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update cpu and gpu type generators #181

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a5b3d3c
update cpu and gpu type generators
DeJayDev Feb 20, 2025
fcdb73a
docs
DeJayDev Feb 20, 2025
91439a0
add requirements for scripts
DeJayDev Feb 24, 2025
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2 changes: 1 addition & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,6 @@ yarn-error.log*

.vercel
.env*.local

.venv

helpers/__pycache__/** */
500 changes: 252 additions & 248 deletions docs/references/cpu-types.md
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76 changes: 38 additions & 38 deletions docs/references/gpu-types.md
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Expand Up @@ -7,42 +7,42 @@ The following list contains all GPU types available on RunPod.
For more information, see [GPU pricing](https://www.runpod.io/gpu-instance/pricing).

<!--
Table last generated: 2024-12-27
Table last generated: 2025-02-19
-->

| GPU ID | Display Name | Memory (GB) |
| ------------------------------ | -------------- | ----------- |
| NVIDIA A100 80GB PCIe | A100 PCIe | 80 |
| NVIDIA A100-SXM4-80GB | A100 SXM | 80 |
| NVIDIA A30 | A30 | 24 |
| NVIDIA A40 | A40 | 48 |
| NVIDIA H100 NVL | H100 NVL | 94 |
| NVIDIA H100 PCIe | H100 PCIe | 80 |
| NVIDIA H100 80GB HBM3 | H100 SXM | 80 |
| NVIDIA H200 | H200 SXM | 143 |
| NVIDIA L4 | L4 | 24 |
| NVIDIA L40 | L40 | 48 |
| NVIDIA L40S | L40S | 48 |
| AMD Instinct MI300X OAM | MI300X | 192 |
| NVIDIA RTX 2000 Ada Generation | RTX 2000 Ada | 16 |
| NVIDIA GeForce RTX 3070 | RTX 3070 | 8 |
| NVIDIA GeForce RTX 3080 | RTX 3080 | 10 |
| NVIDIA GeForce RTX 3080 Ti | RTX 3080 Ti | 12 |
| NVIDIA GeForce RTX 3090 | RTX 3090 | 24 |
| NVIDIA GeForce RTX 3090 Ti | RTX 3090 Ti | 24 |
| NVIDIA RTX 4000 Ada Generation | RTX 4000 Ada | 20 |
| NVIDIA GeForce RTX 4070 Ti | RTX 4070 Ti | 12 |
| NVIDIA GeForce RTX 4080 | RTX 4080 | 16 |
| NVIDIA GeForce RTX 4080 SUPER | RTX 4080 SUPER | 16 |
| NVIDIA GeForce RTX 4090 | RTX 4090 | 24 |
| NVIDIA RTX 5000 Ada Generation | RTX 5000 Ada | 32 |
| NVIDIA RTX 6000 Ada Generation | RTX 6000 Ada | 48 |
| NVIDIA RTX A2000 | RTX A2000 | 6 |
| NVIDIA RTX A4000 | RTX A4000 | 16 |
| NVIDIA RTX A4500 | RTX A4500 | 20 |
| NVIDIA RTX A5000 | RTX A5000 | 24 |
| NVIDIA RTX A6000 | RTX A6000 | 48 |
| Tesla V100-PCIE-16GB | Tesla V100 | 16 |
| Tesla V100-FHHL-16GB | V100 FHHL | 16 |
| Tesla V100-SXM2-16GB | V100 SXM2 | 16 |
| Tesla V100-SXM2-32GB | V100 SXM2 32GB | 32 |
| GPU ID | Display Name | Memory (GB) |
|------------------------------------|------------------|---------------|
| AMD Instinct MI300X OAM | MI300X | 192 |
| NVIDIA A100 80GB PCIe | A100 PCIe | 80 |
| NVIDIA A100-SXM4-80GB | A100 SXM | 80 |
| NVIDIA A30 | A30 | 24 |
| NVIDIA A40 | A40 | 48 |
| NVIDIA GeForce RTX 3070 | RTX 3070 | 8 |
| NVIDIA GeForce RTX 3080 | RTX 3080 | 10 |
| NVIDIA GeForce RTX 3080 Ti | RTX 3080 Ti | 12 |
| NVIDIA GeForce RTX 3090 | RTX 3090 | 24 |
| NVIDIA GeForce RTX 3090 Ti | RTX 3090 Ti | 24 |
| NVIDIA GeForce RTX 4070 Ti | RTX 4070 Ti | 12 |
| NVIDIA GeForce RTX 4080 | RTX 4080 | 16 |
| NVIDIA GeForce RTX 4080 SUPER | RTX 4080 SUPER | 16 |
| NVIDIA GeForce RTX 4090 | RTX 4090 | 24 |
| NVIDIA H100 80GB HBM3 | H100 SXM | 80 |
| NVIDIA H100 NVL | H100 NVL | 94 |
| NVIDIA H100 PCIe | H100 PCIe | 80 |
| NVIDIA H200 | H200 SXM | 141 |
| NVIDIA L4 | L4 | 24 |
| NVIDIA L40 | L40 | 48 |
| NVIDIA L40S | L40S | 48 |
| NVIDIA RTX 2000 Ada Generation | RTX 2000 Ada | 16 |
| NVIDIA RTX 4000 Ada Generation | RTX 4000 Ada | 20 |
| NVIDIA RTX 4000 SFF Ada Generation | RTX 4000 Ada SFF | 20 |
| NVIDIA RTX 5000 Ada Generation | RTX 5000 Ada | 32 |
| NVIDIA RTX 6000 Ada Generation | RTX 6000 Ada | 48 |
| NVIDIA RTX A2000 | RTX A2000 | 6 |
| NVIDIA RTX A4000 | RTX A4000 | 16 |
| NVIDIA RTX A4500 | RTX A4500 | 20 |
| NVIDIA RTX A5000 | RTX A5000 | 24 |
| NVIDIA RTX A6000 | RTX A6000 | 48 |
| Tesla V100-FHHL-16GB | V100 FHHL | 16 |
| Tesla V100-PCIE-16GB | Tesla V100 | 16 |
| Tesla V100-SXM2-16GB | V100 SXM2 | 16 |
| Tesla V100-SXM2-32GB | V100 SXM2 32GB | 32 |
75 changes: 29 additions & 46 deletions helpers/gpu_types.py
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@@ -1,69 +1,52 @@
import os
from datetime import datetime

import pandas as pd
import requests
from dotenv import load_dotenv
from tabulate import tabulate

load_dotenv()
response = requests.post(
"https://api.runpod.io/graphql",
headers={
"content-type": "application/json"
},
json={
"query": "query GpuTypes { gpuTypes { id displayName memoryInGb } }"
})

response.raise_for_status()

api_key = os.getenv("API_KEY")
# URL and headers for the POST request
url = "https://api.runpod.io/graphql"
headers = {"content-type": "application/json", "api_key": api_key}
gpu_data = response.json()
gpus = gpu_data["data"]["gpuTypes"]

# The GraphQL query
data = {"query": "query GpuTypes { gpuTypes { id displayName memoryInGb } }"}
gpus_df = pd.DataFrame(gpus)

# Send the POST request
response = requests.post(url, headers=headers, json=data)
gpus_df = gpus_df[(gpus_df["id"].str.lower() != "unknown")]

# Check if the request was successful
if response.status_code == 200:
# Parse the response JSON
gpu_data = response.json()
gpus_df.sort_values(by="displayName").reset_index(drop=True, inplace=True)

# Extract GPU data
gpus = gpu_data["data"]["gpuTypes"]
file_path = os.path.join(
os.path.dirname(__file__), "../docs/references/gpu-types.md"
)

# Sort the GPUs by display name
gpus_sorted = sorted(gpus, key=lambda x: x["displayName"])
table = tabulate(gpus_df, headers=["GPU ID", "Display Name", "Memory (GB)"], tablefmt="github", showindex=False)

# Writing to a markdown file
# relative path
# os.path.join(os.path.dirname(__file__), "gpu-types.md")
file_path = os.path.join(
os.path.dirname(__file__), "../docs/references/gpu-types.md"
)

with open(file_path, "w") as file:
# Write the table headers
date = datetime.now().strftime("%Y-%m-%d")
file.write(
f"""---
with open(file_path, "w") as file:
# Write the table headers
date = datetime.now().strftime("%Y-%m-%d")
file.write(
f"""---
title: GPU types
---

The following list contains all GPU types available on RunPod.

For more information, see [GPU pricing](https://www.runpod.io/gpu-instance/pricing).

<!--
Table last generated: {date}
-->
| GPU ID | Display Name | Memory (GB) |
| ------ | ------------ | ----------- |
"""
)

# Write each GPU data as a row in the table
for gpu in gpus_sorted:
if gpu["id"] == "unknown":
pass
else:
file.write(
f"| {gpu['id']} | {gpu['displayName']} | {gpu['memoryInGb']} |\n"
)
{table}
""")

print("Markdown file with GPU data created successfully.")
else:
print("Failed to retrieve data: ", response.status_code)
print("Markdown file with GPU data created successfully.")
114 changes: 32 additions & 82 deletions helpers/sls_cpu_types.py
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Original file line number Diff line number Diff line change
@@ -1,94 +1,47 @@
import io
import os
from datetime import datetime

import pandas as pd
import requests
from dotenv import load_dotenv
from tabulate import tabulate

load_dotenv()
response = requests.post(
"https://api.runpod.io/graphql",
headers={
"content-type": "application/json"
},
json={
"query": "query CpuTypes { cpuTypes { displayName cores threadsPerCore } }"
})

api_key = os.getenv("API_KEY")
response.raise_for_status()

# URL and headers for the POST request
url = "https://api.runpod.io/graphql"
headers = {"content-type": "application/json", "api_key": api_key}
cpu_data = response.json()
cpus = cpu_data["data"]["cpuTypes"]

# The GraphQL query
data = {"query": "query CpuTypes { cpuTypes { displayName cores threadsPerCore } }"}
cpus_df = pd.DataFrame(cpus)

# Send the POST request
response = requests.post(url, headers=headers, json=data)
cpus_df = cpus_df[
(cpus_df["displayName"].str.lower() != "unknown")
& (~cpus_df["cores"].isna())
& (~cpus_df["threadsPerCore"].isna())
]

# Check if the request was successful
if response.status_code == 200:
# Parse the response JSON
cpu_data = response.json()
cpus_df['displayName'].str.replace(r'\s{2,}', ' ', regex=True).str.strip()
cpus_df.dropna(how="all")
cpus_df.sort_values(by="displayName").reset_index(drop=True, inplace=True)

# Extract CPU data
cpus = cpu_data["data"]["cpuTypes"]
file_path = os.path.join(
os.path.dirname(__file__), "../docs/references/cpu-types.md"
)

# Filter out empty CPU types and rows where all values are NaN
filtered_cpus = [
cpu
for cpu in cpus
if cpu["displayName"]
and cpu["displayName"].lower() != "unknown"
and not pd.isna(cpu["cores"])
and not pd.isna(cpu["threadsPerCore"])
and not all(pd.isna(value) for value in cpu.values())
]
table = tabulate(cpus_df, headers=["Display Name", "Cores", "Threads Per Core"], tablefmt="github", showindex=False)

# Convert to DataFrame
new_df = pd.DataFrame(filtered_cpus)

# Writing to a markdown file
file_path = os.path.join(
os.path.dirname(__file__), "../docs/references/cpu-types.md"
)

# Check if the file already exists
if os.path.exists(file_path):
with open(file_path, "r") as file:
lines = file.readlines()

# Find where the table ends
table_end_index = 0
for i, line in enumerate(lines):
if line.strip() == "" and i > 0:
table_end_index = i
break

# Extract the current table
current_table = "".join(lines[:table_end_index])

# Convert the current table to a DataFrame
current_df = pd.read_csv(io.StringIO(current_table), sep="|").iloc[:, 1:-1]

# Append the new data to the current table
updated_df = pd.concat([current_df, new_df], ignore_index=True)

else:
# If the file does not exist, start a new DataFrame
updated_df = new_df

# Remove rows where all values are NaN
updated_df = updated_df.dropna(how="all")

# Sort the DataFrame alphabetically by displayName
updated_df = updated_df.sort_values(by="displayName").reset_index(drop=True)

# Convert the updated DataFrame to markdown table format
updated_table = tabulate(
updated_df, headers="keys", tablefmt="pipe", showindex=False
)

with open(file_path, "w") as file:
# Write the headers and table
date = datetime.now().strftime("%Y-%m-%d")
file.write(
f"""---
with open(file_path, "w") as file:
# Write the headers and table
date = datetime.now().strftime("%Y-%m-%d")
file.write(
f"""---
title: Serverless CPU types
---

Expand All @@ -97,10 +50,7 @@
<!--
Table last generated: {date}
-->
{updated_table}
"""
)
{table}
""")

print("Markdown file with CPU data updated successfully.")
else:
print("Failed to retrieve data: ", response.status_code)
print("Markdown file with CPU data updated successfully.")
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