Cloud ML Computing Part 1: Train Robot Models in Google Colab
The Short Version
Launch the LeRobot Colab Notebook and select your GPU type (A100 or T4).
Confirm you have enough compute units, and load a checkpoint file if resuming.
Log in with your Hugging Face token to access Trossen Robotics Community datasets or your own repo ID.
Edit the YAML config to set batch size, learning rate, and training steps (we used 50 episodes, batch size 8, 80,000 steps).
Estimate resources by timing the first steps and checking the usage rate in the Resources tab, then add a buffer.
Monitor early epochs to confirm sufficient compute units for uninterrupted training.
Upload the trained model to Hugging Face or download the outputs locally when finished.
Who this is for
Robotics researchers
ML developers
Imitation learning practitioners
Aloha Kit users
Robotics enthusiasts without high-end local hardware
As machine learning continues to expand into robotics, the need for efficient and accessible tools has never been greater. Cloud ML Computing Part 1 introduces a Google Colab Notebook from Trossen Robotics that lets you train robotic models on cloud-based GPUs like the A100 and T4—no expensive local hardware required. In our tests, an A100 GPU trained the Action Chunking Transformer (ACT) model for a peg insertion task in just 5 hours.
Whether you're working with our Aloha Kits or any other dataset, this notebook makes it incredibly easy to train models in the cloud.
Training machine learning models can be computationally demanding, especially for tasks in robotics like imitation learning, where a large amount of data and high-performance hardware is required. GPUs can dramatically speed up the process, cutting training times down significantly. Google Colab provides an accessible way for users to utilize powerful GPUs like the A100 and T4, making it an excellent option for those without access to high-end local hardware.
In our tests, using an A100 GPU, we trained the ACT model for a peg insertion task with 50 episodes and 80,000 steps in just 5 hours—utilizing 70 compute units. This notebook is designed to let you tap into that power with minimal setup, making model training faster and easier than ever.
What does the Colab Notebook do?
Seamless Setup: The notebook guides you through selecting the appropriate GPU, downloading datasets from Hugging Face, and configuring training parameters. It even supports resuming training from a checkpoint, ensuring you don't lose progress.
Access to Datasets: By linking directly to the Trossen Robotics Community on Hugging Face, the notebook makes it simple to choose from various datasets. You can also use your own data—paste the repository ID into the provided cell, and you're ready to go.
Flexible Training Parameters: We've built in customization options, letting you adjust batch sizes, learning rates, and training steps through a YAML file. For instance, the batch size for our ACT model was set to 8, ensuring efficient processing of each training step while still maintaining high performance.
GPU Utilization: The notebook monitors the initial training epochs and confirms sufficient compute units, so long training sessions proceed without hitches. For our peg insertion task, utilizing 70 compute units over a 5-hour training period was optimal, and the model achieved solid results—demonstrating how leveraging cloud GPUs can significantly boost training efficiency.
Why does cloud-based training matter for robotics?
Robotic tasks like fine manipulation require models to be trained efficiently and quickly, which is difficult without the proper hardware. Our notebook bridges this gap by letting you harness the power of cloud-based GPUs, making training accessible to a much broader audience. Whether you're a researcher, developer, or enthusiast, you can now train advanced models like ACT on your own tasks—improving your robot's performance without worrying about expensive hardware.
How do you use the notebook?
Start with the basics: After launching the notebook, select your GPU type (A100, T4) and ensure you have enough compute units for the session. If you're resuming training, make sure your checkpoint file is available.
Log into Hugging Face: Enter your Hugging Face token to log in and access datasets from the Trossen Robotics Community or any dataset you choose.
Set training parameters: Adjust parameters like batch size and learning rate by editing the provided YAML configuration file. For example, we used 50 episodes, batch size 8, and 80,000 training steps for our ACT model on the peg insertion task.
Upload or download results: Once training is complete, store the model on Hugging Face for easy access and sharing, or download the outputs locally to safeguard your data.
What results did the ACT model achieve?
In our trials, the ACT model was trained for a peg insertion task involving 50 episodes with a batch size of 8, running for 80,000 steps. Using an A100 GPU, training completed in just 5 hours, utilizing 70 compute units. This demonstrates the efficiency and performance improvements cloud-based GPUs can offer—reducing training times by more than half compared to local machines.
ACT peg insertion run | Value |
GPU | A100 |
Episodes | 50 |
Batch size | 8 |
Training steps | 80,000 |
Training time | 5 hours |
Compute units | 70 |
By the end of training, the ACT model showed remarkable performance in fine manipulation tasks such as peg insertion, highlighting the capabilities of low-cost, cloud-based model training for robotics. Using the Colab notebook ensures you're not limited by your local hardware, opening up the possibility for more complex and computationally intensive tasks.
How do you estimate the compute units you'll need?
Once you've connected to a GPU or CPU and started training, you can easily estimate the compute units required for your entire session. Open the Resources tab (as shown in the image) to see the approximate usage rate—how many compute units are consumed per hour.
You can also estimate total training time by timing how long the first few steps take. For example, if 100 steps take 20 seconds and your full session includes 80,000 steps, you can calculate the total training time from that rate. In our example, that works out to roughly 4.44 hours.
With the training time and the compute unit usage rate (e.g., 15.9 compute units per hour, as shown in the image), you can then estimate the total compute units required. For 4.44 hours at 15.9 units per hour, that comes to about 70 compute units.
Always add a buffer of extra compute units to accommodate any variability in the training process. This method gives you a quick, practical way to estimate the resources you'll need before committing to the full training run.
Get Started Today!
Ready to simplify your robotic model training? Check out our LeRobot Colab Notebook today and experience how easy and efficient cloud-based training can be. Whether you're a beginner or an expert, this tool is designed to accelerate your workflows and get you closer to deploying powerful models in the real world. Explore more at Trossen Robotics.
For more details and a step-by-step walk-through, you can watch our video tutorial.
Happy experimenting!
Get Started Today!?
Launch the LeRobot Colab Notebook and select your GPU type (A100 or T4).
Confirm you have enough compute units, and load a checkpoint file if resuming.
Log in with your Hugging Face token to access Trossen Robotics Community datasets or your own repo ID.
_Learn more about Trossen Robotics and Trossen SDK for your deployment._
Frequently Asked Questions
What is Cloud ML Computing Part 1 about?
It introduces our Google Colab Notebook for training robotic models on cloud-based GPUs, letting you harness top-tier hardware without the cost or complexity of maintaining it yourself.
Which GPUs can I use in Google Colab?
Colab provides access to powerful GPUs like the A100 and T4, making it an excellent option for those without high-end local hardware.
How long does training take on an A100 GPU?
In our tests, we trained the ACT model for a peg insertion task with 50 episodes and 80,000 steps in just 5 hours, utilizing 70 compute units.
Where do the datasets come from?
The notebook links directly to the Trossen Robotics Community on Hugging Face, and you can also use your own data by pasting the repository ID into the provided cell.
Can I customize the training parameters?
Yes. You can adjust batch sizes, learning rates, and training steps through a YAML file—for our ACT model we set the batch size to 8.
How much faster is cloud training than a local machine?
Leveraging cloud-based GPUs reduced training times by more than half compared to local machines.
How do I estimate the compute units I'll need?
Time your first few steps, use the usage rate shown in the Resources tab (e.g., 15.9 units per hour), calculate total training time, and add a buffer for variability.
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