Dealing with geo coordinates

Location on maps are often provided in a Coordinate Reference System (CRS). In computer vision projects, however, we need to translate them to pixels. Plotting and transformations can be accomplished with the rasterio Python module.

Below I share a useful snippet showing how to convert CRS locations to pixel coordinates:

import matplotlib.pyplot as plt
import geopandas
import rasterio as rs
from rasterio.plot import show
import pandas as pd
from shapely.geometry import Point
from PIL import Image, ImageDraw

fig, axs = plt.subplots(1, 2)
# open the image and its annotations
img_path = "trees_counting_mount/polish_ortophoto/1_2000/images/66579_623608_8.135.12.19_cropped_2000.tif"
label_path = "trees_counting_mount/polish_ortophoto/1_2000/annotations/66579_623608_8.135.12.19_cropped_2000.csv"
raster =, crs="EPSG:2180")
label_df = pd.read_csv(label_path)

# extract selected points
geometry = list(map(Point, label_df[["x", "y"]].values))
idxs = [0, 100, 200]
Ps = [geometry[idx] for idx in idxs]

# plot points in two alternative ways!
ax = axs[0]
show(raster, ax=ax)
# variant 1: plot geo-coordinates with geopandas.GeoDataFrame
geo_df = geopandas.GeoDataFrame(None, geometry=Ps, crs="EPSG:2180")
ax = axs[1]
# variant 2: convert geo-coordinates to pixel locations
ys, xs = rs.transform.rowcol(raster.transform, [P.x for P in Ps], [P.y for P in Ps])
img =
img_draw = ImageDraw.Draw(img)

for x, y in zip(xs, ys):
  img_draw.ellipse( (x-150,y-150,x+150,y+150), fill="yellow")


plt.title("Points in geo-coordinates (left) and image pixel coordinates (right).")

Setting up CUDA tools properly

CUDA is a computing platform for graphical processing units (GPUs) developed by NVIDIA, widely used to accelerate machine-learning. Existing frameworks, such as Tensorflow or PyTorch, utilize it under the hood not asking user for any specific coding. However, it is still necessary to set its dependencies, particularly the compiler nvcc, properly to benefit of acceleration. In this short note, I share an interesting use-case that occurred when prototyping on Kaggle Docker image and NVIDIA Docker image.

Compatibility of CUDA tools and targeted libraries

It turns out that one of Kaggle images was released with incompatible CUDA dependencies: compilation tools were not aligned with PyTorch, as revealed when attempting to compile detectron2, an object detection library by Facebook.

(base) maciej.skorski@shared-notebooks:~$ docker images
REPOSITORY                        TAG                        IMAGE ID       CREATED        SIZE   latest                     87983e20c290   4 weeks ago    48.1GB
nvidia/cuda                       11.6.2-devel-ubuntu20.04   e1687ea9fbf2   7 weeks ago    5.75GB   <none>                     2b12fe42f372   2 months ago   50.2GB

(base) maciej.skorski@shared-notebooks:~$ docker run -d \
  -it \
  --name kaggle-test \
  --runtime=nvidia \
  --mount type=bind,source=/home/maciej.skorski,target=/home \

(base) maciej.skorski@shared-notebooks:~$ docker exec -it kaggle-test python -m pip install 'git+'
      The detected CUDA version (12.1) mismatches the version that was used to compile
      PyTorch (11.8). Please make sure to use the same CUDA versions.

In order to compile detectron2, it was necessary to align the CUDA toolkit version. Rather than trying to install it manually – which is known to be an error-prone task – a working solution was to change the Kaggle image. It turns out that the gap was bridged in a subsequent release:

(base) maciej.skorski@shared-notebooks:~$ docker run 87983e20c290 nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
(base) maciej.skorski@shared-notebooks:~$ docker run 2b12fe42f372 nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0

And indeed, the Facebook library installed smoothly under the new image ๐Ÿ‘

(base) maciej.skorski@shared-notebooks:~$ docker run -d \
   -it \
   --name kaggle-test \
   --runtime=nvidia \
   --mount type=bind,source=/home/maciej.skorski,target=/home \
(base) maciej.skorski@shared-notebooks:~$ docker exec -it kaggle-test python -m pip install 'git+'
Collecting git+
Successfully built detectron2 fvcore antlr4-python3-runtime pycocotools

Compatibility of CUDA tools and GPU drivers

The compiler version should not be significantly newer than that that of the driver, as presented by nvidia-smi:

(base) maciej.skorski@shared-notebooks:~$ nvidia-smi
Thu Aug 10 14:56:44 2023       
| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
| 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  | 00000000:00:04.0 Off |                    0 |
| N/A   69C    P0    30W /  70W |  12262MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|    0   N/A  N/A      8635      C   ...detectron_venv/bin/python    12260MiB |

Consider the simple CUDA script querying the GPU device properties:


#include <stdio.h> 

int main() {
  int nDevices;

  for (int i = 0; i < nDevices; i++) {
    cudaDeviceProp prop;
    cudaGetDeviceProperties(&prop, i);
    printf("Device Number: %d\n", i);
    printf("  Name: %s\n",;
    printf("  Integrated: %d\n", prop.integrated);
    printf("  Compute capability: %d.%d\n", prop.major, prop.minor );
    printf("  Peak Memory Bandwidth (GB/s): %f\n\n",
    printf( "  Total global mem: %ld\n", prop.totalGlobalMem );
    printf( "  Multiprocessor count: %d\n", prop.multiProcessorCount );

This code compiles and presents GPU properties only under the image equipped with the matching major compiler version (select the appropriate image here):

(base) maciej.skorski@shared-notebooks:~$ docker run -d \
  -it \
  --name nvidia-cuda \
  --runtime=nvidia \
  --mount type=bind,source=$(pwd),target=/home \
  --privileged \

docker exec -it nvidia-cuda sh -c "nvcc /home/ -o /home/query_GPU && /home/query_GPU"
Device Number: 0
  Name: Tesla T4
  Integrated: 0
  Compute capability: 7.5
  Peak Memory Bandwidth (GB/s): 320.064000

  Total global mem: 15634661376
  Multiprocessor count: 40

However, the container doesn’t even start with a mismatching major version:

(base) maciej.skorski@shared-notebooks:~$ docker run -d \
>   -it \
>   --name nvidia-cuda \
>   --runtime=nvidia \
>   --mount type=bind,source=$(pwd),target=/home \
>   --privileged \
>   nvidia/cuda:12.2.0-devel-ubuntu20.04
docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #1: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'
nvidia-container-cli: requirement error: unsatisfied condition: cuda>=12.2, please update your driver to a newer version, or use an earlier cuda container: unknown.

Fixing Reproducibility of Scientific Repos

As the last example, consider the recent cuZK project which implements some state-of-the-art cryptographic protocols on GPU. The original code was missing dependencies and compilation instructions, therefore I shared a working fork version.

To work with the code, let’s use the NVIDIA Docker image with the appropriate version, here I selected the tag 11.6.2-devel-ubuntu20.04. Checkout the code and start a container mounting the working directory with the GitHub code, like below:

docker run -d \
   -it \
   --name nvidia-cuda \
   --runtime=nvidia \
   --mount type=bind,source=$(pwd),target=/home \
   --privileged \

To work with the code, we need few more dependencies within the container:

apt-get update
apt-get install -y git libgmp3-dev

After adjusting the headers in Makefile, the CUDA code can be compiled and run

root@7816e1643c2a:/home/cuZK/test# make
root@7816e1643c2a:/home/cuZK/test# ls
BLS377   Makefile      core          msmtesta  testb  libgmp.a     msmtestb
root@7816e1643c2a:/home/cuZK/test# ./msmtestb
Please enter the MSM scales (e.g. 20 represents 2^20) 

Lego Bricks in LaTeX

Who does not enjoy lego bricks, raise a hand! In this post, I am sharing an elegant and efficient way of plotting bricks under 3d view in TikZ. Briefly speaking, it utilizes canvas transforms to plot facets, and describes boundaries of studs in a simple way with cylindrical coordinates based on the azimuth angle (localizing extreme edges might be a challenge on its own).
While there are other packages, like TikZbricks, this method seems simpler in terms of complexity and brings some educational value in terms of cylinders geometry.

And here is the code (see also this online note)





%  elevation and azimuth for 3D-view


    % cube by rectangle facets
    \begin{scope}[canvas is yx plane at z=\posz,transform shape]
    \draw[fill=#8] (\posy,\posx) rectangle ++(\cubey,\cubex);
    \begin{scope}[canvas is yx plane at z=\posz+\cubez,transform shape]
    \draw[fill=#8] (\posy,\posx) rectangle ++(\cubey,\cubex);
    \begin{scope}[canvas is yz plane at x=\posx+\cubex,transform shape]
    \draw[fill=#8] (\posy,\posz) rectangle ++(\cubey,\cubez) node[pos=.5] {#7};
    \begin{scope}[canvas is xz plane at y=\posy+\cubey,transform shape]
    \draw[fill=#8] (\posx,\posz) rectangle ++(\cubex,\cubez);

    % studs by arcs and extreme edges
    \foreach \i in {1,...,\cubey}{
        \foreach \j in {1,...,\cubex}{
            % upper part - full circle
            \draw [thin] (\posx-0.5+\j,\posy-0.5+\i,\posz+\cubez+0.15) circle (\pinradius);
            % lower part - arc
            \begin{scope}[canvas is xy plane at z=\posz+\cubez]
            \draw[thin] ([shift=(\rotz:\pinradius)] \posx-0.5+\j,\posy-0.5+\i) arc (\rotz:\rotz-180:\pinradius);
                % edges easily identified in cylindrical coordinates! 
                \pgfcoordinate{edge1_top}{ \pgfpointcylindrical{\rotz}{\pinradius}{\posz+\cubez+0.15} };
                \pgfcoordinate{edge1_bottom}{ \pgfpointcylindrical{\rotz}{\pinradius}{\posz+\cubez} };
                \draw[] (edge1_top) -- (edge1_bottom);
                \pgfcoordinate{edge1_top}{ \pgfpointcylindrical{\rotz+180}{\pinradius}{\posz+\cubez+0.15} };
                \pgfcoordinate{edge1_bottom}{ \pgfpointcylindrical{\rotz+180}{\pinradius}{\posz+\cubez} };
                \draw[] (edge1_top) -- (edge1_bottom);

    % draw axes
    \coordinate (O) at (0,0,0);
    \coordinate (A) at (5,0,0);
    \coordinate (B) at (0,5,0);
    \coordinate (C) at (0,0,5);
    \draw[-latex] (O) -- (A) node[below] {$x$};
    \draw[-latex] (O) -- (B) node[above] {$y$};
    \draw[-latex] (O) -- (C) node[left] {$z$};
    % draw brick


Cylinders in LaTeX the Easy and Correct Way

Drawing cylinders in vector graphic is a common task. It is less trivial as it looks at first glance, due to the challenge of finding a proper projection. In this post, I share a simple and robust recipe using the tikz-3dplot package of LaTeX. As opposed to many examples shared online, this approach automatically identifies the boundary of a cylinder, under a given perspective. The trick is to identify edges using the azimuth angle in cylindrical coordinates ๐Ÿ’ช.


\tikzstyle{every node}=[font=\small]
\draw[ultra thin,-latex] (0,0,0) -- (6,0,0) node[anchor=north east]{$x$};
\draw[ultra thin,-latex] (0,0,0) -- (0,6,0) node[anchor=north west]{$y$};
\draw[ultra thin,-latex] (0,0,0) -- (0,0,6) node[anchor=south]{$z$};
\draw [thick](0,0,4) circle (3);
\begin{scope}[canvas is xy plane at z=0]
\draw[thick, dashed] ([shift=(\rotz:3)] 0,0,0) arc (\rotz:\rotz+180:3);
\draw[thick] ([shift=(\rotz:3)] 0,0,0) arc (\rotz:\rotz-180:3);
% manual edges
\draw [dotted, red](1.9,-2.34,0) -- (1.9,-2.34,4) node[midway, left]{};
\draw [dotted, red](-1.9,2.35,0) -- (-1.9,2.35,4);
% automatic edges !
\pgfcoordinate{edge1_top}{ \pgfpointcylindrical{\rotz}{3}{4} };
\pgfcoordinate{edge1_bottom}{ \pgfpointcylindrical{\rotz}{3}{0} };
\draw[thick] (edge1_top) -- (edge1_bottom);
\pgfcoordinate{edge2_top}{ \pgfpointcylindrical{\rotz+180}{3}{4} };
\pgfcoordinate{edge2_bottom}{ \pgfpointcylindrical{\rotz+180}{3}{0} };
\draw[thick] (edge2_top) -- (edge2_bottom);


And we can enjoy this output, comparing with manual edges adapted from this post:

Properly drawn cylinder

Automating Accessors and Mutators Tests

In object-oriented programming, there are plenty of accessors and mutators to test. This post demonstrates that this effort can be automated with reflection ๐Ÿš€. The inspiration came from discussions I had with my students during our software-engineering class: how to increase code coverage without lots of manual effort? ๐Ÿค”

Roughly speaking, the reflection mechanism allows the code to analyse itself. At runtime, we are able to construct calls based on extracted class properties. The idea is not novel, see for instance this gist. To add the value and improve the presentation, I modernized and completed the code to a fully featured project on GitHub with CI/CD on GitHub Actions and Code Coverage connected ๐Ÿ˜Ž.

Here is how the testing class looks like. Java reflection accesses classes, extracts fields and their types and constructs calls with type-matching values accordingly:

// tester class
class AutoTests {
    private static final Class[] classToTest = new Class[]{ 
        // the list of classes to test
        PersonClass.class, AnimalClass.class

   public void correctGettersSetters() {
      for (Class aClass : classToTest) {
        Object instance;
        try {      
           instance = aClass.getDeclaredConstructor().newInstance();
           Field[] declaredFields = aClass.getDeclaredFields();
           for(Field f: declaredFields) {
              // get the field getter and setter, following the Java naming convention (!)
              String name = f.getName();
              name = name.substring(0,1).toUpperCase() + name.substring(1);
              String getterName = "get" + name;
              String setterName = "set" + name;
              Method getterMethod = aClass.getMethod(getterName);
              Method setterMethod = aClass.getMethod(setterName, getterMethod.getReturnType());
              // prepare a test value based on the filed type 
              Object testVal = null;
              Class<?> fType = f.getType();
              if (fType.isAssignableFrom(Integer.class)) {
                  testVal = 1234;
              } else if (fType.isAssignableFrom(String.class)) {
                  testVal = "abcd";
              // test by composing the setter and getter
              setterMethod.invoke(instance, testVal);
              Object result = getterMethod.invoke(instance);
              System.out.printf("Testing class=%s field=%s...\n", aClass.getName(), f.getName());
              assertThat(result).as("in class %s fields %s", aClass.getName(), f.getName()).isEqualTo(testVal);
        } catch(Exception e) {

And here is one more demo available online.

Finally, a disclaimer: accessors and mutators may deserve smarter tests than what stated here – depending on a use-case.

Tracking Training Performance of LDA Models

In my recent open source contribution I enabled callbacks in the scalable (multi-core) implementation of Latent Dirichlet Alocation in the gensim library โ€‹1โ€‹. This will, in turn, allow users for faster and more accurate turning of the popular topic extraction model.

An obvious use case is monitoring and early stopping of training, with popular coherence metrics such as \(U_{mass}\) and \(C_V\) โ€‹2โ€‹. On the News20Group dataset, the training performance looks as follows:

Training performance of Multi-Core LDA on 20 Newsgroups data, monitored by callbacks.

The achieved scores are decent, actually better than reported in the literatureโ€‹3โ€‹ – but this may be due to preprocessing not early stopping. A full example is shared in this Kaggle notebook.

  1. 1.
    R Rehr Uv Rek, P Sojka. Software Framework for Topic Modelling with Large Corpora. Unpublished. Published online 2010. doi:10.13140/2.1.2393.1847
  2. 2.
    Rรถder M, Both A, Hinneburg A. Exploring the Space of Topic Coherence Measures. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. Published online February 2, 2015. doi:10.1145/2684822.2685324
  3. 3.
    Zhang Z, Fang M, Chen L, Namazi Rad MR. Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Published online 2022. doi:10.18653/v1/2022.naacl-main.285

On UML Approach to Management Antipatterns

Therefore speak I to them in parables, because seeing, they see not, and hearing, they hear not, neither do they understand.

Matthew 13:13

Ever wondered how miserable some โ€œprestigiousโ€ businesses are, and how they manage to make their employees make up for poor project management? Me too! A classical situation that contributes to crisis is miscommunication to subcontractors or employees. Let’s see how UML can be used to study such antipatterns. They happen unintentionally, don’t they? ๐Ÿค”

This is a real-world use-case from a prestigious legal office located in Warsaw, Poland. I have been asked to capture project management antipatterns, as an external observer and modeller.

One use case was: an expert subcontractor asked proactively, in fact several times, to be put in the communication loop with the client. But the office executives didn’t find it necessary (why would they, huh?). Until… Guess when? The deadline! The subcontractor was caught by surprise: please deliver for the customer by today! But wait, what customer…? ๐Ÿค”

Another use case: the office rushed promising the client something they couldn’t deliver, and reached out for its experts for help pretty late.. Guess when? On the deadline day!

Here is the UML model that I promised, a good illustration of this poor management practice! I will use a sequence diagram, a powerful tool to explore interactions ๐Ÿ’ช

You certainly agree this is not professional but would probably argue that this doesn’t happen to ErnstYoung, PWC and other big companies… Would you?

Working with Abstract Syntax Trees

Visualizing code as a syntax tree is both funny and useful, as seen from impressive applications such as creating lineage of SQL which helps to understand complex queries in business. Abstract syntax trees are not only widely used in industry but are still a subject of top academic researchโ€‹1,2โ€‹.

This post demonstrates how to work with AST in Python by parsing C code with CLang/LLVMโ€‹3โ€‹ and visualizing by graphviz.

Parsing is relatively simple, particularly to users that have had already similar experiences with abstract trees, such as parsing XMLs. My advice for beginners is to avoid code factoring, but leverage functional coding features in Python. The example below shows how to extract declarations of functions and details of arguments:

from clang.cindex import Index, Config, CursorKind, TypeKind

SCRIPT_PATH = "./tcpdump/print-ppp.c"

# C99 is a proper C code standard for tcpdump, as per their docs
index = Index.create()
translation_unit = index.parse(SCRIPT_PATH, args=["-std=c99"])

# filter to nodes in the root script (ignore imported!)
script_node = translation_unit.cursor
all_nodes = script_node.get_children()
all_nodes = filter(lambda c: == SCRIPT_PATH, all_nodes)

# filter to function nodes
func_nodes = filter(lambda c: c.kind == CursorKind.FUNCTION_DECL, all_nodes)

# print attributes and their types for each function
for fn in func_nodes:
    for arg in fn.get_arguments():
        t = arg.type
        # handle pointers by describing their pointees
        if t.kind == TypeKind.POINTER:
            declr = t.get_pointee().get_declaration()
            declr = t.get_declaration()
            f'arg declared in {arg.location.file}:L{arg.extent.start.line},C{arg.extent.start.column}-L{arg.extent.end.line},C{arg.extent.end.column}',
            f'{declr.spelling} declared in {declr.location.file}:L{declr.location.line}'

Which gives the following output when tested on the tcpdump project

     struct netdissect_options * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L403,C39-L403,C59 netdissect_options declared in ./tcpdump/netdissect.h:L161
     const unsigned char TypeKind.ELABORATED arg declared in ./tcpdump/print-ppp.c:L403,C61-L403,C73 u_char declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_char.h:L30
     const unsigned int TypeKind.ELABORATED arg declared in ./tcpdump/print-ppp.c:L403,C75-L403,C86 u_int declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_int.h:L30
     struct netdissect_options * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L1359,C10-L1359,C33 netdissect_options declared in ./tcpdump/netdissect.h:L161
     const unsigned char * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L1360,C10-L1360,C25 u_char declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_char.h:L30
     unsigned int TypeKind.ELABORATED arg declared in ./tcpdump/print-ppp.c:L1360,C27-L1360,C39 u_int declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_int.h:L30

However, the funny part comes from visualization. This is easy with graphviz

from graphviz import Digraph

dot = Digraph(strict=True)
dot.attr(rankdir="LR", size="20,100", fontsize="6")

node_args = {"fontsize": "8pt", "edgefontsize": "6pt"}

for fn in func_nodes:
    fn_node_name = f"{fn.spelling}\nL{fn.location.line}"
    dot.node(fn_node_name, **node_args)
    for i, arg in enumerate(fn.get_arguments(), start=1):
        arg_node_name = arg.type.get_canonical().spelling
        dot.node(arg_node_name, **node_args)
        dot.edge(fn_node_name, arg_node_name)
        t = arg.type
        # handle pointers by describing their pointees
        if t.kind == TypeKind.POINTER:
            declr = t.get_pointee().get_declaration()
            declr = t.get_declaration()
        declr_file = f"{declr.location.file}"
        dot.node(declr_file, **node_args)
            arg_node_name, declr_file, label=f"L{declr.location.line}", fontsize="6pt"

from IPython.display import display_svg

We can now enjoy the pretty informative graph ๐Ÿ˜Ž It shows that multiple functions share only few types of arguments and gives precise information about their origin.

The fully working example is shared here as a Colab notebook.

  1. 1.
    Grafberger S, Groth P, Stoyanovich J, Schelter S. Data distribution debugging in machine learning pipelines. The VLDB Journal. Published online January 31, 2022:1103-1126. doi:10.1007/s00778-021-00726-w
  2. 2.
    Fu H, Liu C, Wu B, Li F, Tan J, Sun J. CatSQL            โ€ฏ: Towards Real World Natural Language to SQL Applications. Proc VLDB Endow. Published online February 2023:1534-1547. doi:10.14778/3583140.3583165
  3. 3.
    Lattner C, Adve V. LLVM: A compilation framework for lifelong program analysis &amp; transformation. International Symposium on Code Generation and Optimization, 2004 CGO 2004. doi:10.1109/cgo.2004.1281665

Customized Jupyter environments on Google Cloud

Kaggle docker images come with a huge list of pre-installed packages for machine-learning, including the support of GPU computing. They run within a container as a Jupyter application accessed by users through its web interface. Running a custom image boils down to these steps

  • ๐Ÿ’ก pulling the right version from the container registry
  • โ— publishing with appropriate parameters (--runtime flag important for GPU support)

Below we can see how it looks like

(base) maciej.skorski@shared-notebooks:~$ docker pull
v128: Pulling from kaggle-gpu-images/python
d5fd17ec1767: Pulling fs layer 
(base) maciej.skorski@shared-notebooks:~$ sudo docker run \
>    --name "/payload-container" \
>    --runtime "nvidia" \
>    --volume "/home/jupyter:/home/jupyter" \
>    --mount type=bind,source=/opt/deeplearning/jupyter/,destination=/opt/jupyter/.jupyter/,readonly \
>    --log-driver "json-file" \
>    --restart "always" \
>    --publish "" \
>    --network "bridge" \
>    --expose "8080/tcp" \
>    --label "kaggle-lang"="python" \
>    --detach \
>    --tty \
>    --entrypoint "/" \
>    "" \
>    "/" 

The following test in Python shell shows that we can indeed use GPU ๐Ÿ™‚

root@cf1b6f63d729:/# ipython
Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:53) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.33.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import torch

In [2]: torch.cuda.is_available()
Out[2]: True

In [3]: torch.Tensor([1,2,3]).to(0)
Out[3]: tensor([1., 2., 3.], device='cuda:0')

Repairing user-managed notebooks on Google Cloud

In this note, I am sharing a case study on debugging and fixing jupyter-lab access issues.

The diagnostic script can be run on a VM instance as shown below:

(base) maciej.skorski@shared-notebooks:~$ sudo /opt/deeplearning/bin/

Vertex Workbench Diagnostic Tool

Running system diagnostics...

Checking Docker service status...               [OK]
Checking Proxy Agent status...                  [OK]
Checking Jupyter service status in container...         [ERROR] Jupyter service is not running
Checking internal Jupyter API status...         [ERROR] Jupyter API is not active
Checking boot disk (/dev/sda1) space...         [OK]
Checking data disk (/dev/sdb) space...          [OK]
Checking DNS        [OK]
Checking DNS      [OK]

System's health status is degraded

Diagnostic tool will collect the following information: 

  System information
  System Log /var/log/
  Docker information
  Jupyter service status
  Network information
  Proxy configuration: /opt/deeplearning/proxy-agent-config.json
  Conda environment information
  pip environment information
  GCP instance information

Do you want to continue (y/n)? n

Jupyter service runs from a container, but it somehow stopped in this case ๐Ÿ˜ณ

(base) maciej.skorski@shared-notebooks:~$ docker container ls

Not a problem! We can restart the container, but carefully choosing the parameters to expose it properly (ports, mounted folders etc). The appropriate docker command can be retrieved from a running container on a similar healthy instance by docker inspect

(base) maciej.skorski@kaggle-test-shared:~$ docker inspect \
>   --format "$(curl -s" 3f5b6d709ccc

docker run \
  --name "/payload-container" \
  --runtime "runc" \
  --volume "/home/jupyter:/home/jupyter" \
  --mount type=bind,source=/opt/deeplearning/jupyter/,destination=/opt/jupyter/.jupyter/,readonly \
  --log-driver "json-file" \
  --restart "always" \
  --publish "" \
  --network "bridge" \
  --hostname "3f5b6d709ccc" \
  --expose "8080/tcp" \
  --env "TENSORBOARD_PROXY_URL=/proxy/%PORT%/" \
  --env "LIT_PROXY_URL=/proxy/%PORT%/" \
  --env "PATH=/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin" \
  --env "LC_ALL=C.UTF-8" \
  --env "LANG=C.UTF-8" \
  --env "DL_ANACONDA_HOME=/opt/conda" \
  --env "SHELL=/bin/bash" \
  --env "LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:/usr/local/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64::/opt/conda/lib" \
  --env "CONTAINER_NAME=tf2-cpu/2-11" \
  --env "KMP_BLOCKTIME=0" \
  --env "KMP_AFFINITY=granularity=fine,verbose,compact,1,0" \
  --env "KMP_SETTINGS=false" \
  --env "NODE_OPTIONS=--max-old-space-size=4096" \
  --env "ENABLE_MULTI_ENV=false" \
  --env "LIBRARY_PATH=:/opt/conda/lib" \
  --env "TENSORFLOW_VERSION=2.11.0" \
  --env "KMP_WARNINGS=0" \
  --env "PROJ_LIB=/opt/conda/share/proj" \
  --env "TESSERACT_PATH=/usr/bin/tesseract" \
  --env "PYTHONPATH=:/opt/facets/facets_overview/python/" \
  --env "PYTHONUSERBASE=/root/.local" \
  --env "MPLBACKEND=agg" \
  --env "GIT_COMMIT=7e2b36e4a2ac3ef3df74db56b1fd132d56620e8a" \
  --env "BUILD_DATE=20230419-235653" \
  --label "build-date"="20230419-235653" \
  --label ""="Container: TensorFlow 2-11" \
  --label "git-commit"="7e2b36e4a2ac3ef3df74db56b1fd132d56620e8a" \
  --label "kaggle-lang"="python" \
  --label ""="ubuntu" \
  --label "org.opencontainers.image.version"="20.04" \
  --label "tensorflow-version"="2.11.0" \
  --detach \
  --tty \
  --entrypoint "/" \
  "" \

Now the check goes OK ๐Ÿ™‚

(base) maciej.skorski@shared-notebooks:~$ sudo /opt/deeplearning/bin/

Vertex Workbench Diagnostic Tool

Running system diagnostics...

Checking Docker service status...               [OK]
Checking Proxy Agent status...                  [OK]
Checking Jupyter service status in container... [OK]
Checking internal Jupyter API status...         [OK]
Checking boot disk (/dev/sda1) space...         [OK]
Checking data disk (/dev/sdb) space...          [OK]
Checking DNS        [OK]
Checking DNS      [OK]