I have 2 x T4 GPUs. What training/tuning can I do with them?

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I have a Poweredge R740 server running two T4 GPUs. Currently I have a mythomax-L2-13B model running on them in parallel with a chat UI and it runs nice.
The server has 128G memory and dual Intel Xeon Silver 4114 processors with 10 physical cores per processor.
Let’s say I have a time budget of 7 days (168 hours) that I can dedicate the GPUs to training/tuning something. What can I realistically do in that frame of time with what?

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I asked Grok this question and here’s what it responded with. Is any of this true? Can I actually tune my own model on my own hardware with 10-50K pieces of data in 3 hours? GPT told me it would take years.

Given your setup with two NVIDIA T4 GPUs, dual Intel Xeon Silver 4114 processors, and 128GB of RAM, here’s a strategic approach to what you could realistically achieve in 168 hours:

Model Tuning

  1. Fine-Tuning Mythomax-L2-13B for Specific Tasks:

  • Objective: Enhance the model’s performance on specific tasks or domains (e.g., medical text, legal documents, customer support).

  • Strategy:

    • Dataset: Use a smaller, curated dataset relevant to your task. Aim for something around 10k to 50k examples to keep training manageable within your time frame.

    • Hyperparameters: Adjust learning rate, batch size, and number of epochs. Start with a lower learning rate (e.g., 5e-5) for fine-tuning to prevent overfitting.

    • Expected Time: Depending on the dataset size and complexity, you might spend:

      • Data Preparation: 1-2 days for data cleaning, formatting.

      • Training: With your setup, each epoch on this dataset might take 1-3 hours. Run for 10-20 epochs, giving you a total of about 10-60 hours of training time.

    • Outcome: You could see significant improvements in domain-specific performance or task accuracy.

Model Training from Scratch

  • Training a Smaller Model:

    • Objective: If you’re interested in experimenting with model architecture or learning from scratch, consider a smaller model:

      • Model Size: Something like a 1B parameter model would be feasible.

      • Dataset: Use a general language corpus or a domain-specific one if available.

      • Strategy:

        • Data: Again, manage dataset size to fit within your time constraints; perhaps 100k to 500k samples.

        • Training Time: Building a model from scratch would be more time-consuming. Each epoch might take several hours, but you’ll likely need fewer epochs due to the model’s size.

        • Expected Time: Data preparation could take 2 days, training might consume most of the remaining time, with each epoch taking about 4-8 hours, aiming for 5-10 epochs.

      • Outcome: A model tailored to your needs but with a smaller capacity compared to pre-trained models like Mythomax.

Transfer Learning

  • Using Pre-trained Models for New Tasks:

    • Objective: Adapt a different pre-trained model for a new task without starting from scratch.

    • Strategy:

      • Model Choice: Pick a model similar in size or slightly smaller than Mythomax (e.g., Llama or OPT models).

      • Adaptation: Use your GPUs for transfer learning where you adjust only the last few layers or introduce new layers for task-specific output.

      • Time: Data preparation 1 day, fine-tuning could be done in 5-10 epochs, each taking around 2-4 hours.

      • Outcome: A model that performs well on a new task with less computational overhead.

Considerations

  • Resource Management: Monitor GPU usage, memory, and CPU load. Consider using tools like nvidia-smi for GPU monitoring and adjust batch sizes or model parameters if you run into memory constraints.

  • Checkpointing: Regularly save model checkpoints to avoid losing progress in case of system failures or to evaluate model performance at different stages.

  • Validation: Use part of your dataset for validation to assess model performance during training, allowing for early stopping if necessary.

Given these strategies, you can either enhance your existing model or explore new models or tasks, all within your time budget. Remember, the exact time taken can vary based on the specifics of your data, the complexity of the task, and how efficiently you manage your resources.

EDIT: I just had Grok create a tuning script and attempt to tune my model on my hardware. It ran out of memory as expected. Tried a few more tricks with it and then it explained to me that I need 11 more GPUs. Yeah, Grok was wrong as heeelll.

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Oh…:sweat_smile: Depending on the model’s architecture and size, the difference in required time can be several to several hundred times, or even more than a hundred times (because there are models with less than 0.5B and models with far more than 1000B, like ChatGPT), and there is also a huge difference in required time depending on the training algorithm and training content, so I think you have to give the generative AI quite detailed information in order to get a correct answer.
If you were to train a large model from scratch on your own, it might actually take a year or more… Large companies are using parallel processing with huge numbers of enterprise GPUs to shorten the time. Why not ask Grok about ways to fine-tune it quickly and relatively inexpensively?