Chat (meta-llama/Llama-2-70b-chat-hf)
POST {{baseUrl}}/chat/completions
Request Body
{"model"=>"meta-llama/Llama-2-70b-chat-hf", "messages"=>[{"role"=>"system", "content"=>"You are a helpful assistant."}, {"role"=>"user", "content"=>"Write a program to load data from S3 with Ray and train using PyTorch."}], "temperature"=>0.7}
RESPONSES
status: OK
{"id":"meta-llama/Llama-2-70b-chat-hf-7821cf9d-03c6-4ff7-b99c-73c9799c93b7","object":"text_completion","created":1713803412,"model":"meta-llama/Llama-2-70b-chat-hf","choices":[{"message":{"role":"assistant","content":" Sure, here's an example program that uses Ray and PyTorch to load data from Amazon S3 and train a machine learning model:\n```\nimport ray\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Set up Ray\nray.init(num_cpus=4) # Use 4 CPUs for distributed training\n\n# Define the model and loss function\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.fc1 = nn.Linear(784, 128) # input layer (784) -\u003e hidden layer (128)\n self.fc2 = nn.Linear(128, 10) # hidden layer (128) -\u003e output layer (10)\n\n def forward(self, x):\n x = torch.relu(self.fc1(x)) # activation function for hidden layer\n x = self.fc2(x)\n return x\n\nnet = Net()\n\n# Define the loss function and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.01)\n\n# Load the data from S3\nbucket_name = \"your-bucket-name\"\ndata_key = \"path/to/data.pth\"\n\n# Use Ray's S3 client to download the data\ns3 = ray.experimental.s3.S3Client()\ndata = s3.get_object(bucket_name, data_key)\n\n# Load the data into a PyTorch tensor\ndata = torch.tensor(data)\n\n# Normalize the data\nmean = data.mean(dim=0)\nstd = data.std(dim=0)\ndata = (data - mean) / std\n\n# Split the data into training and validation sets\ntrain_size = int(0.8 * len(data))\ntrain_data = data[:train_size]\nval_data = data[train_size:]\n\n# Create a PyTorch DataLoader for the training data\ntrain_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)\n\n# Create a PyTorch DataLoader for the validation data\nval_loader = torch.utils.data.DataLoader(val_data, batch_size=32, shuffle=False)\n\n# Train the model\nfor epoch in range(10): # loop over the dataset multiple times\n for i, batch in enumerate(train_loader):\n # Get the inputs and labels\n inputs, labels = batch\n\n # Zero the gradients\n optimizer.zero_grad()\n\n # Forward pass\n outputs = net(inputs)\n\n # Compute the loss\n loss = criterion(outputs, labels)\n\n # Backward pass\n loss.backward()\n\n # Update the model parameters\n optimizer.step()\n\n # Print the loss at each epoch\n if i % 100 == 0:\n print(f\"Epoch {epoch+1}, Batch {i}, Loss: {loss.item():.4f}\")\n\n # Print the validation loss\n val_loss = 0\n correct = 0\n with torch.no_grad():\n for batch in val_loader:\n inputs, labels = batch\n outputs = net(inputs)\n loss = criterion(outputs, labels)\n val_loss += loss.item()\n _, predicted = torch.max(outputs, dim=1)\n correct += (predicted == labels).sum().item()\n\n val_accuracy = correct / len(val_data)\n print(f\"Epoch {epoch+1}, Validation Loss: {val_loss.item():.4f}, Accuracy: {val_accuracy:.4f}\")\n```\nIn this example, we first set up Ray with 4 CPUs for distributed training. We then define a simple neural network model and loss function using PyTorch.\n\nNext, we load the data from an Amazon S3 bucket using Ray's S3 client. We normalize the data and split it into training and validation sets. We create PyTorch DataLoaders for both sets.\n\nWe then train the model for 10 epochs, printing the loss at each epoch and the validation loss and accuracy after each epoch.\n\nNote that this is just an example program and may not work as-is for your specific use case. You may need to modify the code to fit your specific needs, such as changing the model architecture, loss function, or hyperparameters.","tool_calls":null,"tool_call_id":null},"index":0,"finish_reason":"stop","logprobs":null}],"usage":{"prompt_tokens":45,"completion_tokens":1110,"total_tokens":1155}}