A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 18:30 UTC
[Build/CI] Upgrade jinja2 to get 3 moderate CVE fixes (#14839)
Signed-off-by: Russell Bryant <rbryant@redhat.com>
Russell Bryant committed
Mar 15, 2025 at 01:38 UTC
ee3778d5fc0a075dc04dada5a3bbf2af5275a243
5 files changed
+5
-5
requirements/build.txt
+1
-1
@@ -6,4 +6,4 @@ setuptools>=61
6
setuptools-scm>=8
7
torch==2.6.0
8
wheel
9
-jinja2
9
+jinja2>=3.1.6
requirements/rocm-build.txt
+1
-1
@@ -11,5 +11,5 @@ packaging
11
setuptools>=61
12
setuptools-scm>=8
13
wheel
14
-jinja2
14
+jinja2>=3.1.6
15
amdsmi==6.2.4
requirements/test.txt
+1
-1
@@ -181,7 +181,7 @@ iniconfig==2.0.0
181
# via pytest
182
isort==5.13.2
183
# via datamodel-code-generator
184
-jinja2==3.1.4
184
+jinja2==3.1.6
185
# via
186
# datamodel-code-generator
187
# torch
requirements/tpu.txt
+1
-1
@@ -6,7 +6,7 @@ cmake>=3.26
6
packaging
7
setuptools-scm>=8
8
wheel
9
-jinja2
9
+jinja2>=3.1.6
10
ray[default]
11
ray[data]
12
requirements/xpu.txt
+1
-1
@@ -7,7 +7,7 @@ packaging
7
setuptools-scm>=8
8
setuptools>=75.8.0
9
wheel
10
-jinja2
10
+jinja2>=3.1.6
11
datasets # for benchmark scripts
12
13
torch==2.6.0+xpu