Update app.py
Browse files
app.py
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@@ -57,6 +57,17 @@ The prompt we use for every video is "A robotic arm with a gripper and a small c
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"""
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def create_key(seed=0):
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return jax.random.PRNGKey(seed)
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@@ -300,8 +311,12 @@ def create_demo(process, max_images=12, default_num_images=4):
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format="avi",
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interactive=False)
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"""
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perfo_description = """
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The Table on the right shows the performances of our models running on different nodes.
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To make the benchmark, we loaded one of our model on every GPUs of the node. We then retrieve an episode of our simulation.
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For every frame of the episode, we preprocess the image (resize, canny, ...) and process the Canny image on the GPUs.
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We repeated this procedure for different Batch Size (BS).
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We can see that the greater the BS the greater the FPS. By increazing the BS, we make a profit on the parallelization of the GPUs.
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"""
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def create_key(seed=0):
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return jax.random.PRNGKey(seed)
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format="avi",
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interactive=False)
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with gr.Row():
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with gr.Column():
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gr.Markdown(perfo_description)
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with gr.Column():
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gr.Image("./perfo_rtx.png",
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interactive=False)
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