# Fine tuning GPU options

#### Sample Comparison

**Overview:**

In this exercise, we compare the performance and cost-effectiveness of three different model training pipelines: A100 (Balanced), H100 (Fast), and L40S (Low Cost). The training was conducted using the following parameters:

* Dataset: 10MB zip file
* Steps: 500

```
Training Configuration
{
  "resolution": 1024,
  "repeats": 100,
  "learning_rate": 0.0004,
  "lr_scheduler": "constant",
  "optimizer_type": "Adafactor",
  "num_train_epochs": 500,
  "steps": 500,
  "gradient_accumulation_steps": 2,
  "center_crop": false,
  "lora_rank": 32,
  "noise_offset": 0,
  "max_grad_norm": 0,
  "bucket_steps": 64,
  "weight_decay": 0.01,
  "relative_step": false,
  "auto_caption": false,
  "content_or_style": "balanced",
  "batch_size": 2,
  "linear": 16,
  "linear_alpha": 16,
  "prompt": "driving a F1 car",
  "iterations": 300,
  "captioning": true,
  "priority": "QUALITY"
}

```

Comparison Table:

| Pipeline Name | Characteristics | Charge Rate (/s) | # of Seconds | Total Cost |
| ------------- | --------------- | ---------------- | ------------ | ---------- |
| A100          | Balanced        | $0.002           | 1868.67      | $3.74      |
| H100          | Fast            | $0.0043          | 1085.85      | $4.67      |
| L40S          | Low Cost        | $0.0014          | 2532.14      | $3.54      |

***

**Insights:**

1. A100 (Balanced):

   1. Offers a balanced performance with moderate cost and time.
   2. Suitable for scenarios where a trade-off between speed and cost is acceptable.

2. H100 (Fast):

   1. Significantly faster but at a higher charge rate.
   2. Ideal for time-sensitive tasks where speed is prioritized over cost.

3. L40S (Low Cost):

   1. Lowest cost option but takes the longest time to complete.
   2. Best suited for non-urgent tasks where cost efficiency is more important.


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