Azure Optimized Stack With Deepspeed For Training Hyperscale Models

In recent years, large-scale deep learning models based on transformers and trained on huge amounts of data have been used for new products and many cognitive tasks. These models have grown in size and size and customer needs for training and refinement have grown accordingly. Training and fine-tuning these types of models requires a complex and distributed architecture, and tuning these architectures requires several manual and error-prone steps. With this new optimized stack, AzureML enables a better experience in terms of usability and performance by providing an easy-to-use learning pipeline. The recommended AzureML stack includes: hardware, operating system, VM image, Docker image (with optimized PyTorch, DeepSpeed, ONNX Runtime and other Python packages) for performance and scalability without complexity. Optimized stack for scalable distributed training on Azure A possible experimental setup consists of the NDm A100 v4 series that includes two AMD EPYC 7V12 64-Core CPU slots, 1.7 TB of main memory and eight A100 80 GB GPUS. A balanced PCIe topology is used to connect 4 GPUs to each CPU and each GPU has its own 200Gb/s NVIDIA Mellanox HDR InfiniBand agnostic topology. 1.7 TB of main memory and DeepSpeed ​​library offloading capabilities enable scaling to large model sizes. This setup can be used in both AzureML studio and Azure VMSS, but the AzureML studio solution is recommended because it’s the easiest way to set up and run the setup in the right and easy way. Differences between the distributed architecture and the AzureML training facility The proposed AzureML stack enables efficient training of 2x larger model sizes (2 trillion vs. 1 trillion parameters), scaling to 2x more GPUs (1024 vs. 512), and up to 1.8x higher compute throughput/GPU (150 TFLOP vs. 1 trillion ). 81 TFLOPs). This stack also has the potential to offer an almost linear scalability in terms of increasing model size and increasing the number of GPUs. Thanks to DeepSpeed ​​​​ZeRO-3 with CPU offload capabilities and this new AzureML stack, the 157 TFLOP efficient performance/GPU is maintained as the model grows from 175 billion to 2 trillion parameters and, given the model size (e.g. .175 billion in the graph below), a linear scaling is achieved if the number of GPUs increases. More detailed results are described in the extensive deepspeed technical blog. one. performance/GPU vs. model size from 175 billion to 2 trillion parameters (BS/GPU=8); si. Linear increases the performance scale by increasing the number of GPU devices for the 175B model (BS/GPU=16).

title: “Azure Optimized Stack With Deepspeed For Training Hyperscale Models Klmat” ShowToc: true date: “2022-11-23” author: “James Martin”

In recent years, large-scale deep learning models based on transformers and trained on huge amounts of data have been used for new products and many cognitive tasks. These models have grown in size and size and customer needs for training and refinement have grown accordingly. Training and fine-tuning these types of models requires a complex and distributed architecture, and tuning these architectures requires several manual and error-prone steps. With this new optimized stack, AzureML enables a better experience in terms of usability and performance by providing an easy-to-use learning pipeline. The recommended AzureML stack includes: hardware, operating system, VM image, Docker image (with optimized PyTorch, DeepSpeed, ONNX Runtime and other Python packages) for performance and scalability without complexity. Optimized stack for scalable distributed training on Azure A possible experimental setup consists of the NDm A100 v4 series that includes two AMD EPYC 7V12 64-Core CPU slots, 1.7 TB of main memory and eight A100 80 GB GPUS. A balanced PCIe topology is used to connect 4 GPUs to each CPU and each GPU has its own 200Gb/s NVIDIA Mellanox HDR InfiniBand agnostic topology. 1.7 TB of main memory and DeepSpeed ​​library offloading capabilities enable scaling to large model sizes. This setup can be used in both AzureML studio and Azure VMSS, but the AzureML studio solution is recommended because it’s the easiest way to set up and run the setup in the right and easy way. Differences between the distributed architecture and the AzureML training facility The proposed AzureML stack enables efficient training of 2x larger model sizes (2 trillion vs. 1 trillion parameters), scaling to 2x more GPUs (1024 vs. 512), and up to 1.8x higher compute throughput/GPU (150 TFLOP vs. 1 trillion ). 81 TFLOPs). This stack also has the potential to offer an almost linear scalability in terms of increasing model size and increasing the number of GPUs. Thanks to DeepSpeed ​​​​ZeRO-3 with CPU offload capabilities and this new AzureML stack, the 157 TFLOP efficient performance/GPU is maintained as the model grows from 175 billion to 2 trillion parameters and, given the model size (e.g. .175 billion in the graph below), a linear scaling is achieved if the number of GPUs increases. More detailed results are described in the extensive deepspeed technical blog. one. performance/GPU vs. model size from 175 billion to 2 trillion parameters (BS/GPU=8); si. Linear increases the performance scale by increasing the number of GPU devices for the 175B model (BS/GPU=16).

title: “Azure Optimized Stack With Deepspeed For Training Hyperscale Models Klmat” ShowToc: true date: “2022-12-17” author: “Frances Huss”

In recent years, large-scale deep learning models based on transformers and trained on huge amounts of data have been used for new products and many cognitive tasks. These models have grown in size and size and customer needs for training and refinement have grown accordingly. Training and fine-tuning these types of models requires a complex and distributed architecture, and tuning these architectures requires several manual and error-prone steps. With this new optimized stack, AzureML enables a better experience in terms of usability and performance by providing an easy-to-use learning pipeline. The recommended AzureML stack includes: hardware, operating system, VM image, Docker image (with optimized PyTorch, DeepSpeed, ONNX Runtime and other Python packages) for performance and scalability without complexity. Optimized stack for scalable distributed training on Azure A possible experimental setup consists of the NDm A100 v4 series that includes two AMD EPYC 7V12 64-Core CPU slots, 1.7 TB of main memory and eight A100 80 GB GPUS. A balanced PCIe topology is used to connect 4 GPUs to each CPU and each GPU has its own 200Gb/s NVIDIA Mellanox HDR InfiniBand agnostic topology. 1.7 TB of main memory and DeepSpeed ​​library offloading capabilities enable scaling to large model sizes. This setup can be used in both AzureML studio and Azure VMSS, but the AzureML studio solution is recommended because it’s the easiest way to set up and run the setup in the right and easy way. Differences between the distributed architecture and the AzureML training facility The proposed AzureML stack enables efficient training of 2x larger model sizes (2 trillion vs. 1 trillion parameters), scaling to 2x more GPUs (1024 vs. 512), and up to 1.8x higher compute throughput/GPU (150 TFLOP vs. 1 trillion ). 81 TFLOPs). This stack also has the potential to offer an almost linear scalability in terms of increasing model size and increasing the number of GPUs. Thanks to DeepSpeed ​​​​ZeRO-3 with CPU offload capabilities and this new AzureML stack, the 157 TFLOP efficient performance/GPU is maintained as the model grows from 175 billion to 2 trillion parameters and, given the model size (e.g. .175 billion in the graph below), a linear scaling is achieved if the number of GPUs increases. More detailed results are described in the extensive deepspeed technical blog. one. performance/GPU vs. model size from 175 billion to 2 trillion parameters (BS/GPU=8); si. Linear increases the performance scale by increasing the number of GPU devices for the 175B model (BS/GPU=16).

title: “Azure Optimized Stack With Deepspeed For Training Hyperscale Models Klmat” ShowToc: true date: “2022-11-12” author: “Sandra Reeves”

In recent years, large-scale deep learning models based on transformers and trained on huge amounts of data have been used for new products and many cognitive tasks. These models have grown in size and size and customer needs for training and refinement have grown accordingly. Training and fine-tuning these types of models requires a complex and distributed architecture, and tuning these architectures requires several manual and error-prone steps. With this new optimized stack, AzureML enables a better experience in terms of usability and performance by providing an easy-to-use learning pipeline. The recommended AzureML stack includes: hardware, operating system, VM image, Docker image (with optimized PyTorch, DeepSpeed, ONNX Runtime and other Python packages) for performance and scalability without complexity. Optimized stack for scalable distributed training on Azure A possible experimental setup consists of the NDm A100 v4 series that includes two AMD EPYC 7V12 64-Core CPU slots, 1.7 TB of main memory and eight A100 80 GB GPUS. A balanced PCIe topology is used to connect 4 GPUs to each CPU and each GPU has its own 200Gb/s NVIDIA Mellanox HDR InfiniBand agnostic topology. 1.7 TB of main memory and DeepSpeed ​​library offloading capabilities enable scaling to large model sizes. This setup can be used in both AzureML studio and Azure VMSS, but the AzureML studio solution is recommended because it’s the easiest way to set up and run the setup in the right and easy way. Differences between the distributed architecture and the AzureML training facility The proposed AzureML stack enables efficient training of 2x larger model sizes (2 trillion vs. 1 trillion parameters), scaling to 2x more GPUs (1024 vs. 512), and up to 1.8x higher compute throughput/GPU (150 TFLOP vs. 1 trillion ). 81 TFLOPs). This stack also has the potential to offer an almost linear scalability in terms of increasing model size and increasing the number of GPUs. Thanks to DeepSpeed ​​​​ZeRO-3 with CPU offload capabilities and this new AzureML stack, the 157 TFLOP efficient performance/GPU is maintained as the model grows from 175 billion to 2 trillion parameters and, given the model size (e.g. .175 billion in the graph below), a linear scaling is achieved if the number of GPUs increases. More detailed results are described in the extensive deepspeed technical blog. one. performance/GPU vs. model size from 175 billion to 2 trillion parameters (BS/GPU=8); si. Linear increases the performance scale by increasing the number of GPU devices for the 175B model (BS/GPU=16).

title: “Azure Optimized Stack With Deepspeed For Training Hyperscale Models Klmat” ShowToc: true date: “2022-10-31” author: “Steven Morgia”

In recent years, large-scale deep learning models based on transformers and trained on huge amounts of data have been used for new products and many cognitive tasks. These models have grown in size and size and customer needs for training and refinement have grown accordingly. Training and fine-tuning these types of models requires a complex and distributed architecture, and tuning these architectures requires several manual and error-prone steps. With this new optimized stack, AzureML enables a better experience in terms of usability and performance by providing an easy-to-use learning pipeline. The recommended AzureML stack includes: hardware, operating system, VM image, Docker image (with optimized PyTorch, DeepSpeed, ONNX Runtime and other Python packages) for performance and scalability without complexity. Optimized stack for scalable distributed training on Azure A possible experimental setup consists of the NDm A100 v4 series that includes two AMD EPYC 7V12 64-Core CPU slots, 1.7 TB of main memory and eight A100 80 GB GPUS. A balanced PCIe topology is used to connect 4 GPUs to each CPU and each GPU has its own 200Gb/s NVIDIA Mellanox HDR InfiniBand agnostic topology. 1.7 TB of main memory and DeepSpeed ​​library offloading capabilities enable scaling to large model sizes. This setup can be used in both AzureML studio and Azure VMSS, but the AzureML studio solution is recommended because it’s the easiest way to set up and run the setup in the right and easy way. Differences between the distributed architecture and the AzureML training facility The proposed AzureML stack enables efficient training of 2x larger model sizes (2 trillion vs. 1 trillion parameters), scaling to 2x more GPUs (1024 vs. 512), and up to 1.8x higher compute throughput/GPU (150 TFLOP vs. 1 trillion ). 81 TFLOPs). This stack also has the potential to offer an almost linear scalability in terms of increasing model size and increasing the number of GPUs. Thanks to DeepSpeed ​​​​ZeRO-3 with CPU offload capabilities and this new AzureML stack, the 157 TFLOP efficient performance/GPU is maintained as the model grows from 175 billion to 2 trillion parameters and, given the model size (e.g. .175 billion in the graph below), a linear scaling is achieved if the number of GPUs increases. More detailed results are described in the extensive deepspeed technical blog. one. performance/GPU vs. model size from 175 billion to 2 trillion parameters (BS/GPU=8); si. Linear increases the performance scale by increasing the number of GPU devices for the 175B model (BS/GPU=16).