Remove batches api in docs & example (#7400)
This commit is contained in:
Jinn
@@ -13,7 +13,6 @@
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"\n",
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"- `chat/completions`\n",
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"- `completions`\n",
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"- `batches`\n",
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"\n",
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"Check out other tutorials to learn about [vision APIs](https://docs.sglang.ai/backend/openai_api_vision.html) for vision-language models and [embedding APIs](https://docs.sglang.ai/backend/openai_api_embeddings.html) for embedding models."
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]
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@@ -278,290 +277,6 @@
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"For OpenAI compatible structured outputs API, refer to [Structured Outputs](https://docs.sglang.ai/backend/structured_outputs.html#OpenAI-Compatible-API) for more details.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Batches\n",
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"\n",
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"Batches API for chat completions and completions are also supported. You can upload your requests in `jsonl` files, create a batch job, and retrieve the results when the batch job is completed (which takes longer but costs less).\n",
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"\n",
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"The batches APIs are:\n",
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"\n",
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"- `batches`\n",
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"- `batches/{batch_id}/cancel`\n",
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"- `batches/{batch_id}`\n",
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"\n",
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"Here is an example of a batch job for chat completions, completions are similar.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import time\n",
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"from openai import OpenAI\n",
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"\n",
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"client = OpenAI(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
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"\n",
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"requests = [\n",
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" {\n",
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" \"custom_id\": \"request-1\",\n",
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" \"method\": \"POST\",\n",
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" \"url\": \"/chat/completions\",\n",
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" \"body\": {\n",
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" \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
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" \"messages\": [\n",
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" {\"role\": \"user\", \"content\": \"Tell me a joke about programming\"}\n",
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" ],\n",
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" \"max_tokens\": 50,\n",
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" },\n",
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" },\n",
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" {\n",
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" \"custom_id\": \"request-2\",\n",
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" \"method\": \"POST\",\n",
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" \"url\": \"/chat/completions\",\n",
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" \"body\": {\n",
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" \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
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" \"messages\": [{\"role\": \"user\", \"content\": \"What is Python?\"}],\n",
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" \"max_tokens\": 50,\n",
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" },\n",
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" },\n",
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"]\n",
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"\n",
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"input_file_path = \"batch_requests.jsonl\"\n",
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"\n",
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"with open(input_file_path, \"w\") as f:\n",
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" for req in requests:\n",
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" f.write(json.dumps(req) + \"\\n\")\n",
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"\n",
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"with open(input_file_path, \"rb\") as f:\n",
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" file_response = client.files.create(file=f, purpose=\"batch\")\n",
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"\n",
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"batch_response = client.batches.create(\n",
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" input_file_id=file_response.id,\n",
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" endpoint=\"/v1/chat/completions\",\n",
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" completion_window=\"24h\",\n",
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")\n",
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"\n",
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"print_highlight(f\"Batch job created with ID: {batch_response.id}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"while batch_response.status not in [\"completed\", \"failed\", \"cancelled\"]:\n",
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" time.sleep(3)\n",
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" print(f\"Batch job status: {batch_response.status}...trying again in 3 seconds...\")\n",
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" batch_response = client.batches.retrieve(batch_response.id)\n",
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"\n",
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"if batch_response.status == \"completed\":\n",
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" print(\"Batch job completed successfully!\")\n",
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" print(f\"Request counts: {batch_response.request_counts}\")\n",
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"\n",
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" result_file_id = batch_response.output_file_id\n",
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" file_response = client.files.content(result_file_id)\n",
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" result_content = file_response.read().decode(\"utf-8\")\n",
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"\n",
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" results = [\n",
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" json.loads(line) for line in result_content.split(\"\\n\") if line.strip() != \"\"\n",
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" ]\n",
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"\n",
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" for result in results:\n",
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" print_highlight(f\"Request {result['custom_id']}:\")\n",
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" print_highlight(f\"Response: {result['response']}\")\n",
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"\n",
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" print_highlight(\"Cleaning up files...\")\n",
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" # Only delete the result file ID since file_response is just content\n",
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" client.files.delete(result_file_id)\n",
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"else:\n",
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" print_highlight(f\"Batch job failed with status: {batch_response.status}\")\n",
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" if hasattr(batch_response, \"errors\"):\n",
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" print_highlight(f\"Errors: {batch_response.errors}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It takes a while to complete the batch job. You can use these two APIs to retrieve the batch job status or cancel the batch job.\n",
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"\n",
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"1. `batches/{batch_id}`: Retrieve the batch job status.\n",
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"2. `batches/{batch_id}/cancel`: Cancel the batch job.\n",
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"\n",
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"Here is an example to check the batch job status."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import time\n",
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"from openai import OpenAI\n",
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"\n",
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"client = OpenAI(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
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"\n",
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"requests = []\n",
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"for i in range(20):\n",
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" requests.append(\n",
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" {\n",
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" \"custom_id\": f\"request-{i}\",\n",
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" \"method\": \"POST\",\n",
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" \"url\": \"/chat/completions\",\n",
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" \"body\": {\n",
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" \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
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" \"messages\": [\n",
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" {\n",
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" \"role\": \"system\",\n",
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" \"content\": f\"{i}: You are a helpful AI assistant\",\n",
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" },\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Write a detailed story about topic. Make it very long.\",\n",
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" },\n",
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" ],\n",
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" \"max_tokens\": 64,\n",
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" },\n",
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" }\n",
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" )\n",
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"\n",
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"input_file_path = \"batch_requests.jsonl\"\n",
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"with open(input_file_path, \"w\") as f:\n",
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" for req in requests:\n",
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" f.write(json.dumps(req) + \"\\n\")\n",
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"\n",
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"with open(input_file_path, \"rb\") as f:\n",
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" uploaded_file = client.files.create(file=f, purpose=\"batch\")\n",
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"\n",
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"batch_job = client.batches.create(\n",
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" input_file_id=uploaded_file.id,\n",
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" endpoint=\"/v1/chat/completions\",\n",
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" completion_window=\"24h\",\n",
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")\n",
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"\n",
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"print_highlight(f\"Created batch job with ID: {batch_job.id}\")\n",
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"print_highlight(f\"Initial status: {batch_job.status}\")\n",
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"\n",
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"time.sleep(10)\n",
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"\n",
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"max_checks = 5\n",
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"for i in range(max_checks):\n",
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" batch_details = client.batches.retrieve(batch_id=batch_job.id)\n",
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"\n",
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" print_highlight(\n",
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" f\"Batch job details (check {i+1} / {max_checks}) // ID: {batch_details.id} // Status: {batch_details.status} // Created at: {batch_details.created_at} // Input file ID: {batch_details.input_file_id} // Output file ID: {batch_details.output_file_id}\"\n",
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" )\n",
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" print_highlight(\n",
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" f\"<strong>Request counts: Total: {batch_details.request_counts.total} // Completed: {batch_details.request_counts.completed} // Failed: {batch_details.request_counts.failed}</strong>\"\n",
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" )\n",
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"\n",
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" time.sleep(3)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here is an example to cancel a batch job."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import time\n",
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"from openai import OpenAI\n",
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"import os\n",
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"\n",
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"client = OpenAI(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
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"\n",
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"requests = []\n",
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"for i in range(5000):\n",
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" requests.append(\n",
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" {\n",
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" \"custom_id\": f\"request-{i}\",\n",
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" \"method\": \"POST\",\n",
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" \"url\": \"/chat/completions\",\n",
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" \"body\": {\n",
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" \"model\": \"qwen/qwen2.5-0.5b-instruct\",\n",
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" \"messages\": [\n",
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" {\n",
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" \"role\": \"system\",\n",
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" \"content\": f\"{i}: You are a helpful AI assistant\",\n",
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" },\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"Write a detailed story about topic. Make it very long.\",\n",
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" },\n",
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" ],\n",
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" \"max_tokens\": 128,\n",
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" },\n",
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" }\n",
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" )\n",
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"\n",
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"input_file_path = \"batch_requests.jsonl\"\n",
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"with open(input_file_path, \"w\") as f:\n",
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" for req in requests:\n",
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" f.write(json.dumps(req) + \"\\n\")\n",
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"\n",
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"with open(input_file_path, \"rb\") as f:\n",
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" uploaded_file = client.files.create(file=f, purpose=\"batch\")\n",
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"\n",
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"batch_job = client.batches.create(\n",
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" input_file_id=uploaded_file.id,\n",
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" endpoint=\"/v1/chat/completions\",\n",
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" completion_window=\"24h\",\n",
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")\n",
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"\n",
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"print_highlight(f\"Created batch job with ID: {batch_job.id}\")\n",
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"print_highlight(f\"Initial status: {batch_job.status}\")\n",
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"\n",
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"time.sleep(10)\n",
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"\n",
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"try:\n",
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" cancelled_job = client.batches.cancel(batch_id=batch_job.id)\n",
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" print_highlight(f\"Cancellation initiated. Status: {cancelled_job.status}\")\n",
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" assert cancelled_job.status == \"cancelling\"\n",
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"\n",
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" # Monitor the cancellation process\n",
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" while cancelled_job.status not in [\"failed\", \"cancelled\"]:\n",
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" time.sleep(3)\n",
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" cancelled_job = client.batches.retrieve(batch_job.id)\n",
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" print_highlight(f\"Current status: {cancelled_job.status}\")\n",
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"\n",
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" # Verify final status\n",
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" assert cancelled_job.status == \"cancelled\"\n",
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" print_highlight(\"Batch job successfully cancelled\")\n",
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"\n",
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"except Exception as e:\n",
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" print_highlight(f\"Error during cancellation: {e}\")\n",
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" raise e\n",
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"\n",
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"finally:\n",
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" try:\n",
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" del_response = client.files.delete(uploaded_file.id)\n",
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" if del_response.deleted:\n",
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" print_highlight(\"Successfully cleaned up input file\")\n",
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" if os.path.exists(input_file_path):\n",
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" os.remove(input_file_path)\n",
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" print_highlight(\"Successfully deleted local batch_requests.jsonl file\")\n",
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" except Exception as e:\n",
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" print_highlight(f\"Error cleaning up: {e}\")\n",
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" raise e"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -1,94 +0,0 @@
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"""
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Usage:
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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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python openai_batch_chat.py
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Note: Before running this script,
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you should create the input.jsonl file with the following content:
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{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world! List 3 NBA players and tell a story"}],"max_tokens": 300}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo-0125", "messages": [{"role": "system", "content": "You are an assistant. "},{"role": "user", "content": "Hello world! List three capital and tell a story"}],"max_tokens": 500}}
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"""
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import json
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import time
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import openai
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class OpenAIBatchProcessor:
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def __init__(self):
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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self.client = client
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def process_batch(self, input_file_path, endpoint, completion_window):
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# Upload the input file
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with open(input_file_path, "rb") as file:
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uploaded_file = self.client.files.create(file=file, purpose="batch")
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# Create the batch job
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batch_job = self.client.batches.create(
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input_file_id=uploaded_file.id,
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endpoint=endpoint,
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completion_window=completion_window,
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)
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# Monitor the batch job status
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while batch_job.status not in ["completed", "failed", "cancelled"]:
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time.sleep(3) # Wait for 3 seconds before checking the status again
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print(
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f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
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)
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batch_job = self.client.batches.retrieve(batch_job.id)
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# Check the batch job status and errors
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if batch_job.status == "failed":
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print(f"Batch job failed with status: {batch_job.status}")
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print(f"Batch job errors: {batch_job.errors}")
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return None
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# If the batch job is completed, process the results
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if batch_job.status == "completed":
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# print result of batch job
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print("batch", batch_job.request_counts)
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result_file_id = batch_job.output_file_id
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# Retrieve the file content from the server
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file_response = self.client.files.content(result_file_id)
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result_content = file_response.read() # Read the content of the file
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# Save the content to a local file
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result_file_name = "batch_job_chat_results.jsonl"
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with open(result_file_name, "wb") as file:
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file.write(result_content) # Write the binary content to the file
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# Load data from the saved JSONL file
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results = []
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with open(result_file_name, "r", encoding="utf-8") as file:
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for line in file:
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json_object = json.loads(
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line.strip()
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) # Parse each line as a JSON object
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results.append(json_object)
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return results
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else:
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print(f"Batch job failed with status: {batch_job.status}")
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return None
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# Initialize the OpenAIBatchProcessor
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processor = OpenAIBatchProcessor()
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# Process the batch job
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input_file_path = "input.jsonl"
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endpoint = "/v1/chat/completions"
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completion_window = "24h"
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# Process the batch job
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results = processor.process_batch(input_file_path, endpoint, completion_window)
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# Print the results
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print(results)
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@@ -1,93 +0,0 @@
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"""
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Usage:
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python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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python openai_batch_complete.py
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Note: Before running this script,
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you should create the input.jsonl file with the following content:
|
||||
{"custom_id": "request-1", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 3 names of famous soccer player: ", "max_tokens": 200}}
|
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{"custom_id": "request-2", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 6 names of famous basketball player: ", "max_tokens": 400}}
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{"custom_id": "request-3", "method": "POST", "url": "/v1/completions", "body": {"model": "gpt-3.5-turbo-instruct", "prompt": "List 6 names of famous basketball player: ", "max_tokens": 400}}
|
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"""
|
||||
|
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import json
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import time
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||||
|
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import openai
|
||||
|
||||
|
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class OpenAIBatchProcessor:
|
||||
def __init__(self):
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client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
|
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|
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self.client = client
|
||||
|
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def process_batch(self, input_file_path, endpoint, completion_window):
|
||||
|
||||
# Upload the input file
|
||||
with open(input_file_path, "rb") as file:
|
||||
uploaded_file = self.client.files.create(file=file, purpose="batch")
|
||||
|
||||
# Create the batch job
|
||||
batch_job = self.client.batches.create(
|
||||
input_file_id=uploaded_file.id,
|
||||
endpoint=endpoint,
|
||||
completion_window=completion_window,
|
||||
)
|
||||
|
||||
# Monitor the batch job status
|
||||
while batch_job.status not in ["completed", "failed", "cancelled"]:
|
||||
time.sleep(3) # Wait for 3 seconds before checking the status again
|
||||
print(
|
||||
f"Batch job status: {batch_job.status}...trying again in 3 seconds..."
|
||||
)
|
||||
batch_job = self.client.batches.retrieve(batch_job.id)
|
||||
|
||||
# Check the batch job status and errors
|
||||
if batch_job.status == "failed":
|
||||
print(f"Batch job failed with status: {batch_job.status}")
|
||||
print(f"Batch job errors: {batch_job.errors}")
|
||||
return None
|
||||
|
||||
# If the batch job is completed, process the results
|
||||
if batch_job.status == "completed":
|
||||
|
||||
# print result of batch job
|
||||
print("batch", batch_job.request_counts)
|
||||
|
||||
result_file_id = batch_job.output_file_id
|
||||
# Retrieve the file content from the server
|
||||
file_response = self.client.files.content(result_file_id)
|
||||
result_content = file_response.read() # Read the content of the file
|
||||
|
||||
# Save the content to a local file
|
||||
result_file_name = "batch_job_complete_results.jsonl"
|
||||
with open(result_file_name, "wb") as file:
|
||||
file.write(result_content) # Write the binary content to the file
|
||||
# Load data from the saved JSONL file
|
||||
results = []
|
||||
with open(result_file_name, "r", encoding="utf-8") as file:
|
||||
for line in file:
|
||||
json_object = json.loads(
|
||||
line.strip()
|
||||
) # Parse each line as a JSON object
|
||||
results.append(json_object)
|
||||
|
||||
return results
|
||||
else:
|
||||
print(f"Batch job failed with status: {batch_job.status}")
|
||||
return None
|
||||
|
||||
|
||||
# Initialize the OpenAIBatchProcessor
|
||||
processor = OpenAIBatchProcessor()
|
||||
|
||||
# Process the batch job
|
||||
input_file_path = "input.jsonl"
|
||||
endpoint = "/v1/completions"
|
||||
completion_window = "24h"
|
||||
|
||||
# Process the batch job
|
||||
results = processor.process_batch(input_file_path, endpoint, completion_window)
|
||||
|
||||
# Print the results
|
||||
print(results)
|
||||
Reference in New Issue
Block a user