26. Execution Statistics#

This table contains the latest execution statistics.

Document

Modified

Method

Run Time (s)

Status

aiyagari_jax

2025-04-29 04:38

cache

64.3

✅

arellano

2025-04-29 04:38

cache

21.29

✅

autodiff

2025-04-29 04:38

cache

13.06

✅

hopenhayn

2025-04-29 04:39

cache

24.54

✅

ifp_egm

2025-04-29 04:41

cache

143.29

✅

intro

2025-04-29 04:41

cache

1.19

✅

inventory_dynamics

2025-04-29 04:41

cache

9.46

✅

inventory_ssd

2025-04-29 05:17

cache

2161.41

✅

jax_intro

2025-04-29 05:18

cache

39.1

✅

jax_nn

2025-04-29 05:19

cache

97.23

✅

job_search

2025-04-29 05:20

cache

9.69

✅

keras

2025-04-29 05:20

cache

27.04

✅

kesten_processes

2025-04-29 05:20

cache

14.46

✅

lucas_model

2025-04-29 05:21

cache

19.92

✅

markov_asset

2025-04-29 05:21

cache

11.21

✅

mle

2025-04-29 05:21

cache

15.02

✅

newtons_method

2025-04-29 05:24

cache

182.59

✅

opt_invest

2025-04-29 05:25

cache

21.99

✅

opt_savings_1

2025-04-29 05:25

cache

48.33

✅

opt_savings_2

2025-04-29 05:26

cache

19.87

✅

overborrowing

2025-04-29 05:26

cache

23.15

✅

short_path

2025-04-29 05:26

cache

4.01

✅

status

2025-04-29 05:26

cache

2.1

✅

troubleshooting

2025-04-29 04:41

cache

1.19

✅

wealth_dynamics

2025-04-29 05:29

cache

158.03

✅

zreferences

2025-04-29 04:41

cache

1.19

✅

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

Tue Apr 29 05:26:38 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version: 12.8     |
|-----------------------------------------+------------------------+----------------------+
| 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                       Off |   00000000:00:1E.0 Off |                    0 |
| N/A   33C    P0             34W /   70W |     109MiB /  15360MiB |      2%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A            6578      C   ...da3/envs/quantecon/bin/python        106MiB |
+-----------------------------------------------------------------------------------------+

/home/runner/miniconda3/envs/quantecon/lib/python3.12/pty.py:95: 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()