IOV and InfiniBand support. These VMs communicate over the low latency and high-bandwidth InfiniBand network, which is much more performant than Ethernet-based connectivity. SR-IOV for InfiniBand enables near bare-metal performance for any MPI library (MPI is used by many distributed training frameworks and tooling, including NVIDIA's NCCL software.) These SKUs are intended to meet the needs of computationally intensive, GPU-acclerated machine learning workloads. For more information, see Accelerating Distributed Training in Azure Machine Learning with SR-IOV.

Typically, VM SKUs with an 'r' in their name contain the required InfiniBand hardware, and those without an 'r' typically do not. ('r' is a reference to RDMA, which stands for "remote direct memory access.") For instance, the VM SKU Standard_NC24rs_v3 is InfiniBand-enabled, but the SKU Standard_NC24s_v3 is not. Aside from the InfiniBand capabilities, the specs between these two SKUs are largely the same – both have 24 cores, 448 GB RAM, 4 GPUs of the same SKU, etc. Learn more about RDMA- and InfiniBand-enabled machine SKUs.


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The older-generation machine SKU Standard_NC24r is RDMA-enabled, but it does not contain SR-IOV hardware required for InfiniBand.

If you create an AmlCompute cluster of one of these RDMA-capable, InfiniBand-enabled sizes, the OS image will come with the Mellanox OFED driver required to enable InfiniBand preinstalled and preconfigured.

Next steps

  • Deploy machine learning models to Azure
  • Deploy and score a machine learning model by using a managed online endpoint (preview)
  • Reference architecture for distributed deep learning training in Azure


Can you train ML model on Azure? ›

Azure Machine Learning provides several ways to train your models, from code-first solutions using the SDK to low-code solutions such as automated machine learning and the visual designer.

What is distributed training in machine learning? ›

In distributed training the workload to train a model is split up and shared among multiple mini processors, called worker nodes. These worker nodes work in parallel to speed up model training.

Does Azure ML support PyTorch? ›

Whether you're training a deep learning PyTorch model from the ground-up or you're bringing an existing model into the cloud, you can use AzureML to scale out open-source training jobs using elastic cloud compute resources. You can build, deploy, version, and monitor production-grade models with AzureML.

Does TensorFlow support distributed training? ›

distribute. Strategy is a TensorFlow API to distribute training across multiple GPUs, multiple machines, or TPUs. Using this API, you can distribute your existing models and training code with minimal code changes.

Is Azure good for ML? ›

Microsoft Azure ML is an excellent tool as it helps to develop end to end data science analytics solution from data exploration to model deployment.

What are the main 3 types of ML models? ›

Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression. The type of model you should choose depends on the type of target that you want to predict.

What are 3 advantages of distributed systems? ›

Advantages of Distributed Systems

So nodes can easily share data with other nodes. More nodes can easily be added to the distributed system i.e. it can be scaled as required. Failure of one node does not lead to the failure of the entire distributed system. Other nodes can still communicate with each other.

Is PyTorch or TensorFlow better? ›

TensorFlow offers better visualization, which allows developers to debug better and track the training process. PyTorch, however, provides only limited visualization. TensorFlow also beats PyTorch in deploying trained models to production, thanks to the TensorFlow Serving framework.

Does PyTorch use GPU or CPU? ›

By default, the tensors are generated on the CPU. Even the model is initialized on the CPU. Thus one has to manually ensure that the operations are done using GPU.

Does PyTorch work on GPU? ›

PyTorch is an open source machine learning framework that enables you to perform scientific and tensor computations. You can use PyTorch to speed up deep learning with GPUs.

How do you train multiple GPU models? ›

Data Parallelism
  1. At the start time the main process replicates the model once from gpu 0 to the rest of gpus.
  2. Then for each batch: each gpu consumes each own mini-batch of data directly. during backward , once the local gradients are ready, they are then averaged across all processes.

Is TensorFlow better on CPU or GPU? ›

They noticed that the performance of TensorFlow depends significantly on the CPU for a small-size dataset. Also, they found it is more important to use a graphic processing unit (GPU) when training a large-size dataset.

Should I use TensorFlow GPU or CPU? ›

Generally it uses both, the CPU and the GPU (assuming you are using a GPU-enabled TensorFlow). What actually gets used depends on the actual operations that your code is using.

Which is better for machine learning Azure or AWS? ›

Both are great options for developing and deploying machine learning models, but each has its strengths and weaknesses. AWS Sagemaker is a great platform for building simple models and deploying them in the cloud with minimal setup. However, Azure ML might be a more versatile choice for predictive analytics.

Do you need GPU for ML? ›

Do I need a GPU for machine learning? Machine learning, a subset of AI, is the ability of computer systems to learn to make decisions and predictions from observations and data. A GPU is a specialized processing unit with enhanced mathematical computation capability, making it ideal for machine learning.

Do you need good GPU ML? ›

Almost any NVIDIA graphics card will work, with newer and higher-end models generally offering better performance. Fortunately, most ML / AI applications that have GPU acceleration work well with single precision (FP32).

What is the difference between ML model and ML algorithm? ›

Specifically, you learned: Machine learning algorithms are procedures that are implemented in code and are run on data. Machine learning models are output by algorithms and are comprised of model data and a prediction algorithm.

What are the 2 types of learning ML? ›

These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

What are the 2 types of machine learning models? ›

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

How do I start learning distributed? ›

Therefore, your quest to learn about distributed systems will start with Raft.
What this will teach you is:
  1. What is consensus.
  2. How to implement it.
  3. How to apply it to solve the problem of replicating state machines.
28 Oct 2020

Is AWS a distributed system? ›

With AWS High-Performance Computing (HPC), you can accelerate innovation with fast networking and virtually unlimited distributed computing infrastructure.

Is Kubernetes a distributed system? ›

Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example: Kubernetes can easily manage a canary deployment for your system.

What are the main challenges in distributed system? ›

Challenges and Failures of a Distributed System are:
  • Heterogeneity.
  • Scalability.
  • Openness.
  • Transparency.
  • Concurrency.
  • Security.
  • Failure Handling.

What are the main problems of distributed system? ›

Issues in Distributed Systems
  • the lack of global knowledge.
  • naming.
  • scalability.
  • compatibility.
  • process synchronization (requires global knowledge)
  • resource management (requires global knowledge)
  • security.
  • fault tolerance, error recovery.

What are the two limitations in a distributed system? ›

Absence of global clock make more difficult the algorithm for designing and debugging of distributed system. 2. Absence of Shared Memory: Distributed systems have not any physically shared memory, all computers in the distributed system have their own specific physical memory.

What programming language is used for distributed systems? ›

Erlang is a distributed language which combines functional programming with message passing. Units of distribution in Erlang are processes. These processes may be co-located on the same node or distributed amongst a cluster.

What are the three main characteristics of a distributed system? ›

Key characteristics of distributed systems
  • Resource sharing.
  • Openess.
  • Concurrency.
  • Scalability.
  • Fault Tolerance.
  • Transparency.

What are distributed systems skills? ›

Here are the top skills you will need to master to design distributed computing systems that work:
  • Sharding. ...
  • Distributed Databases. ...
  • Paxos/Raft algorithms. ...
  • Tools for Distributed Computation. ...
  • Distributed File Systems. ...
  • Remote Procedure Call (RPC) ...
  • Distributed Messaging Systems. ...
  • Distributed Ledgers.
27 Jul 2021

Does Tesla use PyTorch or TensorFlow? ›

Even Tesla is using PyTorch to develop full self-driving capabilities for its vehicles, including AutoPilot and Smart Summon. It is very easy to try and execute new research ideas in PyTorch; for example, switching to PyTorch decreased our iteration time on research ideas in generative modeling from weeks to days.

Does Apple use TensorFlow or PyTorch? ›

For deployment of trained models on Apple devices, they use coremltools, Apple's open-source unified conversion tool, to convert their favorite PyTorch and TensorFlow models to the Core ML model package format.

Does Microsoft use PyTorch? ›

PyTorch is an open-source deep-learning framework that accelerates the path from research to production. Data scientists at Microsoft use PyTorch as the primary framework to develop models that enable new experiences in Microsoft 365, Bing, Xbox, and more.

Is TensorFlow or PyTorch faster? ›

If you're working with high-performance models and large datasets, then TensorFlow is the way to go. However, if you're working with low-performance models and large datasets, then PyTorch is a better option. PyTorch can handle low-performance models such as prototypes with greater speed than TensorFlow.

Does PyTorch train faster than TensorFlow? ›

TensorFlow and PyTorch implementations show equal accuracy. However, the training time of TensorFlow is substantially higher, but the memory usage was lower. PyTorch allows quicker prototyping than TensorFlow, but TensorFlow may be a better option if custom features are needed in the neural network.

Is TensorFlow faster than PyTorch on CPU? ›

PyTorch vs TensorFlow: Performance Comparison

Even though both PyTorch and TensorFlow provide similar fast performance when it comes to speed, both the frameworks have advantages and disadvantages in specific scenarios. The performance of Python is faster for PyTorch.

Does GPU have NumPy? ›

Does NumPy automatically make use of GPU hardware? NumPy doesn't natively support GPU s. However, there are tools and libraries to run NumPy on GPU s. Numba is a Python compiler that can compile Python code to run on multicore CPUs and CUDA-enabled GPU s.

Can PyTorch run without CUDA? ›

No CUDA. To install PyTorch via pip, and do not have a CUDA-capable or ROCm-capable system or do not require CUDA/ROCm (i.e. GPU support), in the above selector, choose OS: Linux, Package: Pip, Language: Python and Compute Platform: CPU. Then, run the command that is presented to you.

Is TPU faster than GPU for PyTorch? ›

TensorFlow's big advantage over PyTorch lies in Google's very own Tensor Processing Units (TPUs), a specially designed computer that is far faster than GPUs for most neural network computations.

How can I distribute training across multiple machines? ›

There are generally two ways to distribute computation across multiple devices: Data parallelism, where a single model gets replicated on multiple devices or multiple machines. Each of them processes different batches of data, then they merge their results.

What are the 2 types of GPU? ›

GPUs come in two basic types: integrated and discrete. An integrated GPU does not come on its own separate card at all and is instead embedded alongside the CPU.

Can 2 GPU work at the same time? ›

Two GPUs are ideal for multi-monitor gaming. Dual cards can share the workload and provide better frame rates, higher resolutions, and extra filters. Additional cards can make it possible to take advantage of newer technologies such as 4K Displays.

How much faster is training on GPU? ›

GPU vs CPU Performance in Deep Learning Models

Generally speaking, GPUs are 3X faster than CPUs.

Is TensorFlow faster than Numpy? ›

Tensorflow is consistently much slower than Numpy in my tests. Shouldn't Tensorflow be much faster since it uses GPU and Numpy uses only CPU? I am running Ubuntu and have not changed anything to affect BLAS (that I am aware of). This always depends on the task.

Is TensorFlow GPU deprecated? ›

WARNING:tensorflow:From :1: is_gpu_available (from tensorflow. python. framework. test_util) is deprecated and will be removed in a future version.

Why is GPU better than CPU for AI? ›

While CPUs can process many general tasks in a fast, sequential manner, GPUs use parallel computing to break down massively complex problems into multiple smaller simultaneous calculations. This makes them ideal for handling the massively distributed computational processes required for machine learning.

Can I use TensorFlow GPU without CUDA? ›

Installing tensorflow without CUDA is just for getting started quickly. But after you want to get serious with tensorflow, you should install CUDA yourself so that multiple tensorflow environments can reuse the same CUDA installation and it allows you to install latest tensorflow version like tensorflow 2.0.

Are tensor cores better than CUDA cores? ›

Tensor cores can compute a lot faster than the CUDA cores. CUDA cores perform one operation per clock cycle, whereas tensor cores can perform multiple operations per clock cycle. Everything comes with a cost, and here, the cost is accuracy. Accuracy takes a hit to boost the computation speed.

How do I create a machine learning model in Azure? ›

Each of these stages maps to a set of modules in Azure ML Studio.
  1. Step 1: preprocess your data. For this step, you'll be using the modules under Data Transformation . ...
  2. Step 2: split into train/test. ...
  3. Step 3: select and/or create your features. ...
  4. Step 4–5–6: train, score and evaluate your model. ...
  5. Step 7: deploy selected model.
14 Jun 2020

Can I run TensorFlow on Azure? ›

You can easily run distributed TensorFlow jobs and Azure ML will manage the orchestration for you. Azure ML supports running distributed TensorFlow jobs with both Horovod and TensorFlow's built-in distributed training API. For more information about distributed training, see the Distributed GPU training guide.

How do I train my Azure custom model? ›

  1. Custom model input requirements.
  2. Training data tips.
  3. Upload your training data.
  4. Create a project in the Form Recognizer Studio.
  5. Label your data.
  6. Train your model.
  7. Test the model.
  8. Next steps.
15 Nov 2022

How do you deploy a trained model in Azure? ›

Workflow for deploying a model
  1. Register the model.
  2. Prepare an entry script.
  3. Prepare an inference configuration.
  4. Deploy the model locally to ensure everything works.
  5. Choose a compute target.
  6. Deploy the model to the cloud.
  7. Test the resulting web service.
16 Nov 2022

How many rows of data do you need to train an ML model? ›

Your dataset must always include at least 1,000 rows.

How long does training a ML model take? ›

, 50% of respondents said it took 8–90 days to deploy one model, with only 14% saying they could deploy in less than a week.

What are the 7 steps to making a machine learning model? ›

It can be broken down into 7 major steps :
  1. Collecting Data: As you know, machines initially learn from the data that you give them. ...
  2. Preparing the Data: After you have your data, you have to prepare it. ...
  3. Choosing a Model: ...
  4. Training the Model: ...
  5. Evaluating the Model: ...
  6. Parameter Tuning: ...
  7. Making Predictions.
15 Nov 2022

Does Azure ML use MLflow? ›

Azure Machine Learning uses MLflow Tracking for metric logging and artifact storage for your experiments, whether you created the experiments via the Azure Machine Learning Python SDK, the Azure Machine Learning CLI, or Azure Machine Learning studio.

Does Azure provide GPU? ›

NDm A100 v4-series virtual machine is a new flagship addition to the Azure GPU family, designed for high-end Deep Learning training and tightly-coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single virtual machine (VM) and eight NVIDIA Ampere A100 80GB Tensor Core GPUs.

Is Azure better than Python? ›

Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. Azure Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while Python can be primarily classified under "Languages".

Does Nvidia use Azure? ›

NVIDIA AI Enterprise — the globally adopted software of the NVIDIA AI platform — is certified and supported on Microsoft Azure instances with NVIDIA A100 GPUs. Support for Azure instances with NVIDIA H100 GPUs will be added in a future software release.

Can I learn Azure in a month? ›

intelligence and machine learning

We recommend that you work through the eBook in this order. The author estimates that you can learn to build and run secure, highly available applications in Azure in about 45 minutes a day for 30 days—basically a month of lunch hours.

What are the 3 deployment modes that can be used for Azure? ›

Cloud deployment options

There are several ways to deploy cloud resources. Options for deployment in Azure include public, private and hybrid cloud. All three choices provide similar benefits – including cost-effectiveness, performance, reliability and scale.

Is Azure training worth? ›

Earning a certification is one of the quickest ways to increase your salary. According to Zip Recruiter, the average salary for an expert in Azure Solutions Architecture is raking in $152,142 a year. Even the associate-level Azure certs can command an average salary of $125,000.


1. From Remote Graphics Workstation to Machine Learning – GPU for every workload in Azure : Build 2018
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2. Get Started with Azure Machine Learning
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3. Taking Models to the Next Level with Azure Machine Learning - Amy Boyd
4. Tips and tricks for distributed large model training
5. Deploying your First Machine Learning Model with Azure ML
6. Build Recap | Responsible AI Dashboard and Scorecard in Azure Machine Learning
(Microsoft Developer)
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