❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)


Introduction.
Why is large model training needed?.
Scaling creates training and model efficiency.
Larger models = more efficient, less training, less data.
Larger models can learn with few shot learning.
Democratizing largescale language models with OPT175B.
Challenges of large model training.
What is PyTorch Distributed?.
Features Overview.
DistributedDataParallel.
FullyShardedDataParallel.
FSDP Auto wrapping.
FSDP Auto wrapping example.
FSDP CPU Offload, Backward Prefetch policies.
FSDP Mixed Precision control.
Pipeline.
Example Auto Partitioning.
Pipeline + DDP (PDP).
Memory Saving Features.
Activation Checkpointing.
Activation Offloading.
Activation Checkpointing & Offloading.
Parameter Offloading.
Memory Saving Feature & Training Paradigms.
Experiments & Insights.
Model Implementation.
Scaling Efficiency Varying # GPUs.
Scaling Efficiency Varying World Size.
Scaling Efficiency Varying Batch Size.
Model Scale Limit.
Impact of Network Bandwidth.
Best Practices.
Best Practices FSDP.
Profiling & Troubleshooting.
Profiling & Troubleshooting for Large Scale Model Training.
Uber Prof (Experimental) Profiling & Troubleshooting tool.
Demonstration.
Combining DCGM + Profiling.
Profiling for Large Scale Model Training.
Nvidia NSights multinode, multigpu Profiling.
PyTorch Profiler Distributed Training Profiling (single node multigpu).
Try it now.
Resources.
Closing Notes.


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
Scaling ML workloads with PyTorch OD39 is an online course that teaches learners how to scale their machine learning workloads with PyTorch. Learners will gain an understanding of why large model training is needed, how to create training and model efficiency, and how to use PyTorch Distributed to scale their models. They will also learn about features such as DistributedDataParallel, FullyShardedDataParallel, Auto wrapping, CPU Offload, Backward Prefetch policies, Mixed Precision control, Pipeline, Example Auto Partitioning, Pipeline + DDP (PDP), Memory Saving Features, Activation Checkpointing, Activation Offloading, Parameter Offloading, Memory Saving Feature & Training Paradigms, Experiments & Insights, Model Implementation, Scaling Efficiency Varying # GPUs, Scaling Efficiency Varying World Size, Scaling Efficiency Varying Batch Size, Model Scale Limit, Impact of Network Bandwidth, Best Practices, Best Practices FSDP, Profiling & Troubleshooting, Profiling & Troubleshooting for Large Scale Model Training, Uber Prof (Experimental) Profiling & Troubleshooting tool, Demonstration, Combining DCGM + Profiling, Profiling for Large Scale Model Training, Nvidia NSights multinode, multigpu Profiling, PyTorch Profiler Distributed Training Profiling (single node multigpu), Try it now, Resources, and Closing Notes.

Learners can learn about the importance of large model training and how to create training and model efficiency. They can also learn how to use PyTorch Distributed to scale their models, including features such as DistributedDataParallel, FullyShardedDataParallel, Auto wrapping, CPU Offload, Backward Prefetch policies, and Mixed Precision control. Learners can also learn about Memory Saving Features, Activation Checkpointing, Activation Offloading, Parameter Offloading, Memory Saving Feature & Training Paradigms, Experiments & Insights, Model Implementation, Scaling Efficiency Varying # GPUs, Scaling Efficiency Varying World Size, Scaling Efficiency Varying Batch Size, Model Scale Limit, Impact of Network Bandwidth, Best Practices, Best Practices FSDP, Profiling & Troubleshooting, Profiling & Troubleshooting for Large Scale Model Training, Uber Prof (Experimental) Profiling & Troubleshooting tool, Demonstration, Combining DCGM + Profiling, Profiling for Large Scale Model Training, Nvidia NSights multinode, multigpu Profiling, PyTorch Profiler Distributed Training Profiling (single node multigpu), Try it now, Resources, and Closing Notes.

In this course, learners can gain an understanding of the importance of large model training and how to create training and model efficiency. They can also learn how to use PyTorch Distributed to scale their models, including features such as DistributedDataParallel, FullyShardedDataParallel, Auto wrapping, CPU Offload, Backward Prefetch policies, and Mixed Precision control. Learners can also learn about Memory Saving Features, Activation Checkpointing, Activation Offloading, Parameter Offloading, Memory Saving Feature & Training Paradigms, Experiments & Insights, Model Implementation, Scaling Efficiency Varying # GPUs, Scaling Efficiency Varying World Size, Scaling Efficiency Varying Batch Size, Model Scale Limit, Impact of Network Bandwidth, Best Practices, Best Practices FSDP, Profiling & Troubleshooting, Profiling & Troubleshooting for Large Scale Model Training, Uber Prof (Experimental) Profiling & Troubleshooting tool, Demonstration, Combining DCGM + Profiling, Profiling for Large Scale Model Training, Nvidia NSights multinode, multigpu Profiling, PyTorch Profiler Distributed Training Profiling (single node multigpu), Try it now, Resources, and Closing Notes.

In this course, learners can learn about the importance of large model training and how to create training and model efficiency. They can also learn how to use PyTorch Distributed to scale their models, including features such as DistributedDataParallel, FullyShardedDataParallel, Auto wrapping, CPU Offload, Backward Prefetch policies, and Mixed Precision control. Learners can also gain an understanding of Memory Saving Features, Activation Checkpointing, Activation Offloading, Parameter Offloading, Memory Saving Feature & Training Paradigms, Experiments & Insights, Model Implementation, Scaling Efficiency Varying # GPUs, Scaling Efficiency Varying World Size, Scaling Efficiency Varying Batch Size, Model Scale Limit, Impact of Network Bandwidth, Best Practices, Best Practices FSDP, Profiling & Troubleshooting, Profiling & Troubleshooting for Large Scale Model Training, Uber Prof (Experimental) Profiling & Troubleshooting tool, Demonstration, Combining DCGM + Profiling, Profiling for Large Scale Model Training, Nvidia NSights multinode, multigpu Profiling, PyTorch Profiler Distributed Training Profiling (single node multigpu), Try it now, Resources, and Closing Notes.

In this course, learners can learn about the importance of large model training and how to create training and model efficiency. They can also learn how to use PyTorch Distributed to scale their models, including features such as DistributedDataParallel, FullyShardedDataParallel, Auto wrapping, CPU Offload, Backward Prefetch policies, and Mixed Precision control. Learners can also gain an understanding of Memory Saving Features, Activation Checkpointing, Activation Offloading, Parameter Offloading, Memory Saving Feature & Training Paradigms, Experiments & Insights, Model Implementation, Scaling Efficiency Varying # GPUs, Scaling Efficiency Varying World Size, Scaling Efficiency Varying Batch Size, Model Scale Limit, Impact of Network Bandwidth, Best Practices, Best Practices FSDP, Profiling & Troubleshooting, Profiling & Troubleshooting for Large Scale Model Training, Uber Prof (Experimental) Profiling & Troubleshooting tool, Demonstration, Combining DCGM + Profiling, Profiling for Large Scale Model Training, Nvidia NSights multinode, multigpu Profiling, PyTorch Profiler Distributed Training Profiling (single node multigpu), Try it now, Resources, and Closing Notes. Learners can also learn about the challenges of large model training and how to use best practices to optimize their models.

[Applications]
After taking this course, participants should be able to apply the knowledge gained to scale ML workloads with PyTorch OD39. Participants should be able to understand the challenges of large model training, the features of PyTorch Distributed, and the memory saving features. They should also be able to use the best practices for FSDP, profile and troubleshoot for large scale model training, and use the Nvidia NSights multinode, multigpu Profiling. Finally, participants should be able to combine DCGM and profiling for large scale model training.

[Career Paths]
Three job positions recommended for learners of this course are:

1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models and algorithms. They must have a strong understanding of the fundamentals of machine learning, as well as the ability to scale ML workloads with PyTorch. This role is becoming increasingly important as organizations look to leverage the power of machine learning to gain competitive advantages.

2. Data Scientist: Data Scientists are responsible for analyzing large datasets and uncovering insights that can be used to inform business decisions. They must have a strong understanding of data analysis techniques, as well as the ability to scale ML workloads with PyTorch. This role is becoming increasingly important as organizations look to leverage the power of data to gain competitive advantages.

3. AI/ML Developer: AI/ML Developers are responsible for developing and deploying AI/ML applications. They must have a strong understanding of the fundamentals of AI/ML, as well as the ability to scale ML workloads with PyTorch. This role is becoming increasingly important as organizations look to leverage the power of AI/ML to gain competitive advantages.

The trend for these roles is that they are becoming increasingly important as organizations look to leverage the power of machine learning, data, and AI/ML to gain competitive advantages. As such, these roles are in high demand and will continue to be in the future.