24. Execution Statistics#

This table contains the latest execution statistics.

Document

Modified

Method

Run Time (s)

Status

aiyagari_jax

2024-08-12 03:21

cache

68.16

arellano

2024-08-12 03:21

cache

20.5

autodiff

2024-08-12 03:21

cache

9.53

hopenhayn

2024-08-12 03:24

cache

130.79

ifp_egm

2024-08-12 03:26

cache

119.51

intro

2024-08-12 03:26

cache

1.09

inventory_dynamics

2024-08-12 03:26

cache

39.45

inventory_ssd

2024-08-12 03:58

cache

1920.47

jax_intro

2024-08-12 03:59

cache

27.78

job_search

2024-08-12 03:59

cache

9.53

kesten_processes

2024-08-12 03:59

cache

13.41

lucas_model

2024-08-12 03:59

cache

15.49

markov_asset

2024-08-12 04:00

cache

11.48

mle

2024-08-12 04:00

cache

14.49

newtons_method

2024-08-12 04:02

cache

125.57

opt_invest

2024-08-12 04:02

cache

22.94

opt_savings_1

2024-08-12 04:03

cache

31.92

opt_savings_2

2024-08-12 04:03

cache

20.99

overborrowing

2024-08-12 04:05

cache

123.48

short_path

2024-08-12 04:05

cache

3.02

status

2024-08-12 04:05

cache

2.11

troubleshooting

2024-08-12 03:26

cache

1.09

wealth_dynamics

2024-08-12 04:09

cache

216.57

zreferences

2024-08-12 03:26

cache

1.09

These lectures are built on linux instances through github actions that has access to a gpu. These lectures make use of the nvidia T4 card.

You can check the backend used by JAX using:

import jax
# Check if JAX is using GPU
print(f"JAX backend: {jax.devices()[0].platform}")
JAX backend: gpu

and the hardware we are running on:

!nvidia-smi
/opt/conda/envs/quantecon/lib/python3.11/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
  pid, fd = os.forkpty()
Mon Aug 12 04:05:53 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.54.03              Driver Version: 535.54.03    CUDA Version: 12.5     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       On  | 00000001:00:00.0 Off |                  Off |
| N/A   62C    P0              37W /  70W |    107MiB / 16384MiB |      2%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+