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scrape.py
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#!/usr/bin/env python
from lxml import etree
import re
import json
import locale
import ec2
import os
import requests
import pickle
import boto3
from six.moves.urllib import request as urllib2
# Following advice from https://stackoverflow.com/a/1779324/216138
# The locale must be installed in the system, and it must be one where ',' is
# the thousans separator and '.' is the decimal fraction separator.
locale.setlocale(locale.LC_ALL, "en_US.UTF-8")
class Instance(object):
def __init__(self):
self.arch = []
self.api_description = None
self.availability_zones = {}
self.base_performance = None
self.burst_minutes = None
self.clock_speed_ghz = None
self.compute_capability = 0
self.devices = 0
self.drive_size = None
self.ebs_iops = 0
self.ebs_max_bandwidth = 0
self.ebs_only = True
self.ebs_optimized = False
self.ebs_throughput = 0
self.ebs_as_nvme = False
self.ebs_baseline_throughput = 0
self.ebs_baseline_iops = 0
self.ebs_baseline_bandwidth = 0
self.ECU = 0
self.enhanced_networking = None
self.family = ""
self.FPGA = 0
self.generation = None
self.GPU = 0
self.GPU_memory = 0
self.GPU_model = None
self.includes_swap_partition = False
self.instance_type = ""
self.intel_avx = None
self.intel_avx2 = None
self.intel_avx512 = None
self.intel_turbo = None
self.linux_virtualization_types = []
self.memory = 0
self.network_performance = None
self.num_drives = None
self.nvme_ssd = False
self.physical_processor = None
self.placement_group_support = True
self.pretty_name = ""
self.pricing = {}
self.regions = {}
self.size = 0
self.ssd = False
self.storage_needs_initialization = False
self.trim_support = False
self.vCPU = 0
self.vpc = None
self.vpc_only = True
self.emr = False
def get_type_prefix(self):
"""h1, i3, d2, etc"""
return self.instance_type.split(".")[0]
def get_ipv6_support(self):
"""Fancy parsing not needed for ipv6 support.
"IPv6 is supported on all current generation instance types and the
C3, R3, and I2 previous generation instance types."
- https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-types.html
FIXME: This should be a @property, but this project is still Python 2. Yikes!
"""
ipv4_only_families = (
"cg1",
"m1",
"m3",
"c1",
"cc2",
"g2",
"m2",
"cr1",
"hs1",
"t1",
)
return self.get_type_prefix() not in ipv4_only_families
def to_dict(self):
d = dict(
family=self.family,
instance_type=self.instance_type,
pretty_name=self.pretty_name,
arch=self.arch,
vCPU=self.vCPU,
GPU=self.GPU,
GPU_model=self.GPU_model,
GPU_memory=self.GPU_memory,
compute_capability=self.compute_capability,
FPGA=self.FPGA,
ECU=self.ECU,
base_performance=self.base_performance,
burst_minutes=self.burst_minutes,
memory=self.memory,
ebs_optimized=self.ebs_optimized,
ebs_throughput=self.ebs_throughput,
ebs_iops=self.ebs_iops,
ebs_as_nvme=self.ebs_as_nvme,
ebs_max_bandwidth=self.ebs_max_bandwidth,
ebs_baseline_throughput=self.ebs_baseline_throughput,
ebs_baseline_iops=self.ebs_baseline_iops,
ebs_baseline_bandwidth=self.ebs_baseline_bandwidth,
network_performance=self.network_performance,
enhanced_networking=self.enhanced_networking,
placement_group_support=self.placement_group_support,
pricing=self.pricing,
vpc=self.vpc,
linux_virtualization_types=self.linux_virtualization_types,
generation=self.generation,
vpc_only=self.vpc_only,
ipv6_support=self.get_ipv6_support(),
physical_processor=self.physical_processor,
clock_speed_ghz=self.clock_speed_ghz,
intel_avx=self.intel_avx,
intel_avx2=self.intel_avx2,
intel_avx512=self.intel_avx512,
intel_turbo=self.intel_turbo,
emr=self.emr,
availability_zones=self.availability_zones,
regions=self.regions,
)
if self.ebs_only:
d["storage"] = None
else:
d["storage"] = dict(
ssd=self.ssd,
trim_support=self.trim_support,
nvme_ssd=self.nvme_ssd,
storage_needs_initialization=self.storage_needs_initialization,
includes_swap_partition=self.includes_swap_partition,
devices=self.num_drives,
size=self.drive_size,
size_unit=self.size_unit,
)
return d
def __repr__(self):
return "<Instance {}>".format(self.instance_type)
def sanitize_instance_type(instance_type):
"""Typos and other bad data are common in the instance type columns for some reason"""
# Remove random whitespace
instance_type = re.sub(r"\s+", "", instance_type, flags=re.UNICODE)
# Correct typos
typo_corrections = {
"x1.16large": "x1.16xlarge", # https://github.com/powdahound/ec2instances.info/issues/199
"i3.4xlxarge": "i3.4xlarge", # https://github.com/powdahound/ec2instances.info/issues/227
"i3.16large": "i3.16xlarge", # https://github.com/powdahound/ec2instances.info/issues/227
"p4d.2xlarge": "p4d.24xlarge", # as of 2020-11-15
}
return typo_corrections.get(instance_type, instance_type)
def totext(elt):
s = etree.tostring(elt, method="text", encoding="unicode").strip()
return re.sub(r"\*\d$", "", s)
def transform_size(size):
if size == "u":
return "micro"
if size == "sm":
return "small"
if size == "med":
return "medium"
m = re.search("^(x+)l$", size)
if m:
xs = len(m.group(1))
if xs == 1:
return "xlarge"
else:
return str(xs) + "xlarge"
assert size == "lg", "Unable to parse size: %s" % (size,)
return "large"
def transform_region(reg):
region_map = {
"eu-ireland": "eu-west-1",
"eu-frankfurt": "eu-central-1",
"apac-sin": "ap-southeast-1",
"apac-syd": "ap-southeast-2",
"apac-tokyo": "ap-northeast-1",
}
if reg in region_map:
return region_map[reg]
m = re.search(r"^([^0-9]*)(-(\d))?$", reg)
assert m, "Can't parse region: %s" % (reg,)
base = m.group(1)
num = m.group(3) or "1"
return base + "-" + num
def add_ebs_pricing(imap, data):
for region_spec in data["config"]["regions"]:
region = transform_region(region_spec["region"])
for t_spec in region_spec["instanceTypes"]:
typename = t_spec["type"]
for i_spec in t_spec["sizes"]:
i_type = i_spec["size"]
if i_type not in imap:
print(
"ERROR: Got EBS pricing data for unknown instance type: {}".format(
i_type
)
)
continue
inst = imap[i_type]
inst.pricing.setdefault(region, {})
# print "%s/%s" % (region, i_type)
for col in i_spec["valueColumns"]:
inst.pricing[region]["ebs"] = col["prices"]["USD"]
def add_pricing_info(instances):
for i in instances:
i.pricing = {}
by_type = {i.instance_type: i for i in instances}
ec2.add_pricing(by_type)
# EBS cost surcharge as per https://aws.amazon.com/ec2/pricing/on-demand/#EBS-Optimized_Instances
ebs_pricing_url = (
"https://a0.awsstatic.com/pricing/1/ec2/pricing-ebs-optimized-instances.min.js"
)
pricing = fetch_data(ebs_pricing_url)
add_ebs_pricing(by_type, pricing)
def fetch_data(url):
content = urllib2.urlopen(url).read().decode()
try:
pricing = json.loads(content)
except ValueError:
# if the data isn't compatible JSON, try to parse as jsonP
json_string = re.search(r"callback\((.*)\);", content).groups()[
0
] # extract javascript object
json_string = re.sub(r"(\w+):", r'"\1":', json_string) # convert to json
pricing = json.loads(json_string)
return pricing
def add_eni_info(instances):
client = boto3.client('ec2', region_name='us-east-1')
pager = client.get_paginator("describe_instance_types")
responses = pager.paginate(Filters=[{'Name': 'instance-type', 'Values': ['*']}])
for response in responses:
instance_types = response['InstanceTypes']
by_type = {i.instance_type: i for i in instances}
for instance_type_info in instance_types:
instance_type = instance_type_info['InstanceType']
max_enis = instance_type_info['NetworkInfo']['MaximumNetworkInterfaces']
ip_per_eni = instance_type_info['NetworkInfo']['Ipv4AddressesPerInterface']
if instance_type not in by_type:
print(
"WARNING: Ignoring ENI data for unknown instance type: {}".format(
instance_type
)
)
continue
if not by_type[instance_type].vpc:
print(
f"WARNING: DescribeInstanceTypes API does not have network info for {instance_type}, scraping instead"
)
by_type[instance_type].vpc = {
"max_enis": max_enis,
"ips_per_eni": ip_per_eni,
}
def add_ebs_info(instances):
"""
Six tables on this page:
5 of them: EBS optimized by default and baseline:
Baseline performance metrics for instances with asterisk (unsupported for now, see comment below)
Instance type | Maximum bandwidth (Mib/s) | Maximum throughput (MiB/s, 128 KiB I/O) | Maximum IOPS (16 KiB I/O)
Instance type | Baseline bandwidth (Mib/s) | Baseline throughput (MiB/s, 128 KiB I/O) | Baseline IOPS (16 KiB I/O)
Table 6: Not EBS optimized by default
Instance type | Maximum bandwidth (Mib/s) | Maximum throughput (MiB/s, 128 KiB I/O) | Maximum IOPS (16 KiB I/O)
TODO: Support the asterisk on type names in the first table, which means:
"These instance types can support maximum performance for 30 minutes at least once every 24 hours. For example,
c5.large instances can deliver 281 MB/s for 30 minutes at least once every 24 hours. If you have a workload
that requires sustained maximum performance for longer than 30 minutes, select an instance type based on the
following baseline performance."
"""
def parse_ebs_combined_table(by_type, table):
for row in table.xpath("tr"):
if row.xpath("th"):
continue
cols = row.xpath("td")
# remove last character which is a superscript with other info
instance_type = sanitize_instance_type(totext(cols[0]))[:-1]
if len(cols) == 4:
ebs_baseline_bandwidth = locale.atof(totext(cols[1]))
ebs_baseline_throughput = locale.atof(totext(cols[2]))
ebs_baseline_iops = locale.atof(totext(cols[3]))
ebs_max_bandwidth = locale.atof(totext(cols[1]))
ebs_throughput = locale.atof(totext(cols[2]))
ebs_iops = locale.atof(totext(cols[3]))
elif len(cols) == 7:
ebs_baseline_bandwidth = locale.atof(totext(cols[1]))
ebs_max_bandwidth = locale.atof(totext(cols[2]))
ebs_baseline_throughput = locale.atof(totext(cols[3]))
ebs_throughput = locale.atof(totext(cols[4]))
ebs_baseline_iops = locale.atof(totext(cols[5]))
ebs_iops = locale.atof(totext(cols[6]))
if instance_type not in by_type:
print(f"ERROR: Ignoring EBS info for unknown instance {instance_type}")
else:
by_type[instance_type].ebs_optimized = True
by_type[instance_type].ebs_optimized_by_default = True
by_type[instance_type].ebs_baseline_throughput = ebs_baseline_throughput
by_type[instance_type].ebs_baseline_iops = ebs_baseline_iops
by_type[instance_type].ebs_baseline_bandwidth = ebs_baseline_bandwidth
by_type[instance_type].ebs_throughput = ebs_throughput
by_type[instance_type].ebs_iops = ebs_iops
by_type[instance_type].ebs_max_bandwidth = ebs_max_bandwidth
def parse_ebs_nondefault_table(by_type, table):
for row in table.xpath("tr"):
if row.xpath("th"):
continue
cols = row.xpath("td")
# remove last character which is a superscript with other info
instance_type = sanitize_instance_type(totext(cols[0]))[:-1]
ebs_max_bandwidth = locale.atof(totext(cols[1]))
ebs_throughput = locale.atof(totext(cols[2]))
ebs_iops = locale.atof(totext(cols[3]))
if instance_type not in by_type:
print(f"ERROR: Ignoring EBS info for unknown instance {instance_type}")
else:
if ebs_max_bandwidth:
by_type[instance_type].ebs_optimized = True
by_type[instance_type].ebs_optimized_by_default = False
by_type[instance_type].ebs_throughput = ebs_throughput
by_type[instance_type].ebs_iops = ebs_iops
by_type[instance_type].ebs_max_bandwidth = ebs_max_bandwidth
by_type = {i.instance_type: i for i in instances}
# Canonical URL for this info is https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-optimized.html
# ebs_url = "https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-optimized.partial.html"
ebs_url = "https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-optimized.html"
tree = etree.parse(urllib2.urlopen(ebs_url), etree.HTMLParser())
tables = tree.xpath('//div[@class="table-contents"]//table')
for t in [0, 1, 2, 3, 4]:
parse_ebs_combined_table(by_type, tables[t])
parse_ebs_nondefault_table(by_type, tables[5])
def add_linux_ami_info(instances):
"""Add information about which virtualization options are supported.
Note that only HVM is supported for Windows instances so that info is not
given its own column.
"""
checkmark_char = "\u2713"
url = "http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/"
tree = etree.parse(urllib2.urlopen(url), etree.HTMLParser())
table = tree.xpath('//div[@class="aws-table"]/table')[0]
rows = table.xpath(".//tr[./td]")[1:] # ignore header
for r in rows:
supported_types = []
family_id = totext(r[0]).lower()
if not family_id:
continue
# We only check the primary EBS-backed values here since the 'storage'
# column will already be able to tell users whether or not the instance
# they're looking at can use EBS and/or instance-store AMIs.
try:
if totext(r[1]) == checkmark_char:
supported_types.append("HVM")
if len(r) >= 4 and totext(r[3]) == checkmark_char:
supported_types.append("PV")
except Exception as e:
# 2018-08-01: handle missing cells on last row in this table...
print("Exception while parsing AMI info for {}: {}".format(family_id, e))
# Apply types for this instance family to all matching instances
for i in instances:
i_family_id = i.instance_type.split(".")[0]
if i_family_id == family_id:
i.linux_virtualization_types = supported_types
# http://aws.amazon.com/amazon-linux-ami/instance-type-matrix/ page is
# missing info about both older (t1, m1, c1, m2) and newer exotic (cg1,
# cr1, hi1, hs1, cc2) instance type generations.
# Adding "manual" info about older generations
# Some background info at https://github.com/powdahound/ec2instances.info/pull/161
for i in instances:
i_family_id = i.instance_type.split(".")[0]
if i_family_id in ("cc2", "cg1", "hi1", "hs1"):
if not "HVM" in i.linux_virtualization_types:
i.linux_virtualization_types.append("HVM")
if i_family_id in ("t1", "m1", "m2", "c1", "hi1", "hs1"):
if not "PV" in i.linux_virtualization_types:
i.linux_virtualization_types.append("PV")
def add_vpconly_detail(instances):
# A few legacy instances can be launched in EC2 Classic, the rest is VPC only
# https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-classic-platform.html#ec2-classic-instance-types
classic_families = (
"m1",
"m3",
"t1",
"c1",
"c3",
"cc2",
"cr1",
"m2",
"r3",
"d2",
"hs1",
"i2",
"g2",
)
for i in instances:
for family in classic_families:
if i.instance_type.startswith(family):
i.vpc_only = False
def add_instance_storage_details(instances):
"""Add information about instance storage features."""
client = boto3.client('ec2', region_name='us-east-1')
pager = client.get_paginator("describe_instance_types")
responses = pager.paginate(Filters=[{'Name': 'instance-storage-supported', 'Values': ['true']},{'Name': 'instance-type', 'Values': ['*']}])
for response in responses:
instance_types = response['InstanceTypes']
for i in instances:
for instance_type in instance_types:
if i.instance_type == instance_type["InstanceType"]:
storage_info = instance_type["InstanceStorageInfo"]
if storage_info:
nvme_support = storage_info["NvmeSupport"]
disk = storage_info["Disks"][0]
i.ebs_only = False
i.num_drives = disk["Count"]
i.drive_size = disk["SizeInGB"]
i.size_unit = "GB"
i.ssd = "ssd" == disk["Type"]
i.nvme_ssd = nvme_support in ['supported', 'required']
def add_t2_credits(instances):
# Canonical URL for this info is
# http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/t2-credits-baseline-concepts.html
# url = "https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-credits-baseline-concepts.partial.html"
# It seems it's no longer dynamically loaded
url = "http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/t2-credits-baseline-concepts.html"
tree = etree.parse(urllib2.urlopen(url), etree.HTMLParser())
table = tree.xpath('//div[@class="table-contents"]//table')[1]
rows = table.xpath(".//tr[./td]")
assert len(rows) > 0, "Failed to find T2 CPU credit info"
by_type = {i.instance_type: i for i in instances}
for r in rows:
if len(r) > 1:
inst_type = totext(r[0])
if not inst_type in by_type:
print(
f"WARNING: Skipping unknown instance type '{inst_type}' in CPU credit info table"
)
continue
inst = by_type[inst_type]
creds_per_hour = locale.atof(totext(r[1]))
inst.base_performance = creds_per_hour / 60
inst.burst_minutes = creds_per_hour * 24 / inst.vCPU
def add_pretty_names(instances):
family_names = {
"c1": "C1 High-CPU",
"c3": "C3 High-CPU",
"c4": "C4 High-CPU",
"c5": "C5 High-CPU",
"c5d": "C5 High-CPU",
"cc2": "Cluster Compute",
"cg1": "Cluster GPU",
"cr1": "High Memory Cluster",
"g4": "G4 Accelerated Computing",
"hi1": "HI1. High I/O",
"hs1": "High Storage",
"i3": "I3 High I/O",
"m1": "M1 General Purpose",
"m2": "M2 High Memory",
"m3": "M3 General Purpose",
"m4": "M4 General Purpose",
"m5": "M5 General Purpose",
"m5d": "M5 General Purpose",
"g3": "G3 Graphics GPU",
"g4": "G4 Graphics and Machine Learning GPU",
"g5": "G5 Graphics and Machine Learning GPU",
"g6": "G6 Graphics and Machine Learning GPU",
"gr6": "Gr6 Graphics and Machine Learning GPU High RAM ratio",
"p2": "P2 General Purpose GPU",
"p3": "P3 High Performance GPU",
"p4d": "P4D Highest Performance GPU",
"r3": "R3 High-Memory",
"r4": "R4 High-Memory",
"x1": "X1 Extra High-Memory",
}
for i in instances:
pieces = i.instance_type.split(".")
family = pieces[0]
short = pieces[1]
prefix = family_names.get(family, family.upper())
extra = None
if short.startswith("8x"):
extra = "Eight"
elif short.startswith("4x"):
extra = "Quadruple"
elif short.startswith("2x"):
extra = "Double"
elif short.startswith("10x"):
extra = "Deca"
elif short.startswith("x"):
extra = ""
bits = [prefix]
if extra is not None:
bits.extend([extra, "Extra"])
short = "Large"
bits.append(short.capitalize())
i.pretty_name = " ".join([b for b in bits if b])
def add_emr_info(instances):
url = "https://b0.p.awsstatic.com/pricing/2.0/meteredUnitMaps/elasticmapreduce/USD/current/elasticmapreduce.json"
pricing = fetch_data(url)
region_map = {value: key for key, value in ec2.get_region_descriptions().items()}
emr_prices = {}
for region in pricing["regions"]:
emr_prices[region] = {}
for inst_type in pricing["regions"][region]:
_inst_name = inst_type.replace("Instance-instancetype-", "")
_price = pricing["regions"][region][inst_type]["price"]
emr_prices[region][_inst_name] = _price
for inst in instances:
for region in inst.pricing:
try:
emr_price = {}
region = ec2.canonicalize_location(region, False)
# The frontend expects ["emr"]["emr"] for some reason
emr_price["emr"] = emr_prices[region_map[region]][inst.instance_type]
inst.pricing[region]["emr"] = emr_price
# TODO: this is set for the whole instance when it should be per-region
inst.emr = True
except KeyError:
pass
def add_gpu_info(instances):
"""
Add info about GPUs from the manually-curated dictionaries below. They are
manually curated because GPU models and their corresponding CUDA Compute
Capability are not listed in a structured form anywhere in the AWS docs.
This function will print a warning if it encounters an instance with
.GPU > 0 for which GPU information is not included in the dictionaries
below. This may indicate that AWS has added a new GPU instance type. If you
see such a warning and want to fill in the missing information, check
https://aws.amazon.com/ec2/instance-types/#Accelerated_Computing for
descriptions of the instance types and https://en.wikipedia.org/wiki/CUDA
for information on the CUDA compute capability of different Nvidia GPU
models.
For G5 instances, please reference the following:
https://aws.amazon.com/ec2/instance-types/g5/
https://github.com/vantage-sh/ec2instances.info/issues/593
"""
gpu_data = {
"g2.2xlarge": {
# No longer listed in AWS docs linked above. Alternative source is
# https://medium.com/@manku_timma1/part-1-g2-2xlarge-gpu-basics-805ad40a37a4
# The model has 2 units, 4G of memory each, but AWS exposes only 1 unit per instance
"gpu_model": "NVIDIA GRID K520",
"compute_capability": 3.0,
"gpu_count": 1,
"cuda_cores": 3072,
"gpu_memory": 4,
},
"g2.8xlarge": {
# No longer listed in AWS docs linked above. Alternative source is
# https://aws.amazon.com/blogs/aws/new-g2-instance-type-with-4x-more-gpu-power/
"gpu_model": "NVIDIA GRID K520",
"compute_capability": 3.0,
"gpu_count": 4,
"cuda_cores": 6144,
"gpu_memory": 16,
},
"g3s.xlarge": {
"gpu_model": "NVIDIA Tesla M60",
"compute_capability": 5.2,
"gpu_count": 1,
"cuda_cores": 2048,
"gpu_memory": 8,
},
"g3.4xlarge": {
"gpu_model": "NVIDIA Tesla M60",
"compute_capability": 5.2,
"gpu_count": 1,
"cuda_cores": 2048,
"gpu_memory": 8,
},
"g3.8xlarge": {
"gpu_model": "NVIDIA Tesla M60",
"compute_capability": 5.2,
"gpu_count": 2,
"cuda_cores": 4096,
"gpu_memory": 16,
},
"g3.16xlarge": {
"gpu_model": "NVIDIA Tesla M60",
"compute_capability": 5.2,
"gpu_count": 4,
"cuda_cores": 8192,
"gpu_memory": 32,
},
"g4dn.xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g4dn.2xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g4dn.4xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g4dn.8xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g4dn.16xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g4dn.12xlarge": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 4,
"cuda_cores": 10240,
"gpu_memory": 64,
},
"g4dn.metal": {
"gpu_model": "NVIDIA T4 Tensor Core",
"compute_capability": 7.5,
"gpu_count": 8,
"cuda_cores": 20480,
"gpu_memory": 128,
},
"p2.xlarge": {
"gpu_model": "NVIDIA Tesla K80",
"compute_capability": 3.7,
"gpu_count": 1,
"cuda_cores": 2496,
"gpu_memory": 12,
},
"p2.8xlarge": {
"gpu_model": "NVIDIA Tesla K80",
"compute_capability": 3.7,
"gpu_count": 4,
"cuda_cores": 19968,
"gpu_memory": 96,
},
"p2.16xlarge": {
"gpu_model": "NVIDIA Tesla K80",
"compute_capability": 3.7,
"gpu_count": 8,
"cuda_cores": 39936,
"gpu_memory": 192,
},
"p3.2xlarge": {
"gpu_model": "NVIDIA Tesla V100",
"compute_capability": 7.0,
"gpu_count": 1,
"cuda_cores": 5120,
"gpu_memory": 16,
},
"p3.8xlarge": {
"gpu_model": "NVIDIA Tesla V100",
"compute_capability": 7.0,
"gpu_count": 4,
"cuda_cores": 20480,
"gpu_memory": 64,
},
"p3.16xlarge": {
"gpu_model": "NVIDIA Tesla V100",
"compute_capability": 7.0,
"gpu_count": 8,
"cuda_cores": 40960,
"gpu_memory": 128,
},
"p3dn.24xlarge": {
"gpu_model": "NVIDIA Tesla V100",
"compute_capability": 7.0,
"gpu_count": 8,
"cuda_cores": 40960,
"gpu_memory": 256,
},
"g5.xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 1,
"cuda_cores": 9616,
"gpu_memory": 24,
},
"g5.2xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 1,
"cuda_cores": 9616,
"gpu_memory": 24,
},
"g5.4xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 1,
"cuda_cores": 9616,
"gpu_memory": 24,
},
"g5.8xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 1,
"cuda_cores": 9616,
"gpu_memory": 24,
},
"g5.16xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 1,
"cuda_cores": 9616,
"gpu_memory": 24,
},
"g5.12xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 4,
"cuda_cores": 38464,
"gpu_memory": 96,
},
"g5.24xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 4,
"cuda_cores": 38464,
"gpu_memory": 96,
},
"g5.48xlarge": {
"gpu_model": "NVIDIA A10G",
"compute_capability": 8.6,
"gpu_count": 8,
"cuda_cores": 76928,
"gpu_memory": 192,
},
"g6.xlarge": {
# GPU core count found from the whitepaper
# https://images.nvidia.com/aem-dam/Solutions/Data-Center/l4/nvidia-ada-gpu-architecture-whitepaper-v2.1.pdf
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"g6.2xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"g6.4xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"g6.8xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"gr6.4xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"gr6.8xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"g6.16xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 1,
"cuda_cores": 7424,
"gpu_memory": 24,
},
"g6.12xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 4,
"cuda_cores": 29696,
"gpu_memory": 96,
},
"g6.24xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 4,
"cuda_cores": 29696,
"gpu_memory": 96,
},
"g6.48xlarge": {
"gpu_model": "NVIDIA L4",
"compute_capability": 8.9,
"gpu_count": 8,
"cuda_cores": 59392,
"gpu_memory": 192,
},
"p4d.24xlarge": {
"gpu_model": "NVIDIA A100",
"compute_capability": 8.0,
"gpu_count": 8,
"cuda_cores": 55296, # Source: Asked Matthew Wilson at AWS as this isn't public anywhere.
"gpu_memory": 320,
},
"p4de.24xlarge": {
"gpu_model": "NVIDIA A100",
"compute_capability": 8.0,
"gpu_count": 8,
"cuda_cores": 55296,
"gpu_memory": 640,
},
"g5g.xlarge": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g5g.2xlarge": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g5g.4xlarge": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g5g.8xlarge": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 1,
"cuda_cores": 2560,
"gpu_memory": 16,
},
"g5g.16xlarge": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 2,
"cuda_cores": 5120,
"gpu_memory": 32,
},
"g5g.metal": {
"gpu_model": "NVIDIA T4G Tensor Core",
"compute_capability": 7.5,
"gpu_count": 2,
"cuda_cores": 5120,
"gpu_memory": 32,
},
"g4ad.xlarge": {
"gpu_model": "AMD Radeon Pro V520",
"compute_capability": 0,
"gpu_count": 1,
"gpu_memory": 8,
},
"g4ad.2xlarge": {
"gpu_model": "AMD Radeon Pro V520",
"compute_capability": 0,
"gpu_count": 1,
"gpu_memory": 8,
},
"g4ad.4xlarge": {
"gpu_model": "AMD Radeon Pro V520",
"compute_capability": 0,
"gpu_count": 1,
"gpu_memory": 8,
},
"g4ad.8xlarge": {
"gpu_model": "AMD Radeon Pro V520",
"compute_capability": 0,
"gpu_count": 2,
"gpu_memory": 16,
},
"g4ad.16xlarge": {
"gpu_model": "AMD Radeon Pro V520",
"compute_capability": 0,
"gpu_count": 4,
"gpu_memory": 32,
},
"trn1.2xlarge": {
"gpu_model": "AWS Inferentia",
"compute_capability": 0,
"gpu_count": 1,
"gpu_memory": 32,
},
"trn1.32xlarge": {
"gpu_model": "AWS Inferentia",
"compute_capability": 0,
"gpu_count": 16,
"gpu_memory": 512,
},
"trn1n.32xlarge": {
"gpu_model": "AWS Inferentia",
"compute_capability": 0,
"gpu_count": 16,
"gpu_memory": 512,
},