[{'from': 'human', 'value': 'I want to implement Colossal Ai distributed training in my training script, what are the steps needed to convert my pytorch training script into using the Colossal Ai distributed training library?'}, {'from': 'gpt', 'value': "Colossal AI is a high-performance distributed deep learning library, built on top of PyTorch. To convert your PyTorch training script to use Colossal AI for distributed training, follow these steps:\n\nInstall Colossal AI:\n1. First, install the library by running the following command:\n\n```\npip install colossalai\n```\n\n2. Import required libraries:\nIn your training script, import the necessary Colossal AI libraries, such as:\n```python\nimport torch\nimport colossalai\nfrom colossalai.core import global_context as gpc\nfrom colossalai.initializer import init_config, init_dist\nfrom colossalai.utils import get_world_size\n```\n\n3. Initialize the configuration:\nLoad the configuration file and initialize the distributed environment using the init_config and init_dist functions. Make sure you have a valid configuration file (usually in YAML format)\n```python\nconfig = 'path/to/your/config.yaml'\ninit_config(config)\ninit_dist()\n```\n\n4. Modify your DataLoader:\nReplace your original PyTorch DataLoader with Colossal AI's DataLoader, which supports parallel data loading.\n```python\nfrom colossalai import DataLoader\n# ...\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n```\n\n5. Convert your model to a parallel model:\nModify your model by wrapping it with Colossal AI's parallel engine. For example, if you're using a 2D parallelism with pipeline and tensor slicing, you can use the PipelineModule and ParallelLinear layers:\n```python\nfrom colossalai.nn import PipelineModule, ParallelLinear\n# ...\nclass MyModel(PipelineModule):\n    def __init__(self):\n        super().__init__()\n        self.linear1 = ParallelLinear(in_features, hidden_features)\n        # ...\n    # ...\nmodel = MyModel()\n```\n\n6. Replace the optimizer:\nUse Colossal AI's distributed optimizer to replace the original PyTorch optimizer.\n```python\nfrom colossalai.optim import Adam\n# ...\noptimizer = Adam(model.parameters(), lr=learning_rate)\n```\n\n7. Update the training loop:\nIn the training loop, use Colossal AI's backward and step functions for gradient calculation and optimization.\n```python\nfrom colossalai.engine import backward, step\n# ...\nfor epoch in range(num_epochs):\n    for batch in train_loader:\n        # ...\n        loss = criterion(output, target)\n        backward(loss)\n        step(optimizer)\n        # ...\n```\n\n8. Finalize the distributed environment:\nAfter the training is complete, finalize the distributed environment.\n``` python\ngpc.destroy()\n```\n\nThis is a basic outline to convert your PyTorch training script into using the Colossal AI distributed training library. The actual implementation may vary depending on the specifics of your training script and the parallelism strategies you choose. Be sure to refer to Colossal AI's documentation and examples for more detailed guidance."}, {'from': 'human', 'value': 'Well done. Can you give a complete beginner script for me?'}]