Spaces:
Running
Running
feat: cleanup
Browse files- dev/inference/wandb-backend.ipynb +32 -109
dev/inference/wandb-backend.ipynb
CHANGED
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"cells": [
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"cell_type": "code",
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"run_ids = ['
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"ENTITY, PROJECT = '
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text =
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"latest_only =
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"suffix = '' # mainly for duplicate inference runs with a deleted version\n",
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"add_clip_32 =
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"run_ids = ['
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"ENTITY, PROJECT = '
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text =
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"latest_only = True
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"suffix = '' # mainly for duplicate inference runs with a deleted version\n",
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"add_clip_32 =
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:absl:Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: \n",
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"INFO:absl:Unable to initialize backend 'gpu': NOT_FOUND: Could not find registered platform with name: \"cuda\". Available platform names are: TPU Interpreter Host\n"
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"source": [
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"batch_size = 8\n",
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"num_images = 128\n",
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"cell_type": "code",
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"id": "c6a878fa-4bf5-4978-abb5-e235841d765b",
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"text": [
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"Working with z of shape (1, 256, 16, 16) = 65536 dimensions.\n"
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],
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"source": [
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"vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
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"clip = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
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"id": "a500dd07-dbc3-477d-80d4-2b73a3b83ef3",
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{
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"data": {
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"text/plain": [
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"ShardedDeviceArray([4.6051702, 4.6051702, 4.6051702, 4.6051702, 4.6051702,\n",
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" 4.6051702, 4.6051702, 4.6051702], dtype=float32)"
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"execution_count": 7,
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{
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"ename": "SyntaxError",
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"evalue": "EOL while scanning string literal (1745443972.py, line 60)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;36m File \u001b[0;32m\"/tmp/ipykernel_402605/1745443972.py\"\u001b[0;36m, line \u001b[0;32m60\u001b[0m\n\u001b[0;31m for i in tqdm(range(num_images // jax.device_count()), desc='Generating Images):\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
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],
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"source": [
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"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
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"last_inference_version = get_last_inference_version(run_id)\n",
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" columns = ['Caption'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
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" \n",
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" if latest_only:\n",
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"
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" #assert last_inference_version is None or version > last_inference_version\n",
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" else:\n",
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" if last_inference_version is None:\n",
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" # we should start from v0\n",
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"\n",
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" # generate images\n",
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" images = []\n",
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" for i in tqdm(range(num_images // jax.device_count()), desc='Generating Images):\n",
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" key, subkey = jax.random.split(key)\n",
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" encoded_images = p_generate(tokenized_prompt, shard_prng_key(subkey), model_params)\n",
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" encoded_images = encoded_images.sequences[..., 1:]\n",
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" run = None # ensure we don't log on this run"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fdcd09d6-079c-461a-a81a-d9e650d3b099",
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"metadata": {},
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"outputs": [],
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"source": [
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"p_clip32"
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"id": "7d86ceee-c9ac-4860-abad-410cadd16c3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"clip_inputs['attention_mask'].shape, clip_inputs['pixel_values'].shape"
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" for run in tqdm(runs):\n",
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" log_run(run)"
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"metadata": {
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"cells": [
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"cell_type": "code",
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"execution_count": null,
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"id": "4ff2a984-b8b2-4a69-89cf-0d16da2393c8",
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"id": "23e00271-941c-4e1b-b6a9-107a1b77324d",
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"metadata": {},
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"outputs": [],
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"source": [
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"run_ids = ['3kaut6e8']\n",
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"ENTITY, PROJECT = 'wandb', 'hf-flax-dalle-mini'\n",
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text = False\n",
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"latest_only = True # log only latest or all versions\n",
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"suffix = '' # mainly for duplicate inference runs with a deleted version\n",
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"add_clip_32 = True"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "92f4557c-fd7f-4edc-81c2-de0b0a10c270",
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"metadata": {},
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"outputs": [],
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"source": [
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"run_ids = ['k76r0v39']\n",
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"ENTITY, PROJECT = 'dalle-mini', 'dalle-mini' # used only for training run\n",
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text = True\n",
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"latest_only = True # log only latest or all versions\n",
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"suffix = '' # mainly for duplicate inference runs with a deleted version\n",
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"add_clip_32 = False"
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]
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"batch_size = 8\n",
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"num_images = 128\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6a878fa-4bf5-4978-abb5-e235841d765b",
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"source": [
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"vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
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"clip = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
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"cell_type": "code",
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"source": [
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"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
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"last_inference_version = get_last_inference_version(run_id)\n",
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" columns = ['Caption'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
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" \n",
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" if latest_only:\n",
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" assert last_inference_version is None or version > last_inference_version\n",
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" else:\n",
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" if last_inference_version is None:\n",
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" # we should start from v0\n",
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"\n",
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" # generate images\n",
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" images = []\n",
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" for i in tqdm(range(num_images // jax.device_count()), desc='Generating Images'):\n",
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" key, subkey = jax.random.split(key)\n",
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" encoded_images = p_generate(tokenized_prompt, shard_prng_key(subkey), model_params)\n",
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" encoded_images = encoded_images.sequences[..., 1:]\n",
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" run = None # ensure we don't log on this run"
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"execution_count": null,
|
|
|
359 |
" for run in tqdm(runs):\n",
|
360 |
" log_run(run)"
|
361 |
]
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
362 |
}
|
363 |
],
|
364 |
"metadata": {
|