-
Notifications
You must be signed in to change notification settings - Fork 1
250 lines (215 loc) · 8.58 KB
/
ec2-pipeline.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
name: CML-EC2-Runner
on:
workflow_dispatch:
jobs:
build-and-push-ecr-image:
name: Continuous Delivery
runs-on: ubuntu-latest
steps:
- name: Checkout Code
uses: actions/checkout@v3
- name: Install Utilities
run: |
sudo apt-get update
sudo apt-get install -y jq unzip
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v1
with:
aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
aws-region: ${{ secrets.AWS_REGION }}
- name: Login to Amazon ECR
id: login-ecr
uses: aws-actions/amazon-ecr-login@v1
- name: Build, tag, and push image to Amazon ECR
id: build-image
env:
ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }}
IMAGE_TAG: latest
run: |
# Build a docker container and
# push it to ECR so that it can
# be deployed to ECS.
docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
launch-runner:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: iterative/setup-cml@v2
- name: Deploy runner on AWS EC2
env:
REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
run: |
cml runner launch \
--cloud=aws \
--name=session-08 \
--cloud-region=ap-south-1 \
--cloud-type=g4dn.xlarge \
--cloud-hdd-size=64 \
--cloud-spot \
--single \
--labels=cml-gpu \
--idle-timeout=100
train-and-report:
runs-on: [self-hosted, cml-gpu]
needs: launch-runner
timeout-minutes: 20
# container:
# image: docker://pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime
# options: --gpus all
# runs-on: ubuntu-latest
steps:
# - name: Set node environment
# run: |
# apt-get remove nodejs
# apt-get remove npm
# curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
# chmod +x ~/.nvm/nvm.sh
# ls -a ~
# nvm -v
# nvm install 20
# node -v
# npm -v
# - uses: actions/setup-node@v4
# with:
# node-version: 20
# - run: npm ci
# - run: npm test
# - uses: actions/checkout@v2
- name: Display CUDA Version
run: |
echo "CUDA Version:"
nvcc --version || true
- name: Display cuDNN Version
run: |
echo "cuDNN Version:"
cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 || true
- name: Verify EC2 Instance
run: |
echo "Checking instance information..."
# Check if we're on EC2
TOKEN=$(curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600")
curl -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/instance-type
echo "Checking system resources..."
lscpu
free -h
df -h
nvidia-smi # This will show GPU if available
echo "Checking environment..."
env | grep AWS || true
hostname
whoami
pwd
# Install the AWS CLI if not already available
if ! command -v aws &> /dev/null; then
apt-get update
apt-get install -y awscli
fi
# Get ECR login command and execute it
$(aws ecr get-login --no-include-email --region ap-south-1)
aws ecr get-login-password --region ap-south-1 | docker login --username AWS --password-stdin 306093656765.dkr.ecr.ap-south-1.amazonaws.com
- name: Set up AWS CLI credentials
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
AWS_DEFAULT_REGION: ap-south-1 # Change to your desired region
run: |
# Create the AWS config and credentials files
mkdir -p ~/.aws
echo "[default]" > ~/.aws/config
echo "region=${AWS_DEFAULT_REGION}" >> ~/.aws/config
echo "[default]" > ~/.aws/credentials
echo "aws_access_key_id=${AWS_ACCESS_KEY_ID}" >> ~/.aws/credentials
echo "aws_secret_access_key=${AWS_SECRET_ACCESS_KEY}" >> ~/.aws/credentials
- name: Test AWS CLI
run: |
# Now you can run any AWS CLI command
aws s3 ls # Example command to list S3 buckets
# - name: Authenticate with AWS ECR
# uses: aws-actions/configure-aws-credentials@v4
# with:
# aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
# aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
# aws-region: ${{ secrets.AWS_REGION }}
# - name: AWS ECR
# run: |
# aws configure aws_access_key_id=${{ secrets.AWS_ACCESS_KEY_ID }} aws_secret_access_key=${{ secrets.AWS_SECRET_ACCESS_KEY }}
# - name: Install Docker
# run: |
# curl -fsSL https://get.docker.com -o get-docker.sh
# sh get-docker.sh
# - name: Login to Amazon ECR
# id: login-ecr
# uses: aws-actions/amazon-ecr-login@v2
# - name: CUDA Check
# run: |
# docker run --gpus all -it pytorch/pytorch:2.3.1-cuda11.8-cudnn8-runtime python3 -c "
# import torch;
# print(f'CUDA Available: {torch.cuda.is_available()}');
# if torch.cuda.is_available():
# print(f'Device: {torch.cuda.get_device_name(0)}')"
- name: Pull Docker image from ECR
run: |
docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
ls -a
- name: Run DVC commands in container
run: |
mkdir -p model_storage
docker run --gpus=all \
-v "$(pwd)/model_storage:/workspace/model_storage" \
-e AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }} \
-e AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }} \
-e AWS_DEFAULT_REGION=${{ secrets.AWS_REGION }} \
${{ secrets.AWS_ECR_LOGIN_URI }}/${{ secrets.ECR_REPOSITORY_NAME }}:latest \
/bin/bash -c "
dvc pull -r myremote && \
mkdir -p model_storage && \
dvc repro -f
"
# # Wait a moment to ensure the container has started
# sleep 5
ls model_storage/
# # Print logs from the container
# docker logs $CONTAINER_ID
# # Stop the container after retrieving logs
# docker stop $CONTAINER_ID
- name: List files in folder
run: |
ls -l ./
- name: Install jq
run: |
sudo apt-get update
sudo apt-get install -y jq
- name: Get latest commit ID from the repository
id: get_commit_id
env:
REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
run: |
repo="ajithvcoder/emlo4-session-08-ajithvcoder"
latest_commit=$(curl -s -H "Authorization: token $REPO_TOKEN" \
"https://api.github.com/repos/$repo/commits?per_page=1" | \
jq -r '.[0].sha')
echo "COMMIT_ID=$latest_commit" >> $GITHUB_ENV
- name: List files in folder
run: |
ls -l ./model_storage
- name: Read best checkpoint file name
id: read_checkpoint
run: |
checkpoint_file=$(head -n 1 ./model_storage/best_model_checkpoint.txt)
echo "CHECKPOINT_FILE=$checkpoint_file" >> $GITHUB_ENV
- name: Upload checkpoint to S3
run: |
checkpoint_path="${{ env.CHECKPOINT_FILE }}" # Use the checkpoint path from the file
bucket_name="mybucket-emlo-mumbai/session-08-checkpoint/" # Change to your S3 bucket name
s3_key="session-08-checkpoint/${{ env.COMMIT_ID }}/$(basename "$checkpoint_path")" # Define S3 key
echo "Uploading $checkpoint_path to s3://$bucket_name/$s3_key"
aws s3 cp "$checkpoint_path" "s3://$bucket_name/$s3_key"
- name: Clean previous images and containers
run: |
docker system prune -f